From 75fed1cb6e04ee9c493ab1acefa86338c6e3ade1 Mon Sep 17 00:00:00 2001
From: wangchongwu <759291707@qq.com>
Date: Mon, 10 Nov 2025 14:40:53 +0800
Subject: [PATCH] =?UTF-8?q?ceres=E5=8F=96=E6=B6=88=E5=BF=BD=E7=95=A5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 3rdParty/ceres_1.14/bin/ceres-debug.dll       |  Bin 0 -> 14047232 bytes
 3rdParty/ceres_1.14/bin/ceres.dll             |  Bin 0 -> 1647104 bytes
 3rdParty/ceres_1.14/bin/gflags.dll            |  Bin 0 -> 140800 bytes
 3rdParty/ceres_1.14/bin/gflags_debug.dll      |  Bin 0 -> 557056 bytes
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 3rdParty/ceres_1.14/bin/glog.dll              |  Bin 0 -> 142336 bytes
 3rdParty/ceres_1.14/bin/glogd.dll             |  Bin 0 -> 356864 bytes
 3rdParty/ceres_1.14/include/ceres/version.h   |   48 +
 3rdParty/ceres_1.14/lib/ceres-debug.lib       |  Bin 0 -> 137044 bytes
 3rdParty/ceres_1.14/lib/gflags_debug.lib      |  Bin 0 -> 30516 bytes
 .../ceres_1.14/lib/gflags_nothreads_debug.lib |  Bin 0 -> 31346 bytes
 3rdParty/ceres_1.14/lib/glogd.lib             |  Bin 0 -> 55222 bytes
 3rdParty/ceres_2.2/bin/ceres-debug.dll        |  Bin 0 -> 26604544 bytes
 3rdParty/ceres_2.2/bin/ceres.dll              |  Bin 0 -> 3622912 bytes
 3rdParty/ceres_2.2/bin/gflags.dll             |  Bin 0 -> 130048 bytes
 3rdParty/ceres_2.2/bin/gflags_debug.dll       |  Bin 0 -> 810496 bytes
 3rdParty/ceres_2.2/bin/gflags_nothreads.dll   |  Bin 0 -> 128512 bytes
 .../ceres_2.2/bin/gflags_nothreads_debug.dll  |  Bin 0 -> 809984 bytes
 3rdParty/ceres_2.2/bin/glog.dll               |  Bin 0 -> 145920 bytes
 3rdParty/ceres_2.2/bin/glogd.dll              |  Bin 0 -> 749056 bytes
 .../Core/arch/AltiVec/MatrixProductCommon.h   |  159 --
 .../Core/arch/AltiVec/MatrixVectorProduct.h   | 2400 -----------------
 .../ceres_2.2/include/ceres/internal/config.h |    2 +-
 .../ceres_2.2/include/ceres/internal/export.h |    1 -
 3rdParty/ceres_2.2/include/ceres/version.h    |   49 +
 .../include/{ => eigen3}/Eigen/Cholesky       |    0
 .../include/{ => eigen3}/Eigen/CholmodSupport |    2 +-
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 .../Eigen/src/Core/ArithmeticSequence.h       |   63 +-
 .../{ => eigen3}/Eigen/src/Core/Array.h       |   10 +-
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 .../{ => eigen3}/Eigen/src/Core/BandMatrix.h  |    0
 .../{ => eigen3}/Eigen/src/Core/Block.h       |   31 +-
 .../Eigen/src/Core/BooleanRedux.h             |   54 +-
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 .../include/{ => eigen3}/Eigen/src/Core/Dot.h |   11 +-
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 .../{ => eigen3}/Eigen/src/Core/Fuzzy.h       |    0
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 .../Eigen/src/Core/GenericPacketMath.h        |   24 +-
 .../Eigen/src/Core/GlobalFunctions.h          |    0
 .../include/{ => eigen3}/Eigen/src/Core/IO.h  |    0
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 .../include/{ => eigen3}/Eigen/src/Core/Map.h |    0
 .../{ => eigen3}/Eigen/src/Core/MapBase.h     |    0
 .../Eigen/src/Core/MathFunctions.h            |  181 +-
 .../Eigen/src/Core/MathFunctionsImpl.h        |    0
 .../{ => eigen3}/Eigen/src/Core/Matrix.h      |   19 +-
 .../{ => eigen3}/Eigen/src/Core/MatrixBase.h  |   18 +-
 .../{ => eigen3}/Eigen/src/Core/NestByValue.h |    0
 .../{ => eigen3}/Eigen/src/Core/NoAlias.h     |    0
 .../{ => eigen3}/Eigen/src/Core/NumTraits.h   |   60 +-
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 .../Eigen/src/Core/PlainObjectBase.h          |    0
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 .../include/{ => eigen3}/Eigen/src/Core/Ref.h |    4 +-
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 .../Eigen/src/Core/StlIterators.h             |    0
 .../{ => eigen3}/Eigen/src/Core/Stride.h      |    6 +-
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 .../Eigen/src/Core/Transpositions.h           |    0
 .../Eigen/src/Core/TriangularMatrix.h         |   15 +-
 .../{ => eigen3}/Eigen/src/Core/VectorBlock.h |    0
 .../Eigen/src/Core/VectorwiseOp.h             |    0
 .../{ => eigen3}/Eigen/src/Core/Visitor.h     |    0
 .../Eigen/src/Core/arch/AVX/Complex.h         |   16 +-
 .../Eigen/src/Core/arch/AVX/MathFunctions.h   |    0
 .../Eigen/src/Core/arch/AVX/PacketMath.h      |   24 +-
 .../Eigen/src/Core/arch/AVX/TypeCasting.h     |    0
 .../Eigen/src/Core/arch/AVX512/Complex.h      |   60 +-
 .../src/Core/arch/AVX512/MathFunctions.h      |    5 +-
 .../Eigen/src/Core/arch/AVX512/PacketMath.h   |   95 +-
 .../Eigen/src/Core/arch/AVX512/TypeCasting.h  |    0
 .../Eigen/src/Core/arch/AltiVec/Complex.h     |   44 +-
 .../src/Core/arch/AltiVec/MathFunctions.h     |   41 +-
 .../src/Core/arch/AltiVec/MatrixProduct.h     | 1765 ++++++------
 .../Core/arch/AltiVec/MatrixProductCommon.h   |  221 ++
 .../src/Core/arch/AltiVec/MatrixProductMMA.h  |  346 +--
 .../Eigen/src/Core/arch/AltiVec/PacketMath.h  |  116 +-
 .../Eigen/src/Core/arch/CUDA/Complex.h        |   13 +-
 .../Eigen/src/Core/arch/Default/BFloat16.h    |   16 +-
 .../Eigen/src/Core/arch/Default/ConjHelper.h  |    0
 .../arch/Default/GenericPacketMathFunctions.h |  103 +-
 .../Default/GenericPacketMathFunctionsFwd.h   |    6 -
 .../Eigen/src/Core/arch/Default/Half.h        |   18 +-
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 .../Eigen/src/Core/arch/GPU/MathFunctions.h   |    0
 .../Eigen/src/Core/arch/GPU/PacketMath.h      |  223 +-
 .../Eigen/src/Core/arch/GPU/TypeCasting.h     |    3 +-
 .../src/Core/arch/HIP/hcc/math_constants.h    |    0
 .../Eigen/src/Core/arch/MSA/Complex.h         |   11 +-
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 .../Eigen/src/Core/arch/MSA/PacketMath.h      |    0
 .../Eigen/src/Core/arch/NEON/Complex.h        |   46 +-
 .../Core/arch/NEON/GeneralBlockPanelKernel.h  |    2 +-
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 .../Eigen/src/Core/arch/NEON/PacketMath.h     |  298 +-
 .../Eigen/src/Core/arch/NEON/TypeCasting.h    |  339 ++-
 .../Eigen/src/Core/arch/SSE/Complex.h         |   23 +-
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 .../Eigen/src/Core/arch/SSE/PacketMath.h      |    8 +-
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 .../Eigen/src/Core/arch/ZVector/PacketMath.h  |   28 +-
 .../src/Core/functors/AssignmentFunctors.h    |    0
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 .../Eigen/src/Core/functors/UnaryFunctors.h   |    0
 .../Core/products/GeneralBlockPanelKernel.h   |   16 +-
 .../src/Core/products/GeneralMatrixMatrix.h   |   12 +-
 .../products/GeneralMatrixMatrixTriangular.h  |   35 +-
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 .../Core/products/GeneralMatrixMatrix_BLAS.h  |    0
 .../src/Core/products/GeneralMatrixVector.h   |   33 +-
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 .../Eigen/src/Core/products/Parallelizer.h    |    0
 .../Core/products/SelfadjointMatrixMatrix.h   |   42 +-
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 .../Core/products/SelfadjointMatrixVector.h   |    0
 .../products/SelfadjointMatrixVector_BLAS.h   |    0
 .../src/Core/products/SelfadjointProduct.h    |    0
 .../Core/products/SelfadjointRank2Update.h    |    0
 .../Core/products/TriangularMatrixMatrix.h    |   36 +-
 .../products/TriangularMatrixMatrix_BLAS.h    |    0
 .../Core/products/TriangularMatrixVector.h    |   42 +-
 .../products/TriangularMatrixVector_BLAS.h    |   64 +-
 .../Core/products/TriangularSolverMatrix.h    |    0
 .../products/TriangularSolverMatrix_BLAS.h    |    0
 .../Core/products/TriangularSolverVector.h    |    0
 .../Eigen/src/Core/util/BlasUtil.h            |    0
 .../src/Core/util/ConfigureVectorization.h    |   23 +-
 .../Eigen/src/Core/util/Constants.h           |    2 +-
 .../src/Core/util/DisableStupidWarnings.h     |   94 +-
 .../Eigen/src/Core/util/ForwardDeclarations.h |    0
 .../Eigen/src/Core/util/IndexedViewHelper.h   |    2 +-
 .../Eigen/src/Core/util/IntegralConstant.h    |    6 +-
 .../Eigen/src/Core/util/MKL_support.h         |    0
 .../{ => eigen3}/Eigen/src/Core/util/Macros.h |   79 +-
 .../{ => eigen3}/Eigen/src/Core/util/Memory.h |  121 +-
 .../{ => eigen3}/Eigen/src/Core/util/Meta.h   |   48 +-
 .../Eigen/src/Core/util/NonMPL2.h             |    0
 .../src/Core/util/ReenableStupidWarnings.h    |    0
 .../Eigen/src/Core/util/ReshapedHelper.h      |    0
 .../Eigen/src/Core/util/StaticAssert.h        |    0
 .../Eigen/src/Core/util/SymbolicIndex.h       |    0
 .../Eigen/src/Core/util/XprHelper.h           |    0
 .../src/Eigenvalues/ComplexEigenSolver.h      |    5 +-
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 .../src/Eigenvalues/ComplexSchur_LAPACKE.h    |    0
 .../Eigen/src/Eigenvalues/EigenSolver.h       |    0
 .../src/Eigenvalues/GeneralizedEigenSolver.h  |   33 +-
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 .../src/Eigenvalues/HessenbergDecomposition.h |    0
 .../src/Eigenvalues/MatrixBaseEigenvalues.h   |    0
 .../Eigen/src/Eigenvalues/RealQZ.h            |    0
 .../Eigen/src/Eigenvalues/RealSchur.h         |   53 +-
 .../Eigen/src/Eigenvalues/RealSchur_LAPACKE.h |    0
 .../src/Eigenvalues/SelfAdjointEigenSolver.h  |    0
 .../SelfAdjointEigenSolver_LAPACKE.h          |    0
 .../src/Eigenvalues/Tridiagonalization.h      |    3 +-
 .../Eigen/src/Geometry/AlignedBox.h           |    0
 .../Eigen/src/Geometry/AngleAxis.h            |    0
 .../Eigen/src/Geometry/EulerAngles.h          |    0
 .../Eigen/src/Geometry/Homogeneous.h          |    0
 .../Eigen/src/Geometry/Hyperplane.h           |    0
 .../Eigen/src/Geometry/OrthoMethods.h         |    0
 .../Eigen/src/Geometry/ParametrizedLine.h     |    0
 .../Eigen/src/Geometry/Quaternion.h           |    0
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 .../{ => eigen3}/Eigen/src/Geometry/Scaling.h |    0
 .../Eigen/src/Geometry/Transform.h            |    5 +-
 .../Eigen/src/Geometry/Translation.h          |    0
 .../{ => eigen3}/Eigen/src/Geometry/Umeyama.h |    6 +-
 .../Eigen/src/Geometry/arch/Geometry_SIMD.h   |    0
 .../Eigen/src/Householder/BlockHouseholder.h  |    0
 .../Eigen/src/Householder/Householder.h       |    2 +-
 .../src/Householder/HouseholderSequence.h     |   12 +-
 .../BasicPreconditioners.h                    |    0
 .../src/IterativeLinearSolvers/BiCGSTAB.h     |    6 +-
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 .../{ => eigen3}/Eigen/src/LU/FullPivLU.h     |    0
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 .../Eigen/src/LU/PartialPivLU_LAPACKE.h       |    0
 .../Eigen/src/LU/arch/InverseSize4.h          |   24 +-
 .../Eigen/src/MetisSupport/MetisSupport.h     |    0
 .../Eigen/src/OrderingMethods/Amd.h           |    0
 .../Eigen/src/OrderingMethods/Eigen_Colamd.h  |    0
 .../Eigen/src/OrderingMethods/Ordering.h      |    0
 .../Eigen/src/PaStiXSupport/PaStiXSupport.h   |    0
 .../Eigen/src/PardisoSupport/PardisoSupport.h |    0
 .../Eigen/src/QR/ColPivHouseholderQR.h        |    0
 .../src/QR/ColPivHouseholderQR_LAPACKE.h      |    0
 .../src/QR/CompleteOrthogonalDecomposition.h  |    0
 .../Eigen/src/QR/FullPivHouseholderQR.h       |    0
 .../{ => eigen3}/Eigen/src/QR/HouseholderQR.h |    0
 .../Eigen/src/QR/HouseholderQR_LAPACKE.h      |    0
 .../src/SPQRSupport/SuiteSparseQRSupport.h    |   10 +-
 .../{ => eigen3}/Eigen/src/SVD/BDCSVD.h       |   27 +-
 .../{ => eigen3}/Eigen/src/SVD/JacobiSVD.h    |    1 -
 .../Eigen/src/SVD/JacobiSVD_LAPACKE.h         |    0
 .../{ => eigen3}/Eigen/src/SVD/SVDBase.h      |    0
 .../Eigen/src/SVD/UpperBidiagonalization.h    |   11 +-
 .../src/SparseCholesky/SimplicialCholesky.h   |    0
 .../SparseCholesky/SimplicialCholesky_impl.h  |    0
 .../Eigen/src/SparseCore/AmbiVector.h         |    0
 .../Eigen/src/SparseCore/CompressedStorage.h  |    0
 .../ConservativeSparseSparseProduct.h         |    0
 .../Eigen/src/SparseCore/MappedSparseMatrix.h |    0
 .../Eigen/src/SparseCore/SparseAssign.h       |    0
 .../Eigen/src/SparseCore/SparseBlock.h        |    7 +-
 .../Eigen/src/SparseCore/SparseColEtree.h     |    0
 .../src/SparseCore/SparseCompressedBase.h     |    0
 .../src/SparseCore/SparseCwiseBinaryOp.h      |    0
 .../Eigen/src/SparseCore/SparseCwiseUnaryOp.h |    0
 .../Eigen/src/SparseCore/SparseDenseProduct.h |    0
 .../src/SparseCore/SparseDiagonalProduct.h    |    0
 .../Eigen/src/SparseCore/SparseDot.h          |    0
 .../Eigen/src/SparseCore/SparseFuzzy.h        |    0
 .../Eigen/src/SparseCore/SparseMap.h          |    1 -
 .../Eigen/src/SparseCore/SparseMatrix.h       |    4 +-
 .../Eigen/src/SparseCore/SparseMatrixBase.h   |    5 +-
 .../Eigen/src/SparseCore/SparsePermutation.h  |    0
 .../Eigen/src/SparseCore/SparseProduct.h      |    1 -
 .../Eigen/src/SparseCore/SparseRedux.h        |    0
 .../Eigen/src/SparseCore/SparseRef.h          |    0
 .../src/SparseCore/SparseSelfAdjointView.h    |    0
 .../Eigen/src/SparseCore/SparseSolverBase.h   |    0
 .../SparseSparseProductWithPruning.h          |   24 +-
 .../Eigen/src/SparseCore/SparseTranspose.h    |    0
 .../src/SparseCore/SparseTriangularView.h     |    0
 .../Eigen/src/SparseCore/SparseUtil.h         |    0
 .../Eigen/src/SparseCore/SparseVector.h       |    2 -
 .../Eigen/src/SparseCore/SparseView.h         |    0
 .../Eigen/src/SparseCore/TriangularSolver.h   |    4 +-
 .../Eigen/src/SparseLU/SparseLU.h             |   18 +-
 .../Eigen/src/SparseLU/SparseLUImpl.h         |    0
 .../Eigen/src/SparseLU/SparseLU_Memory.h      |    0
 .../Eigen/src/SparseLU/SparseLU_Structs.h     |    4 +-
 .../src/SparseLU/SparseLU_SupernodalMatrix.h  |    7 +-
 .../Eigen/src/SparseLU/SparseLU_Utils.h       |    0
 .../Eigen/src/SparseLU/SparseLU_column_bmod.h |    0
 .../Eigen/src/SparseLU/SparseLU_column_dfs.h  |    0
 .../src/SparseLU/SparseLU_copy_to_ucol.h      |    0
 .../Eigen/src/SparseLU/SparseLU_gemm_kernel.h |  280 ++
 .../src/SparseLU/SparseLU_heap_relax_snode.h  |    5 +
 .../Eigen/src/SparseLU/SparseLU_kernel_bmod.h |    3 +-
 .../Eigen/src/SparseLU/SparseLU_panel_bmod.h  |    3 +-
 .../Eigen/src/SparseLU/SparseLU_panel_dfs.h   |    0
 .../Eigen/src/SparseLU/SparseLU_pivotL.h      |    0
 .../Eigen/src/SparseLU/SparseLU_pruneL.h      |    0
 .../Eigen/src/SparseLU/SparseLU_relax_snode.h |    0
 .../Eigen/src/SparseQR/SparseQR.h             |    2 +-
 .../Eigen/src/StlSupport/StdDeque.h           |    0
 .../Eigen/src/StlSupport/StdList.h            |    0
 .../Eigen/src/StlSupport/StdVector.h          |    0
 .../Eigen/src/StlSupport/details.h            |    0
 .../Eigen/src/SuperLUSupport/SuperLUSupport.h |    0
 .../Eigen/src/UmfPackSupport/UmfPackSupport.h |    0
 .../{ => eigen3}/Eigen/src/misc/Image.h       |    0
 .../{ => eigen3}/Eigen/src/misc/Kernel.h      |    0
 .../{ => eigen3}/Eigen/src/misc/RealSvd2x2.h  |    0
 .../{ => eigen3}/Eigen/src/misc/blas.h        |    0
 .../{ => eigen3}/Eigen/src/misc/lapack.h      |    0
 .../{ => eigen3}/Eigen/src/misc/lapacke.h     |    0
 .../Eigen/src/misc/lapacke_mangling.h         |    0
 .../Eigen/src/plugins/ArrayCwiseBinaryOps.h   |   93 +-
 .../Eigen/src/plugins/ArrayCwiseUnaryOps.h    |    0
 .../Eigen/src/plugins/BlockMethods.h          |    0
 .../Eigen/src/plugins/CommonCwiseBinaryOps.h  |    0
 .../Eigen/src/plugins/CommonCwiseUnaryOps.h   |    0
 .../Eigen/src/plugins/IndexedViewMethods.h    |    6 +-
 .../Eigen/src/plugins/MatrixCwiseBinaryOps.h  |   48 +-
 .../Eigen/src/plugins/MatrixCwiseUnaryOps.h   |    0
 .../Eigen/src/plugins/ReshapedMethods.h       |    0
 .../eigen3/signature_of_eigen3_matrix_library |    1 +
 .../eigen3/unsupported/Eigen/AdolcForward     |  159 ++
 .../eigen3/unsupported/Eigen/AlignedVector3   |  234 ++
 .../eigen3/unsupported/Eigen/ArpackSupport    |   30 +
 .../include/eigen3/unsupported/Eigen/AutoDiff |   46 +
 .../include/eigen3/unsupported/Eigen/BVH      |   95 +
 .../eigen3/unsupported/Eigen/CXX11/Tensor     |  137 +
 .../unsupported/Eigen/CXX11/TensorSymmetry    |   42 +
 .../eigen3/unsupported/Eigen/CXX11/ThreadPool |   74 +
 .../Eigen/CXX11/src/Tensor/Tensor.h           |  554 ++++
 .../Eigen/CXX11/src/Tensor/TensorArgMax.h     |  329 +++
 .../Eigen/CXX11/src/Tensor/TensorAssign.h     |  247 ++
 .../Eigen/CXX11/src/Tensor/TensorBase.h       | 1176 ++++++++
 .../Eigen/CXX11/src/Tensor/TensorBlock.h      | 1559 +++++++++++
 .../CXX11/src/Tensor/TensorBroadcasting.h     | 1093 ++++++++
 .../Eigen/CXX11/src/Tensor/TensorChipping.h   |  518 ++++
 .../CXX11/src/Tensor/TensorConcatenation.h    |  377 +++
 .../CXX11/src/Tensor/TensorContraction.h      | 1023 +++++++
 .../src/Tensor/TensorContractionBlocking.h    |   73 +
 .../CXX11/src/Tensor/TensorContractionCuda.h  |    6 +
 .../CXX11/src/Tensor/TensorContractionGpu.h   | 1413 ++++++++++
 .../src/Tensor/TensorContractionMapper.h      |  575 ++++
 .../CXX11/src/Tensor/TensorContractionSycl.h  | 1650 ++++++++++++
 .../src/Tensor/TensorContractionThreadPool.h  | 1679 ++++++++++++
 .../Eigen/CXX11/src/Tensor/TensorConversion.h |  456 ++++
 .../CXX11/src/Tensor/TensorConvolution.h      | 1132 ++++++++
 .../CXX11/src/Tensor/TensorConvolutionSycl.h  |  544 ++++
 .../Eigen/CXX11/src/Tensor/TensorCostModel.h  |  214 ++
 .../Eigen/CXX11/src/Tensor/TensorCustomOp.h   |  347 +++
 .../Eigen/CXX11/src/Tensor/TensorDevice.h     |  137 +
 .../Eigen/CXX11/src/Tensor/TensorDeviceCuda.h |    6 +
 .../CXX11/src/Tensor/TensorDeviceDefault.h    |  104 +
 .../Eigen/CXX11/src/Tensor/TensorDeviceGpu.h  |  389 +++
 .../Eigen/CXX11/src/Tensor/TensorDeviceSycl.h | 1048 +++++++
 .../CXX11/src/Tensor/TensorDeviceThreadPool.h |  409 +++
 .../CXX11/src/Tensor/TensorDimensionList.h    |  236 ++
 .../Eigen/CXX11/src/Tensor/TensorDimensions.h |  490 ++++
 .../Eigen/CXX11/src/Tensor/TensorEvalTo.h     |  236 ++
 .../Eigen/CXX11/src/Tensor/TensorEvaluator.h  |  983 +++++++
 .../Eigen/CXX11/src/Tensor/TensorExecutor.h   |  703 +++++
 .../Eigen/CXX11/src/Tensor/TensorExpr.h       |  388 +++
 .../Eigen/CXX11/src/Tensor/TensorFFT.h        |  669 +++++
 .../Eigen/CXX11/src/Tensor/TensorFixedSize.h  |  379 +++
 .../Eigen/CXX11/src/Tensor/TensorForcedEval.h |  237 ++
 .../src/Tensor/TensorForwardDeclarations.h    |  191 ++
 .../Eigen/CXX11/src/Tensor/TensorFunctors.h   |  488 ++++
 .../Eigen/CXX11/src/Tensor/TensorGenerator.h  |  302 +++
 .../CXX11/src/Tensor/TensorGlobalFunctions.h  |   33 +
 .../src/Tensor/TensorGpuHipCudaDefines.h      |   99 +
 .../src/Tensor/TensorGpuHipCudaUndefines.h    |   44 +
 .../Eigen/CXX11/src/Tensor/TensorIO.h         |   79 +
 .../Eigen/CXX11/src/Tensor/TensorImagePatch.h |  603 +++++
 .../Eigen/CXX11/src/Tensor/TensorIndexList.h  |  738 +++++
 .../Eigen/CXX11/src/Tensor/TensorInflation.h  |  247 ++
 .../CXX11/src/Tensor/TensorInitializer.h      |   82 +
 .../Eigen/CXX11/src/Tensor/TensorIntDiv.h     |  263 ++
 .../Eigen/CXX11/src/Tensor/TensorLayoutSwap.h |  216 ++
 .../Eigen/CXX11/src/Tensor/TensorMacros.h     |   98 +
 .../Eigen/CXX11/src/Tensor/TensorMap.h        |  327 +++
 .../Eigen/CXX11/src/Tensor/TensorMeta.h       |  311 +++
 .../Eigen/CXX11/src/Tensor/TensorMorphing.h   | 1102 ++++++++
 .../Eigen/CXX11/src/Tensor/TensorPadding.h    |  708 +++++
 .../Eigen/CXX11/src/Tensor/TensorPatch.h      |  291 ++
 .../Eigen/CXX11/src/Tensor/TensorRandom.h     |  322 +++
 .../Eigen/CXX11/src/Tensor/TensorReduction.h  |  998 +++++++
 .../CXX11/src/Tensor/TensorReductionCuda.h    |    6 +
 .../CXX11/src/Tensor/TensorReductionGpu.h     |  966 +++++++
 .../CXX11/src/Tensor/TensorReductionSycl.h    |  582 ++++
 .../Eigen/CXX11/src/Tensor/TensorRef.h        |  454 ++++
 .../Eigen/CXX11/src/Tensor/TensorReverse.h    |  465 ++++
 .../Eigen/CXX11/src/Tensor/TensorScan.h       |  528 ++++
 .../Eigen/CXX11/src/Tensor/TensorScanSycl.h   |  513 ++++
 .../Eigen/CXX11/src/Tensor/TensorShuffling.h  |  471 ++++
 .../Eigen/CXX11/src/Tensor/TensorStorage.h    |  161 ++
 .../Eigen/CXX11/src/Tensor/TensorStriding.h   |  346 +++
 .../Eigen/CXX11/src/Tensor/TensorTrace.h      |  303 +++
 .../Eigen/CXX11/src/Tensor/TensorTraits.h     |  264 ++
 .../Eigen/CXX11/src/Tensor/TensorUInt128.h    |  249 ++
 .../CXX11/src/Tensor/TensorVolumePatch.h      |  629 +++++
 .../src/TensorSymmetry/DynamicSymmetry.h      |  293 ++
 .../CXX11/src/TensorSymmetry/StaticSymmetry.h |  236 ++
 .../Eigen/CXX11/src/TensorSymmetry/Symmetry.h |  338 +++
 .../TensorSymmetry/util/TemplateGroupTheory.h |  669 +++++
 .../Eigen/CXX11/src/ThreadPool/Barrier.h      |   67 +
 .../Eigen/CXX11/src/ThreadPool/EventCount.h   |  249 ++
 .../src/ThreadPool/NonBlockingThreadPool.h    |  486 ++++
 .../Eigen/CXX11/src/ThreadPool/RunQueue.h     |  236 ++
 .../Eigen/CXX11/src/ThreadPool/ThreadCancel.h |   23 +
 .../CXX11/src/ThreadPool/ThreadEnvironment.h  |   40 +
 .../Eigen/CXX11/src/ThreadPool/ThreadLocal.h  |  301 +++
 .../src/ThreadPool/ThreadPoolInterface.h      |   48 +
 .../Eigen/CXX11/src/ThreadPool/ThreadYield.h  |   20 +
 .../Eigen/CXX11/src/util/CXX11Meta.h          |  537 ++++
 .../Eigen/CXX11/src/util/CXX11Workarounds.h   |   88 +
 .../Eigen/CXX11/src/util/EmulateArray.h       |  261 ++
 .../Eigen/CXX11/src/util/MaxSizeVector.h      |  158 ++
 .../eigen3/unsupported/Eigen/EulerAngles      |   43 +
 .../include/eigen3/unsupported/Eigen/FFT      |  419 +++
 .../eigen3/unsupported/Eigen/IterativeSolvers |   51 +
 .../eigen3/unsupported/Eigen/KroneckerProduct |   36 +
 .../unsupported/Eigen/LevenbergMarquardt      |   49 +
 .../eigen3/unsupported/Eigen/MPRealSupport    |  213 ++
 .../eigen3/unsupported/Eigen/MatrixFunctions  |  504 ++++
 .../unsupported/Eigen/MoreVectorization       |   24 +
 .../unsupported/Eigen/NonLinearOptimization   |  140 +
 .../eigen3/unsupported/Eigen/NumericalDiff    |   56 +
 .../eigen3/unsupported/Eigen/OpenGLSupport    |  322 +++
 .../eigen3/unsupported/Eigen/Polynomials      |  137 +
 .../include/eigen3/unsupported/Eigen/Skyline  |   39 +
 .../eigen3/unsupported/Eigen/SparseExtra      |   54 +
 .../eigen3/unsupported/Eigen/SpecialFunctions |  103 +
 .../include/eigen3/unsupported/Eigen/Splines  |   35 +
 .../Eigen/src/AutoDiff/AutoDiffJacobian.h     |  108 +
 .../Eigen/src/AutoDiff/AutoDiffScalar.h       |  730 +++++
 .../Eigen/src/AutoDiff/AutoDiffVector.h       |  220 ++
 .../unsupported/Eigen/src/BVH/BVAlgorithms.h  |  293 ++
 .../eigen3/unsupported/Eigen/src/BVH/KdBVH.h  |  223 ++
 .../ArpackSelfAdjointEigenSolver.h            |  790 ++++++
 .../Eigen/src/EulerAngles/EulerAngles.h       |  355 +++
 .../Eigen/src/EulerAngles/EulerSystem.h       |  305 +++
 .../unsupported/Eigen/src/FFT/ei_fftw_impl.h  |  261 ++
 .../Eigen/src/FFT/ei_kissfft_impl.h           |  449 +++
 .../IterativeSolvers/ConstrainedConjGrad.h    |  187 ++
 .../Eigen/src/IterativeSolvers/DGMRES.h       |  511 ++++
 .../Eigen/src/IterativeSolvers/GMRES.h        |  335 +++
 .../Eigen/src/IterativeSolvers/IDRS.h         |  436 +++
 .../Eigen/src/IterativeSolvers/IncompleteLU.h |   90 +
 .../IterativeSolvers/IterationController.h    |  154 ++
 .../Eigen/src/IterativeSolvers/MINRES.h       |  267 ++
 .../Eigen/src/IterativeSolvers/Scaling.h      |  193 ++
 .../KroneckerProduct/KroneckerTensorProduct.h |  305 +++
 .../Eigen/src/LevenbergMarquardt/LMcovar.h    |   84 +
 .../Eigen/src/LevenbergMarquardt/LMonestep.h  |  202 ++
 .../Eigen/src/LevenbergMarquardt/LMpar.h      |  160 ++
 .../Eigen/src/LevenbergMarquardt/LMqrsolv.h   |  188 ++
 .../LevenbergMarquardt/LevenbergMarquardt.h   |  396 +++
 .../src/MatrixFunctions/MatrixExponential.h   |  441 +++
 .../src/MatrixFunctions/MatrixFunction.h      |  569 ++++
 .../src/MatrixFunctions/MatrixLogarithm.h     |  373 +++
 .../Eigen/src/MatrixFunctions/MatrixPower.h   |  705 +++++
 .../src/MatrixFunctions/MatrixSquareRoot.h    |  368 +++
 .../Eigen/src/MatrixFunctions/StemFunction.h  |  117 +
 .../src/MoreVectorization/MathFunctions.h     |   95 +
 .../HybridNonLinearSolver.h                   |  601 +++++
 .../LevenbergMarquardt.h                      |  657 +++++
 .../Eigen/src/NonLinearOptimization/chkder.h  |   66 +
 .../Eigen/src/NonLinearOptimization/covar.h   |   70 +
 .../Eigen/src/NonLinearOptimization/dogleg.h  |  107 +
 .../Eigen/src/NonLinearOptimization/fdjac1.h  |   79 +
 .../Eigen/src/NonLinearOptimization/lmpar.h   |  298 ++
 .../Eigen/src/NonLinearOptimization/qrsolv.h  |   91 +
 .../Eigen/src/NonLinearOptimization/r1mpyq.h  |   30 +
 .../Eigen/src/NonLinearOptimization/r1updt.h  |   99 +
 .../Eigen/src/NonLinearOptimization/rwupdt.h  |   49 +
 .../Eigen/src/NumericalDiff/NumericalDiff.h   |  130 +
 .../Eigen/src/Polynomials/Companion.h         |  280 ++
 .../Eigen/src/Polynomials/PolynomialSolver.h  |  428 +++
 .../Eigen/src/Polynomials/PolynomialUtils.h   |  143 +
 .../Eigen/src/Skyline/SkylineInplaceLU.h      |  352 +++
 .../Eigen/src/Skyline/SkylineMatrix.h         |  862 ++++++
 .../Eigen/src/Skyline/SkylineMatrixBase.h     |  212 ++
 .../Eigen/src/Skyline/SkylineProduct.h        |  295 ++
 .../Eigen/src/Skyline/SkylineStorage.h        |  259 ++
 .../Eigen/src/Skyline/SkylineUtil.h           |   89 +
 .../SparseExtra/BlockOfDynamicSparseMatrix.h  |  122 +
 .../Eigen/src/SparseExtra/BlockSparseMatrix.h | 1079 ++++++++
 .../src/SparseExtra/DynamicSparseMatrix.h     |  404 +++
 .../Eigen/src/SparseExtra/MarketIO.h          |  282 ++
 .../src/SparseExtra/MatrixMarketIterator.h    |  247 ++
 .../Eigen/src/SparseExtra/RandomSetter.h      |  349 +++
 .../BesselFunctionsArrayAPI.h                 |  286 ++
 .../BesselFunctionsBFloat16.h                 |   68 +
 .../BesselFunctionsFunctors.h                 |  357 +++
 .../SpecialFunctions/BesselFunctionsHalf.h    |   66 +
 .../SpecialFunctions/BesselFunctionsImpl.h    | 1959 ++++++++++++++
 .../BesselFunctionsPacketMath.h               |  118 +
 .../SpecialFunctions/HipVectorCompatibility.h |   67 +
 .../SpecialFunctionsArrayAPI.h                |  167 ++
 .../SpecialFunctionsBFloat16.h                |   58 +
 .../SpecialFunctionsFunctors.h                |  330 +++
 .../SpecialFunctions/SpecialFunctionsHalf.h   |   58 +
 .../SpecialFunctions/SpecialFunctionsImpl.h   | 2045 ++++++++++++++
 .../SpecialFunctionsPacketMath.h              |   79 +
 .../arch/AVX/BesselFunctions.h                |   46 +
 .../arch/AVX/SpecialFunctions.h               |   16 +
 .../arch/AVX512/BesselFunctions.h             |   46 +
 .../arch/AVX512/SpecialFunctions.h            |   16 +
 .../arch/GPU/SpecialFunctions.h               |  369 +++
 .../arch/NEON/BesselFunctions.h               |   54 +
 .../arch/NEON/SpecialFunctions.h              |   34 +
 .../unsupported/Eigen/src/Splines/Spline.h    |  507 ++++
 .../Eigen/src/Splines/SplineFitting.h         |  431 +++
 .../unsupported/Eigen/src/Splines/SplineFwd.h |   93 +
 3rdParty/ceres_2.2/include/glog/export.h      |   43 -
 .../ceres_2.2/include/glog/log_severity.h     |   24 +-
 3rdParty/ceres_2.2/include/glog/logging.h     |  532 +---
 3rdParty/ceres_2.2/include/glog/platform.h    |   58 -
 3rdParty/ceres_2.2/include/glog/raw_logging.h |   51 +-
 3rdParty/ceres_2.2/include/glog/stl_logging.h |    6 +-
 3rdParty/ceres_2.2/include/glog/vlog_is_on.h  |   43 +-
 3rdParty/ceres_2.2/lib/ceres-debug.lib        |  Bin 132690 -> 132690 bytes
 3rdParty/ceres_2.2/lib/ceres.lib              |  Bin 130656 -> 130656 bytes
 .../lib/ceres_cuda_kernels-debug.lib          |  Bin 0 -> 2239490 bytes
 3rdParty/ceres_2.2/lib/ceres_cuda_kernels.lib |  Bin 0 -> 1378330 bytes
 .../lib/cmake/Ceres/CeresConfig.cmake         |    6 +-
 .../lib/cmake/Ceres/CeresConfigVersion.cmake  |    5 +
 .../lib/cmake/Ceres/CeresTargets-debug.cmake  |   10 +
 .../cmake/Ceres/CeresTargets-release.cmake    |   10 +
 .../lib/cmake/Ceres/CeresTargets.cmake        |   29 +-
 .../gflags-nonamespace-targets-debug.cmake    |   10 +
 .../gflags-nonamespace-targets-release.cmake  |   10 +
 .../gflags/gflags-nonamespace-targets.cmake   |   35 +-
 .../cmake/gflags/gflags-targets-debug.cmake   |   10 +
 .../cmake/gflags/gflags-targets-release.cmake |   10 +
 .../lib/cmake/gflags/gflags-targets.cmake     |   35 +-
 .../lib/cmake/glog/glog-config-version.cmake  |   11 +-
 .../lib/cmake/glog/glog-config.cmake          |   12 +-
 .../lib/cmake/glog/glog-modules.cmake         |   18 -
 .../lib/cmake/glog/glog-targets-debug.cmake   |   19 +
 .../lib/cmake/glog/glog-targets.cmake         |   23 +-
 3rdParty/ceres_2.2/lib/gflags.lib             |  Bin 29946 -> 29946 bytes
 3rdParty/ceres_2.2/lib/gflags_debug.lib       |  Bin 30516 -> 30516 bytes
 3rdParty/ceres_2.2/lib/gflags_nothreads.lib   |  Bin 0 -> 30848 bytes
 .../ceres_2.2/lib/gflags_nothreads_debug.lib  |  Bin 0 -> 31346 bytes
 3rdParty/ceres_2.2/lib/glog.lib               |  Bin 67180 -> 55062 bytes
 3rdParty/ceres_2.2/lib/glogd.lib              |  Bin 65560 -> 55222 bytes
 3rdParty/ceres_2.2/lib/pkgconfig/libglog.pc   |   11 -
 .../share/eigen3/cmake/Eigen3Config.cmake     |   37 +
 .../eigen3/cmake/Eigen3ConfigVersion.cmake    |   70 +
 .../share/eigen3/cmake/Eigen3Targets.cmake    |  106 +
 .../share/eigen3/cmake/UseEigen3.cmake        |    6 +
 594 files changed, 70485 insertions(+), 6096 deletions(-)
 create mode 100644 3rdParty/ceres_1.14/bin/ceres-debug.dll
 create mode 100644 3rdParty/ceres_1.14/bin/ceres.dll
 create mode 100644 3rdParty/ceres_1.14/bin/gflags.dll
 create mode 100644 3rdParty/ceres_1.14/bin/gflags_debug.dll
 create mode 100644 3rdParty/ceres_1.14/bin/gflags_nothreads.dll
 create mode 100644 3rdParty/ceres_1.14/bin/gflags_nothreads_debug.dll
 create mode 100644 3rdParty/ceres_1.14/bin/glog.dll
 create mode 100644 3rdParty/ceres_1.14/bin/glogd.dll
 create mode 100644 3rdParty/ceres_1.14/include/ceres/version.h
 create mode 100644 3rdParty/ceres_1.14/lib/ceres-debug.lib
 create mode 100644 3rdParty/ceres_1.14/lib/gflags_debug.lib
 create mode 100644 3rdParty/ceres_1.14/lib/gflags_nothreads_debug.lib
 create mode 100644 3rdParty/ceres_1.14/lib/glogd.lib
 create mode 100644 3rdParty/ceres_2.2/bin/ceres-debug.dll
 create mode 100644 3rdParty/ceres_2.2/bin/ceres.dll
 create mode 100644 3rdParty/ceres_2.2/bin/gflags.dll
 create mode 100644 3rdParty/ceres_2.2/bin/gflags_debug.dll
 create mode 100644 3rdParty/ceres_2.2/bin/gflags_nothreads.dll
 create mode 100644 3rdParty/ceres_2.2/bin/gflags_nothreads_debug.dll
 create mode 100644 3rdParty/ceres_2.2/bin/glog.dll
 create mode 100644 3rdParty/ceres_2.2/bin/glogd.dll
 delete mode 100644 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
 delete mode 100644 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h
 create mode 100644 3rdParty/ceres_2.2/include/ceres/version.h
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Cholesky (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/CholmodSupport (96%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Core (98%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Dense (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Eigen (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Eigenvalues (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Geometry (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Householder (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/IterativeLinearSolvers (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Jacobi (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/KLUSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/LU (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/MetisSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/OrderingMethods (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/PaStiXSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/PardisoSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/QR (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/QtAlignedMalloc (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SPQRSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SVD (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/Sparse (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SparseCholesky (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SparseCore (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SparseLU (97%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SparseQR (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/StdDeque (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/StdList (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/StdVector (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/SuperLUSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/UmfPackSupport (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Cholesky/LDLT.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Cholesky/LLT.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Cholesky/LLT_LAPACKE.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/CholmodSupport/CholmodSupport.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/ArithmeticSequence.h (98%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/Array.h (96%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/ArrayBase.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/ArrayWrapper.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/Assign.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/AssignEvaluator.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/Assign_MKL.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/BandMatrix.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/Block.h (91%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/BooleanRedux.h (68%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CommaInitializer.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/ConditionEstimator.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CoreEvaluators.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CoreIterators.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CwiseBinaryOp.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CwiseNullaryOp.h (99%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CwiseTernaryOp.h (100%)
 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/CwiseUnaryOp.h (100%)
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 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/products/GeneralBlockPanelKernel.h (99%)
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 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/Core/util/IntegralConstant.h (98%)
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 rename 3rdParty/ceres_2.2/include/{ => eigen3}/Eigen/src/QR/FullPivHouseholderQR.h (100%)
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diff --git a/3rdParty/ceres_1.14/bin/ceres.dll b/3rdParty/ceres_1.14/bin/ceres.dll
new file mode 100644
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diff --git a/3rdParty/ceres_1.14/bin/gflags.dll b/3rdParty/ceres_1.14/bin/gflags.dll
new file mode 100644
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new file mode 100644
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diff --git a/3rdParty/ceres_1.14/bin/gflags_nothreads.dll b/3rdParty/ceres_1.14/bin/gflags_nothreads.dll
new file mode 100644
index 0000000000000000000000000000000000000000..be8aa2f47c8fb128c28335348a3c516ce3419083
GIT binary patch
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diff --git a/3rdParty/ceres_1.14/bin/gflags_nothreads_debug.dll b/3rdParty/ceres_1.14/bin/gflags_nothreads_debug.dll
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diff --git a/3rdParty/ceres_1.14/bin/glogd.dll b/3rdParty/ceres_1.14/bin/glogd.dll
new file mode 100644
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diff --git a/3rdParty/ceres_1.14/include/ceres/version.h b/3rdParty/ceres_1.14/include/ceres/version.h
new file mode 100644
index 0000000..3ccd4c8
--- /dev/null
+++ b/3rdParty/ceres_1.14/include/ceres/version.h
@@ -0,0 +1,48 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: mierle@gmail.com (Keir Mierle)
+
+#ifndef CERES_PUBLIC_VERSION_H_
+#define CERES_PUBLIC_VERSION_H_
+
+#define CERES_VERSION_MAJOR 1
+#define CERES_VERSION_MINOR 14
+#define CERES_VERSION_REVISION 0
+
+// Classic CPP stringifcation; the extra level of indirection allows the
+// preprocessor to expand the macro before being converted to a string.
+#define CERES_TO_STRING_HELPER(x) #x
+#define CERES_TO_STRING(x) CERES_TO_STRING_HELPER(x)
+
+// The Ceres version as a string; for example "1.9.0".
+#define CERES_VERSION_STRING CERES_TO_STRING(CERES_VERSION_MAJOR) "." \
+                             CERES_TO_STRING(CERES_VERSION_MINOR) "." \
+                             CERES_TO_STRING(CERES_VERSION_REVISION)
+
+#endif  // CERES_PUBLIC_VERSION_H_
diff --git a/3rdParty/ceres_1.14/lib/ceres-debug.lib b/3rdParty/ceres_1.14/lib/ceres-debug.lib
new file mode 100644
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diff --git a/3rdParty/ceres_1.14/lib/gflags_debug.lib b/3rdParty/ceres_1.14/lib/gflags_debug.lib
new file mode 100644
index 0000000000000000000000000000000000000000..6fffdf6f29259960765d630c2b79975eee838ca8
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diff --git a/3rdParty/ceres_1.14/lib/gflags_nothreads_debug.lib b/3rdParty/ceres_1.14/lib/gflags_nothreads_debug.lib
new file mode 100644
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diff --git a/3rdParty/ceres_1.14/lib/glogd.lib b/3rdParty/ceres_1.14/lib/glogd.lib
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diff --git a/3rdParty/ceres_2.2/bin/ceres-debug.dll b/3rdParty/ceres_2.2/bin/ceres-debug.dll
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diff --git a/3rdParty/ceres_2.2/bin/gflags.dll b/3rdParty/ceres_2.2/bin/gflags.dll
new file mode 100644
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diff --git a/3rdParty/ceres_2.2/bin/gflags_nothreads.dll b/3rdParty/ceres_2.2/bin/gflags_nothreads.dll
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diff --git a/3rdParty/ceres_2.2/bin/gflags_nothreads_debug.dll b/3rdParty/ceres_2.2/bin/gflags_nothreads_debug.dll
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new file mode 100644
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diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
deleted file mode 100644
index bf01dba..0000000
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
+++ /dev/null
@@ -1,159 +0,0 @@
-//#define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
-#ifdef EIGEN_POWER_USE_PREFETCH
-#define EIGEN_POWER_PREFETCH(p)  prefetch(p)
-#else
-#define EIGEN_POWER_PREFETCH(p)
-#endif
-
-namespace Eigen {
-
-namespace internal {
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_ALWAYS_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_extra_cols(
-  const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index offsetB,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask);
-
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_cols(
-  const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index offsetB,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask);
-
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs);
-
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int StorageOrder, bool Complex, int N>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,N>& acc, const DataMapper& res, Index row, Index col);
-
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha);
-
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag);
-
-// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
-template<typename Packet, typename Packetc, int N>
-EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,N>& taccReal, PacketBlock<Packet,N>& taccImag, PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2)
-{
-  acc1.packet[0].v = vec_mergeh(taccReal.packet[0], taccImag.packet[0]);
-  if (N > 1) {
-    acc1.packet[1].v = vec_mergeh(taccReal.packet[1], taccImag.packet[1]);
-  }
-  if (N > 2) {
-    acc1.packet[2].v = vec_mergeh(taccReal.packet[2], taccImag.packet[2]);
-  }
-  if (N > 3) {
-    acc1.packet[3].v = vec_mergeh(taccReal.packet[3], taccImag.packet[3]);
-  }
-
-  acc2.packet[0].v = vec_mergel(taccReal.packet[0], taccImag.packet[0]);
-  if (N > 1) {
-    acc2.packet[1].v = vec_mergel(taccReal.packet[1], taccImag.packet[1]);
-  }
-  if (N > 2) {
-    acc2.packet[2].v = vec_mergel(taccReal.packet[2], taccImag.packet[2]);
-  }
-  if (N > 3) {
-    acc2.packet[3].v = vec_mergel(taccReal.packet[3], taccImag.packet[3]);
-  }
-}
-
-template<typename Packet, typename Packetc, int N>
-EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,N>& taccReal, PacketBlock<Packet,N>& taccImag, PacketBlock<Packetc,N*2>& tRes, PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2)
-{
-  bcouple_common<Packet, Packetc, N>(taccReal, taccImag, acc1, acc2);
-
-  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
-  if (N > 1) {
-    acc1.packet[1] = padd<Packetc>(tRes.packet[1], acc1.packet[1]);
-  }
-  if (N > 2) {
-    acc1.packet[2] = padd<Packetc>(tRes.packet[2], acc1.packet[2]);
-  }
-  if (N > 3) {
-    acc1.packet[3] = padd<Packetc>(tRes.packet[3], acc1.packet[3]);
-  }
-
-  acc2.packet[0] = padd<Packetc>(tRes.packet[0+N], acc2.packet[0]);
-  if (N > 1) {
-    acc2.packet[1] = padd<Packetc>(tRes.packet[1+N], acc2.packet[1]);
-  }
-  if (N > 2) {
-    acc2.packet[2] = padd<Packetc>(tRes.packet[2+N], acc2.packet[2]);
-  }
-  if (N > 3) {
-    acc2.packet[3] = padd<Packetc>(tRes.packet[3+N], acc2.packet[3]);
-  }
-}
-
-// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
-template<typename Scalar, typename Packet>
-EIGEN_ALWAYS_INLINE Packet ploadRhs(const Scalar* rhs)
-{
-  return ploadu<Packet>(rhs);
-}
-
-} // end namespace internal
-} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h b/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h
deleted file mode 100644
index bb84ac9..0000000
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h
+++ /dev/null
@@ -1,2400 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
-#define EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
-
-#include "../../InternalHeaderCheck.h"
-
-#if defined(__MMA__) && !EIGEN_ALTIVEC_DISABLE_MMA
-#if EIGEN_COMP_LLVM || (__GNUC__ > 10 || __GNUC_MINOR__ >= 3)
-#define USE_GEMV_MMA
-#endif
-
-#if !EIGEN_COMP_LLVM && (__GNUC__ == 10 && __GNUC_MINOR__ <= 3)
-// Only allow one vector_pair in buggy gcc - gcc 10.3 has a bug
-#define GCC_ONE_VECTORPAIR_BUG
-#endif
-#endif
-
-//#define USE_SLOWER_GEMV_MMA   // MMA is currently not as fast as VSX in complex double GEMV (revisit when gcc is improved)
-
-//#define EIGEN_POWER_USE_GEMV_PREFETCH
-#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
-#define EIGEN_POWER_GEMV_PREFETCH(p)  prefetch(p)
-#else
-#define EIGEN_POWER_GEMV_PREFETCH(p)
-#endif
-
-#ifdef __has_builtin
-#if !__has_builtin(__builtin_vsx_assemble_pair)
-#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
-#endif
-#if !__has_builtin(__builtin_vsx_disassemble_pair)
-#define __builtin_vsx_disassemble_pair __builtin_mma_disassemble_pair
-#endif
-#endif
-
-#if EIGEN_COMP_LLVM
-#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
-  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
-#else
-#if (__GNUC__ <= 10)
-#if (__GNUC_MINOR__ > 3)
-#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
-  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
-#else
-#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
-  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
-#endif
-#else
-#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
-  __builtin_vsx_build_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
-#endif
-#endif
-
-#define GEMV_IS_COMPLEX_COMPLEX ((sizeof(LhsPacket) == 16) && (sizeof(RhsPacket) == 16))
-#define GEMV_IS_FLOAT           (ResPacketSize == (16 / sizeof(float)))
-#define GEMV_IS_SCALAR          (sizeof(ResPacket) != 16)
-#define GEMV_IS_COMPLEX_FLOAT   (ResPacketSize == (16 / sizeof(std::complex<float>)))
-
-/** \internal multiply and add and store results */
-template<typename ResPacket, typename ResScalar>
-EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResPacket& palpha, ResPacket& data)
-{
-    pstoreu(res, pmadd(data, palpha, ploadu<ResPacket>(res)));
-}
-
-template<typename ResScalar>
-EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResScalar& alpha, ResScalar& data)
-{
-    *res += (alpha * data);
-}
-
-#define GEMV_UNROLL(func, N) \
-  func(0, N) func(1, N) func(2, N) func(3, N) \
-  func(4, N) func(5, N) func(6, N) func(7, N)
-
-#define GEMV_UNROLL_HALF(func, N) \
-  func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
-
-#define GEMV_GETN(N) (((N) * ResPacketSize) >> 2)
-
-#define GEMV_LOADPACKET_COL(iter) \
-  lhs.template load<LhsPacket, LhsAlignment>(i + ((iter) * LhsPacketSize), j)
-
-#ifdef USE_GEMV_MMA
-#define GEMV_UNROLL3(func, N, which) \
-  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) \
-  func(4, N, which) func(5, N, which) func(6, N, which) func(7, N, which)
-
-#define GEMV_UNUSED_VAR(iter, N, which) \
-  if (GEMV_GETN(N) <= iter) { \
-    EIGEN_UNUSED_VARIABLE(which##iter); \
-  }
-
-#define GEMV_UNUSED_EXTRA_VAR(iter, N, which) \
-  if (N <= iter) { \
-    EIGEN_UNUSED_VARIABLE(which##iter); \
-  }
-
-#define GEMV_UNUSED_EXTRA(N, which) \
-  GEMV_UNROLL3(GEMV_UNUSED_EXTRA_VAR, N, which)
-
-#define GEMV_UNUSED(N, which) \
-  GEMV_UNROLL3(GEMV_UNUSED_VAR, N, which)
-
-#define GEMV_INIT_MMA(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    __builtin_mma_xxsetaccz(&e##iter); \
-  }
-
-#if EIGEN_COMP_LLVM
-#define GEMV_LOADPAIR_COL_MMA(iter1, iter2) \
-  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_COL(iter2), GEMV_LOADPACKET_COL((iter2) + 1));
-#else
-#define GEMV_LOADPAIR_COL_MMA(iter1, iter2) \
-  const LhsScalar& src##iter1 = lhs(i + ((iter1 * 32) / sizeof(LhsScalar)), j); \
-  b##iter1 = *reinterpret_cast<__vector_pair *>(const_cast<LhsScalar *>(&src##iter1));
-#endif
-
-#define GEMV_LOAD1A_COL_MMA(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    if (GEMV_IS_FLOAT) { \
-      g##iter = GEMV_LOADPACKET_COL(iter); \
-      EIGEN_UNUSED_VARIABLE(b##iter); \
-    } else { \
-      GEMV_LOADPAIR_COL_MMA(iter, iter << 1) \
-      EIGEN_UNUSED_VARIABLE(g##iter); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(b##iter); \
-    EIGEN_UNUSED_VARIABLE(g##iter); \
-  }
-
-#define GEMV_WORK1A_COL_MMA(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    if (GEMV_IS_FLOAT) { \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, a0, g##iter); \
-    } else { \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, b##iter, a0); \
-    } \
-  }
-
-#define GEMV_LOAD1B_COL_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN(N) > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      GEMV_LOADPAIR_COL_MMA(iter2, iter2) \
-      EIGEN_UNUSED_VARIABLE(b##iter3); \
-    } else { \
-      GEMV_LOADPAIR_COL_MMA(iter2, iter2 << 1) \
-      GEMV_LOADPAIR_COL_MMA(iter3, iter3 << 1) \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(b##iter2); \
-    EIGEN_UNUSED_VARIABLE(b##iter3); \
-  } \
-  EIGEN_UNUSED_VARIABLE(g##iter2); \
-  EIGEN_UNUSED_VARIABLE(g##iter3);
-
-#define GEMV_WORK1B_COL_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN(N) > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      LhsPacket h[2]; \
-      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(h), &b##iter2); \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, a0, h[0]); \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, a0, h[1]); \
-    } else { \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, b##iter2, a0); \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, b##iter3, a0); \
-    } \
-  }
-
-#if EIGEN_COMP_LLVM
-#define GEMV_LOAD_COL_MMA(N) \
-  if (GEMV_GETN(N) > 1) { \
-    GEMV_UNROLL_HALF(GEMV_LOAD1B_COL_MMA, (N >> 1)) \
-  } else { \
-    GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N) \
-  }
-
-#define GEMV_WORK_COL_MMA(N) \
-  if (GEMV_GETN(N) > 1) { \
-    GEMV_UNROLL_HALF(GEMV_WORK1B_COL_MMA, (N >> 1)) \
-  } else { \
-    GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N) \
-  }
-#else
-#define GEMV_LOAD_COL_MMA(N) \
-  GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N)
-
-#define GEMV_WORK_COL_MMA(N) \
-  GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N)
-#endif
-
-#define GEMV_DISASSEMBLE_MMA(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    __builtin_mma_disassemble_acc(&result##iter.packet, &e##iter); \
-    if (!GEMV_IS_FLOAT) { \
-      result##iter.packet[0][1] = result##iter.packet[1][0]; \
-      result##iter.packet[2][1] = result##iter.packet[3][0]; \
-    } \
-  }
-
-#define GEMV_LOADPAIR2_COL_MMA(iter1, iter2) \
-  b##iter1 = *reinterpret_cast<__vector_pair *>(res + i + ((iter2) * ResPacketSize));
-
-#define GEMV_LOAD2_COL_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN(N) > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      GEMV_LOADPAIR2_COL_MMA(iter2, iter2); \
-      EIGEN_UNUSED_VARIABLE(b##iter3); \
-    } else { \
-      GEMV_LOADPAIR2_COL_MMA(iter2, iter2 << 1); \
-      GEMV_LOADPAIR2_COL_MMA(iter3, iter3 << 1); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(b##iter2); \
-    EIGEN_UNUSED_VARIABLE(b##iter3); \
-  }
-
-#if EIGEN_COMP_LLVM
-#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4) \
-  ResPacket f##iter2[2]; \
-  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(f##iter2), &b##iter2); \
-  f##iter2[0] = pmadd(result##iter2.packet[0], palpha, f##iter2[0]); \
-  f##iter2[1] = pmadd(result##iter3.packet[(iter2 == iter3) ? 2 : 0], palpha, f##iter2[1]); \
-  GEMV_BUILDPAIR_MMA(b##iter2, f##iter2[0], f##iter2[1]);
-#else
-#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4) \
-  if (GEMV_IS_FLOAT) { \
-    __asm__ ("xvmaddasp %0,%x1,%x3\n\txvmaddasp %L0,%x2,%x3" : "+&d" (b##iter2) : "wa" (result##iter3.packet[0]), "wa" (result##iter2.packet[0]), "wa" (palpha)); \
-  } else { \
-    __asm__ ("xvmaddadp %0,%x1,%x3\n\txvmaddadp %L0,%x2,%x3" : "+&d" (b##iter2) : "wa" (result##iter2.packet[2]), "wa" (result##iter2.packet[0]), "wa" (palpha)); \
-  }
-#endif
-
-#define GEMV_WORK2_COL_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN(N) > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter2); \
-    } else { \
-      GEMV_WORKPAIR2_COL_MMA(iter2, iter2, iter2 << 1); \
-      GEMV_WORKPAIR2_COL_MMA(iter3, iter3, iter3 << 1); \
-    } \
-  }
-
-#define GEMV_STOREPAIR2_COL_MMA(iter1, iter2) \
-  *reinterpret_cast<__vector_pair *>(res + i + ((iter2) * ResPacketSize)) = b##iter1;
-
-#define GEMV_STORE_COL_MMA(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    if (GEMV_IS_FLOAT) { \
-      storeMaddData<ResPacket, ResScalar>(res + i + (iter * ResPacketSize), palpha, result##iter.packet[0]); \
-    } else { \
-      GEMV_LOADPAIR2_COL_MMA(iter, iter << 1) \
-      GEMV_WORKPAIR2_COL_MMA(iter, iter, iter << 1) \
-      GEMV_STOREPAIR2_COL_MMA(iter, iter << 1) \
-    } \
-  }
-
-#define GEMV_STORE2_COL_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN(N) > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      GEMV_STOREPAIR2_COL_MMA(iter2, iter2); \
-    } else { \
-      GEMV_STOREPAIR2_COL_MMA(iter2, iter2 << 1) \
-      GEMV_STOREPAIR2_COL_MMA(iter3, iter3 << 1) \
-    } \
-  }
-
-#define GEMV_PROCESS_COL_ONE_MMA(N) \
-  GEMV_UNROLL(GEMV_INIT_MMA, N) \
-  Index j = j2; \
-  __vector_pair b0, b1, b2, b3, b4, b5, b6, b7; \
-  do { \
-    LhsPacket g0, g1, g2, g3, g4, g5, g6, g7; \
-    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0)); \
-    GEMV_UNROLL(GEMV_PREFETCH, N) \
-    GEMV_LOAD_COL_MMA(N) \
-    GEMV_WORK_COL_MMA(N) \
-  } while (++j < jend); \
-  GEMV_UNROLL(GEMV_DISASSEMBLE_MMA, N) \
-  if (GEMV_GETN(N) <= 1) { \
-    GEMV_UNROLL(GEMV_STORE_COL_MMA, N) \
-  } else { \
-    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_MMA, (N >> 1)) \
-    GEMV_UNROLL_HALF(GEMV_WORK2_COL_MMA, (N >> 1)) \
-    GEMV_UNROLL_HALF(GEMV_STORE2_COL_MMA, (N >> 1)) \
-  } \
-  i += (ResPacketSize * N);
-#endif
-
-#define GEMV_INIT(iter, N) \
-  if (N > iter) { \
-    c##iter = pset1<ResPacket>(ResScalar(0)); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(c##iter); \
-  }
-
-#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
-#define GEMV_PREFETCH(iter, N) \
-  if (GEMV_GETN(N) > ((iter >> 1) + ((N >> 1) * (iter & 1)))) { \
-    lhs.prefetch(i + (iter * LhsPacketSize) + prefetch_dist, j); \
-  }
-#else
-#define GEMV_PREFETCH(iter, N)
-#endif
-
-#define GEMV_WORK_COL(iter, N) \
-  if (N > iter) { \
-    c##iter = pcj.pmadd(GEMV_LOADPACKET_COL(iter), a0, c##iter); \
-  }
-
-#define GEMV_STORE_COL(iter, N) \
-  if (N > iter) { \
-    pstoreu(res + i + (iter * ResPacketSize), pmadd(c##iter, palpha, ploadu<ResPacket>(res + i + (iter * ResPacketSize)))); \
-  }
-
-/** \internal main macro for gemv_col - initialize accumulators, multiply and add inputs, and store results */
-#define GEMV_PROCESS_COL_ONE(N) \
-  GEMV_UNROLL(GEMV_INIT, N) \
-  Index j = j2; \
-  do { \
-    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0)); \
-    GEMV_UNROLL(GEMV_PREFETCH, N) \
-    GEMV_UNROLL(GEMV_WORK_COL, N) \
-  } while (++j < jend); \
-  GEMV_UNROLL(GEMV_STORE_COL, N) \
-  i += (ResPacketSize * N);
-
-#ifdef USE_GEMV_MMA
-#define GEMV_PROCESS_COL(N) \
-  GEMV_PROCESS_COL_ONE_MMA(N)
-#else
-#define GEMV_PROCESS_COL(N) \
-  GEMV_PROCESS_COL_ONE(N)
-#endif
-
-/** \internal perform a matrix multiply and accumulate of packet a and packet b */
-#ifdef USE_GEMV_MMA
-template<typename LhsPacket, typename RhsPacket, bool accumulate>
-EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b)
-{
-    if (accumulate)
-    {
-        __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-    }
-    else
-    {
-        __builtin_mma_xvf32ger(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-    }
-}
-
-/** \internal perform a matrix multiply and accumulate of vector_pair a and packet b */
-template<typename LhsPacket, typename RhsPacket, bool accumulate>
-EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, __vector_pair& a, const LhsPacket& b)
-{
-    if (accumulate)
-    {
-        __builtin_mma_xvf64gerpp(acc, a, (__vector unsigned char)b);
-    }
-    else
-    {
-        __builtin_mma_xvf64ger(acc, a, (__vector unsigned char)b);
-    }
-}
-#endif
-
-template<typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
-EIGEN_STRONG_INLINE void gemv_col(
-    Index rows, Index cols,
-    const LhsMapper& alhs,
-    const RhsMapper& rhs,
-    ResScalar* res, Index resIncr,
-    ResScalar alpha)
-{
-    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
-
-    typedef typename Traits::LhsPacket LhsPacket;
-    typedef typename Traits::RhsPacket RhsPacket;
-    typedef typename Traits::ResPacket ResPacket;
-
-    EIGEN_UNUSED_VARIABLE(resIncr);
-    eigen_internal_assert(resIncr == 1);
-
-    // The following copy tells the compiler that lhs's attributes are not modified outside this function
-    // This helps GCC to generate proper code.
-    LhsMapper lhs(alhs);
-    RhsMapper rhs2(rhs);
-
-    conj_helper<LhsScalar, RhsScalar, false, false> cj;
-    conj_helper<LhsPacket, RhsPacket, false, false> pcj;
-
-    const Index lhsStride = lhs.stride();
-    // TODO: for padded aligned inputs, we could enable aligned reads
-    enum {
-        LhsAlignment = Unaligned,
-        ResPacketSize = Traits::ResPacketSize,
-        LhsPacketSize = Traits::LhsPacketSize,
-        RhsPacketSize = Traits::RhsPacketSize,
-    };
-
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    const Index n8 = rows - 8 * ResPacketSize + 1;
-    const Index n4 = rows - 4 * ResPacketSize + 1;
-    const Index n2 = rows - 2 * ResPacketSize + 1;
-#endif
-    const Index n1 = rows - 1 * ResPacketSize + 1;
-#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
-    const Index prefetch_dist = 64 * LhsPacketSize;
-#endif
-
-    // TODO: improve the following heuristic:
-    const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
-    ResPacket palpha = pset1<ResPacket>(alpha);
-
-    for (Index j2 = 0; j2 < cols; j2 += block_cols)
-    {
-        Index jend = numext::mini(j2 + block_cols, cols);
-        Index i = 0;
-        ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
-#ifdef USE_GEMV_MMA
-        __vector_quad e0, e1, e2, e3, e4, e5, e6, e7;
-        PacketBlock<ResPacket, 4> result0, result1, result2, result3, result4, result5, result6, result7;
-        GEMV_UNUSED(8, e)
-        GEMV_UNUSED(8, result)
-        GEMV_UNUSED_EXTRA(1, c)
-#endif
-#ifndef GCC_ONE_VECTORPAIR_BUG
-        while (i < n8)
-        {
-            GEMV_PROCESS_COL(8)
-        }
-        if (i < n4)
-        {
-            GEMV_PROCESS_COL(4)
-        }
-        if (i < n2)
-        {
-            GEMV_PROCESS_COL(2)
-        }
-        if (i < n1)
-#else
-        while (i < n1)
-#endif
-        {
-            GEMV_PROCESS_COL_ONE(1)
-        }
-        for (;i < rows;++i)
-        {
-            ResScalar d0(0);
-            Index j = j2;
-            do {
-                d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
-            } while (++j < jend);
-            res[i] += alpha * d0;
-        }
-    }
-}
-
-const Packet16uc p16uc_COMPLEX32_XORFLIP = { 0x44,0x55,0x66,0x77, 0x00,0x11,0x22,0x33, 0xcc,0xdd,0xee,0xff, 0x88,0x99,0xaa,0xbb };
-const Packet16uc p16uc_COMPLEX64_XORFLIP = { 0x88,0x99,0xaa,0xbb, 0xcc,0xdd,0xee,0xff, 0x00,0x11,0x22,0x33, 0x44,0x55,0x66,0x77 };
-
-#ifdef _BIG_ENDIAN
-const Packet16uc p16uc_COMPLEX32_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX64_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX32_NEGATE    = { 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX64_NEGATE    = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
-#else
-const Packet16uc p16uc_COMPLEX32_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
-const Packet16uc p16uc_COMPLEX64_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
-const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = { 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
-const Packet16uc p16uc_COMPLEX32_NEGATE    = { 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80 };
-const Packet16uc p16uc_COMPLEX64_NEGATE    = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
-#endif
-
-#ifdef _BIG_ENDIAN
-#define COMPLEX_DELTA  0
-#else
-#define COMPLEX_DELTA  2
-#endif
-
-/** \internal packet conjugate (same as pconj but uses the constants in pcplxflipconj for better code generation) */
-EIGEN_ALWAYS_INLINE Packet2cf pconj2(const Packet2cf& a) {
-    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR)));
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pconj2(const Packet1cd& a) {
-    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR)));
-}
-
-/** \internal packet conjugate with real & imaginary operation inverted */
-EIGEN_ALWAYS_INLINE Packet2cf pconjinv(const Packet2cf& a) {
-#ifdef __POWER8_VECTOR__
-    return Packet2cf(Packet4f(vec_neg(Packet2d(a.v))));
-#else
-    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR2)));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pconjinv(const Packet1cd& a) {
-    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR2)));
-}
-
-#if defined(_ARCH_PWR8) && (!EIGEN_COMP_LLVM || __clang_major__ >= 12)
-#define PERMXOR_GOOD  // Clang had a bug with vec_permxor and endianness prior to version 12
-#endif
-
-/** \internal flip the real & imaginary results and packet conjugate */
-EIGEN_ALWAYS_INLINE Packet2cf pcplxflipconj(Packet2cf a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR, p16uc_COMPLEX32_XORFLIP)));
-#else
-    return pcplxflip(pconj2(a));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pcplxflipconj(Packet1cd a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR, p16uc_COMPLEX64_XORFLIP)));
-#else
-    return pcplxflip(pconj2(a));
-#endif
-}
-
-/** \internal packet conjugate and flip the real & imaginary results */
-EIGEN_ALWAYS_INLINE Packet2cf pcplxconjflip(Packet2cf a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR2, p16uc_COMPLEX32_XORFLIP)));
-#else
-    return pconj2(pcplxflip(a));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pcplxconjflip(Packet1cd a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR2, p16uc_COMPLEX64_XORFLIP)));
-#else
-    return pconj2(pcplxflip(a));
-#endif
-}
-
-/** \internal packet negate */
-EIGEN_ALWAYS_INLINE Packet2cf pnegate2(Packet2cf a)
-{
-#ifdef __POWER8_VECTOR__
-    return Packet2cf(vec_neg(a.v));
-#else
-    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_NEGATE)));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pnegate2(Packet1cd a)
-{
-#ifdef __POWER8_VECTOR__
-    return Packet1cd(vec_neg(a.v));
-#else
-    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_NEGATE)));
-#endif
-}
-
-/** \internal flip the real & imaginary results and negate */
-EIGEN_ALWAYS_INLINE Packet2cf pcplxflipnegate(Packet2cf a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_NEGATE, p16uc_COMPLEX32_XORFLIP)));
-#else
-    return pcplxflip(pnegate2(a));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pcplxflipnegate(Packet1cd a)
-{
-#ifdef PERMXOR_GOOD
-    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_NEGATE, p16uc_COMPLEX64_XORFLIP)));
-#else
-    return pcplxflip(pnegate2(a));
-#endif
-}
-
-/** \internal flip the real & imaginary results */
-EIGEN_ALWAYS_INLINE Packet2cf pcplxflip2(Packet2cf a)
-{
-    return Packet2cf(Packet4f(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX32_XORFLIP)));
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd pcplxflip2(Packet1cd a)
-{
-#ifdef EIGEN_VECTORIZE_VSX
-    return Packet1cd(__builtin_vsx_xxpermdi(a.v, a.v, 2));
-#else
-    return Packet1cd(Packet2d(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX64_XORFLIP)));
-#endif
-}
-
-/** \internal load half a vector with one complex value */
-EIGEN_ALWAYS_INLINE Packet4f pload_complex_half(std::complex<float>* src)
-{
-    Packet4f t;
-#ifdef EIGEN_VECTORIZE_VSX
-    // Load float64/two float32 (doubleword alignment)
-    __asm__("lxsdx %x0,%y1" : "=wa" (t) : "Z" (*src));
-#else
-    *reinterpret_cast<std::complex<float>*>(reinterpret_cast<float*>(&t) + COMPLEX_DELTA) = *src;
-#endif
-    return t;
-}
-
-/** \internal load two vectors from the real and imaginary portions of a complex value */
-template<typename RhsScalar>
-EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet4f& r, Packet4f& i)
-{
-#ifdef _ARCH_PWR9
-    __asm__("lxvwsx %x0,%y1" : "=wa" (r) : "Z" (*(reinterpret_cast<float*>(src) + 0)));
-    __asm__("lxvwsx %x0,%y1" : "=wa" (i) : "Z" (*(reinterpret_cast<float*>(src) + 1)));
-#else
-    Packet4f t = pload_complex_half(src);
-    r = vec_splat(t, COMPLEX_DELTA + 0);
-    i = vec_splat(t, COMPLEX_DELTA + 1);
-#endif
-}
-
-template<typename RhsScalar>
-EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet2d& r, Packet2d& i)
-{
-#ifdef EIGEN_VECTORIZE_VSX
-    __asm__("lxvdsx %x0,%y1" : "=wa" (r) : "Z" (*(reinterpret_cast<double*>(src) + 0)));
-    __asm__("lxvdsx %x0,%y1" : "=wa" (i) : "Z" (*(reinterpret_cast<double*>(src) + 1)));
-#else
-    Packet2d t = ploadu<Packet2d>(reinterpret_cast<double*>(src));
-    r = vec_splat(t, 0);
-    i = vec_splat(t, 1);
-#endif
-}
-
-#ifndef __POWER8_VECTOR__
-const Packet16uc p16uc_MERGEE = { 0x00, 0x01, 0x02, 0x03, 0x10, 0x11, 0x12, 0x13, 0x08, 0x09, 0x0A, 0x0B, 0x18, 0x19, 0x1A, 0x1B };
-
-const Packet16uc p16uc_MERGEO = { 0x04, 0x05, 0x06, 0x07, 0x14, 0x15, 0x16, 0x17, 0x0C, 0x0D, 0x0E, 0x0F, 0x1C, 0x1D, 0x1E, 0x1F };
-#endif
-
-/** \internal load two vectors from the interleaved real & imaginary values of src */
-template<typename RhsScalar>
-EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet4f& r, Packet4f& i)
-{
-    Packet4f t = ploadu<Packet4f>(reinterpret_cast<float*>(src));
-#ifdef __POWER8_VECTOR__
-    r = vec_mergee(t, t);
-    i = vec_mergeo(t, t);
-#else
-    r = vec_perm(t, t, p16uc_MERGEE);
-    i = vec_perm(t, t, p16uc_MERGEO);
-#endif
-}
-
-template<typename RhsScalar>
-EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet2d& r, Packet2d& i)
-{
-    return pload_realimag(src, r, i);
-}
-
-/** \internal load and splat a complex value into a vector - column-wise */
-EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine(std::complex<float>* src)
-{
-#ifdef EIGEN_VECTORIZE_VSX
-    Packet4f ret;
-    __asm__("lxvdsx %x0,%y1" : "=wa" (ret) : "Z" (*(reinterpret_cast<double*>(src) + 0)));
-    return ret;
-#else
-    return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double *>(src)));
-#endif
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine(std::complex<double>* src)
-{
-    return ploadu<Packet1cd>(src).v;
-}
-
-/** \internal load a complex value into a vector - row-wise */
-EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine_row(std::complex<float>* src)
-{
-    return ploadu<Packet2cf>(src).v;
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine_row(std::complex<double>* src)
-{
-    return ploadu<Packet1cd>(src).v;
-}
-
-/** \internal load a scalar or a vector from complex location */
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet4f pload_complex(std::complex<float>* src)
-{
-    if (GEMV_IS_SCALAR) {
-        return pload_complex_half(src);
-    }
-    else
-    {
-        return ploadu<Packet4f>(reinterpret_cast<float*>(src));
-    }
-}
-
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet2d pload_complex(std::complex<double>* src)
-{
-    return ploadu<Packet2d>(reinterpret_cast<double*>(src));
-}
-
-/** \internal load from a complex vector and convert to a real vector */
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet4f pload_complex(Packet2cf* src)
-{
-    return src->v;
-}
-
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet2d pload_complex(Packet1cd* src)
-{
-    return src->v;
-}
-
-/** \internal load a full vector from complex location - column-wise */
-EIGEN_ALWAYS_INLINE Packet4f pload_complex_full(std::complex<float>* src)
-{
-    return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double *>(src)));
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_complex_full(std::complex<double>* src)
-{
-    return ploadu<Packet1cd>(src).v;
-}
-
-/** \internal load a full vector from complex location - row-wise */
-EIGEN_ALWAYS_INLINE Packet4f pload_complex_full_row(std::complex<float>* src)
-{
-    return ploadu<Packet2cf>(src).v;
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_complex_full_row(std::complex<double>* src)
-{
-    return pload_complex_full(src);
-}
-
-/** \internal load a vector from a real-only scalar location - column-wise */
-EIGEN_ALWAYS_INLINE Packet4f pload_real(float* src)
-{
-    return pset1<Packet4f>(*src);
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_real(double* src)
-{
-    return pset1<Packet2d>(*src);
-}
-
-EIGEN_ALWAYS_INLINE Packet4f pload_real(Packet4f& src)
-{
-    return src;
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_real(Packet2d& src)
-{
-    return src;
-}
-
-/** \internal load a vector from a real-only vector location */
-EIGEN_ALWAYS_INLINE Packet4f pload_real_full(float* src)
-{
-    Packet4f ret = ploadu<Packet4f>(src);
-    return vec_mergeh(ret, ret);
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_real_full(double* src)
-{
-    return pload_real(src);
-}
-
-EIGEN_ALWAYS_INLINE Packet4f pload_real_full(std::complex<float>* src)
-{
-    return pload_complex_full(src);   // Just for compilation
-}
-
-EIGEN_ALWAYS_INLINE Packet2d pload_real_full(std::complex<double>* src)
-{
-    return pload_complex_full(src);   // Just for compilation
-}
-
-/** \internal load a vector from a real-only scalar location - row-wise */
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet4f pload_real_row(float* src)
-{
-    if (GEMV_IS_SCALAR) {
-        return pload_real_full(src);
-    }
-    else {
-        return ploadu<Packet4f>(src);
-    }
-}
-
-template<typename ResPacket>
-EIGEN_ALWAYS_INLINE Packet2d pload_real_row(double* src)
-{
-    return pload_real(src);
-}
-
-EIGEN_ALWAYS_INLINE Packet2cf padd(Packet2cf& a, std::complex<float>& b)
-{
-    EIGEN_UNUSED_VARIABLE(b);
-    return a;  // Just for compilation
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd padd(Packet1cd& a, std::complex<double>& b)
-{
-    EIGEN_UNUSED_VARIABLE(b);
-    return a;  // Just for compilation
-}
-
-/** \internal set a scalar from complex location */
-template<typename Scalar, typename ResScalar>
-EIGEN_ALWAYS_INLINE Scalar pset1_realimag(ResScalar& alpha, int which, int conj)
-{
-    return (which) ? ((conj) ? -alpha.real() : alpha.real()) : ((conj) ? -alpha.imag() : alpha.imag());
-}
-
-/** \internal set a vector from complex location */
-template<typename Scalar, typename ResScalar, typename ResPacket, int which>
-EIGEN_ALWAYS_INLINE Packet2cf pset1_complex(std::complex<float>& alpha)
-{
-    Packet2cf ret;
-    ret.v[COMPLEX_DELTA + 0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
-    ret.v[COMPLEX_DELTA + 1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
-    ret.v[2 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 0];
-    ret.v[3 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 1];
-    return ret;
-}
-
-template<typename Scalar, typename ResScalar, typename ResPacket, int which>
-EIGEN_ALWAYS_INLINE Packet1cd pset1_complex(std::complex<double>& alpha)
-{
-    Packet1cd ret;
-    ret.v[0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
-    ret.v[1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
-    return ret;
-}
-
-/** \internal zero out a vector for real or complex forms */
-template<typename Packet>
-EIGEN_ALWAYS_INLINE Packet pset_zero()
-{
-    return pset1<Packet>(__UNPACK_TYPE__(Packet)(0));
-}
-
-template<>
-EIGEN_ALWAYS_INLINE Packet2cf pset_zero<Packet2cf>()
-{
-    return Packet2cf(pset1<Packet4f>(float(0)));
-}
-
-template<>
-EIGEN_ALWAYS_INLINE Packet1cd pset_zero<Packet1cd>()
-{
-    return Packet1cd(pset1<Packet2d>(double(0)));
-}
-
-/** \internal initialize a vector from another vector */
-template<typename Packet, typename LhsPacket, typename RhsPacket>
-EIGEN_ALWAYS_INLINE Packet pset_init(Packet& c1)
-{
-    if (GEMV_IS_COMPLEX_COMPLEX) {
-        EIGEN_UNUSED_VARIABLE(c1);
-        return pset_zero<Packet>();
-    }
-    else
-    {
-        return c1;  // Intentionally left uninitialized
-    }
-}
-
-template<typename PResPacket, typename ResPacket, typename ResScalar, typename Scalar>
-struct alpha_store
-{
-    alpha_store<PResPacket, ResPacket, ResScalar, Scalar>(ResScalar& alpha) {
-        separate.r = pset1_complex<Scalar, ResScalar, ResPacket, 0x3>(alpha);
-        separate.i = pset1_complex<Scalar, ResScalar, ResPacket, 0x0>(alpha);
-    }
-    struct ri {
-        PResPacket r;
-        PResPacket i;
-    } separate;
-};
-
-/** \internal multiply and add for complex math */
-template<typename ScalarPacket, typename AlphaData>
-EIGEN_ALWAYS_INLINE ScalarPacket pmadd_complex(ScalarPacket& c0, ScalarPacket& c2, ScalarPacket& c4, AlphaData& b0)
-{
-    return pmadd(c2, b0.separate.i.v, pmadd(c0, b0.separate.r.v, c4));
-}
-
-/** \internal store and madd for complex math */
-template<typename Scalar, typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar, typename AlphaData>
-EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, AlphaData& b0, ResScalar* res)
-{
-    PResPacket c2 = pcplxflipconj(c0);
-    if (GEMV_IS_SCALAR) {
-        ScalarPacket c4 = ploadu<ScalarPacket>(reinterpret_cast<Scalar*>(res));
-        ScalarPacket c3 = pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0);
-        pstoreu(reinterpret_cast<Scalar*>(res), c3);
-    } else {
-        ScalarPacket c4 = pload_complex<ResPacket>(res);
-        PResPacket c3 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
-        pstoreu(res, c3);
-    }
-}
-
-template<typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar, typename AlphaData, Index ResPacketSize, Index iter2>
-EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, PResPacket& c1, AlphaData& b0, ResScalar* res)
-{
-    PResPacket c2 = pcplxflipconj(c0);
-    PResPacket c3 = pcplxflipconj(c1);
-#if !defined(_ARCH_PWR10)
-    ScalarPacket c4 = pload_complex<ResPacket>(res + (iter2 * ResPacketSize));
-    ScalarPacket c5 = pload_complex<ResPacket>(res + ((iter2 + 1) * ResPacketSize));
-    PResPacket c6 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
-    PResPacket c7 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c5, b0));
-    pstoreu(res + (iter2 * ResPacketSize), c6);
-    pstoreu(res + ((iter2 + 1) * ResPacketSize), c7);
-#else
-    __vector_pair a = *reinterpret_cast<__vector_pair *>(res + (iter2 * ResPacketSize));
-#if EIGEN_COMP_LLVM
-    PResPacket c6[2];
-    __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(c6), &a);
-    c6[0] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c6[0].v, b0));
-    c6[1] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c6[1].v, b0));
-    GEMV_BUILDPAIR_MMA(a, c6[0].v, c6[1].v);
-#else
-    if (GEMV_IS_COMPLEX_FLOAT) {
-        __asm__ ("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.r.v), "wa" (c0.v), "wa" (c1.v));
-        __asm__ ("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.i.v), "wa" (c2.v), "wa" (c3.v));
-    } else {
-        __asm__ ("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.r.v), "wa" (c0.v), "wa" (c1.v));
-        __asm__ ("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.i.v), "wa" (c2.v), "wa" (c3.v));
-    }
-#endif
-    *reinterpret_cast<__vector_pair *>(res + (iter2 * ResPacketSize)) = a;
-#endif
-}
-
-/** \internal load lhs packet */
-template<typename Scalar, typename LhsScalar, typename LhsMapper, typename LhsPacket>
-EIGEN_ALWAYS_INLINE LhsPacket loadLhsPacket(LhsMapper& lhs, Index i, Index j)
-{
-    if (sizeof(Scalar) == sizeof(LhsScalar)) {
-        const LhsScalar& src = lhs(i + 0, j);
-        return LhsPacket(pload_real_full(const_cast<LhsScalar*>(&src)));
-    }
-    return lhs.template load<LhsPacket, Unaligned>(i + 0, j);
-}
-
-/** \internal madd for complex times complex */
-template<typename ComplexPacket, typename RealPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
-EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_complex(RealPacket& a, RealPacket& b, RealPacket& c)
-{
-    if (ConjugateLhs && ConjugateRhs) {
-        return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
-    }
-    else if (Negate && !ConjugateLhs && ConjugateRhs) {
-        return vec_nmsub(a, b, c);
-    }
-    else {
-        return vec_madd(a, b, c);
-    }
-}
-
-/** \internal madd for complex times real */
-template<typename ComplexPacket, typename RealPacket, bool Conjugate>
-EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_real(RealPacket& a, RealPacket& b, RealPacket& c)
-{
-    if (Conjugate) {
-        return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
-    }
-    else {
-        return vec_madd(a, b, c);
-    }
-}
-
-template<typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_generic(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
-{
-    conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
-    RhsPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pset1<RhsPacket>(*b);
-    }
-    else {
-        b0 = ploadu<RhsPacket>(b);
-    }
-    c0 = pcj.pmadd(a0, b0, c0);
-}
-
-/** \internal core multiply operation for vectors - complex times complex */
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0, ResPacket& c1)
-{
-    ScalarPacket br, bi;
-    if (StorageOrder == ColMajor) {
-        pload_realimag<RhsScalar>(b, br, bi);
-    }
-    else {
-        pload_realimag_row<RhsScalar>(b, br, bi);
-    }
-    if (ConjugateLhs && !ConjugateRhs) a0 = pconj2(a0);
-    LhsPacket a1 = pcplxflipconj(a0);
-    ScalarPacket cr = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, false>(a0.v, br, c0.v);
-    ScalarPacket ci = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, true>(a1.v, bi, c1.v);
-    c1 = ResPacket(ci);
-    c0 = PResPacket(cr);
-}
-
-/** \internal core multiply operation for vectors - real times complex */
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_real_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
-{
-    ScalarPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pload_complex_full(b);
-    }
-    else {
-        b0 = pload_complex_full_row(b);
-    }
-    ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateRhs>(a0, b0, c0.v);
-    c0 = PResPacket(cri);
-}
-
-/** \internal core multiply operation for vectors - complex times real */
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_real(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
-{
-    ScalarPacket a1 = pload_complex<ResPacket>(&a0);
-    ScalarPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pload_real(b);
-    }
-    else {
-        b0 = pload_real_row<ResPacket>(b);
-    }
-    ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateLhs>(a1, b0, c0.v);
-    c0 = PResPacket(cri);
-}
-
-#define GEMV_MULT_COMPLEX_COMPLEX(LhsType, RhsType, ResType) \
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, ResType& c1) \
-{ \
-    gemv_mult_complex_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0, c1); \
-}
-
-GEMV_MULT_COMPLEX_COMPLEX(Packet2cf, std::complex<float>,  Packet2cf)
-GEMV_MULT_COMPLEX_COMPLEX(Packet1cd, std::complex<double>, Packet1cd)
-
-#define GEMV_MULT_REAL_COMPLEX(LhsType, RhsType, ResType) \
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, RhsType&) \
-{ \
-    gemv_mult_real_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
-}
-
-GEMV_MULT_REAL_COMPLEX(float,    std::complex<float>,  Packet2cf)
-GEMV_MULT_REAL_COMPLEX(double,   std::complex<double>, Packet1cd)
-GEMV_MULT_REAL_COMPLEX(Packet4f, std::complex<float>,  Packet2cf)
-GEMV_MULT_REAL_COMPLEX(Packet2d, std::complex<double>, Packet1cd)
-
-#define GEMV_MULT_COMPLEX_REAL(LhsType, RhsType, ResType1, ResType2) \
-template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType1& c0, ResType2&) \
-{ \
-    gemv_mult_complex_real<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
-}
-
-GEMV_MULT_COMPLEX_REAL(Packet2cf,             float, Packet2cf, std::complex<float>)
-GEMV_MULT_COMPLEX_REAL(Packet1cd,            double, Packet1cd, std::complex<double>)
-GEMV_MULT_COMPLEX_REAL(std::complex<float>,   float, Packet2cf, std::complex<float>)
-GEMV_MULT_COMPLEX_REAL(std::complex<double>, double, Packet1cd, std::complex<double>)
-
-#ifdef USE_GEMV_MMA
-/** \internal convert packet to real form */
-template<typename T>
-EIGEN_ALWAYS_INLINE T convertReal(T a)
-{
-    return a;
-}
-
-EIGEN_ALWAYS_INLINE Packet4f convertReal(Packet2cf a)
-{
-    return a.v;
-}
-
-EIGEN_ALWAYS_INLINE Packet2d convertReal(Packet1cd a)
-{
-    return a.v;
-}
-
-/** \internal convert packet to complex form */
-template<typename T>
-EIGEN_ALWAYS_INLINE T convertComplex(T a)
-{
-    return a;
-}
-
-EIGEN_ALWAYS_INLINE Packet2cf convertComplex(Packet4f a)
-{
-    return Packet2cf(a);
-}
-
-EIGEN_ALWAYS_INLINE Packet1cd convertComplex(Packet2d a)
-{
-    return Packet1cd(a);
-}
-
-/** \internal load a vector from a complex location (for MMA version) */
-template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
-EIGEN_ALWAYS_INLINE void pload_complex_MMA(SLhsPacket& a)
-{
-    a = SLhsPacket(pload_complex<ResPacket>(&a));
-}
-
-template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
-EIGEN_ALWAYS_INLINE void pload_complex_MMA(__vector_pair&)
-{
-    // Pass thru
-}
-
-/** \internal perform a matrix multiply and accumulate (positive and negative) of packet a and packet b */
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, RhsPacket& a, LhsPacket& b)
-{
-    if (NegativeAccumulate)
-    {
-        __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-    }
-    else {
-        __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
-    }
-}
-
-/** \internal perform a matrix multiply and accumulate (positive and negative) of vector_pair a and packet b */
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, __vector_pair& a, Packet2d& b)
-{
-    if (NegativeAccumulate)
-    {
-        __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
-    }
-    else {
-        __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
-    }
-}
-
-template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
-EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad*, __vector_pair&, Packet4f&)
-{
-    // Just for compilation
-}
-
-/** \internal madd for complex times complex (MMA version) */
-template<typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
-EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c)
-{
-    if (ConjugateLhs && ConjugateRhs) {
-        RealPacket b2 = pconj2(convertComplex(b)).v;
-        return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a.v);
-    }
-    else if (Negate && !ConjugateLhs && ConjugateRhs) {
-        return pger_vecMMA<RealPacket, RealPacket, true>(c, b, a.v);
-    }
-    else {
-        return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a.v);
-    }
-}
-
-template<typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
-EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c)
-{
-    if (ConjugateLhs && ConjugateRhs) {
-        RealPacket b2 = pconj2(convertComplex(b)).v;
-        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
-    }
-    else if (Negate && !ConjugateLhs && ConjugateRhs) {
-        return pger_vecMMA<RealPacket, __vector_pair, true>(c, a, b);
-    }
-    else {
-        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
-    }
-}
-
-/** \internal madd for complex times real (MMA version) */
-template<typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
-EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c)
-{
-    RealPacket a2 = convertReal(a);
-    if (Conjugate) {
-        RealPacket b2 = pconj2(convertComplex(b)).v;
-        if (StorageOrder == ColMajor) {
-            return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a2);
-        } else {
-            return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b2);
-        }
-    }
-    else {
-        if (StorageOrder == ColMajor) {
-            return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a2);
-        } else {
-            return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b);
-        }
-    }
-}
-
-/** \internal madd for real times complex (MMA version) */
-template<typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
-EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c)
-{
-    if (Conjugate) {
-        RealPacket b2 = pconj2(convertComplex(b)).v;
-        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
-    }
-    else {
-        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
-    }
-}
-
-/** \internal core multiply operation for vectors (MMA version) - complex times complex */
-template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
-{
-    ScalarPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pload_realimag_combine(b);
-    } else {
-        b0 = pload_realimag_combine_row(b);
-    }
-    pmadd_complex_complex_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ConjugateRhs, false>(a0, b0, c0);
-}
-
-/** \internal core multiply operation for vectors (MMA version) - complex times real */
-template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_real_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
-{
-    pload_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, ResPacket>(a0);
-    ScalarPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pload_real(b);
-    }
-    else {
-        b0 = pload_real_row<ResPacket>(b);
-    }
-    pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ColMajor>(a0, b0, c0);
-}
-
-/** \internal core multiply operation for vectors (MMA version) - real times complex */
-template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_real_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
-{
-    ScalarPacket b0;
-    if (StorageOrder == ColMajor) {
-        b0 = pload_complex_full(b);
-    }
-    else {
-        b0 = pload_complex_full_row(b);
-    }
-    pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateRhs, (sizeof(RhsScalar) == sizeof(std::complex<float>)) ? StorageOrder : ColMajor>(a0, b0, c0);
-}
-
-#define GEMV_MULT_COMPLEX_COMPLEX_MMA(LhsType, RhsType) \
-template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
-{ \
-    gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
-}
-
-GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet2cf,     std::complex<float>)
-GEMV_MULT_COMPLEX_COMPLEX_MMA(__vector_pair, std::complex<float>)
-GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet1cd,     std::complex<double>)
-
-/** \internal core multiply operation for vectors (MMA version) - complex times complex */
-template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(__vector_pair& a0, std::complex<double>* b, __vector_quad* c0)
-{
-    if (sizeof(LhsScalar) == 16) {
-        gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0);
-    }
-    else {
-        gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0);
-    }
-}
-
-#define GEMV_MULT_REAL_COMPLEX_MMA(LhsType, RhsType) \
-template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
-{ \
-    gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
-}
-
-GEMV_MULT_REAL_COMPLEX_MMA(Packet4f, std::complex<float>)
-GEMV_MULT_REAL_COMPLEX_MMA(Packet2d, std::complex<double>)
-
-#define GEMV_MULT_COMPLEX_REAL_MMA(LhsType, RhsType) \
-template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
-EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
-{ \
-    gemv_mult_complex_real_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
-}
-
-GEMV_MULT_COMPLEX_REAL_MMA(Packet2cf,     float)
-GEMV_MULT_COMPLEX_REAL_MMA(Packet1cd,     double)
-GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, float)
-GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, double)
-
-/** \internal disassemble MMA accumulator results into packets */
-template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE void disassembleResults2(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
-{
-    __builtin_mma_disassemble_acc(&result0.packet, c0);
-    if (sizeof(LhsPacket) == 16) {
-        if (sizeof(RhsPacket) == 16) {
-            ScalarPacket tmp0, tmp2;
-            tmp2 = vec_mergeh(result0.packet[2], result0.packet[3]);
-            tmp0 = vec_mergeh(result0.packet[0], result0.packet[1]);
-            result0.packet[3] = vec_mergel(result0.packet[3], result0.packet[2]);
-            result0.packet[1] = vec_mergel(result0.packet[1], result0.packet[0]);
-            result0.packet[2] = tmp2;
-            result0.packet[0] = tmp0;
-
-            if (ConjugateLhs) {
-                result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-                result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
-            } else if (ConjugateRhs) {
-                result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
-                result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
-            } else {
-                result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
-                result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
-            }
-            result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
-            result0.packet[2] = vec_add(result0.packet[2], result0.packet[3]);
-        } else {
-            result0.packet[0][1] = result0.packet[1][1];
-            result0.packet[2][1] = result0.packet[3][1];
-        }
-    }
-}
-
-template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE void disassembleResults4(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
-{
-    __builtin_mma_disassemble_acc(&result0.packet, c0);
-    if (GEMV_IS_COMPLEX_COMPLEX) {
-        if (ConjugateLhs) {
-            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-            result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
-        } else {
-            if (ConjugateRhs) {
-                result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
-            } else {
-                result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
-            }
-        }
-        result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
-    } else if (sizeof(LhsPacket) == sizeof(std::complex<float>)) {
-        if (ConjugateLhs) {
-            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-        }
-    } else {
-        result0.packet[0] = vec_mergee(result0.packet[0], result0.packet[1]);
-    }
-}
-
-template <typename Scalar, typename ScalarPacket, int ResPacketSize, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE void disassembleResults(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
-{
-    if (!GEMV_IS_COMPLEX_FLOAT) {
-        disassembleResults2<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
-    } else {
-        disassembleResults4<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
-    }
-}
-#endif
-
-#define GEMV_GETN_COMPLEX(N) (((N) * ResPacketSize) >> 1)
-
-#define GEMV_LOADPACKET_COL_COMPLEX(iter) \
-  loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + ((iter) * ResPacketSize), j)
-
-#define GEMV_LOADPACKET_COL_COMPLEX_DATA(iter) \
-  convertReal(GEMV_LOADPACKET_COL_COMPLEX(iter))
-
-#ifdef USE_GEMV_MMA
-#define GEMV_INIT_COL_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    __builtin_mma_xxsetaccz(&e0##iter); \
-  }
-
-#if EIGEN_COMP_LLVM
-#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2) \
-  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1)); \
-  EIGEN_UNUSED_VARIABLE(f##iter1);
-#else
-#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2) \
-  if (sizeof(LhsPacket) == 16) { \
-    const LhsScalar& src = lhs(i + ((32 * iter1) / sizeof(LhsScalar)), j); \
-    a##iter1 = *reinterpret_cast<__vector_pair *>(const_cast<LhsScalar *>(&src)); \
-    EIGEN_UNUSED_VARIABLE(f##iter1); \
-  } else { \
-    f##iter1 = lhs.template load<PLhsPacket, Unaligned>(i + ((iter2) * ResPacketSize), j); \
-    GEMV_BUILDPAIR_MMA(a##iter1, vec_splat(convertReal(f##iter1), 0), vec_splat(convertReal(f##iter1), 1)); \
-  }
-#endif
-
-#define GEMV_LOAD1_COL_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter); \
-      EIGEN_UNUSED_VARIABLE(a##iter); \
-    } else { \
-      GEMV_LOADPAIR_COL_COMPLEX_MMA(iter, iter << 1) \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(a##iter); \
-    EIGEN_UNUSED_VARIABLE(f##iter); \
-  }
-
-#define GEMV_WORK1_COL_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(f##iter, b, &e0##iter); \
-    } else { \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter, b, &e0##iter); \
-    } \
-  }
-
-#define GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter1, iter2) \
-  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1));
-
-#define GEMV_LOAD2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter1) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2); \
-      EIGEN_UNUSED_VARIABLE(a##iter3) \
-    } else { \
-      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2 << 1); \
-      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter3, iter3 << 1); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(a##iter2); \
-    EIGEN_UNUSED_VARIABLE(a##iter3); \
-  } \
-  EIGEN_UNUSED_VARIABLE(f##iter2); \
-  EIGEN_UNUSED_VARIABLE(f##iter3);
-
-#define GEMV_WORK2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter1) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      PLhsPacket g[2]; \
-      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(g), &a##iter2); \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(g[0], b, &e0##iter2); \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(g[1], b, &e0##iter3); \
-    } else { \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter2, b, &e0##iter2); \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter3, b, &e0##iter3); \
-    } \
-  }
-
-#if EIGEN_COMP_LLVM
-#define GEMV_LOAD_COL_COMPLEX_MMA(N) \
-  if (GEMV_GETN_COMPLEX(N) > 1) { \
-    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_COMPLEX_MMA, (N >> 1)) \
-  } else { \
-    GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N) \
-  }
-
-#define GEMV_WORK_COL_COMPLEX_MMA(N) \
-  if (GEMV_GETN_COMPLEX(N) > 1) { \
-    GEMV_UNROLL_HALF(GEMV_WORK2_COL_COMPLEX_MMA, (N >> 1)) \
-  } else { \
-    GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N) \
-  }
-#else
-#define GEMV_LOAD_COL_COMPLEX_MMA(N) \
-  GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N)
-
-#define GEMV_WORK_COL_COMPLEX_MMA(N) \
-  GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N)
-#endif
-
-#define GEMV_DISASSEMBLE_COMPLEX_MMA(iter) \
-  disassembleResults<Scalar, ScalarPacket, ResPacketSize, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter, result0##iter);
-
-#define GEMV_STORE_COL_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    GEMV_DISASSEMBLE_COMPLEX_MMA(iter); \
-    c0##iter = PResPacket(result0##iter.packet[0]); \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (iter * ResPacketSize)); \
-    } else { \
-      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + ((iter << 1) * ResPacketSize)); \
-      c0##iter = PResPacket(result0##iter.packet[2]); \
-      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (((iter << 1) + 1) * ResPacketSize)); \
-    } \
-  }
-
-#define GEMV_STORE2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter1) { \
-    GEMV_DISASSEMBLE_COMPLEX_MMA(iter2); \
-    GEMV_DISASSEMBLE_COMPLEX_MMA(iter3); \
-    c0##iter2 = PResPacket(result0##iter2.packet[0]); \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      c0##iter3 = PResPacket(result0##iter3.packet[0]); \
-      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2>(c0##iter2, c0##iter3, alpha_data, res + i); \
-    } else { \
-      c0##iter3 = PResPacket(result0##iter2.packet[2]); \
-      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2 << 1>(c0##iter2, c0##iter3, alpha_data, res + i); \
-      c0##iter2 = PResPacket(result0##iter3.packet[0]); \
-      c0##iter3 = PResPacket(result0##iter3.packet[2]); \
-      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter3 << 1>(c0##iter2, c0##iter3, alpha_data, res + i); \
-    } \
-  }
-
-#define GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N) \
-  GEMV_UNROLL(GEMV_INIT_COL_COMPLEX_MMA, N) \
-  Index j = j2; \
-  do { \
-    const RhsScalar& b1 = rhs2(j, 0); \
-    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
-    GEMV_UNROLL(GEMV_PREFETCH, N) \
-    GEMV_LOAD_COL_COMPLEX_MMA(N) \
-    GEMV_WORK_COL_COMPLEX_MMA(N) \
-  } while (++j < jend); \
-  if (GEMV_GETN(N) <= 2) { \
-    GEMV_UNROLL(GEMV_STORE_COL_COMPLEX_MMA, N) \
-  } else { \
-    GEMV_UNROLL_HALF(GEMV_STORE2_COL_COMPLEX_MMA, (N >> 1)) \
-  } \
-  i += (ResPacketSize * N);
-#endif
-
-#define GEMV_INIT_COMPLEX(iter, N) \
-  if (N > iter) { \
-    c0##iter = pset_zero<PResPacket>(); \
-    c1##iter = pset_init<ResPacket, LhsPacket, RhsPacket>(c1##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(c0##iter); \
-    EIGEN_UNUSED_VARIABLE(c1##iter); \
-  }
-
-#define GEMV_WORK_COL_COMPLEX(iter, N) \
-  if (N > iter) { \
-    f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter); \
-    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(f##iter, b, c0##iter, c1##iter); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(f##iter); \
-  }
-
-#define GEMV_STORE_COL_COMPLEX(iter, N) \
-  if (N > iter) { \
-    if (GEMV_IS_COMPLEX_COMPLEX) { \
-      c0##iter = padd(c0##iter, c1##iter); \
-    } \
-    pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (iter * ResPacketSize)); \
-  }
-
-/** \internal main macro for gemv_complex_col - initialize accumulators, multiply and add inputs, and store results */
-#define GEMV_PROCESS_COL_COMPLEX_ONE(N) \
-  GEMV_UNROLL(GEMV_INIT_COMPLEX, N) \
-  Index j = j2; \
-  do { \
-    const RhsScalar& b1 = rhs2(j, 0); \
-    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
-    GEMV_UNROLL(GEMV_PREFETCH, N) \
-    GEMV_UNROLL(GEMV_WORK_COL_COMPLEX, N) \
-  } while (++j < jend); \
-  GEMV_UNROLL(GEMV_STORE_COL_COMPLEX, N) \
-  i += (ResPacketSize * N);
-
-#if defined(USE_GEMV_MMA) && (EIGEN_COMP_LLVM || defined(USE_SLOWER_GEMV_MMA))
-#define USE_GEMV_COL_COMPLEX_MMA
-#endif
-
-#ifdef USE_GEMV_COL_COMPLEX_MMA
-#define GEMV_PROCESS_COL_COMPLEX(N) \
-  GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N)
-#else
-#if defined(USE_GEMV_MMA) && (__GNUC__ > 10)
-#define GEMV_PROCESS_COL_COMPLEX(N) \
-  if (sizeof(Scalar) != sizeof(LhsPacket)) { \
-    GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N) \
-  } else { \
-    GEMV_PROCESS_COL_COMPLEX_ONE(N) \
-  }
-#else
-#define GEMV_PROCESS_COL_COMPLEX(N) \
-  GEMV_PROCESS_COL_COMPLEX_ONE(N)
-#endif
-#endif
-
-template<typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
-EIGEN_STRONG_INLINE void gemv_complex_col(
-    Index rows, Index cols,
-    const LhsMapper& alhs,
-    const RhsMapper& rhs,
-    ResScalar* res, Index resIncr,
-    ResScalar alpha)
-{
-    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
-
-    typedef typename Traits::LhsPacket LhsPacket;
-    typedef typename Traits::RhsPacket RhsPacket;
-    typedef typename Traits::ResPacket ResPacket;
-
-    typedef typename packet_traits<Scalar>::type ScalarPacket;
-    typedef typename packet_traits<LhsScalar>::type PLhsPacket;
-    typedef typename packet_traits<ResScalar>::type PResPacket;
-    typedef gemv_traits<ResPacket, ResPacket> PTraits;
-
-    EIGEN_UNUSED_VARIABLE(resIncr);
-    eigen_internal_assert(resIncr == 1);
-
-    // The following copy tells the compiler that lhs's attributes are not modified outside this function
-    // This helps GCC to generate proper code.
-    LhsMapper lhs(alhs);
-    RhsMapper rhs2(rhs);
-
-    conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
-
-    const Index lhsStride = lhs.stride();
-    // TODO: for padded aligned inputs, we could enable aligned reads
-    enum {
-        LhsAlignment = Unaligned,
-        ResPacketSize = PTraits::ResPacketSize,
-        LhsPacketSize = PTraits::LhsPacketSize,
-        RhsPacketSize = PTraits::RhsPacketSize,
-    };
-#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
-    const Index prefetch_dist = 64 * LhsPacketSize;
-#endif
-
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    const Index n8 = rows - 8 * ResPacketSize + 1;
-    const Index n4 = rows - 4 * ResPacketSize + 1;
-    const Index n2 = rows - 2 * ResPacketSize + 1;
-#endif
-    const Index n1 = rows - 1 * ResPacketSize + 1;
-
-    // TODO: improve the following heuristic:
-    const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
-
-    typedef alpha_store<PResPacket, ResPacket, ResScalar, Scalar> AlphaData;
-    AlphaData alpha_data(alpha);
-
-    for (Index j2 = 0; j2 < cols; j2 += block_cols)
-    {
-        Index jend = numext::mini(j2 + block_cols, cols);
-        Index i = 0;
-        PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
-        ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
-        PLhsPacket f0, f1, f2, f3, f4, f5, f6, f7;
-#ifdef USE_GEMV_MMA
-        __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
-        __vector_pair a0, a1, a2, a3, a4, a5, a6, a7;
-        PacketBlock<ScalarPacket, 4> result00, result01, result02, result03, result04, result05, result06, result07;
-        GEMV_UNUSED(8, e0)
-        GEMV_UNUSED(8, result0)
-        GEMV_UNUSED(8, a)
-        GEMV_UNUSED(8, f)
-#if !defined(GCC_ONE_VECTORPAIR_BUG) && defined(USE_GEMV_COL_COMPLEX_MMA)
-        if (GEMV_IS_COMPLEX_COMPLEX || !GEMV_IS_COMPLEX_FLOAT)
-#endif
-#endif
-#ifndef GCC_ONE_VECTORPAIR_BUG
-        {
-            while (i < n8)
-            {
-                GEMV_PROCESS_COL_COMPLEX(8)
-            }
-        }
-        while (i < n4)
-        {
-            GEMV_PROCESS_COL_COMPLEX(4)
-        }
-        if (i < n2)
-        {
-            GEMV_PROCESS_COL_COMPLEX(2)
-        }
-        if (i < n1)
-#else
-        while (i < n1)
-#endif
-        {
-            GEMV_PROCESS_COL_COMPLEX_ONE(1)
-        }
-        for (;i < rows;++i)
-        {
-            ResScalar d0(0);
-            Index j = j2;
-            do {
-                d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
-            } while (++j < jend);
-            res[i] += alpha * d0;
-        }
-    }
-}
-
-template <typename Scalar, int N> struct ScalarBlock {
-    Scalar scalar[N];
-};
-
-#ifdef USE_GEMV_MMA
-static Packet16uc p16uc_ELEMENT_3 = { 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f, 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f };
-
-/** \internal predux (add elements of a vector) from a MMA accumulator - real results */
-template<typename ResScalar, typename ResPacket>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0, __vector_quad* acc1)
-{
-    PacketBlock<ResPacket, 4> result0, result1;
-    __builtin_mma_disassemble_acc(&result0.packet, acc0);
-    __builtin_mma_disassemble_acc(&result1.packet, acc1);
-    result0.packet[0] = vec_mergeh(result0.packet[0], result1.packet[0]);
-    result0.packet[1] = vec_mergeo(result0.packet[1], result1.packet[1]);
-    result0.packet[2] = vec_mergel(result0.packet[2], result1.packet[2]);
-    result0.packet[3] = vec_perm(result0.packet[3], result1.packet[3], p16uc_ELEMENT_3);
-    result0.packet[0] = vec_add(vec_add(result0.packet[0], result0.packet[2]), vec_add(result0.packet[1], result0.packet[3]));
-    return *reinterpret_cast<ScalarBlock<ResScalar, 2> *>(&result0.packet[0]);
-}
-
-template<>
-EIGEN_ALWAYS_INLINE ScalarBlock<double, 2> predux_real<double, Packet2d>(__vector_quad* acc0, __vector_quad* acc1)
-{
-    PacketBlock<Packet2d, 4> result0, result1;
-    __builtin_mma_disassemble_acc(&result0.packet, acc0);
-    __builtin_mma_disassemble_acc(&result1.packet, acc1);
-    result0.packet[0] = vec_add(vec_mergeh(result0.packet[0], result1.packet[0]), vec_mergel(result0.packet[1], result1.packet[1]));
-    return *reinterpret_cast<ScalarBlock<double, 2> *>(&result0.packet[0]);
-}
-
-/** \internal add complex results together */
-template<typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<float>, 2> addComplexResults(PacketBlock<Packet4f, 4>& result0, PacketBlock<Packet4f, 4>& result1)
-{
-    ScalarBlock<std::complex<float>, 2> cc0;
-    result0.packet[0] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[0]), reinterpret_cast<Packet2d>(result1.packet[0])));
-    result0.packet[2] = reinterpret_cast<Packet4f>(vec_mergel(reinterpret_cast<Packet2d>(result0.packet[2]), reinterpret_cast<Packet2d>(result1.packet[2])));
-    result0.packet[0] = vec_add(result0.packet[0], result0.packet[2]);
-    if (GEMV_IS_COMPLEX_COMPLEX) {
-        result0.packet[1] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[1]), reinterpret_cast<Packet2d>(result1.packet[1])));
-        result0.packet[3] = reinterpret_cast<Packet4f>(vec_mergel(reinterpret_cast<Packet2d>(result0.packet[3]), reinterpret_cast<Packet2d>(result1.packet[3])));
-        result0.packet[1] = vec_add(result0.packet[1], result0.packet[3]);
-        if (ConjugateLhs) {
-            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-            result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
-        } else if (ConjugateRhs) {
-            result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
-        } else {
-            result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
-        }
-        result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
-    } else {
-        if (ConjugateLhs && (sizeof(LhsPacket) == sizeof(std::complex<float>))) {
-            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-        }
-    }
-    cc0.scalar[0].real(result0.packet[0][0]);
-    cc0.scalar[0].imag(result0.packet[0][1]);
-    cc0.scalar[1].real(result0.packet[0][2]);
-    cc0.scalar[1].imag(result0.packet[0][3]);
-    return cc0;
-}
-
-template<typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<double>, 2> addComplexResults(PacketBlock<Packet2d, 4>&, PacketBlock<Packet2d, 4>&)
-{
-    ScalarBlock<std::complex<double>, 2> cc0;
-    EIGEN_UNUSED_VARIABLE(cc0);
-    return cc0;  // Just for compilation
-}
-
-/** \internal predux (add elements of a vector) from a MMA accumulator - complex results */
-template<typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0, __vector_quad* acc1)
-{
-    PacketBlock<ResPacket, 4> result0, result1;
-    __builtin_mma_disassemble_acc(&result0.packet, acc0);
-    __builtin_mma_disassemble_acc(&result1.packet, acc1);
-    return addComplexResults<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(result0, result1);
-}
-
-template<typename ResScalar, typename ResPacket>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0)
-{
-    PacketBlock<ResPacket, 4> result0;
-    __builtin_mma_disassemble_acc(&result0.packet, acc0);
-    result0.packet[0] = vec_add(vec_mergeh(result0.packet[0], result0.packet[2]), vec_mergel(result0.packet[1], result0.packet[3]));
-    return *reinterpret_cast<ScalarBlock<ResScalar, 2> *>(&result0.packet[0]);
-}
-
-template<typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0)
-{
-    ScalarBlock<ResScalar, 2> cc0;
-    PacketBlock<ResPacket, 4> result0;
-    __builtin_mma_disassemble_acc(&result0.packet, acc0);
-    if (GEMV_IS_COMPLEX_COMPLEX) {
-        if (ConjugateLhs) {
-            result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
-            result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
-        } else if (ConjugateRhs) {
-            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
-            result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
-        } else {
-            result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
-            result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
-        }
-        result0.packet[0] = vec_add(result0.packet[0], __builtin_vsx_xxpermdi(result0.packet[1], result0.packet[1], 2));
-        result0.packet[2] = vec_add(result0.packet[2], __builtin_vsx_xxpermdi(result0.packet[3], result0.packet[3], 2));
-    } else {
-        result0.packet[0] = __builtin_vsx_xxpermdi(result0.packet[0], result0.packet[1], 1);
-        result0.packet[2] = __builtin_vsx_xxpermdi(result0.packet[2], result0.packet[3], 1);
-    }
-    cc0.scalar[0].real(result0.packet[0][0]);
-    cc0.scalar[0].imag(result0.packet[0][1]);
-    cc0.scalar[1].real(result0.packet[2][0]);
-    cc0.scalar[1].imag(result0.packet[2][1]);
-    return cc0;
-}
-#endif
-
-template<typename ResScalar, typename ResPacket>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(ResPacket& a, ResPacket& b)
-{
-    ScalarBlock<ResScalar, 2> cc0;
-    cc0.scalar[0] = predux(a);
-    cc0.scalar[1] = predux(b);
-    return cc0;
-}
-
-template<typename ResScalar, typename ResPacket>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(ResPacket& a, ResPacket& b)
-{
-    return predux_real<ResScalar, ResPacket>(a, b);
-}
-
-#define GEMV_UNROLL_ROW(func, N) \
-  func(0, N) func(1, N) func(2, N) func(3, N) func(4, N) func(5, N) func(6, N) func(7, N)
-
-#define GEMV_UNROLL_ROW_HALF(func, N) \
-  func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
-
-#define GEMV_LOADPACKET_ROW(iter) \
-  lhs.template load<LhsPacket, Unaligned>(i + (iter), j)
-
-#ifdef USE_GEMV_MMA
-#define GEMV_UNROLL3_ROW(func, N, which) \
-  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) \
-  func(4, N, which) func(5, N, which) func(6, N, which) func(7, N, which)
-
-#define GEMV_UNUSED_ROW(N, which) \
-  GEMV_UNROLL3_ROW(GEMV_UNUSED_VAR, N, which)
-
-#define GEMV_INIT_ROW(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    __builtin_mma_xxsetaccz(&c##iter); \
-  }
-
-#define GEMV_LOADPAIR_ROW(iter1, iter2) \
-  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_ROW(iter2), GEMV_LOADPACKET_ROW((iter2) + 1));
-
-#define GEMV_WORK_ROW(iter, N) \
-  if (GEMV_GETN(N) > iter) { \
-    if (GEMV_IS_FLOAT) { \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, a0, GEMV_LOADPACKET_ROW(iter)); \
-    } else { \
-      __vector_pair b##iter; \
-      GEMV_LOADPAIR_ROW(iter, iter << 1) \
-      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, b##iter, a0); \
-    } \
-  }
-
-#define GEMV_PREDUX2(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    if (GEMV_IS_FLOAT) { \
-      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter2, &c##iter3); \
-    } else { \
-      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter1); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(cc##iter1); \
-  }
-#else
-#define GEMV_INIT_ROW(iter, N) \
-  if (N > iter) { \
-    c##iter = pset1<ResPacket>(ResScalar(0)); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(c##iter); \
-  }
-
-#define GEMV_WORK_ROW(iter, N) \
-  if (N > iter) { \
-    c##iter = pcj.pmadd(GEMV_LOADPACKET_ROW(iter), a0, c##iter); \
-  }
-
-#define GEMV_PREDUX2(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    cc##iter1 = predux_real<ResScalar, ResPacket>(c##iter2, c##iter3); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(cc##iter1); \
-  }
-#endif
-
-#define GEMV_MULT(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), a0); \
-    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), a0); \
-  }
-
-#define GEMV_STORE_ROW(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
-    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
-  }
-
-/** \internal main macro for gemv_row - initialize accumulators, multiply and add inputs, predux and store results */
-#define GEMV_PROCESS_ROW(N) \
-  for (; i < n##N; i += N) { \
-    GEMV_UNROLL_ROW(GEMV_INIT_ROW, N) \
-    Index j = 0; \
-    for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
-      RhsPacket a0 = rhs2.template load<RhsPacket, Unaligned>(j); \
-      GEMV_UNROLL_ROW(GEMV_WORK_ROW, N) \
-    } \
-    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX2, (N >> 1)) \
-    for (; j < cols; ++j) { \
-      RhsScalar a0 = rhs2(j); \
-      GEMV_UNROLL_ROW_HALF(GEMV_MULT, (N >> 1)) \
-    } \
-    GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW, (N >> 1)) \
-  }
-
-template<typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
-EIGEN_STRONG_INLINE void gemv_row(
-    Index rows, Index cols,
-    const LhsMapper& alhs,
-    const RhsMapper& rhs,
-    ResScalar* res, Index resIncr,
-    ResScalar alpha)
-{
-    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
-
-    typedef typename Traits::LhsPacket LhsPacket;
-    typedef typename Traits::RhsPacket RhsPacket;
-    typedef typename Traits::ResPacket ResPacket;
-
-    // The following copy tells the compiler that lhs's attributes are not modified outside this function
-    // This helps GCC to generate proper code.
-    LhsMapper lhs(alhs);
-    typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
-
-    eigen_internal_assert(rhs.stride() == 1);
-    conj_helper<LhsScalar, RhsScalar, false, false> cj;
-    conj_helper<LhsPacket, RhsPacket, false, false> pcj;
-
-    // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
-    //       processing 8 rows at once might be counter productive wrt cache.
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
-    const Index n4 = rows - 3;
-    const Index n2 = rows - 1;
-#endif
-
-    // TODO: for padded aligned inputs, we could enable aligned reads
-    enum {
-        LhsAlignment = Unaligned,
-        ResPacketSize = Traits::ResPacketSize,
-        LhsPacketSize = Traits::LhsPacketSize,
-        RhsPacketSize = Traits::RhsPacketSize,
-    };
-
-    Index i = 0;
-#ifdef USE_GEMV_MMA
-    __vector_quad c0, c1, c2, c3, c4, c5, c6, c7;
-    GEMV_UNUSED_ROW(8, c)
-#else
-    ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
-#endif
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
-    GEMV_PROCESS_ROW(8)
-    GEMV_PROCESS_ROW(4)
-    GEMV_PROCESS_ROW(2)
-#endif
-    for (; i < rows; ++i)
-    {
-        ResPacket d0 = pset1<ResPacket>(ResScalar(0));
-        Index j = 0;
-        for (; j + LhsPacketSize <= cols; j += LhsPacketSize)
-        {
-            RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j);
-
-            d0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, d0);
-        }
-        ResScalar dd0 = predux(d0);
-        for (; j < cols; ++j)
-        {
-            dd0 += cj.pmul(lhs(i, j), rhs2(j));
-        }
-        res[i * resIncr] += alpha * dd0;
-    }
-}
-
-#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(Scalar) \
-template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
-struct general_matrix_vector_product<Index, Scalar, LhsMapper, ColMajor, ConjugateLhs, Scalar, RhsMapper, ConjugateRhs, Version> \
-{ \
-    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar; \
-\
-    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
-        Index rows, Index cols, \
-        const LhsMapper& lhs, \
-        const RhsMapper& rhs, \
-        ResScalar* res, Index resIncr, \
-        ResScalar alpha) { \
-        gemv_col<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
-    } \
-};
-
-#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(Scalar) \
-template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
-struct general_matrix_vector_product<Index, Scalar, LhsMapper, RowMajor, ConjugateLhs, Scalar, RhsMapper, ConjugateRhs, Version> \
-{ \
-    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar; \
-\
-    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
-        Index rows, Index cols, \
-        const LhsMapper& lhs, \
-        const RhsMapper& rhs, \
-        ResScalar* res, Index resIncr, \
-        ResScalar alpha) { \
-        gemv_row<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
-    } \
-};
-
-EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(float)
-EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(double)
-EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(float)
-EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(double)
-
-template<typename ResScalar, typename PResPacket, typename ResPacket, typename LhsPacket, typename RhsPacket>
-EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(PResPacket& a0, PResPacket& b0, ResPacket& a1, ResPacket& b1)
-{
-    if (GEMV_IS_COMPLEX_COMPLEX) {
-        a0 = padd(a0, a1);
-        b0 = padd(b0, b1);
-    }
-    return predux_complex<ResScalar, PResPacket>(a0, b0);
-}
-
-#define GEMV_LOADPACKET_ROW_COMPLEX(iter) \
-  loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + (iter), j)
-
-#define GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter) \
-  convertReal(GEMV_LOADPACKET_ROW_COMPLEX(iter))
-
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(which, N) \
-  j = 0; \
-  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
-    const RhsScalar& b1 = rhs2(j); \
-    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
-    GEMV_UNROLL_ROW(which, N) \
-  }
-
-#define GEMV_PROCESS_END_ROW_COMPLEX(N) \
-  for (; j < cols; ++j) { \
-    RhsScalar b0 = rhs2(j); \
-    GEMV_UNROLL_ROW_HALF(GEMV_MULT_COMPLEX, (N >> 1)) \
-  } \
-  GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW_COMPLEX, (N >> 1))
-
-#ifdef USE_GEMV_MMA
-#define GEMV_INIT_ROW_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    __builtin_mma_xxsetaccz(&e0##iter); \
-  }
-
-#define GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter1, iter2) \
-  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter2), GEMV_LOADPACKET_ROW_COMPLEX_DATA((iter2) + 1));
-
-#define GEMV_WORK_ROW_COMPLEX_MMA(iter, N) \
-  if (GEMV_GETN_COMPLEX(N) > iter) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter); \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter); \
-    } else { \
-      __vector_pair a##iter; \
-      GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter, iter << 1) \
-      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter); \
-    } \
-  }
-
-#define GEMV_PREDUX4_COMPLEX_MMA(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    if (GEMV_IS_COMPLEX_FLOAT) { \
-      cc##iter1 = predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter2, &e0##iter3); \
-    } else { \
-      cc##iter1 = predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter1); \
-    } \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(cc##iter1); \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N) \
-  GEMV_UNROLL_ROW(GEMV_INIT_ROW_COMPLEX_MMA, N) \
-  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX_MMA, N)
-
-#define GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N) \
-  for (; i < n##N; i += N) { \
-    GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N) \
-    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_MMA, (N >> 1)) \
-    GEMV_PROCESS_END_ROW_COMPLEX(N); \
-  }
-#endif
-
-#define GEMV_WORK_ROW_COMPLEX(iter, N) \
-  if (N > iter) { \
-    PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter); \
-    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, c0##iter, c1##iter); \
-  }
-
-#define GEMV_PREDUX4_COMPLEX(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    cc##iter1 = predux_complex<ResScalar, PResPacket, ResPacket, LhsPacket, RhsPacket>(c0##iter2, c0##iter3, c1##iter2, c1##iter3); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(cc##iter1); \
-  }
-
-#define GEMV_MULT_COMPLEX(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), b0); \
-    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), b0); \
-  }
-
-#define GEMV_STORE_ROW_COMPLEX(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
-    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
-  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX, N) \
-  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX, N)
-
-/** \internal main macro for gemv_complex_row - initialize accumulators, multiply and add inputs, predux and store results */
-#define GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N) \
-  for (; i < n##N; i += N) { \
-    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
-    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX, (N >> 1)) \
-    GEMV_PROCESS_END_ROW_COMPLEX(N); \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
-  if (GEMV_IS_COMPLEX_COMPLEX) { \
-    c0##iter = padd(c0##iter, c1##iter); \
-  } \
-  dd0 = predux(c0##iter);
-
-#if EIGEN_COMP_LLVM
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) \
-  GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N)
-
-#define GEMV_PROCESS_ROW_COMPLEX_ONE(N) \
-  GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N)
-
-#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) \
-  GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter)
-#else
-// gcc seems to be reading and writing registers unnecessarily to memory.
-// Use the old way for complex double until it is fixed.
-
-#define GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter) \
-  lhs.template load<LhsPacket, LhsAlignment>(i + (iter), j)
-
-#define GEMV_INIT_COMPLEX_OLD(iter, N) \
-  EIGEN_UNUSED_VARIABLE(c0##iter); \
-  if (N > iter) { \
-    c1##iter = pset_zero<ResPacket>(); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(c1##iter); \
-  }
-
-#define GEMV_WORK_ROW_COMPLEX_OLD(iter, N) \
-  if (N > iter) { \
-    LhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter); \
-    c1##iter = pcj.pmadd(a##iter, b0, c1##iter); \
-  }
-
-#define GEMV_PREDUX4_COMPLEX_OLD(iter1, iter2, iter3, N) \
-  if (N > iter1) { \
-    cc##iter1.scalar[0] = predux(c1##iter2); \
-    cc##iter1.scalar[1] = predux(c1##iter3); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(cc##iter1); \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
-  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX_OLD, N) \
-  j = 0; \
-  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
-    RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j); \
-    GEMV_UNROLL_ROW(GEMV_WORK_ROW_COMPLEX_OLD, N) \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N) \
-  for (; i < n##N; i += N) { \
-    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
-    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_OLD, (N >> 1)) \
-    GEMV_PROCESS_END_ROW_COMPLEX(N) \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) \
-  dd0 = predux(c1##iter);
-
-#if (__GNUC__ > 10)
-#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW  1
-#else
-#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW  \
-  (sizeof(Scalar) == sizeof(float)) || GEMV_IS_COMPLEX_COMPLEX
-#endif
-
-#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) \
-  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
-    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
-  } else { \
-    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_ONE(N) \
-  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
-    GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N) \
-  } else { \
-    GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N) \
-  }
-
-#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) \
-  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
-    GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
-  } else { \
-    GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) \
-  }
-#endif
-
-#ifdef USE_GEMV_MMA
-#define GEMV_PROCESS_ROW_COMPLEX(N) \
-  GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N)
-#else
-#define GEMV_PROCESS_ROW_COMPLEX(N) \
-  GEMV_PROCESS_ROW_COMPLEX_ONE(N)
-#endif
-
-template<typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
-EIGEN_STRONG_INLINE void gemv_complex_row(
-    Index rows, Index cols,
-    const LhsMapper& alhs,
-    const RhsMapper& rhs,
-    ResScalar* res, Index resIncr,
-    ResScalar alpha)
-{
-    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
-
-    typedef typename Traits::LhsPacket LhsPacket;
-    typedef typename Traits::RhsPacket RhsPacket;
-    typedef typename Traits::ResPacket ResPacket;
-
-    typedef typename packet_traits<Scalar>::type ScalarPacket;
-    typedef typename packet_traits<LhsScalar>::type PLhsPacket;
-    typedef typename packet_traits<ResScalar>::type PResPacket;
-    typedef gemv_traits<ResPacket, ResPacket> PTraits;
-
-    // The following copy tells the compiler that lhs's attributes are not modified outside this function
-    // This helps GCC to generate proper code.
-    LhsMapper lhs(alhs);
-    typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
-
-    eigen_internal_assert(rhs.stride() == 1);
-    conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
-#if !EIGEN_COMP_LLVM
-    conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
-#endif
-
-    // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
-    //       processing 8 rows at once might be counter productive wrt cache.
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
-    const Index n4 = rows - 3;
-    const Index n2 = rows - 1;
-#endif
-
-    // TODO: for padded aligned inputs, we could enable aligned reads
-    enum {
-        LhsAlignment = Unaligned,
-        ResPacketSize = PTraits::ResPacketSize,
-        LhsPacketSize = PTraits::LhsPacketSize,
-        RhsPacketSize = PTraits::RhsPacketSize,
-    };
-
-    Index i = 0, j;
-    PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
-    ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
-#ifdef USE_GEMV_MMA
-    __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
-    GEMV_UNUSED_ROW(8, e0)
-    GEMV_UNUSED_EXTRA(1, c0)
-    GEMV_UNUSED_EXTRA(1, c1)
-#endif
-    ResScalar dd0;
-#ifndef GCC_ONE_VECTORPAIR_BUG
-    ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
-#ifdef USE_GEMV_MMA
-    if (!GEMV_IS_COMPLEX_COMPLEX)
-#endif
-    {
-        GEMV_PROCESS_ROW_COMPLEX(8)
-    }
-    GEMV_PROCESS_ROW_COMPLEX(4)
-    GEMV_PROCESS_ROW_COMPLEX(2)
-#endif
-    for (; i < rows; ++i)
-    {
-        GEMV_PROCESS_ROW_COMPLEX_SINGLE(1)
-        GEMV_PROCESS_ROW_COMPLEX_PREDUX(0)
-        for (; j < cols; ++j)
-        {
-            dd0 += cj.pmul(lhs(i, j), rhs2(j));
-        }
-        res[i * resIncr] += alpha * dd0;
-    }
-}
-
-#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(Scalar, LhsScalar, RhsScalar) \
-template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
-struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs, Version> \
-{ \
-    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar; \
-\
-    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
-        Index rows, Index cols, \
-        const LhsMapper& lhs, \
-        const RhsMapper& rhs, \
-        ResScalar* res, Index resIncr, \
-        ResScalar alpha) { \
-        gemv_complex_col<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar, RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
-    } \
-};
-
-#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(Scalar, LhsScalar, RhsScalar) \
-template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
-struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs, Version> \
-{ \
-    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar; \
-\
-    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
-        Index rows, Index cols, \
-        const LhsMapper& lhs, \
-        const RhsMapper& rhs, \
-        ResScalar* res, Index resIncr, \
-        ResScalar alpha) { \
-        gemv_complex_row<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar, RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
-    } \
-};
-
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  float,                std::complex<float>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  std::complex<float>,  float)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  std::complex<float>,  std::complex<float>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, double,               std::complex<double>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, double)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, std::complex<double>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  float,                std::complex<float>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  std::complex<float>,  float)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  std::complex<float>,  std::complex<float>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, double,               std::complex<double>)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, double)
-EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, std::complex<double>)
-
-#endif // EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
-
diff --git a/3rdParty/ceres_2.2/include/ceres/internal/config.h b/3rdParty/ceres_2.2/include/ceres/internal/config.h
index 14259cb..5ebfccd 100644
--- a/3rdParty/ceres_2.2/include/ceres/internal/config.h
+++ b/3rdParty/ceres_2.2/include/ceres/internal/config.h
@@ -51,7 +51,7 @@
 #define CERES_NO_SUITESPARSE
 
 // If defined, Ceres was compiled without CUDA.
-#define CERES_NO_CUDA
+// #define CERES_NO_CUDA
 
 // If defined, Ceres was compiled without Apple's Accelerate framework solvers.
 #define CERES_NO_ACCELERATE_SPARSE
diff --git a/3rdParty/ceres_2.2/include/ceres/internal/export.h b/3rdParty/ceres_2.2/include/ceres/internal/export.h
index 610cab8..a563536 100644
--- a/3rdParty/ceres_2.2/include/ceres/internal/export.h
+++ b/3rdParty/ceres_2.2/include/ceres/internal/export.h
@@ -33,7 +33,6 @@
 #  define CERES_DEPRECATED_NO_EXPORT CERES_NO_EXPORT CERES_DEPRECATED
 #endif
 
-/* NOLINTNEXTLINE(readability-avoid-unconditional-preprocessor-if) */
 #if 0 /* DEFINE_NO_DEPRECATED */
 #  ifndef CERES_NO_DEPRECATED
 #    define CERES_NO_DEPRECATED
diff --git a/3rdParty/ceres_2.2/include/ceres/version.h b/3rdParty/ceres_2.2/include/ceres/version.h
new file mode 100644
index 0000000..2d5d000
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/ceres/version.h
@@ -0,0 +1,49 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2023 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: mierle@gmail.com (Keir Mierle)
+
+#ifndef CERES_PUBLIC_VERSION_H_
+#define CERES_PUBLIC_VERSION_H_
+
+#define CERES_VERSION_MAJOR 2
+#define CERES_VERSION_MINOR 2
+#define CERES_VERSION_REVISION 0
+
+// Classic CPP stringifcation; the extra level of indirection allows the
+// preprocessor to expand the macro before being converted to a string.
+#define CERES_TO_STRING_HELPER(x) #x
+#define CERES_TO_STRING(x) CERES_TO_STRING_HELPER(x)
+
+// The Ceres version as a string; for example "1.9.0".
+#define CERES_VERSION_STRING                                    \
+  CERES_TO_STRING(CERES_VERSION_MAJOR)                          \
+  "." CERES_TO_STRING(CERES_VERSION_MINOR) "." CERES_TO_STRING( \
+      CERES_VERSION_REVISION)
+
+#endif  // CERES_PUBLIC_VERSION_H_
diff --git a/3rdParty/ceres_2.2/include/Eigen/Cholesky b/3rdParty/ceres_2.2/include/eigen3/Eigen/Cholesky
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Cholesky
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Cholesky
diff --git a/3rdParty/ceres_2.2/include/Eigen/CholmodSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/CholmodSupport
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/CholmodSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/CholmodSupport
index 1037bd5..bed8924 100644
--- a/3rdParty/ceres_2.2/include/Eigen/CholmodSupport
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/CholmodSupport
@@ -22,7 +22,7 @@ extern "C" {
   * This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
   * It provides the two following main factorization classes:
   * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
-  * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
+  * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
   *
   * For the sake of completeness, this module also propose the two following classes:
   * - class CholmodSimplicialLLT
diff --git a/3rdParty/ceres_2.2/include/Eigen/Core b/3rdParty/ceres_2.2/include/eigen3/Eigen/Core
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/Core
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Core
index bb16c86..5921e15 100644
--- a/3rdParty/ceres_2.2/include/Eigen/Core
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/Core
@@ -83,8 +83,8 @@
 #include <cmath>
 #include <cassert>
 #include <functional>
+#include <sstream>
 #ifndef EIGEN_NO_IO
-  #include <sstream>
   #include <iosfwd>
 #endif
 #include <cstring>
@@ -109,8 +109,7 @@
 #endif
 
 // required for __cpuid, needs to be included after cmath
-// also required for _BitScanReverse on Windows on ARM
-#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
+#if EIGEN_COMP_MSVC && EIGEN_ARCH_i386_OR_x86_64 && !EIGEN_OS_WINCE
   #include <intrin.h>
 #endif
 
@@ -347,7 +346,7 @@ using std::ptrdiff_t;
 #include "src/Core/CoreIterators.h"
 #include "src/Core/ConditionEstimator.h"
 
-#if defined(EIGEN_VECTORIZE_VSX)
+#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
   #include "src/Core/arch/AltiVec/MatrixProduct.h"
 #elif defined EIGEN_VECTORIZE_NEON
   #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
diff --git a/3rdParty/ceres_2.2/include/Eigen/Dense b/3rdParty/ceres_2.2/include/eigen3/Eigen/Dense
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Dense
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Dense
diff --git a/3rdParty/ceres_2.2/include/Eigen/Eigen b/3rdParty/ceres_2.2/include/eigen3/Eigen/Eigen
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Eigen
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/Eigenvalues b/3rdParty/ceres_2.2/include/eigen3/Eigen/Eigenvalues
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Eigenvalues
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Eigenvalues
diff --git a/3rdParty/ceres_2.2/include/Eigen/Geometry b/3rdParty/ceres_2.2/include/eigen3/Eigen/Geometry
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Geometry
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Geometry
diff --git a/3rdParty/ceres_2.2/include/Eigen/Householder b/3rdParty/ceres_2.2/include/eigen3/Eigen/Householder
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Householder
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Householder
diff --git a/3rdParty/ceres_2.2/include/Eigen/IterativeLinearSolvers b/3rdParty/ceres_2.2/include/eigen3/Eigen/IterativeLinearSolvers
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/IterativeLinearSolvers
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/IterativeLinearSolvers
diff --git a/3rdParty/ceres_2.2/include/Eigen/Jacobi b/3rdParty/ceres_2.2/include/eigen3/Eigen/Jacobi
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Jacobi
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Jacobi
diff --git a/3rdParty/ceres_2.2/include/Eigen/KLUSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/KLUSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/KLUSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/KLUSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/LU b/3rdParty/ceres_2.2/include/eigen3/Eigen/LU
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/LU
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/LU
diff --git a/3rdParty/ceres_2.2/include/Eigen/MetisSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/MetisSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/MetisSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/MetisSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/OrderingMethods b/3rdParty/ceres_2.2/include/eigen3/Eigen/OrderingMethods
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/OrderingMethods
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/OrderingMethods
diff --git a/3rdParty/ceres_2.2/include/Eigen/PaStiXSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/PaStiXSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/PaStiXSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/PaStiXSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/PardisoSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/PardisoSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/PardisoSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/PardisoSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/QR b/3rdParty/ceres_2.2/include/eigen3/Eigen/QR
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/QR
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/QR
diff --git a/3rdParty/ceres_2.2/include/Eigen/QtAlignedMalloc b/3rdParty/ceres_2.2/include/eigen3/Eigen/QtAlignedMalloc
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/QtAlignedMalloc
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/QtAlignedMalloc
diff --git a/3rdParty/ceres_2.2/include/Eigen/SPQRSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/SPQRSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SPQRSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SPQRSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/SVD b/3rdParty/ceres_2.2/include/eigen3/Eigen/SVD
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SVD
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SVD
diff --git a/3rdParty/ceres_2.2/include/Eigen/Sparse b/3rdParty/ceres_2.2/include/eigen3/Eigen/Sparse
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/Sparse
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/Sparse
diff --git a/3rdParty/ceres_2.2/include/Eigen/SparseCholesky b/3rdParty/ceres_2.2/include/eigen3/Eigen/SparseCholesky
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SparseCholesky
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SparseCholesky
diff --git a/3rdParty/ceres_2.2/include/Eigen/SparseCore b/3rdParty/ceres_2.2/include/eigen3/Eigen/SparseCore
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SparseCore
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SparseCore
diff --git a/3rdParty/ceres_2.2/include/Eigen/SparseLU b/3rdParty/ceres_2.2/include/eigen3/Eigen/SparseLU
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/SparseLU
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SparseLU
index 047cf0d..37c4a5c 100644
--- a/3rdParty/ceres_2.2/include/Eigen/SparseLU
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/SparseLU
@@ -25,6 +25,8 @@
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
+#include "src/SparseLU/SparseLU_gemm_kernel.h"
+
 #include "src/SparseLU/SparseLU_Structs.h"
 #include "src/SparseLU/SparseLU_SupernodalMatrix.h"
 #include "src/SparseLU/SparseLUImpl.h"
diff --git a/3rdParty/ceres_2.2/include/Eigen/SparseQR b/3rdParty/ceres_2.2/include/eigen3/Eigen/SparseQR
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SparseQR
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SparseQR
diff --git a/3rdParty/ceres_2.2/include/Eigen/StdDeque b/3rdParty/ceres_2.2/include/eigen3/Eigen/StdDeque
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/StdDeque
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/StdDeque
diff --git a/3rdParty/ceres_2.2/include/Eigen/StdList b/3rdParty/ceres_2.2/include/eigen3/Eigen/StdList
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/StdList
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/StdList
diff --git a/3rdParty/ceres_2.2/include/Eigen/StdVector b/3rdParty/ceres_2.2/include/eigen3/Eigen/StdVector
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/StdVector
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/StdVector
diff --git a/3rdParty/ceres_2.2/include/Eigen/SuperLUSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/SuperLUSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/SuperLUSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/SuperLUSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/UmfPackSupport b/3rdParty/ceres_2.2/include/eigen3/Eigen/UmfPackSupport
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/UmfPackSupport
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/UmfPackSupport
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LDLT.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LDLT.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LDLT.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LDLT.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LLT.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LLT.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LLT.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LLT.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LLT_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Cholesky/LLT_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Cholesky/LLT_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/CholmodSupport/CholmodSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/CholmodSupport/CholmodSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ArithmeticSequence.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ArithmeticSequence.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
index d04f726..b6200fa 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/ArithmeticSequence.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArithmeticSequence.h
@@ -172,8 +172,7 @@ seqN(FirstType first, SizeType size)  {
   return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type>(first,size);
 }
 
-
-#if EIGEN_HAS_CXX11
+#ifdef EIGEN_PARSED_BY_DOXYGEN
 
 /** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a incr
   *
@@ -184,15 +183,8 @@ seqN(FirstType first, SizeType size)  {
   *
   * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
   */
-template<typename FirstType,typename LastType>
-auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-                                                   (  typename internal::cleanup_index_type<LastType>::type(l)
-                                                    - typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())))
-{
-  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
-              (typename internal::cleanup_index_type<LastType>::type(l)
-               -typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
-}
+template<typename FirstType,typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr);
 
 /** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
   *
@@ -203,6 +195,22 @@ auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_in
   *
   * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
   */
+template<typename FirstType,typename LastType>
+auto seq(FirstType f, LastType l);
+
+#else // EIGEN_PARSED_BY_DOXYGEN
+
+#if EIGEN_HAS_CXX11
+template<typename FirstType,typename LastType>
+auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                                                   (  typename internal::cleanup_index_type<LastType>::type(l)
+                                                    - typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())))
+{
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l)
+               -typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
+}
+
 template<typename FirstType,typename LastType, typename IncrType>
 auto seq(FirstType f, LastType l, IncrType incr)
   -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
@@ -309,27 +317,13 @@ seq(const symbolic::BaseExpr<FirstTypeDerived> &f, const symbolic::BaseExpr<Last
 }
 #endif // EIGEN_HAS_CXX11
 
-#if EIGEN_HAS_CXX11
-/** \cpp11
-  * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
-  *
-  * \anchor indexing_lastN
-  *
-  *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
-  * 
-  * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
-template<typename SizeType>
-auto lastN(SizeType size)
--> decltype(seqN(Eigen::last+fix<1>()-size, size))
-{
-  return seqN(Eigen::last+fix<1>()-size, size);
-}
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
 
+#if EIGEN_HAS_CXX11 || defined(EIGEN_PARSED_BY_DOXYGEN)
 /** \cpp11
   * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
   *
-  * \anchor indexing_lastN_with_incr
-  *
   * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
   * 
   * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
@@ -339,6 +333,19 @@ auto lastN(SizeType size, IncrType incr)
 {
   return seqN(Eigen::last-(size-fix<1>())*incr, size, incr);
 }
+
+/** \cpp11
+  * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
+  *
+  *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
+  * 
+  * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
+template<typename SizeType>
+auto lastN(SizeType size)
+-> decltype(seqN(Eigen::last+fix<1>()-size, size))
+{
+  return seqN(Eigen::last+fix<1>()-size, size);
+}
 #endif
 
 namespace internal {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Array.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Array.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Array.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Array.h
index 6d50ea4..20c789b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Array.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Array.h
@@ -163,15 +163,7 @@ class Array
 #endif
 
     #if EIGEN_HAS_CXX11
-    /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11
-     *
-     * \only_for_vectors
-     *
-     * This constructor is for 1D array or vectors with more than 4 coefficients.
-     * There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients.
-     *
-     * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
-     * constructor must match the the fixed number of rows (resp. columns) of \c *this.
+    /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
      *
      * Example: \include Array_variadic_ctor_cxx11.cpp
      * Output: \verbinclude Array_variadic_ctor_cxx11.out
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ArrayBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArrayBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ArrayBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArrayBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ArrayWrapper.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArrayWrapper.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ArrayWrapper.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ArrayWrapper.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Assign.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Assign.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Assign.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Assign.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/AssignEvaluator.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/AssignEvaluator.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/AssignEvaluator.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/AssignEvaluator.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Assign_MKL.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Assign_MKL.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Assign_MKL.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Assign_MKL.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/BandMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/BandMatrix.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/BandMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/BandMatrix.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Block.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Block.h
similarity index 91%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Block.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Block.h
index 9d89b60..3206d66 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Block.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Block.h
@@ -260,19 +260,19 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
     }
 
     template<int LoadMode>
-    EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const
+    inline PacketScalar packet(Index rowId, Index colId) const
     {
       return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
     }
 
     template<int LoadMode>
-    EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
+    inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
     {
       m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
     }
 
     template<int LoadMode>
-    EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const
+    inline PacketScalar packet(Index index) const
     {
       return m_xpr.template packet<Unaligned>
               (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
@@ -280,7 +280,7 @@ template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool H
     }
 
     template<int LoadMode>
-    EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val)
+    inline void writePacket(Index index, const PacketScalar& val)
     {
       m_xpr.template writePacket<Unaligned>
          (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
@@ -334,17 +334,6 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
     enum {
       XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
     };
-
-    /** \internal Returns base+offset (unless base is null, in which case returns null).
-      * Adding an offset to nullptr is undefined behavior, so we must avoid it.
-      */
-    template <typename Scalar>
-    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE
-    static Scalar* add_to_nullable_pointer(Scalar* base, Index offset)
-    {
-      return base != NULL ? base+offset : NULL;
-    }
-
   public:
 
     typedef MapBase<BlockType> Base;
@@ -355,9 +344,8 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
       */
     EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     BlockImpl_dense(XprType& xpr, Index i)
-      : Base((BlockRows == 0 || BlockCols == 0) ? NULL : add_to_nullable_pointer(xpr.data(),
-                 i * (    ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
-                       || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride())),
+      : Base(xpr.data() + i * (    ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
+                                || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride()),
              BlockRows==1 ? 1 : xpr.rows(),
              BlockCols==1 ? 1 : xpr.cols()),
         m_xpr(xpr),
@@ -371,8 +359,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
       */
     EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
-      : Base((BlockRows == 0 || BlockCols == 0) ? NULL : add_to_nullable_pointer(xpr.data(),
-                 xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol))),
+      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
         m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
     {
       init();
@@ -384,9 +371,7 @@ class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
     BlockImpl_dense(XprType& xpr,
           Index startRow, Index startCol,
           Index blockRows, Index blockCols)
-      : Base((blockRows == 0 || blockCols == 0) ? NULL : add_to_nullable_pointer(xpr.data(),
-                 xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
-             blockRows, blockCols),
+      : Base(xpr.data()+xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol), blockRows, blockCols),
         m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
     {
       init();
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/BooleanRedux.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/BooleanRedux.h
similarity index 68%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/BooleanRedux.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/BooleanRedux.h
index fa4d7c3..852de8b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/BooleanRedux.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/BooleanRedux.h
@@ -14,56 +14,54 @@ namespace Eigen {
 
 namespace internal {
 
-template<typename Derived, int UnrollCount, int InnerSize>
+template<typename Derived, int UnrollCount, int Rows>
 struct all_unroller
 {
   enum {
-    IsRowMajor = (int(Derived::Flags) & int(RowMajor)),
-    i = (UnrollCount-1) / InnerSize,
-    j = (UnrollCount-1) % InnerSize
+    col = (UnrollCount-1) / Rows,
+    row = (UnrollCount-1) % Rows
   };
 
   EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
   {
-    return all_unroller<Derived, UnrollCount-1, InnerSize>::run(mat) && mat.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i);
+    return all_unroller<Derived, UnrollCount-1, Rows>::run(mat) && mat.coeff(row, col);
   }
 };
 
-template<typename Derived, int InnerSize>
-struct all_unroller<Derived, 0, InnerSize>
+template<typename Derived, int Rows>
+struct all_unroller<Derived, 0, Rows>
 {
   EIGEN_DEVICE_FUNC static inline bool run(const Derived &/*mat*/) { return true; }
 };
 
-template<typename Derived, int InnerSize>
-struct all_unroller<Derived, Dynamic, InnerSize>
+template<typename Derived, int Rows>
+struct all_unroller<Derived, Dynamic, Rows>
 {
   EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
 };
 
-template<typename Derived, int UnrollCount, int InnerSize>
+template<typename Derived, int UnrollCount, int Rows>
 struct any_unroller
 {
   enum {
-    IsRowMajor = (int(Derived::Flags) & int(RowMajor)),
-    i = (UnrollCount-1) / InnerSize,
-    j = (UnrollCount-1) % InnerSize
+    col = (UnrollCount-1) / Rows,
+    row = (UnrollCount-1) % Rows
   };
-
+  
   EIGEN_DEVICE_FUNC static inline bool run(const Derived &mat)
   {
-    return any_unroller<Derived, UnrollCount-1, InnerSize>::run(mat) || mat.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i);
+    return any_unroller<Derived, UnrollCount-1, Rows>::run(mat) || mat.coeff(row, col);
   }
 };
 
-template<typename Derived, int InnerSize>
-struct any_unroller<Derived, 0, InnerSize>
+template<typename Derived, int Rows>
+struct any_unroller<Derived, 0, Rows>
 {
   EIGEN_DEVICE_FUNC static inline bool run(const Derived & /*mat*/) { return false; }
 };
 
-template<typename Derived, int InnerSize>
-struct any_unroller<Derived, Dynamic, InnerSize>
+template<typename Derived, int Rows>
+struct any_unroller<Derived, Dynamic, Rows>
 {
   EIGEN_DEVICE_FUNC static inline bool run(const Derived &) { return false; }
 };
@@ -87,12 +85,12 @@ EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const
   };
   Evaluator evaluator(derived());
   if(unroll)
-    return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, InnerSizeAtCompileTime>::run(evaluator);
+    return internal::all_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
   else
   {
-    for(Index i = 0; i < derived().outerSize(); ++i)
-      for(Index j = 0; j < derived().innerSize(); ++j)
-        if (!evaluator.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i)) return false;
+    for(Index j = 0; j < cols(); ++j)
+      for(Index i = 0; i < rows(); ++i)
+        if (!evaluator.coeff(i, j)) return false;
     return true;
   }
 }
@@ -111,12 +109,12 @@ EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const
   };
   Evaluator evaluator(derived());
   if(unroll)
-    return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, InnerSizeAtCompileTime>::run(evaluator);
+    return internal::any_unroller<Evaluator, unroll ? int(SizeAtCompileTime) : Dynamic, internal::traits<Derived>::RowsAtCompileTime>::run(evaluator);
   else
   {
-    for(Index i = 0; i < derived().outerSize(); ++i)
-      for(Index j = 0; j < derived().innerSize(); ++j)
-        if (evaluator.coeff(IsRowMajor ? i : j, IsRowMajor ? j : i)) return true;
+    for(Index j = 0; j < cols(); ++j)
+      for(Index i = 0; i < rows(); ++i)
+        if (evaluator.coeff(i, j)) return true;
     return false;
   }
 }
@@ -158,7 +156,7 @@ inline bool DenseBase<Derived>::allFinite() const
   return !((derived()-derived()).hasNaN());
 #endif
 }
-
+    
 } // end namespace Eigen
 
 #endif // EIGEN_ALLANDANY_H
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CommaInitializer.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CommaInitializer.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CommaInitializer.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CommaInitializer.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ConditionEstimator.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ConditionEstimator.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ConditionEstimator.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ConditionEstimator.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CoreEvaluators.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CoreEvaluators.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CoreEvaluators.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CoreEvaluators.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CoreIterators.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CoreIterators.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CoreIterators.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CoreIterators.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseBinaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseBinaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseNullaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseNullaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
index ba07e71..289ec51 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseNullaryOp.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseNullaryOp.h
@@ -292,7 +292,7 @@ DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
 }
 
 /**
-  * \copydoc DenseBase::LinSpaced(Index, const DenseBase::Scalar&, const DenseBase::Scalar&)
+  * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
   * Special version for fixed size types which does not require the size parameter.
   */
 template<typename Derived>
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseTernaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseTernaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseTernaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseUnaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseUnaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseUnaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseUnaryView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/CwiseUnaryView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/CwiseUnaryView.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/DenseBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseBase.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/DenseBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseBase.h
index cdd0f5f..9b16db6 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/DenseBase.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseBase.h
@@ -324,9 +324,9 @@ template<typename Derived> class DenseBase
     typedef Transpose<Derived> TransposeReturnType;
     EIGEN_DEVICE_FUNC
     TransposeReturnType transpose();
-    typedef Transpose<const Derived> ConstTransposeReturnType;
+    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
     EIGEN_DEVICE_FUNC
-    const ConstTransposeReturnType transpose() const;
+    ConstTransposeReturnType transpose() const;
     EIGEN_DEVICE_FUNC
     void transposeInPlace();
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/DenseCoeffsBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/DenseCoeffsBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseCoeffsBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/DenseStorage.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseStorage.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/DenseStorage.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DenseStorage.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Diagonal.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Diagonal.h
similarity index 92%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Diagonal.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Diagonal.h
index ad5bccd..3112d2c 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Diagonal.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Diagonal.h
@@ -191,8 +191,7 @@ MatrixBase<Derived>::diagonal()
 
 /** This is the const version of diagonal(). */
 template<typename Derived>
-EIGEN_DEVICE_FUNC inline
-const typename MatrixBase<Derived>::ConstDiagonalReturnType
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalReturnType
 MatrixBase<Derived>::diagonal() const
 {
   return ConstDiagonalReturnType(derived());
@@ -210,18 +209,18 @@ MatrixBase<Derived>::diagonal() const
   *
   * \sa MatrixBase::diagonal(), class Diagonal */
 template<typename Derived>
-EIGEN_DEVICE_FUNC inline Diagonal<Derived, DynamicIndex>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalDynamicIndexReturnType
 MatrixBase<Derived>::diagonal(Index index)
 {
-  return Diagonal<Derived, DynamicIndex>(derived(), index);
+  return DiagonalDynamicIndexReturnType(derived(), index);
 }
 
 /** This is the const version of diagonal(Index). */
 template<typename Derived>
-EIGEN_DEVICE_FUNC inline const Diagonal<const Derived, DynamicIndex>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::ConstDiagonalDynamicIndexReturnType
 MatrixBase<Derived>::diagonal(Index index) const
 {
-  return Diagonal<const Derived, DynamicIndex>(derived(), index);
+  return ConstDiagonalDynamicIndexReturnType(derived(), index);
 }
 
 /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
@@ -238,20 +237,20 @@ MatrixBase<Derived>::diagonal(Index index) const
 template<typename Derived>
 template<int Index_>
 EIGEN_DEVICE_FUNC
-inline Diagonal<Derived, Index_>
+inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index_>::Type
 MatrixBase<Derived>::diagonal()
 {
-  return Diagonal<Derived, Index_>(derived());
+  return typename DiagonalIndexReturnType<Index_>::Type(derived());
 }
 
 /** This is the const version of diagonal<int>(). */
 template<typename Derived>
 template<int Index_>
 EIGEN_DEVICE_FUNC
-inline const Diagonal<const Derived, Index_>
+inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index_>::Type
 MatrixBase<Derived>::diagonal() const
 {
-  return  Diagonal<const Derived, Index_>(derived());
+  return typename ConstDiagonalIndexReturnType<Index_>::Type(derived());
 }
 
 } // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/DiagonalMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/DiagonalMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DiagonalMatrix.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/DiagonalProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DiagonalProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/DiagonalProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/DiagonalProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Dot.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Dot.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Dot.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Dot.h
index abac7ad..5c3441b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Dot.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Dot.h
@@ -18,9 +18,14 @@ namespace internal {
 // with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
 // looking at the static assertions. Thus this is a trick to get better compile errors.
 template<typename T, typename U,
-         bool NeedToTranspose = T::IsVectorAtCompileTime && U::IsVectorAtCompileTime &&
-                ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1) ||
-                 (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))>
+// the NeedToTranspose condition here is taken straight from Assign.h
+         bool NeedToTranspose = T::IsVectorAtCompileTime
+                && U::IsVectorAtCompileTime
+                && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1)
+                      |  // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
+                         // revert to || as soon as not needed anymore.
+                    (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))
+>
 struct dot_nocheck
 {
   typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/EigenBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/EigenBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/EigenBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/EigenBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ForceAlignedAccess.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ForceAlignedAccess.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ForceAlignedAccess.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Fuzzy.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Fuzzy.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Fuzzy.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Fuzzy.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/GeneralProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GeneralProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/GeneralProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GeneralProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/GenericPacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GenericPacketMath.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/GenericPacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GenericPacketMath.h
index af45f39..cf677a1 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/GenericPacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GenericPacketMath.h
@@ -160,7 +160,7 @@ struct eigen_packet_wrapper
 {
   EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
   EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
-  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {};
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() {}
   EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {}
   EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) {
     m_val = v;
@@ -258,7 +258,7 @@ struct ptrue_impl {
 // uses a comparison to zero, so this should still work in most cases. We don't
 // have another option, since the scalar type requires initialization.
 template<typename T>
-struct ptrue_impl<T,
+struct ptrue_impl<T, 
     typename internal::enable_if<is_scalar<T>::value && NumTraits<T>::RequireInitialization>::type > {
   static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){
     return T(1);
@@ -356,16 +356,16 @@ struct bytewise_bitwise_helper {
   EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
     return binary(a, b, bit_and<unsigned char>());
   }
-  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) {
+  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { 
     return binary(a, b, bit_or<unsigned char>());
    }
   EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
     return binary(a, b, bit_xor<unsigned char>());
   }
-  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) {
+  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { 
     return unary(a,bit_not<unsigned char>());
    }
-
+  
  private:
   template<typename Op>
   EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
@@ -440,7 +440,7 @@ struct pselect_impl {
 
 // For scalars, use ternary select.
 template<typename Packet>
-struct pselect_impl<Packet,
+struct pselect_impl<Packet, 
     typename internal::enable_if<is_scalar<Packet>::value>::type > {
   static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
     return numext::equal_strict(mask, Packet(0)) ? b : a;
@@ -807,7 +807,7 @@ Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); }
 template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
 Packet plog2(const Packet& a) {
   typedef typename internal::unpacket_traits<Packet>::type Scalar;
-  return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a));
+  return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a)); 
 }
 
 /** \internal \returns the square-root of \a a (coeff-wise) */
@@ -881,7 +881,7 @@ predux(const Packet& a)
 template <typename Packet>
 EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
     const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::type Scalar; 
   return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
 }
 
@@ -889,14 +889,14 @@ EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
 template <typename Packet>
 EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
     const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::type Scalar; 
   return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<PropagateFast, Scalar>)));
 }
 
 template <int NaNPropagation, typename Packet>
 EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
     const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::type Scalar; 
   return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
 }
 
@@ -904,14 +904,14 @@ EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
 template <typename Packet>
 EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
     const Packet &a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::type Scalar; 
   return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<PropagateFast, Scalar>)));
 }
 
 template <int NaNPropagation, typename Packet>
 EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
     const Packet& a) {
-  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::type Scalar; 
   return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
 }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/GlobalFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GlobalFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/GlobalFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/GlobalFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/IO.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/IO.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/IO.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/IO.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/IndexedView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/IndexedView.h
similarity index 88%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/IndexedView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/IndexedView.h
index 05c2bc9..0847625 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/IndexedView.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/IndexedView.h
@@ -122,10 +122,10 @@ public:
   {}
 
   /** \returns number of rows */
-  Index rows() const { return internal::index_list_size(m_rowIndices); }
+  Index rows() const { return internal::size(m_rowIndices); }
 
   /** \returns number of columns */
-  Index cols() const { return internal::index_list_size(m_colIndices); }
+  Index cols() const { return internal::size(m_colIndices); }
 
   /** \returns the nested expression */
   const typename internal::remove_all<XprType>::type&
@@ -189,16 +189,12 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
   CoeffReturnType coeff(Index row, Index col) const
   {
-    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
-                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
     return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
   Scalar& coeffRef(Index row, Index col)
   {
-    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
-                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
     return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
   }
 
@@ -208,8 +204,6 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
     EIGEN_STATIC_ASSERT_LVALUE(XprType)
     Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
     Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
-                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
     return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
   }
 
@@ -218,8 +212,6 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
   {
     Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
     Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
-                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
     return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
   }
 
@@ -228,8 +220,6 @@ struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
   {
     Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
     Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
-    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
-                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
     return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
   }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Inverse.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Inverse.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Inverse.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Inverse.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Map.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Map.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Map.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Map.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/MapBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MapBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/MapBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MapBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MathFunctions.h
similarity index 90%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MathFunctions.h
index 764c41c..61b78f4 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/MathFunctions.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MathFunctions.h
@@ -588,8 +588,12 @@ struct arg_default_impl<Scalar, true> {
   EIGEN_DEVICE_FUNC
   static inline RealScalar run(const Scalar& x)
   {
-    // There is no official ::arg on device in CUDA/HIP, so we always need to use std::arg.
+    #if defined(EIGEN_HIP_DEVICE_COMPILE)
+    // HIP does not seem to have a native device side implementation for the math routine "arg"
     using std::arg;
+    #else
+    EIGEN_USING_STD(arg);
+    #endif
     return static_cast<RealScalar>(arg(x));
   }
 };
@@ -877,159 +881,13 @@ struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus>
   // no value, error at compile time
 };
 
-template <typename BitsType, typename EnableIf = void>
-struct count_bits_impl {
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(
-        is_integral<BitsType>::value && !NumTraits<BitsType>::IsSigned,
-        THIS_TYPE_IS_NOT_SUPPORTED);
-    int n = CHAR_BIT * sizeof(BitsType);
-    int shift = n / 2;
-    while (bits > 0 && shift > 0) {
-      BitsType y = bits >> shift;
-      if (y > 0) {
-        n -= shift;
-        bits = y;
-      }
-      shift /= 2;
-    }
-    if (shift == 0) {
-      --n;
-    }
-    return n;
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(
-        is_integral<BitsType>::value && !NumTraits<BitsType>::IsSigned,
-        THIS_TYPE_IS_NOT_SUPPORTED);
-    int n = CHAR_BIT * sizeof(BitsType);
-    int shift = n / 2;
-    while (bits > 0 && shift > 0) {
-      BitsType y = bits << shift;
-      if (y > 0) {
-        n -= shift;
-        bits = y;
-      }
-      shift /= 2;
-    }
-    if (shift == 0) {
-      --n;
-    }
-    return n;
-  }
-};
-
-// Count leading zeros.
-template <typename BitsType>
-EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-  return count_bits_impl<BitsType>::clz(bits);
-}
-
-// Count trailing zeros.
-template <typename BitsType>
-EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-  return count_bits_impl<BitsType>::ctz(bits);
-}
-
-#if EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
-
-template <typename BitsType>
-struct count_bits_impl<BitsType, typename enable_if<sizeof(BitsType) <= sizeof(unsigned int)>::type> {
-  static const int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    static const int kLeadingBitsOffset = (sizeof(unsigned int) - sizeof(BitsType)) * CHAR_BIT;
-    return bits == 0 ? kNumBits : __builtin_clz(static_cast<unsigned int>(bits)) - kLeadingBitsOffset;
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    return bits == 0 ? kNumBits : __builtin_ctz(static_cast<unsigned int>(bits));
-  }
-};
-
-template <typename BitsType>
-struct count_bits_impl<
-    BitsType, typename enable_if<sizeof(unsigned int) < sizeof(BitsType) && sizeof(BitsType) <= sizeof(unsigned long)>::type> {
-  static const int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    static const int kLeadingBitsOffset = (sizeof(unsigned long) - sizeof(BitsType)) * CHAR_BIT;
-    return bits == 0 ? kNumBits : __builtin_clzl(static_cast<unsigned long>(bits)) - kLeadingBitsOffset;
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    return bits == 0 ? kNumBits : __builtin_ctzl(static_cast<unsigned long>(bits));
-  }
-};
-
-template <typename BitsType>
-struct count_bits_impl<BitsType, typename enable_if<sizeof(unsigned long) < sizeof(BitsType) &&
-                                                  sizeof(BitsType) <= sizeof(unsigned long long)>::type> {
-  static const int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    static const int kLeadingBitsOffset = (sizeof(unsigned long long) - sizeof(BitsType)) * CHAR_BIT;
-    return bits == 0 ? kNumBits : __builtin_clzll(static_cast<unsigned long long>(bits)) - kLeadingBitsOffset;
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    return bits == 0 ? kNumBits : __builtin_ctzll(static_cast<unsigned long long>(bits));
-  }
-};
-
-#elif EIGEN_COMP_MSVC
-
-template <typename BitsType>
-struct count_bits_impl<BitsType, typename enable_if<sizeof(BitsType) <= sizeof(unsigned long)>::type> {
-  static const int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    unsigned long out;
-    _BitScanReverse(&out, static_cast<unsigned long>(bits));
-    return bits == 0 ? kNumBits : (kNumBits - 1) - static_cast<int>(out);
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    unsigned long out;
-    _BitScanForward(&out, static_cast<unsigned long>(bits));
-    return bits == 0 ? kNumBits : static_cast<int>(out);
-  }
-};
-
-#ifdef _WIN64
-
-template <typename BitsType>
-struct count_bits_impl<
-    BitsType, typename enable_if<sizeof(unsigned long) < sizeof(BitsType) && sizeof(BitsType) <= sizeof(__int64)>::type> {
-  static const int kNumBits = static_cast<int>(sizeof(BitsType) * CHAR_BIT);
-  static EIGEN_DEVICE_FUNC inline int clz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    unsigned long out;
-    _BitScanReverse64(&out, static_cast<unsigned __int64>(bits));
-    return bits == 0 ? kNumBits : (kNumBits - 1) - static_cast<int>(out);
-  }
-
-  static EIGEN_DEVICE_FUNC inline int ctz(BitsType bits) {
-    EIGEN_STATIC_ASSERT(is_integral<BitsType>::value, THIS_TYPE_IS_NOT_SUPPORTED);
-    unsigned long out;
-    _BitScanForward64(&out, static_cast<unsigned __int64>(bits));
-    return bits == 0 ? kNumBits : static_cast<int>(out);
-  }
-};
-
-#endif  // _WIN64
-
-#endif  // EIGEN_COMP_GNUC || EIGEN_COMP_CLANG
-
-template <typename Scalar>
-struct random_default_impl<Scalar, false, true> {
-  static inline Scalar run(const Scalar& x, const Scalar& y) {
-    if (y <= x) return x;
+template<typename Scalar>
+struct random_default_impl<Scalar, false, true>
+{
+  static inline Scalar run(const Scalar& x, const Scalar& y)
+  {
+    if (y <= x)
+      return x;
     // ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself.
     typedef typename make_unsigned<Scalar>::type ScalarU;
     // ScalarX is the widest of ScalarU and unsigned int.
@@ -1174,15 +1032,11 @@ template<typename T> EIGEN_DEVICE_FUNC bool isinf_msvc_helper(T x)
 }
 
 //MSVC defines a _isnan builtin function, but for double only
-#ifndef EIGEN_GPU_COMPILE_PHASE
 EIGEN_DEVICE_FUNC inline bool isnan_impl(const long double& x) { return _isnan(x)!=0; }
-#endif
 EIGEN_DEVICE_FUNC inline bool isnan_impl(const double& x)      { return _isnan(x)!=0; }
 EIGEN_DEVICE_FUNC inline bool isnan_impl(const float& x)       { return _isnan(x)!=0; }
 
-#ifndef EIGEN_GPU_COMPILE_PHASE
 EIGEN_DEVICE_FUNC inline bool isinf_impl(const long double& x) { return isinf_msvc_helper(x); }
-#endif
 EIGEN_DEVICE_FUNC inline bool isinf_impl(const double& x)      { return isinf_msvc_helper(x); }
 EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x)       { return isinf_msvc_helper(x); }
 
@@ -1196,16 +1050,12 @@ EIGEN_DEVICE_FUNC inline bool isinf_impl(const float& x)       { return isinf_ms
   #define EIGEN_TMP_NOOPT_ATTRIB EIGEN_DEVICE_FUNC inline __attribute__((noinline,optimize("no-finite-math-only")))
 #endif
 
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const long double& x) { return __builtin_isnan(x); }
-#endif
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const double& x)      { return __builtin_isnan(x); }
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isnan_impl(const float& x)       { return __builtin_isnan(x); }
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const double& x)      { return __builtin_isinf(x); }
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const float& x)       { return __builtin_isinf(x); }
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<> EIGEN_TMP_NOOPT_ATTRIB bool isinf_impl(const long double& x) { return __builtin_isinf(x); }
-#endif
 
 #undef EIGEN_TMP_NOOPT_ATTRIB
 
@@ -1262,8 +1112,6 @@ EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y)
 {
   return fmin(x, y);
 }
-
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<>
 EIGEN_DEVICE_FUNC
 EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y)
@@ -1275,7 +1123,6 @@ EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y)
   return fminl(x, y);
 #endif
 }
-#endif
 
 template<typename T>
 EIGEN_DEVICE_FUNC
@@ -1295,7 +1142,6 @@ EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y)
 {
   return fmax(x, y);
 }
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<>
 EIGEN_DEVICE_FUNC
 EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y)
@@ -1308,7 +1154,6 @@ EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y)
 #endif
 }
 #endif
-#endif
 
 #if defined(SYCL_DEVICE_ONLY)
 
@@ -1465,8 +1310,8 @@ EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y)
   return fabs(x - y);
 }
 
+#if !defined(EIGEN_GPUCC)
 // HIP and CUDA do not support long double.
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<>
 EIGEN_DEVICE_FUNC
 EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/MathFunctionsImpl.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/MathFunctionsImpl.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MathFunctionsImpl.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Matrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Matrix.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Matrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Matrix.h
index 29c3b5c..f0e59a9 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Matrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Matrix.h
@@ -225,6 +225,8 @@ class Matrix
       return Base::_set(other);
     }
 
+    /* Here, doxygen failed to copy the brief information when using \copydoc */
+
     /**
       * \brief Copies the generic expression \a other into *this.
       * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
@@ -282,15 +284,7 @@ class Matrix
 #endif
 
 #if EIGEN_HAS_CXX11
-    /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients. \cpp11
-     *
-     * \only_for_vectors
-     *
-     * This constructor is for 1D array or vectors with more than 4 coefficients.
-     * There exists C++98 analogue constructors for fixed-size array/vector having 1, 2, 3, or 4 coefficients.
-     *
-     * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
-     * constructor must match the the fixed number of rows (resp. columns) of \c *this.
+    /** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&... args)
      *
      * Example: \include Matrix_variadic_ctor_cxx11.cpp
      * Output: \verbinclude Matrix_variadic_ctor_cxx11.out
@@ -303,8 +297,6 @@ class Matrix
       : Base(a0, a1, a2, a3, args...) {}
 
     /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
-      *
-      * \anchor matrix_constructor_initializer_list
       *
       * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
       *
@@ -488,21 +480,16 @@ class Matrix
 
 #define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
 /** \ingroup matrixtypedefs */                                    \
-/** \brief \noop            */                                    \
 typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix;  \
 /** \ingroup matrixtypedefs */                                    \
-/** \brief \noop            */                                    \
 typedef Matrix<Type, Size, 1>    Vector##SizeSuffix##TypeSuffix;  \
 /** \ingroup matrixtypedefs */                                    \
-/** \brief \noop            */                                    \
 typedef Matrix<Type, 1, Size>    RowVector##SizeSuffix##TypeSuffix;
 
 #define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
 /** \ingroup matrixtypedefs */                                    \
-/** \brief \noop            */                                    \
 typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix;  \
 /** \ingroup matrixtypedefs */                                    \
-/** \brief \noop            */                                    \
 typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
 
 #define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/MatrixBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MatrixBase.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/MatrixBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MatrixBase.h
index d93a7e3..45c3a59 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/MatrixBase.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/MatrixBase.h
@@ -206,22 +206,28 @@ template<typename Derived> class MatrixBase
     EIGEN_DEVICE_FUNC
     DiagonalReturnType diagonal();
 
-    typedef Diagonal<const Derived> ConstDiagonalReturnType;
+    typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
     EIGEN_DEVICE_FUNC
-    const ConstDiagonalReturnType diagonal() const;
+    ConstDiagonalReturnType diagonal() const;
+
+    template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
+    template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };
 
     template<int Index>
     EIGEN_DEVICE_FUNC
-    Diagonal<Derived, Index> diagonal();
+    typename DiagonalIndexReturnType<Index>::Type diagonal();
 
     template<int Index>
     EIGEN_DEVICE_FUNC
-    const Diagonal<const Derived, Index> diagonal() const;
+    typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const;
+
+    typedef Diagonal<Derived,DynamicIndex> DiagonalDynamicIndexReturnType;
+    typedef typename internal::add_const<Diagonal<const Derived,DynamicIndex> >::type ConstDiagonalDynamicIndexReturnType;
 
     EIGEN_DEVICE_FUNC
-    Diagonal<Derived, DynamicIndex> diagonal(Index index);
+    DiagonalDynamicIndexReturnType diagonal(Index index);
     EIGEN_DEVICE_FUNC
-    const Diagonal<const Derived, DynamicIndex> diagonal(Index index) const;
+    ConstDiagonalDynamicIndexReturnType diagonal(Index index) const;
 
     template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
     template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/NestByValue.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NestByValue.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/NestByValue.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NestByValue.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/NoAlias.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NoAlias.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/NoAlias.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NoAlias.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/NumTraits.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NumTraits.h
similarity index 82%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/NumTraits.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NumTraits.h
index 7c2c50b..72eac5a 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/NumTraits.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/NumTraits.h
@@ -98,7 +98,6 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
 }
 }  // namespace numext
 
-// clang-format off
 /** \class NumTraits
   * \ingroup Core_Module
   *
@@ -110,47 +109,45 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
   *
   * The provided data consists of:
   * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
-  *     then \c Real is just a typedef to \a T. If \a T is `std::complex<U>` then \c Real
+  *     then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
   *     is a typedef to \a U.
   * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
   *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
-  *     \a T again. Note however that many Eigen functions such as `internal::sqrt` simply refuse to
+  *     \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
   *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
   *     only intended as a helper for code that needs to explicitly promote types.
-  * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for `std::complex<U>`,
-  *     Literal is defined as \c U.
+  * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
   *     Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
-  * \li A typedef \c Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
+  * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
   *     this means, just use \a T here.
-  * \li An enum value \c IsComplex. It is equal to 1 if \a T is a \c std::complex
+  * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
   *     type, and to 0 otherwise.
-  * \li An enum value \c IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
+  * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
   *     and to \c 0 otherwise.
-  * \li Enum values \c ReadCost, \c AddCost and \c MulCost representing a rough estimate of the number of CPU cycles needed
+  * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
   *     to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
   *     Stay vague here. No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost.
-  * \li An enum value \c IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
-  * \li An enum value \c RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
+  * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
+  * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
   *     be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
-  * \li An `epsilon()` function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">`std::numeric_limits::epsilon()`</a>,
-  *     it returns a \c Real instead of a \a T.
-  * \li A `dummy_precision()` function returning a weak epsilon value. It is mainly used as a default
+  * \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
+  *     it returns a \a Real instead of a \a T.
+  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
   *     value by the fuzzy comparison operators.
-  * \li `highest()` and `lowest()` functions returning the highest and lowest possible values respectively.
-  * \li `digits()` function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is
+  * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
+  * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is
   *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits">std::numeric_limits<T>::digits</a>
   *     which is used as the default implementation if specialized.
-  * \li `digits10()` function returning the number of decimal digits that can be represented without change. This is
+  * \li digits10() function returning the number of decimal digits that can be represented without change. This is
   *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
   *     which is used as the default implementation if specialized.
-  * \li `min_exponent()` and `max_exponent()` functions returning the highest and lowest possible values, respectively,
+  * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively,
   *     such that the radix raised to the power exponent-1 is a normalized floating-point number.  These are equivalent to
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">`std::numeric_limits<T>::min_exponent`</a>/
-  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">`std::numeric_limits<T>::max_exponent`</a>.
-  * \li `infinity()` function returning a representation of positive infinity, if available.
-  * \li `quiet_NaN` function returning a non-signaling "not-a-number", if available.
+  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">std::numeric_limits<T>::min_exponent</a>/
+  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">std::numeric_limits<T>::max_exponent</a>.
+  * \li infinity() function returning a representation of positive infinity, if available.
+  * \li quiet_NaN function returning a non-signaling "not-a-number", if available.
   */
-  // clang-format on
 
 template<typename T> struct GenericNumTraits
 {
@@ -248,25 +245,12 @@ template<> struct NumTraits<double> : GenericNumTraits<double>
   static inline double dummy_precision() { return 1e-12; }
 };
 
-// GPU devices treat `long double` as `double`.
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<> struct NumTraits<long double>
   : GenericNumTraits<long double>
 {
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline long double dummy_precision() { return static_cast<long double>(1e-15l); }
-
-#if defined(EIGEN_ARCH_PPC) && (__LDBL_MANT_DIG__ == 106)
-  // PowerPC double double causes issues with some values
-  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
-  static inline long double epsilon()
-  {
-    // 2^(-(__LDBL_MANT_DIG__)+1)
-    return static_cast<long double>(2.4651903288156618919116517665087e-32l);
-  }
-#endif
+  EIGEN_CONSTEXPR
+  static inline long double dummy_precision() { return 1e-15l; }
 };
-#endif
 
 template<typename _Real> struct NumTraits<std::complex<_Real> >
   : GenericNumTraits<std::complex<_Real> >
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/PartialReduxEvaluator.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/PartialReduxEvaluator.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
index 17c06f0..29abf35 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/PartialReduxEvaluator.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PartialReduxEvaluator.h
@@ -54,17 +54,12 @@ struct packetwise_redux_traits
 /* Value to be returned when size==0 , by default let's return 0 */
 template<typename PacketType,typename Func>
 EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const Func& ) {
-  const typename unpacket_traits<PacketType>::type zero(0);
-  return pset1<PacketType>(zero);
-}
+PacketType packetwise_redux_empty_value(const Func& ) { return pset1<PacketType>(0); }
 
 /* For products the default is 1 */
 template<typename PacketType,typename Scalar>
 EIGEN_DEVICE_FUNC
-PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) {
-  return pset1<PacketType>(Scalar(1));
-}
+PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) { return pset1<PacketType>(1); }
 
 /* Perform the actual reduction */
 template<typename Func, typename Evaluator,
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/PermutationMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PermutationMatrix.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/PermutationMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PermutationMatrix.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/PlainObjectBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PlainObjectBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/PlainObjectBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/PlainObjectBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Product.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Product.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Product.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Product.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ProductEvaluators.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ProductEvaluators.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ProductEvaluators.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ProductEvaluators.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Random.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Random.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Random.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Random.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Redux.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Redux.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Redux.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Redux.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Ref.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Ref.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Ref.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Ref.h
index 07da155..c2a37ea 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Ref.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Ref.h
@@ -312,8 +312,8 @@ template<typename PlainObjectType, int Options, typename StrideType> class Ref
     inline Ref(DenseBase<Derived>& expr)
     #endif
     {
-      EIGEN_STATIC_ASSERT((static_cast<bool>(internal::is_lvalue<Derived>::value)), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
-      EIGEN_STATIC_ASSERT((static_cast<bool>(Traits::template match<Derived>::MatchAtCompileTime)), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
       EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
       // Construction must pass since we will not create temporary storage in the non-const case.
       const bool success = Base::construct(expr.const_cast_derived());
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Replicate.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Replicate.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Replicate.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Replicate.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Reshaped.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Reshaped.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Reshaped.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Reshaped.h
index 882314c..52de73b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Reshaped.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Reshaped.h
@@ -250,7 +250,7 @@ class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
     EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
     inline Index outerStride() const
     {
-      return (((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows()) * m_xpr.innerStride();
+      return ((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows();
     }
 
   protected:
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/ReturnByValue.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ReturnByValue.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/ReturnByValue.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/ReturnByValue.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Reverse.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Reverse.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Reverse.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Reverse.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Select.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Select.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Select.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Select.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/SelfAdjointView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SelfAdjointView.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/SelfAdjointView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SelfAdjointView.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/SelfCwiseBinaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/SelfCwiseBinaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Solve.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Solve.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Solve.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Solve.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/SolveTriangular.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SolveTriangular.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/SolveTriangular.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SolveTriangular.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/SolverBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SolverBase.h
similarity index 93%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/SolverBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SolverBase.h
index e38b3d5..5014610 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/SolverBase.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/SolverBase.h
@@ -110,7 +110,7 @@ class SolverBase : public EigenBase<Derived>
     }
 
     /** \internal the return type of transpose() */
-    typedef Transpose<const Derived> ConstTransposeReturnType;
+    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
     /** \returns an expression of the transposed of the factored matrix.
       *
       * A typical usage is to solve for the transposed problem A^T x = b:
@@ -118,16 +118,16 @@ class SolverBase : public EigenBase<Derived>
       *
       * \sa adjoint(), solve()
       */
-    inline const ConstTransposeReturnType transpose() const
+    inline ConstTransposeReturnType transpose() const
     {
       return ConstTransposeReturnType(derived());
     }
 
     /** \internal the return type of adjoint() */
     typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
-               CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const ConstTransposeReturnType>,
-               const ConstTransposeReturnType
-            >::type AdjointReturnType;
+                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
+                        ConstTransposeReturnType
+                     >::type AdjointReturnType;
     /** \returns an expression of the adjoint of the factored matrix
       *
       * A typical usage is to solve for the adjoint problem A' x = b:
@@ -137,7 +137,7 @@ class SolverBase : public EigenBase<Derived>
       *
       * \sa transpose(), solve()
       */
-    inline const AdjointReturnType adjoint() const
+    inline AdjointReturnType adjoint() const
     {
       return AdjointReturnType(derived().transpose());
     }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/StableNorm.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/StableNorm.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/StableNorm.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/StableNorm.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/StlIterators.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/StlIterators.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/StlIterators.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/StlIterators.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Stride.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Stride.h
similarity index 93%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Stride.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Stride.h
index d164e53..6494d51 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Stride.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Stride.h
@@ -38,14 +38,10 @@ namespace Eigen {
   * \include Map_general_stride.cpp
   * Output: \verbinclude Map_general_stride.out
   *
-  * Both strides can be negative. However, a negative stride of -1 cannot be specified at compile time
+  * Both strides can be negative, however, a negative stride of -1 cannot be specified at compiletime
   * because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
   * not allowed).
   *
-  * Note that for compile-time vectors (ColsAtCompileTime==1 or RowsAtCompile==1),
-  * the inner stride is the pointer increment between two consecutive elements,
-  * regardless of storage layout.
-  *
   * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
   */
 template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime>
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Swap.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Swap.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Swap.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Swap.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Transpose.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Transpose.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Transpose.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Transpose.h
index 741504d..2bc658f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/Transpose.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Transpose.h
@@ -178,7 +178,7 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
   * \sa transposeInPlace(), adjoint() */
 template<typename Derived>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-typename DenseBase<Derived>::TransposeReturnType
+Transpose<Derived>
 DenseBase<Derived>::transpose()
 {
   return TransposeReturnType(derived());
@@ -191,7 +191,7 @@ DenseBase<Derived>::transpose()
   * \sa transposeInPlace(), adjoint() */
 template<typename Derived>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-const typename DenseBase<Derived>::ConstTransposeReturnType
+typename DenseBase<Derived>::ConstTransposeReturnType
 DenseBase<Derived>::transpose() const
 {
   return ConstTransposeReturnType(derived());
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Transpositions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Transpositions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Transpositions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Transpositions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/TriangularMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/TriangularMatrix.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/TriangularMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/TriangularMatrix.h
index bd722cf..fdb8bc1 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/TriangularMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/TriangularMatrix.h
@@ -100,10 +100,12 @@ template<typename Derived> class TriangularBase : public EigenBase<Derived>
       return coeffRef(row,col);
     }
 
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
     EIGEN_DEVICE_FUNC
     inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
     EIGEN_DEVICE_FUNC
     inline Derived& derived() { return *static_cast<Derived*>(this); }
+    #endif // not EIGEN_PARSED_BY_DOXYGEN
 
     template<typename DenseDerived>
     EIGEN_DEVICE_FUNC
@@ -440,6 +442,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
     EIGEN_DEVICE_FUNC
     TriangularViewType& operator=(const MatrixBase<OtherDerived>& other);
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     EIGEN_DEVICE_FUNC
     TriangularViewType& operator=(const TriangularViewImpl& other)
     { return *this = other.derived().nestedExpression(); }
@@ -453,6 +456,7 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
     /** \deprecated */
     EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
     void lazyAssign(const MatrixBase<OtherDerived>& other);
+#endif
 
     /** Efficient triangular matrix times vector/matrix product */
     template<typename OtherDerived>
@@ -520,7 +524,11 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
     /** Swaps the coefficients of the common triangular parts of two matrices */
     template<typename OtherDerived>
     EIGEN_DEVICE_FUNC
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    void swap(TriangularBase<OtherDerived> &other)
+#else
     void swap(TriangularBase<OtherDerived> const & other)
+#endif
     {
       EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
       call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
@@ -544,10 +552,9 @@ template<typename _MatrixType, unsigned int _Mode> class TriangularViewImpl<_Mat
       this->solveInPlace(dst);
     }
 
-    template <typename ProductType>
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha,
-                                                                           bool beta);
-
+    template<typename ProductType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha, bool beta);
   protected:
     EIGEN_DEFAULT_COPY_CONSTRUCTOR(TriangularViewImpl)
     EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TriangularViewImpl)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/VectorBlock.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/VectorBlock.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/VectorBlock.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/VectorBlock.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/VectorwiseOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/VectorwiseOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/VectorwiseOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/VectorwiseOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/Visitor.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Visitor.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/Visitor.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/Visitor.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
index c200620..ab7bd6c 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/Complex.h
@@ -99,9 +99,7 @@ template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<fl
 
 template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
 {
-  const float re = std::real(from);
-  const float im = std::imag(from);
-  return Packet4cf(_mm256_set_ps(im, re, im, re, im, re, im, re));
+  return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from)));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
@@ -169,12 +167,15 @@ template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const P
                          Packet2cf(_mm256_extractf128_ps(a.v, 1))));
 }
 
-
 EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
 
 template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
 {
-  return pdiv_complex(a, b);
+  Packet4cf num = pmul(a, pconj(b));
+  __m256 tmp = _mm256_mul_ps(b.v, b.v);
+  __m256 tmp2    = _mm256_shuffle_ps(tmp,tmp,0xB1);
+  __m256 denom = _mm256_add_ps(tmp, tmp2);
+  return Packet4cf(_mm256_div_ps(num.v, denom));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
@@ -320,7 +321,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
 
 template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
 {
-  return pdiv_complex(a, b);
+  Packet2cd num = pmul(a, pconj(b));
+  __m256d tmp = _mm256_mul_pd(b.v, b.v);
+  __m256d denom = _mm256_hadd_pd(tmp, tmp);
+  return Packet2cd(_mm256_div_pd(num.v, denom));
 }
 
 template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
index 24e01c4..7fc32fd 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h
@@ -285,13 +285,11 @@ template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const
 
 template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a)
 {
-  const Packet8f mask = _mm256_castsi256_ps(_mm256_set1_epi32(0x80000000));
-  return _mm256_xor_ps(a, mask);
+  return _mm256_sub_ps(_mm256_set1_ps(0.0),a);
 }
 template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a)
 {
-  const Packet4d mask = _mm256_castsi256_pd(_mm256_set1_epi64x(0x8000000000000000ULL));
-  return _mm256_xor_pd(a, mask);
+  return _mm256_sub_pd(_mm256_set1_pd(0.0),a);
 }
 
 template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; }
@@ -630,23 +628,11 @@ template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d&
 template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
 
 template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from, uint8_t umask) {
-#ifdef EIGEN_VECTORIZE_AVX512
-  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
-  EIGEN_DEBUG_UNALIGNED_STORE _mm512_mask_storeu_ps(to, mask, _mm512_castps256_ps512(from));
-#else
   Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
-  const Packet8i bit_mask = _mm256_set_epi32(0x7f7f7f7f, 0xbfbfbfbf, 0xdfdfdfdf, 0xefefefef, 0xf7f7f7f7, 0xfbfbfbfb, 0xfdfdfdfd, 0xfefefefe);
+  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
   mask = por<Packet8i>(mask, bit_mask);
   mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
-#if EIGEN_COMP_MSVC
-  // MSVC sometimes seems to use a bogus mask with maskstore.
-  const __m256i ifrom = _mm256_castps_si256(from);
-  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 0), _mm256_extractf128_si256(mask, 0), reinterpret_cast<char*>(to));
-  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 1), _mm256_extractf128_si256(mask, 1), reinterpret_cast<char*>(to + 4));
-#else
-  EIGEN_DEBUG_UNALIGNED_STORE _mm256_maskstore_ps(to, mask, from);
-#endif
-#endif
+  EIGEN_DEBUG_UNALIGNED_STORE return _mm256_maskstore_ps(to, mask, from);
 }
 
 // NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
@@ -1020,7 +1006,7 @@ EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
 
 EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
 #ifdef EIGEN_HAS_FP16_C
-  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT);
+  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
 #else
   EIGEN_ALIGN32 float aux[8];
   pstore(aux, a);
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
similarity index 90%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
index ebf00c2..49c72b3 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/Complex.h
@@ -37,7 +37,7 @@ template<> struct packet_traits<std::complex<float> >  : default_packet_traits
     HasMul    = 1,
     HasDiv    = 1,
     HasNegate = 1,
-    HasSqrt   = EIGEN_HAS_AVX512_MATH,
+    HasSqrt   = 1,
     HasAbs    = 0,
     HasAbs2   = 0,
     HasMin    = 0,
@@ -97,9 +97,7 @@ template<> EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<fl
 
 template<> EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from)
 {
-  const float re = std::real(from);
-  const float im = std::imag(from);
-  return Packet8cf(_mm512_set_ps(im, re, im, re, im, re, im, re, im, re, im, re, im, re, im, re));
+  return Packet8cf(_mm512_castpd_ps(pload1<Packet8d>((const double*)(const void*)&from)));
 }
 
 template<> EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from)
@@ -159,7 +157,11 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf,Packet16f)
 
 template<> EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
 {
-  return pdiv_complex(a, b);
+  Packet8cf num = pmul(a, pconj(b));
+  __m512 tmp = _mm512_mul_ps(b.v, b.v);
+  __m512 tmp2    = _mm512_shuffle_ps(tmp,tmp,0xB1);
+  __m512 denom = _mm512_add_ps(tmp, tmp2);
+  return Packet8cf(_mm512_div_ps(num.v, denom));
 }
 
 template<> EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x)
@@ -190,7 +192,7 @@ template<> struct packet_traits<std::complex<double> >  : default_packet_traits
     HasMul    = 1,
     HasDiv    = 1,
     HasNegate = 1,
-    HasSqrt   = EIGEN_HAS_AVX512_MATH,
+    HasSqrt   = 1,
     HasAbs    = 0,
     HasAbs2   = 0,
     HasMin    = 0,
@@ -251,7 +253,11 @@ template<> EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<do
 
 template<> EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from)
 {
+  #ifdef EIGEN_VECTORIZE_AVX512DQ
+  return Packet4cd(_mm512_broadcast_f64x2(pset1<Packet1cd>(from).v));
+  #else
   return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4( _mm_castpd_ps(pset1<Packet1cd>(from).v))));
+  #endif
 }
 
 template<> EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
@@ -303,11 +309,47 @@ template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const
                          Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
 }
 
+template<> struct conj_helper<Packet4cd, Packet4cd, false,true>
+{
+  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
+  { return padd(pmul(x,y),c); }
+
+  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
+  {
+    return internal::pmul(a, pconj(b));
+  }
+};
+
+template<> struct conj_helper<Packet4cd, Packet4cd, true,false>
+{
+  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
+  { return padd(pmul(x,y),c); }
+
+  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
+  {
+    return internal::pmul(pconj(a), b);
+  }
+};
+
+template<> struct conj_helper<Packet4cd, Packet4cd, true,true>
+{
+  EIGEN_STRONG_INLINE Packet4cd pmadd(const Packet4cd& x, const Packet4cd& y, const Packet4cd& c) const
+  { return padd(pmul(x,y),c); }
+
+  EIGEN_STRONG_INLINE Packet4cd pmul(const Packet4cd& a, const Packet4cd& b) const
+  {
+    return pconj(internal::pmul(a, b));
+  }
+};
+
 EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd,Packet8d)
 
 template<> EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
 {
-  return pdiv_complex(a, b);
+  Packet4cd num = pmul(a, pconj(b));
+  __m512d tmp = _mm512_mul_pd(b.v, b.v);
+  __m512d denom =  padd(_mm512_permute_pd(tmp,0x55), tmp);
+  return Packet4cd(_mm512_div_pd(num.v, denom));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x)
@@ -366,8 +408,6 @@ ptranspose(PacketBlock<Packet4cd,4>& kernel) {
   kernel.packet[0] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<0,2,0,2>::mask))); // [a0 b0 c0 d0]
 }
 
-#if EIGEN_HAS_AVX512_MATH
-
 template<> EIGEN_STRONG_INLINE Packet4cd psqrt<Packet4cd>(const Packet4cd& a) {
   return psqrt_complex<Packet4cd>(a);
 }
@@ -376,8 +416,6 @@ template<> EIGEN_STRONG_INLINE Packet8cf psqrt<Packet8cf>(const Packet8cf& a) {
   return psqrt_complex<Packet8cf>(a);
 }
 
-#endif
-
 } // end namespace internal
 } // end namespace Eigen
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
index 017d6bf..6fd726d 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/MathFunctions.h
@@ -14,7 +14,8 @@ namespace Eigen {
 
 namespace internal {
 
-#if EIGEN_HAS_AVX512_MATH
+// Disable the code for older versions of gcc that don't support many of the required avx512 instrinsics.
+#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG  || EIGEN_COMP_MSVC >= 1923
 
 #define _EIGEN_DECLARE_CONST_Packet16f(NAME, X) \
   const Packet16f p16f_##NAME = pset1<Packet16f>(X)
@@ -325,7 +326,7 @@ Packet16f pexpm1<Packet16f>(const Packet16f& _x) {
 F16_PACKET_FUNCTION(Packet16f, Packet16h, pexpm1)
 BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexpm1)
 
-#endif  // EIGEN_HAS_AVX512_MATH
+#endif
 
 
 template <>
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
index 4ab100c..34d49ab 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/PacketMath.h
@@ -28,13 +28,6 @@ namespace internal {
 #endif
 #endif
 
-// Disable the code for older versions of gcc that don't support many of the required avx512 math instrinsics.
-#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG || EIGEN_COMP_MSVC >= 1923 || EIGEN_COMP_ICC >= 1900
-#define EIGEN_HAS_AVX512_MATH 1
-#else
-#define EIGEN_HAS_AVX512_MATH 0
-#endif
-
 typedef __m512 Packet16f;
 typedef __m512i Packet16i;
 typedef __m512d Packet8d;
@@ -79,14 +72,12 @@ struct packet_traits<half> : default_packet_traits {
     HasMax    = 1,
     HasConj   = 1,
     HasSetLinear = 0,
-    HasLog    = EIGEN_HAS_AVX512_MATH,
-    HasLog1p  = EIGEN_HAS_AVX512_MATH,
-    HasExp    = EIGEN_HAS_AVX512_MATH,
-    HasExpm1  = EIGEN_HAS_AVX512_MATH,
-    HasSqrt   = EIGEN_HAS_AVX512_MATH,
-    HasRsqrt  = EIGEN_HAS_AVX512_MATH,
-    HasBessel = EIGEN_HAS_AVX512_MATH,
-    HasNdtri  = EIGEN_HAS_AVX512_MATH,
+    HasLog    = 1,
+    HasLog1p  = 1,
+    HasExpm1  = 1,
+    HasExp    = 1,
+    HasSqrt   = 1,
+    HasRsqrt  = 1,
     HasSin    = EIGEN_FAST_MATH,
     HasCos    = EIGEN_FAST_MATH,
     HasTanh   = EIGEN_FAST_MATH,
@@ -95,7 +86,9 @@ struct packet_traits<half> : default_packet_traits {
     HasRound  = 1,
     HasFloor  = 1,
     HasCeil   = 1,
-    HasRint   = 1
+    HasRint   = 1,
+    HasBessel = 1,
+    HasNdtri  = 1
   };
 };
 
@@ -116,7 +109,7 @@ template<> struct packet_traits<float>  : default_packet_traits
     HasBlend = 0,
     HasSin = EIGEN_FAST_MATH,
     HasCos = EIGEN_FAST_MATH,
-#if EIGEN_HAS_AVX512_MATH
+#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
     HasLog = 1,
     HasLog1p  = 1,
     HasExpm1  = 1,
@@ -145,7 +138,7 @@ template<> struct packet_traits<double> : default_packet_traits
     AlignedOnScalar = 1,
     size = 8,
     HasHalfPacket = 1,
-#if EIGEN_HAS_AVX512_MATH
+#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
     HasLog  = 1,
     HasExp = 1,
     HasSqrt = EIGEN_FAST_MATH,
@@ -296,20 +289,11 @@ EIGEN_STRONG_INLINE Packet16i psub<Packet16i>(const Packet16i& a,
 
 template <>
 EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
-  // NOTE: MSVC seems to struggle with _mm512_set1_epi32, leading to random results.
-  //       The intel docs give it a relatively high latency as well, so we're probably
-  //       better off with using _mm512_set_epi32 directly anyways.
-  const __m512i mask = _mm512_set_epi32(0x80000000,0x80000000,0x80000000,0x80000000,
-                                        0x80000000,0x80000000,0x80000000,0x80000000,
-                                        0x80000000,0x80000000,0x80000000,0x80000000,
-                                        0x80000000,0x80000000,0x80000000,0x80000000);
-  return _mm512_castsi512_ps(_mm512_xor_epi32(_mm512_castps_si512(a), mask));
+  return _mm512_sub_ps(_mm512_set1_ps(0.0), a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet8d pnegate(const Packet8d& a) {
-  const __m512i mask = _mm512_set_epi64(0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL,
-                                        0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL);
-  return _mm512_castsi512_pd(_mm512_xor_epi64(_mm512_castpd_si512(a), mask));
+  return _mm512_sub_pd(_mm512_set1_pd(0.0), a);
 }
 
 template <>
@@ -702,7 +686,7 @@ EIGEN_STRONG_INLINE Packet8d pload<Packet8d>(const double* from) {
 template <>
 EIGEN_STRONG_INLINE Packet16i pload<Packet16i>(const int* from) {
   EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_si512(
-    reinterpret_cast<const __m512i*>(from));
+      reinterpret_cast<const __m512i*>(from));
 }
 
 template <>
@@ -1442,11 +1426,60 @@ ploadquad(const Eigen::half* from) {
 }
 
 EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) {
+#ifdef EIGEN_HAS_FP16_C
   return _mm512_cvtph_ps(a);
+#else
+  EIGEN_ALIGN64 half aux[16];
+  pstore(aux, a);
+  float f0(aux[0]);
+  float f1(aux[1]);
+  float f2(aux[2]);
+  float f3(aux[3]);
+  float f4(aux[4]);
+  float f5(aux[5]);
+  float f6(aux[6]);
+  float f7(aux[7]);
+  float f8(aux[8]);
+  float f9(aux[9]);
+  float fa(aux[10]);
+  float fb(aux[11]);
+  float fc(aux[12]);
+  float fd(aux[13]);
+  float fe(aux[14]);
+  float ff(aux[15]);
+
+  return _mm512_set_ps(
+      ff, fe, fd, fc, fb, fa, f9, f8, f7, f6, f5, f4, f3, f2, f1, f0);
+#endif
 }
 
 EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) {
+#ifdef EIGEN_HAS_FP16_C
   return _mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
+#else
+  EIGEN_ALIGN64 float aux[16];
+  pstore(aux, a);
+  half h0(aux[0]);
+  half h1(aux[1]);
+  half h2(aux[2]);
+  half h3(aux[3]);
+  half h4(aux[4]);
+  half h5(aux[5]);
+  half h6(aux[6]);
+  half h7(aux[7]);
+  half h8(aux[8]);
+  half h9(aux[9]);
+  half ha(aux[10]);
+  half hb(aux[11]);
+  half hc(aux[12]);
+  half hd(aux[13]);
+  half he(aux[14]);
+  half hf(aux[15]);
+
+  return _mm256_set_epi16(
+      hf.x, he.x, hd.x, hc.x, hb.x, ha.x, h9.x, h8.x,
+      h7.x, h6.x, h5.x, h4.x, h3.x, h2.x, h1.x, h0.x);
+#endif
 }
 
 template<> EIGEN_STRONG_INLINE Packet16h ptrue(const Packet16h& a) {
@@ -1819,7 +1852,7 @@ struct packet_traits<bfloat16> : default_packet_traits {
     HasInsert = 1,
     HasSin = EIGEN_FAST_MATH,
     HasCos = EIGEN_FAST_MATH,
-#if EIGEN_HAS_AVX512_MATH
+#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
 #ifdef EIGEN_VECTORIZE_AVX512DQ
     HasLog = 1,  // Currently fails test with bad accuracy.
     HasLog1p  = 1,
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AVX512/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AVX512/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
similarity index 93%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
index b05d0d3..f424f11 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h
@@ -15,10 +15,8 @@ namespace Eigen {
 
 namespace internal {
 
-inline Packet4ui  p4ui_CONJ_XOR() {
-  return vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
-}
-#ifdef EIGEN_VECTORIZE_VSX
+static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+#ifdef __VSX__
 #if defined(_BIG_ENDIAN)
 static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
 static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
@@ -46,7 +44,7 @@ struct Packet2cf
     v1 = vec_madd(v1, b.v, p4f_ZERO);
     // multiply a_im * b and get the conjugate result
     v2 = vec_madd(v2, b.v, p4f_ZERO);
-    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR())));
+    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR)));
     // permute back to a proper order
     v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
 
@@ -102,8 +100,7 @@ template<> struct packet_traits<std::complex<float> >  : default_packet_traits
     HasAbs2   = 0,
     HasMin    = 0,
     HasMax    = 0,
-    HasSqrt   = 1,
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
     HasBlend  = 1,
 #endif
     HasSetLinear = 0
@@ -130,20 +127,20 @@ template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<
 template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstore((float*)to, from.v); }
 template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstoreu((float*)to, from.v); }
 
-EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>& from0, const std::complex<float>& from1)
+EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>* from0, const std::complex<float>* from1)
 {
   Packet4f res0, res1;
-#ifdef EIGEN_VECTORIZE_VSX
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res0) : "Z" (from0));
-  __asm__ ("lxsdx %x0,%y1" : "=wa" (res1) : "Z" (from1));
+#ifdef __VSX__
+  __asm__ ("lxsdx %x0,%y1" : "=wa" (res0) : "Z" (*from0));
+  __asm__ ("lxsdx %x0,%y1" : "=wa" (res1) : "Z" (*from1));
 #ifdef _BIG_ENDIAN
   __asm__ ("xxpermdi %x0, %x1, %x2, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
 #else
   __asm__ ("xxpermdi %x0, %x2, %x1, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
 #endif
 #else
-  *reinterpret_cast<std::complex<float> *>(&res0) = from0;
-  *reinterpret_cast<std::complex<float> *>(&res1) = from1;
+  *reinterpret_cast<std::complex<float> *>(&res0) = *from0;
+  *reinterpret_cast<std::complex<float> *>(&res1) = *from1;
   res0 = vec_perm(res0, res1, p16uc_TRANSPOSE64_HI);
 #endif
   return Packet2cf(res0);
@@ -167,7 +164,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf
 template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v + b.v); }
 template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v - b.v); }
 template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); }
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR()))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
 
 template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v, b.v)); }
 template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v, b.v)); }
@@ -213,7 +210,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
 
 template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for AltiVec
+  Packet2cf res = pmul(a, pconj(b));
+  Packet4f s = pmul<Packet4f>(b.v, b.v);
+  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
 }
 
 template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
@@ -233,21 +233,21 @@ template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packe
   return Packet2cf(vec_and(eq, vec_perm(eq, eq, p16uc_COMPLEX32_REV)));
 }
 
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
   Packet2cf result;
   result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
   return result;
 }
+#endif
 
 template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a)
 {
   return psqrt_complex<Packet2cf>(a);
 }
-#endif
 
 //---------- double ----------
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
 struct Packet1cd
 {
   EIGEN_STRONG_INLINE Packet1cd() {}
@@ -320,7 +320,6 @@ template<> struct packet_traits<std::complex<double> >  : default_packet_traits
     HasAbs2   = 0,
     HasMin    = 0,
     HasMax    = 0,
-    HasSqrt   = 1,
     HasSetLinear = 0
   };
 };
@@ -376,7 +375,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
 
 template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for AltiVec
+  Packet1cd res = pmul(a,pconj(b));
+  Packet2d s = pmul<Packet2d>(b.v, b.v);
+  return Packet1cd(pdiv(res.v, padd<Packet2d>(s, vec_perm(s, s, p16uc_REVERSE64))));
 }
 
 EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
@@ -407,7 +409,7 @@ template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a)
   return psqrt_complex<Packet1cd>(a);
 }
 
-#endif // EIGEN_VECTORIZE_VSX
+#endif // __VSX__
 } // end namespace internal
 
 } // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
similarity index 65%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
index e3d7616..3a7a329 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h
@@ -40,14 +40,16 @@ Packet4f pcos<Packet4f>(const Packet4f& _x)
   return pcos_float(_x);
 }
 
-#ifdef EIGEN_VECTORIZE_VSX
 #ifndef EIGEN_COMP_CLANG
 template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
 Packet4f prsqrt<Packet4f>(const Packet4f& x)
 {
   return  vec_rsqrt(x);
 }
+#endif
 
+#ifdef __VSX__
+#ifndef EIGEN_COMP_CLANG
 template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
 Packet2d prsqrt<Packet2d>(const Packet2d& x)
 {
@@ -55,7 +57,7 @@ Packet2d prsqrt<Packet2d>(const Packet2d& x)
 }
 #endif
 
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
 Packet4f psqrt<Packet4f>(const Packet4f& x)
 {
   return  vec_sqrt(x);
@@ -67,43 +69,12 @@ Packet2d psqrt<Packet2d>(const Packet2d& x)
   return  vec_sqrt(x);
 }
 
-#if !EIGEN_COMP_CLANG
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet4f prsqrt<Packet4f>(const Packet4f& x)
-{
-  return pset1<Packet4f>(1.0f) / psqrt<Packet4f>(x);
-//  vec_rsqrt returns different results from the generic version
-//  return  vec_rsqrt(x);
-}
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-Packet2d prsqrt<Packet2d>(const Packet2d& x)
-{
-  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
-//  vec_rsqrt returns different results from the generic version
-//  return  vec_rsqrt(x);
-}
-#endif
-
-template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
 Packet2d pexp<Packet2d>(const Packet2d& _x)
 {
   return pexp_double(_x);
 }
-
-template<> EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(vec_sqrt, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
-}
-
-template<> EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf> (const Packet8bf& a){
-  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
-}
-
-#endif  // EIGEN_VECTORIZE_VSX
+#endif
 
 // Hyperbolic Tangent function.
 template <>
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
similarity index 62%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
index ea77496..3f79b97 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
@@ -17,35 +17,24 @@
 
 #include "MatrixProductCommon.h"
 
-#if !defined(EIGEN_ALTIVEC_DISABLE_MMA)
-#define EIGEN_ALTIVEC_DISABLE_MMA 0
+// Since LLVM doesn't support dynamic dispatching, force either always MMA or VSX
+#if EIGEN_COMP_LLVM
+#if !defined(EIGEN_ALTIVEC_DISABLE_MMA) && !defined(EIGEN_ALTIVEC_MMA_ONLY)
+#ifdef __MMA__
+#define EIGEN_ALTIVEC_MMA_ONLY
+#else
+#define EIGEN_ALTIVEC_DISABLE_MMA
 #endif
-
-// Check for MMA builtin support. 
-#if !EIGEN_ALTIVEC_DISABLE_MMA && defined(__has_builtin)
-#if __has_builtin(__builtin_mma_assemble_acc)
-  #define EIGEN_ALTIVEC_MMA_SUPPORT
 #endif
 #endif
 
-// Check if and how we should actually use MMA if supported.
-#if defined(EIGEN_ALTIVEC_MMA_SUPPORT)
-
-#if !defined(EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH)
-#define EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH 0
+#ifdef __has_builtin
+#if __has_builtin(__builtin_mma_assemble_acc)
+  #define ALTIVEC_MMA_SUPPORT
 #endif
-
-// Check if we want to enable dynamic dispatch. Not supported by LLVM.
-#if EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH && !EIGEN_COMP_LLVM
-#define EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH 1
-// Otherwise, use MMA by default if available.
-#elif defined(__MMA__)
-#define EIGEN_ALTIVEC_MMA_ONLY 1
 #endif
 
-#endif // EIGEN_ALTIVEC_MMA_SUPPORT
-
-#if defined(EIGEN_ALTIVEC_MMA_ONLY) || defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#if defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
   #include "MatrixProductMMA.h"
 #endif
 
@@ -175,23 +164,24 @@ EIGEN_STRONG_INLINE void symm_pack_complex_rhs_helper(std::complex<Scalar>* bloc
 
     rir += vectorDelta;
   }
-
-  for(; j < cols; j++)
+  if (j < cols)
   {
-    rii = rir + rows;
+    rii = rir + ((cols - j) * rows);
 
     for(Index i = k2; i < depth; i++)
     {
-      std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, j, rhs);
+      Index k = j;
+      for(; k < cols; k++)
+      {
+        std::complex<Scalar> v = getAdjointVal<Scalar, Index, StorageOrder>(i, k, rhs);
 
-      blockBf[rir] = v.real();
-      blockBf[rii] = v.imag();
+        blockBf[rir] = v.real();
+        blockBf[rii] = v.imag();
 
-      rir += 1;
-      rii += 1;
+        rir += 1;
+        rii += 1;
+      }
     }
-
-    rir += rows;
   }
 }
 
@@ -270,15 +260,19 @@ EIGEN_STRONG_INLINE void symm_pack_rhs_helper(Scalar* blockB, const Scalar* _rhs
     }
   }
 
-  for(; j < cols; j++)
+  if (j < cols)
   {
     for(Index i = k2; i < depth; i++)
     {
-      if(j <= i)
-        blockB[ri] = rhs(i, j);
-      else
-        blockB[ri] = rhs(j, i);
-      ri += 1;
+      Index k = j;
+      for(; k < cols; k++)
+      {
+        if(k <= i)
+          blockB[ri] = rhs(i, k);
+        else
+          blockB[ri] = rhs(k, i);
+        ri += 1;
+      }
     }
   }
 }
@@ -412,18 +406,22 @@ struct symm_pack_lhs<double, Index, Pack1, Pack2_dummy, StorageOrder>
  * and offset and behaves accordingly.
  **/
 
-template<typename Scalar, typename Packet, typename Index, int N>
-EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,N>& block)
+template<typename Scalar, typename Packet, typename Index>
+EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,4>& block)
+{
+  const Index size = 16 / sizeof(Scalar);
+  pstore<Scalar>(to + (0 * size), block.packet[0]);
+  pstore<Scalar>(to + (1 * size), block.packet[1]);
+  pstore<Scalar>(to + (2 * size), block.packet[2]);
+  pstore<Scalar>(to + (3 * size), block.packet[3]);
+}
+
+template<typename Scalar, typename Packet, typename Index>
+EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,2>& block)
 {
   const Index size = 16 / sizeof(Scalar);
   pstore<Scalar>(to + (0 * size), block.packet[0]);
   pstore<Scalar>(to + (1 * size), block.packet[1]);
-  if (N > 2) {
-    pstore<Scalar>(to + (2 * size), block.packet[2]);
-  }
-  if (N > 3) {
-    pstore<Scalar>(to + (3 * size), block.packet[3]);
-  }
 }
 
 // General template for lhs & rhs complex packing.
@@ -449,9 +447,9 @@ struct dhs_cpack {
         PacketBlock<PacketC,8> cblock;
 
         if (UseLhs) {
-          bload<DataMapper, PacketC, Index, 2, StorageOrder, true, 4>(cblock, lhs, j, i);
+          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, j, i);
         } else {
-          bload<DataMapper, PacketC, Index, 2, StorageOrder, true, 4>(cblock, lhs, i, j);
+          bload<DataMapper, PacketC, Index, 2, 0, StorageOrder>(cblock, lhs, i, j);
         }
 
         blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETREAL32);
@@ -478,8 +476,8 @@ struct dhs_cpack {
           ptranspose(blocki);
         }
 
-        storeBlock<Scalar, Packet, Index, 4>(blockAt + rir, blockr);
-        storeBlock<Scalar, Packet, Index, 4>(blockAt + rii, blocki);
+        storeBlock<Scalar, Packet, Index>(blockAt + rir, blockr);
+        storeBlock<Scalar, Packet, Index>(blockAt + rii, blocki);
 
         rir += 4*vectorSize;
         rii += 4*vectorSize;
@@ -499,12 +497,21 @@ struct dhs_cpack {
             cblock.packet[1] = lhs.template loadPacket<PacketC>(i, j + 2);
           }
         } else {
+          std::complex<Scalar> lhs0, lhs1;
           if (UseLhs) {
-            cblock.packet[0] = pload2(lhs(j + 0, i), lhs(j + 1, i));
-            cblock.packet[1] = pload2(lhs(j + 2, i), lhs(j + 3, i));
+            lhs0 = lhs(j + 0, i);
+            lhs1 = lhs(j + 1, i);
+            cblock.packet[0] = pload2(&lhs0, &lhs1);
+            lhs0 = lhs(j + 2, i);
+            lhs1 = lhs(j + 3, i);
+            cblock.packet[1] = pload2(&lhs0, &lhs1);
           } else {
-            cblock.packet[0] = pload2(lhs(i, j + 0), lhs(i, j + 1));
-            cblock.packet[1] = pload2(lhs(i, j + 2), lhs(i, j + 3));
+            lhs0 = lhs(i, j + 0);
+            lhs1 = lhs(i, j + 1);
+            cblock.packet[0] = pload2(&lhs0, &lhs1);
+            lhs0 = lhs(i, j + 2);
+            lhs1 = lhs(i, j + 3);
+            cblock.packet[1] = pload2(&lhs0, &lhs1);
           }
         }
 
@@ -526,50 +533,34 @@ struct dhs_cpack {
       rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
     }
 
-    if (!UseLhs)
+    if (j < rows)
     {
-      if(PanelMode) rir -= (offset*(vectorSize - 1));
-
-      for(; j < rows; j++)
-      {
-        rii = rir + ((PanelMode) ? stride : depth);
-
-        for(Index i = 0; i < depth; i++)
-        {
-          blockAt[rir] = lhs(i, j).real();
-
-          if(Conjugate)
-            blockAt[rii] = -lhs(i, j).imag();
-          else
-            blockAt[rii] =  lhs(i, j).imag();
-
-          rir += 1;
-          rii += 1;
-        }
+      if(PanelMode) rir += (offset*(rows - j - vectorSize));
+      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
 
-        rir += ((PanelMode) ? (2*stride - depth) : depth);
-      }
-    } else {
-      if (j < rows)
+      for(Index i = 0; i < depth; i++)
       {
-        if(PanelMode) rir += (offset*(rows - j - vectorSize));
-        rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
-
-        for(Index i = 0; i < depth; i++)
+        Index k = j;
+        for(; k < rows; k++)
         {
-          Index k = j;
-          for(; k < rows; k++)
-          {
+          if (UseLhs) {
             blockAt[rir] = lhs(k, i).real();
 
             if(Conjugate)
               blockAt[rii] = -lhs(k, i).imag();
             else
               blockAt[rii] =  lhs(k, i).imag();
+          } else {
+            blockAt[rir] = lhs(i, k).real();
 
-            rir += 1;
-            rii += 1;
+            if(Conjugate)
+              blockAt[rii] = -lhs(i, k).imag();
+            else
+              blockAt[rii] =  lhs(i, k).imag();
           }
+
+          rir += 1;
+          rii += 1;
         }
       }
     }
@@ -595,16 +586,16 @@ struct dhs_pack{
         PacketBlock<Packet,4> block;
 
         if (UseLhs) {
-          bload<DataMapper, Packet, Index, 4, StorageOrder, false, 4>(block, lhs, j, i);
+          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, j, i);
         } else {
-          bload<DataMapper, Packet, Index, 4, StorageOrder, false, 4>(block, lhs, i, j);
+          bload<DataMapper, Packet, Index, 4, 0, StorageOrder>(block, lhs, i, j);
         }
         if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
         {
           ptranspose(block);
         }
 
-        storeBlock<Scalar, Packet, Index, 4>(blockA + ri, block);
+        storeBlock<Scalar, Packet, Index>(blockA + ri, block);
 
         ri += 4*vectorSize;
       }
@@ -639,33 +630,21 @@ struct dhs_pack{
       if(PanelMode) ri += vectorSize*(stride - offset - depth);
     }
 
-    if (!UseLhs)
+    if (j < rows)
     {
-      if(PanelMode) ri += offset;
-
-      for(; j < rows; j++)
-      {
-        for(Index i = 0; i < depth; i++)
-        {
-          blockA[ri] = lhs(i, j);
-          ri += 1;
-        }
+      if(PanelMode) ri += offset*(rows - j);
 
-        if(PanelMode) ri += stride - depth;
-      }
-    } else {
-      if (j < rows)
+      for(Index i = 0; i < depth; i++)
       {
-        if(PanelMode) ri += offset*(rows - j);
-
-        for(Index i = 0; i < depth; i++)
+        Index k = j;
+        for(; k < rows; k++)
         {
-          Index k = j;
-          for(; k < rows; k++)
-          {
+          if (UseLhs) {
             blockA[ri] = lhs(k, i);
-            ri += 1;
+          } else {
+            blockA[ri] = lhs(i, k);
           }
+          ri += 1;
         }
       }
     }
@@ -701,7 +680,7 @@ struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, tr
           block.packet[1] = lhs.template loadPacket<Packet2d>(j, i + 1);
         }
 
-        storeBlock<double, Packet2d, Index, 2>(blockA + ri, block);
+        storeBlock<double, Packet2d, Index>(blockA + ri, block);
 
         ri += 2*vectorSize;
       }
@@ -778,7 +757,7 @@ struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, fa
           block.packet[2] = rhs.template loadPacket<Packet2d>(i + 1, j + 0); //[b1 b2]
           block.packet[3] = rhs.template loadPacket<Packet2d>(i + 1, j + 2); //[b3 b4]
 
-          storeBlock<double, Packet2d, Index, 4>(blockB + ri, block);
+          storeBlock<double, Packet2d, Index>(blockB + ri, block);
         }
 
         ri += 4*vectorSize;
@@ -809,17 +788,19 @@ struct dhs_pack<double, Index, DataMapper, Packet2d, StorageOrder, PanelMode, fa
       if(PanelMode) ri += (2*vectorSize)*(stride - offset - depth);
     }
 
-    if(PanelMode) ri += offset;
-
-    for(; j < cols; j++)
+    if (j < cols)
     {
+      if(PanelMode) ri += offset*(cols - j);
+
       for(Index i = 0; i < depth; i++)
       {
-        blockB[ri] = rhs(i, j);
-        ri += 1;
+        Index k = j;
+        for(; k < cols; k++)
+        {
+          blockB[ri] = rhs(i, k);
+          ri += 1;
+        }
       }
-
-      if(PanelMode) ri += stride - depth;
     }
   }
 };
@@ -880,8 +861,8 @@ struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conju
           blocki.packet[1] = -blocki.packet[1];
         }
 
-        storeBlock<double, Packet, Index, 2>(blockAt + rir, blockr);
-        storeBlock<double, Packet, Index, 2>(blockAt + rii, blocki);
+        storeBlock<double, Packet, Index>(blockAt + rir, blockr);
+        storeBlock<double, Packet, Index>(blockAt + rii, blocki);
 
         rir += 2*vectorSize;
         rii += 2*vectorSize;
@@ -960,7 +941,7 @@ struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conju
         PacketBlock<PacketC,4> cblock;
         PacketBlock<Packet,2> blockr, blocki;
 
-        bload<DataMapper, PacketC, Index, 2, ColMajor, false, 4>(cblock, rhs, i, j);
+        bload<DataMapper, PacketC, Index, 2, 0, ColMajor>(cblock, rhs, i, j);
 
         blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL64);
         blockr.packet[1] = vec_perm(cblock.packet[2].v, cblock.packet[3].v, p16uc_GETREAL64);
@@ -974,8 +955,8 @@ struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conju
           blocki.packet[1] = -blocki.packet[1];
         }
 
-        storeBlock<double, Packet, Index, 2>(blockBt + rir, blockr);
-        storeBlock<double, Packet, Index, 2>(blockBt + rii, blocki);
+        storeBlock<double, Packet, Index>(blockBt + rir, blockr);
+        storeBlock<double, Packet, Index>(blockBt + rii, blocki);
 
         rir += 2*vectorSize;
         rii += 2*vectorSize;
@@ -984,26 +965,27 @@ struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conju
       rir += ((PanelMode) ? (2*vectorSize*(2*stride - depth)) : vectorDelta);
     }
 
-    if(PanelMode) rir -= (offset*(2*vectorSize - 1));
-
-    for(; j < cols; j++)
+    if (j < cols)
     {
-      rii = rir + ((PanelMode) ? stride : depth);
+      if(PanelMode) rir += (offset*(cols - j - 2*vectorSize));
+      rii = rir + (((PanelMode) ? stride : depth) * (cols - j));
 
       for(Index i = 0; i < depth; i++)
       {
-        blockBt[rir] = rhs(i, j).real();
+        Index k = j;
+        for(; k < cols; k++)
+        {
+          blockBt[rir] = rhs(i, k).real();
 
-        if(Conjugate)
-          blockBt[rii] = -rhs(i, j).imag();
-        else
-          blockBt[rii] =  rhs(i, j).imag();
+          if(Conjugate)
+            blockBt[rii] = -rhs(i, k).imag();
+          else
+            blockBt[rii] =  rhs(i, k).imag();
 
-        rir += 1;
-        rii += 1;
+          rir += 1;
+          rii += 1;
+        }
       }
-
-      rir += ((PanelMode) ? (2*stride - depth) : depth);
     }
   }
 };
@@ -1013,32 +995,31 @@ struct dhs_cpack<double, Index, DataMapper, Packet, PacketC, StorageOrder, Conju
  **************/
 
 // 512-bits rank1-update of acc. It can either positive or negative accumulate (useful for complex gemm).
-template<typename Packet, bool NegativeAccumulate, int N>
-EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,N>* acc, const Packet& lhsV, const Packet* rhsV)
+template<typename Packet, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,4>* acc, const Packet& lhsV, const Packet* rhsV)
+{
+  if(NegativeAccumulate)
+  {
+    acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
+    acc->packet[1] = vec_nmsub(lhsV, rhsV[1], acc->packet[1]);
+    acc->packet[2] = vec_nmsub(lhsV, rhsV[2], acc->packet[2]);
+    acc->packet[3] = vec_nmsub(lhsV, rhsV[3], acc->packet[3]);
+  } else {
+    acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
+    acc->packet[1] = vec_madd(lhsV, rhsV[1], acc->packet[1]);
+    acc->packet[2] = vec_madd(lhsV, rhsV[2], acc->packet[2]);
+    acc->packet[3] = vec_madd(lhsV, rhsV[3], acc->packet[3]);
+  }
+}
+
+template<typename Packet, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,1>* acc, const Packet& lhsV, const Packet* rhsV)
 {
   if(NegativeAccumulate)
   {
     acc->packet[0] = vec_nmsub(lhsV, rhsV[0], acc->packet[0]);
-    if (N > 1) {
-      acc->packet[1] = vec_nmsub(lhsV, rhsV[1], acc->packet[1]);
-    }
-    if (N > 2) {
-      acc->packet[2] = vec_nmsub(lhsV, rhsV[2], acc->packet[2]);
-    }
-    if (N > 3) {
-      acc->packet[3] = vec_nmsub(lhsV, rhsV[3], acc->packet[3]);
-    }
   } else {
     acc->packet[0] = vec_madd(lhsV, rhsV[0], acc->packet[0]);
-    if (N > 1) {
-      acc->packet[1] = vec_madd(lhsV, rhsV[1], acc->packet[1]);
-    }
-    if (N > 2) {
-      acc->packet[2] = vec_madd(lhsV, rhsV[2], acc->packet[2]);
-    }
-    if (N > 3) {
-      acc->packet[3] = vec_madd(lhsV, rhsV[3], acc->packet[3]);
-    }
   }
 }
 
@@ -1047,11 +1028,11 @@ EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, con
 {
   Packet lhsV = pload<Packet>(lhs);
 
-  pger_common<Packet, NegativeAccumulate, N>(acc, lhsV, rhsV);
+  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
 }
 
-template<typename Scalar, typename Packet, typename Index, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs, Packet &lhsV)
+template<typename Scalar, typename Packet, typename Index>
+EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs, Packet &lhsV, Index remaining_rows)
 {
 #ifdef _ARCH_PWR9
   lhsV = vec_xl_len((Scalar *)lhs, remaining_rows * sizeof(Scalar));
@@ -1063,32 +1044,32 @@ EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs, Packet &lhsV)
 #endif
 }
 
-template<int N, typename Scalar, typename Packet, typename Index, bool NegativeAccumulate, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV)
+template<int N, typename Scalar, typename Packet, typename Index, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV, Index remaining_rows)
 {
   Packet lhsV;
-  loadPacketRemaining<Scalar, Packet, Index, remaining_rows>(lhs, lhsV);
+  loadPacketRemaining<Scalar, Packet, Index>(lhs, lhsV, remaining_rows);
 
-  pger_common<Packet, NegativeAccumulate, N>(acc, lhsV, rhsV);
+  pger_common<Packet, NegativeAccumulate>(acc, lhsV, rhsV);
 }
 
 // 512-bits rank1-update of complex acc. It takes decoupled accumulators as entries. It also takes cares of mixed types real * complex and complex * real.
 template<int N, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
 EIGEN_ALWAYS_INLINE void pgerc_common(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Packet &lhsV, const Packet &lhsVi, const Packet* rhsV, const Packet* rhsVi)
 {
-  pger_common<Packet, false, N>(accReal, lhsV, rhsV);
+  pger_common<Packet, false>(accReal, lhsV, rhsV);
   if(LhsIsReal)
   {
-    pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+    pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
     EIGEN_UNUSED_VARIABLE(lhsVi);
   } else {
     if (!RhsIsReal) {
-      pger_common<Packet, ConjugateLhs == ConjugateRhs, N>(accReal, lhsVi, rhsVi);
-      pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+      pger_common<Packet, ConjugateLhs == ConjugateRhs>(accReal, lhsVi, rhsVi);
+      pger_common<Packet, ConjugateRhs>(accImag, lhsV, rhsVi);
     } else {
       EIGEN_UNUSED_VARIABLE(rhsVi);
     }
-    pger_common<Packet, ConjugateLhs, N>(accImag, lhsVi, rhsV);
+    pger_common<Packet, ConjugateLhs>(accImag, lhsVi, rhsV);
   }
 }
 
@@ -1103,8 +1084,8 @@ EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packe
   pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
 }
 
-template<typename Scalar, typename Packet, typename Index, bool LhsIsReal, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, Packet &lhsV, Packet &lhsVi)
+template<typename Scalar, typename Packet, typename Index, bool LhsIsReal>
+EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, Packet &lhsV, Packet &lhsVi, Index remaining_rows)
 {
 #ifdef _ARCH_PWR9
   lhsV = vec_xl_len((Scalar *)lhs_ptr, remaining_rows * sizeof(Scalar));
@@ -1120,11 +1101,11 @@ EIGEN_ALWAYS_INLINE void loadPacketRemaining(const Scalar* lhs_ptr, const Scalar
 #endif
 }
 
-template<int N, typename Scalar, typename Packet, typename Index, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi)
+template<int N, typename Scalar, typename Packet, typename Index, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi, Index remaining_rows)
 {
   Packet lhsV, lhsVi;
-  loadPacketRemaining<Scalar, Packet, Index, LhsIsReal, remaining_rows>(lhs_ptr, lhs_ptr_imag, lhsV, lhsVi);
+  loadPacketRemaining<Scalar, Packet, Index, LhsIsReal>(lhs_ptr, lhs_ptr_imag, lhsV, lhsVi, remaining_rows);
 
   pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
 }
@@ -1136,142 +1117,132 @@ EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs)
 }
 
 // Zero the accumulator on PacketBlock.
-template<typename Scalar, typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,N>& acc)
+template<typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,4>& acc)
+{
+  acc.packet[0] = pset1<Packet>((Scalar)0);
+  acc.packet[1] = pset1<Packet>((Scalar)0);
+  acc.packet[2] = pset1<Packet>((Scalar)0);
+  acc.packet[3] = pset1<Packet>((Scalar)0);
+}
+
+template<typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,1>& acc)
 {
   acc.packet[0] = pset1<Packet>((Scalar)0);
-  if (N > 1) {
-    acc.packet[1] = pset1<Packet>((Scalar)0);
-  }
-  if (N > 2) {
-    acc.packet[2] = pset1<Packet>((Scalar)0);
-  }
-  if (N > 3) {
-    acc.packet[3] = pset1<Packet>((Scalar)0);
-  }
 }
 
 // Scale the PacketBlock vectors by alpha.
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
 {
   acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
-  if (N > 1) {
-    acc.packet[1] = pmadd(pAlpha, accZ.packet[1], acc.packet[1]);
-  }
-  if (N > 2) {
-    acc.packet[2] = pmadd(pAlpha, accZ.packet[2], acc.packet[2]);
-  }
-  if (N > 3) {
-    acc.packet[3] = pmadd(pAlpha, accZ.packet[3], acc.packet[3]);
-  }
+  acc.packet[1] = pmadd(pAlpha, accZ.packet[1], acc.packet[1]);
+  acc.packet[2] = pmadd(pAlpha, accZ.packet[2], acc.packet[2]);
+  acc.packet[3] = pmadd(pAlpha, accZ.packet[3], acc.packet[3]);
 }
 
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
+{
+  acc.packet[0] = pmadd(pAlpha, accZ.packet[0], acc.packet[0]);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha)
+{
+  acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
+  acc.packet[1] = pmul<Packet>(accZ.packet[1], pAlpha);
+  acc.packet[2] = pmul<Packet>(accZ.packet[2], pAlpha);
+  acc.packet[3] = pmul<Packet>(accZ.packet[3], pAlpha);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,1>& acc, PacketBlock<Packet,1>& accZ, const Packet& pAlpha)
 {
   acc.packet[0] = pmul<Packet>(accZ.packet[0], pAlpha);
-  if (N > 1) {
-    acc.packet[1] = pmul<Packet>(accZ.packet[1], pAlpha);
-  }
-  if (N > 2) {
-    acc.packet[2] = pmul<Packet>(accZ.packet[2], pAlpha);
-  }
-  if (N > 3) {
-    acc.packet[3] = pmul<Packet>(accZ.packet[3], pAlpha);
-  }
 }
 
 // Complex version of PacketBlock scaling.
 template<typename Packet, int N>
 EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag)
 {
-  bscalec_common<Packet, N>(cReal, aReal, bReal);
+  bscalec_common<Packet>(cReal, aReal, bReal);
 
-  bscalec_common<Packet, N>(cImag, aImag, bReal);
+  bscalec_common<Packet>(cImag, aImag, bReal);
 
-  pger_common<Packet, true, N>(&cReal, bImag, aImag.packet);
+  pger_common<Packet, true>(&cReal, bImag, aImag.packet);
 
-  pger_common<Packet, false, N>(&cImag, bImag, aReal.packet);
+  pger_common<Packet, false>(&cImag, bImag, aReal.packet);
 }
 
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet,N>& acc, const Packet& pMask)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet,4>& acc, const Packet& pMask)
 {
   acc.packet[0] = pand(acc.packet[0], pMask);
-  if (N > 1) {
-    acc.packet[1] = pand(acc.packet[1], pMask);
-  }
-  if (N > 2) {
-    acc.packet[2] = pand(acc.packet[2], pMask);
-  }
-  if (N > 3) {
-    acc.packet[3] = pand(acc.packet[3], pMask);
-  }
+  acc.packet[1] = pand(acc.packet[1], pMask);
+  acc.packet[2] = pand(acc.packet[2], pMask);
+  acc.packet[3] = pand(acc.packet[3], pMask);
 }
 
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag, const Packet& pMask)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,4>& aReal, PacketBlock<Packet,4>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,4>& cReal, PacketBlock<Packet,4>& cImag, const Packet& pMask)
 {
-  band<Packet, N>(aReal, pMask);
-  band<Packet, N>(aImag, pMask);
+  band<Packet>(aReal, pMask);
+  band<Packet>(aImag, pMask);
 
-  bscalec<Packet,N>(aReal, aImag, bReal, bImag, cReal, cImag);
+  bscalec<Packet,4>(aReal, aImag, bReal, bImag, cReal, cImag);
 }
 
 // Load a PacketBlock, the N parameters make tunning gemm easier so we can add more accumulators as needed.
-template<typename DataMapper, typename Packet, typename Index, const Index accCols, int StorageOrder, bool Complex, int N>
-EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,N*(Complex?2:1)>& acc, const DataMapper& res, Index row, Index col)
+template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col)
 {
   if (StorageOrder == RowMajor) {
-    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col);
-    if (N > 1) {
-      acc.packet[1] = res.template loadPacket<Packet>(row + 1, col);
-    }
-    if (N > 2) {
-      acc.packet[2] = res.template loadPacket<Packet>(row + 2, col);
-    }
-    if (N > 3) {
-      acc.packet[3] = res.template loadPacket<Packet>(row + 3, col);
-    }
-    if (Complex) {
-      acc.packet[0+N] = res.template loadPacket<Packet>(row + 0, col + accCols);
-      if (N > 1) {
-        acc.packet[1+N] = res.template loadPacket<Packet>(row + 1, col + accCols);
-      }
-      if (N > 2) {
-        acc.packet[2+N] = res.template loadPacket<Packet>(row + 2, col + accCols);
-      }
-      if (N > 3) {
-        acc.packet[3+N] = res.template loadPacket<Packet>(row + 3, col + accCols);
-      }
-    }
+    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
+    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
+    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
+    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
   } else {
-    acc.packet[0] = res.template loadPacket<Packet>(row, col + 0);
-    if (N > 1) {
-      acc.packet[1] = res.template loadPacket<Packet>(row, col + 1);
-    }
-    if (N > 2) {
-      acc.packet[2] = res.template loadPacket<Packet>(row, col + 2);
-    }
-    if (N > 3) {
-      acc.packet[3] = res.template loadPacket<Packet>(row, col + 3);
-    }
-    if (Complex) {
-      acc.packet[0+N] = res.template loadPacket<Packet>(row + accCols, col + 0);
-      if (N > 1) {
-        acc.packet[1+N] = res.template loadPacket<Packet>(row + accCols, col + 1);
-      }
-      if (N > 2) {
-        acc.packet[2+N] = res.template loadPacket<Packet>(row + accCols, col + 2);
-      }
-      if (N > 3) {
-        acc.packet[3+N] = res.template loadPacket<Packet>(row + accCols, col + 3);
-      }
-    }
+    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
+    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
+    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
+    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
   }
 }
 
+// An overload of bload when you have a PacketBLock with 8 vectors.
+template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col)
+{
+  if (StorageOrder == RowMajor) {
+    acc.packet[0] = res.template loadPacket<Packet>(row + 0, col + N*accCols);
+    acc.packet[1] = res.template loadPacket<Packet>(row + 1, col + N*accCols);
+    acc.packet[2] = res.template loadPacket<Packet>(row + 2, col + N*accCols);
+    acc.packet[3] = res.template loadPacket<Packet>(row + 3, col + N*accCols);
+    acc.packet[4] = res.template loadPacket<Packet>(row + 0, col + (N+1)*accCols);
+    acc.packet[5] = res.template loadPacket<Packet>(row + 1, col + (N+1)*accCols);
+    acc.packet[6] = res.template loadPacket<Packet>(row + 2, col + (N+1)*accCols);
+    acc.packet[7] = res.template loadPacket<Packet>(row + 3, col + (N+1)*accCols);
+  } else {
+    acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
+    acc.packet[1] = res.template loadPacket<Packet>(row + N*accCols, col + 1);
+    acc.packet[2] = res.template loadPacket<Packet>(row + N*accCols, col + 2);
+    acc.packet[3] = res.template loadPacket<Packet>(row + N*accCols, col + 3);
+    acc.packet[4] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
+    acc.packet[5] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 1);
+    acc.packet[6] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 2);
+    acc.packet[7] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 3);
+  }
+}
+
+template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,2>& acc, const DataMapper& res, Index row, Index col)
+{
+  acc.packet[0] = res.template loadPacket<Packet>(row + N*accCols, col + 0);
+  acc.packet[1] = res.template loadPacket<Packet>(row + (N+1)*accCols, col + 0);
+}
+
 const static Packet4i mask41 = { -1,  0,  0,  0 };
 const static Packet4i mask42 = { -1, -1,  0,  0 };
 const static Packet4i mask43 = { -1, -1, -1,  0 };
@@ -1302,44 +1273,22 @@ EIGEN_ALWAYS_INLINE Packet2d bmask<Packet2d>(const int remaining_rows)
   }
 }
 
-template<typename Packet, int N>
-EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha, const Packet& pMask)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha, const Packet& pMask)
 {
-  band<Packet, N>(accZ, pMask);
-
-  bscale<Packet, N>(acc, accZ, pAlpha);
-}
+  band<Packet>(accZ, pMask);
 
-template<typename Packet, int N> EIGEN_ALWAYS_INLINE void
-pbroadcastN_old(const __UNPACK_TYPE__(Packet) *a,
-                      Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a0 = pset1<Packet>(a[0]);
-  if (N > 1) {
-    a1 = pset1<Packet>(a[1]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a1);
-  }
-  if (N > 2) {
-    a2 = pset1<Packet>(a[2]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a2);
-  }
-  if (N > 3) {
-    a3 = pset1<Packet>(a[3]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a3);
-  }
+  bscale<Packet>(acc, accZ, pAlpha);
 }
 
-template<>
-EIGEN_ALWAYS_INLINE void pbroadcastN_old<Packet4f,4>(const float* a, Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void pbroadcast4_old(const __UNPACK_TYPE__(Packet)* a, Packet& a0, Packet& a1, Packet& a2, Packet& a3)
 {
-  pbroadcast4<Packet4f>(a, a0, a1, a2, a3);
+  pbroadcast4<Packet>(a, a0, a1, a2, a3);
 }
 
 template<>
-EIGEN_ALWAYS_INLINE void pbroadcastN_old<Packet2d,4>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
+EIGEN_ALWAYS_INLINE void pbroadcast4_old<Packet2d>(const double* a, Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
 {
   a1 = pload<Packet2d>(a);
   a3 = pload<Packet2d>(a + 2);
@@ -1349,96 +1298,89 @@ EIGEN_ALWAYS_INLINE void pbroadcastN_old<Packet2d,4>(const double* a, Packet2d&
   a3 = vec_splat(a3, 1);
 }
 
-template<typename Packet, int N> EIGEN_ALWAYS_INLINE void
-pbroadcastN(const __UNPACK_TYPE__(Packet) *a,
-                      Packet& a0, Packet& a1, Packet& a2, Packet& a3)
-{
-  a0 = pset1<Packet>(a[0]);
-  if (N > 1) {
-    a1 = pset1<Packet>(a[1]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a1);
-  }
-  if (N > 2) {
-    a2 = pset1<Packet>(a[2]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a2);
-  }
-  if (N > 3) {
-    a3 = pset1<Packet>(a[3]);
-  } else {
-    EIGEN_UNUSED_VARIABLE(a3);
-  }
-}
+// PEEL loop factor.
+#define PEEL 7
 
-template<> EIGEN_ALWAYS_INLINE void
-pbroadcastN<Packet4f,4>(const float *a,
-                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+template<typename Scalar, typename Packet, typename Index>
+EIGEN_ALWAYS_INLINE void MICRO_EXTRA_COL(
+  const Scalar* &lhs_ptr,
+  const Scalar* &rhs_ptr,
+  PacketBlock<Packet,1> &accZero,
+  Index remaining_rows,
+  Index remaining_cols)
 {
-  a3 = pload<Packet4f>(a);
-  a0 = vec_splat(a3, 0);
-  a1 = vec_splat(a3, 1);
-  a2 = vec_splat(a3, 2);
-  a3 = vec_splat(a3, 3);
+  Packet rhsV[1];
+  rhsV[0] = pset1<Packet>(rhs_ptr[0]);
+  pger<1,Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
+  lhs_ptr += remaining_rows;
+  rhs_ptr += remaining_cols;
 }
 
-// PEEL loop factor.
-#define PEEL 7
-#define PEEL_ROW 7
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
+EIGEN_STRONG_INLINE void gemm_extra_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index row,
+  Index col,
+  Index remaining_rows,
+  Index remaining_cols,
+  const Packet& pAlpha)
+{
+  const Scalar* rhs_ptr = rhs_base;
+  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
+  PacketBlock<Packet,1> accZero;
 
-#define MICRO_UNROLL_PEEL(func) \
-  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
+  bsetzero<Scalar, Packet>(accZero);
 
-#define MICRO_ZERO_PEEL(peel) \
-  if ((PEEL_ROW > peel) && (peel != 0)) { \
-    bsetzero<Scalar, Packet, accRows>(accZero##peel); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accZero##peel); \
+  Index remaining_depth = (depth & -accRows);
+  Index k = 0;
+  for(; k + PEEL <= remaining_depth; k+= PEEL)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr);
+    EIGEN_POWER_PREFETCH(lhs_ptr);
+    for (int l = 0; l < PEEL; l++) {
+      MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
+    }
   }
-
-#define MICRO_ZERO_PEEL_ROW \
-  MICRO_UNROLL_PEEL(MICRO_ZERO_PEEL);
-
-#define MICRO_WORK_PEEL(peel) \
-  if (PEEL_ROW > peel) { \
-    pbroadcastN<Packet,accRows>(rhs_ptr + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    pger<accRows, Scalar, Packet, false>(&accZero##peel, lhs_ptr + (remaining_rows * peel), rhsV##peel); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+  for(; k < remaining_depth; k++)
+  {
+    MICRO_EXTRA_COL<Scalar, Packet, Index>(lhs_ptr, rhs_ptr, accZero, remaining_rows, remaining_cols);
   }
-
-#define MICRO_WORK_PEEL_ROW \
-  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4], rhsV4[4], rhsV5[4], rhsV6[4], rhsV7[4]; \
-  MICRO_UNROLL_PEEL(MICRO_WORK_PEEL); \
-  lhs_ptr += (remaining_rows * PEEL_ROW); \
-  rhs_ptr += (accRows * PEEL_ROW);
-
-#define MICRO_ADD_PEEL(peel, sum) \
-  if (PEEL_ROW > peel) { \
-    for (Index i = 0; i < accRows; i++) { \
-      accZero##sum.packet[i] += accZero##peel.packet[i]; \
-    } \
+  for(; k < depth; k++)
+  {
+    Packet rhsV[1];
+    rhsV[0] = pset1<Packet>(rhs_ptr[0]);
+    pger<1, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
+    lhs_ptr += remaining_rows;
+    rhs_ptr += remaining_cols;
   }
 
-#define MICRO_ADD_PEEL_ROW \
-  MICRO_ADD_PEEL(4, 0) MICRO_ADD_PEEL(5, 1) MICRO_ADD_PEEL(6, 2) MICRO_ADD_PEEL(7, 3) \
-  MICRO_ADD_PEEL(2, 0) MICRO_ADD_PEEL(3, 1) MICRO_ADD_PEEL(1, 0)
+  accZero.packet[0] = vec_mul(pAlpha, accZero.packet[0]);
+  for(Index i = 0; i < remaining_rows; i++) {
+    res(row + i, col) += accZero.packet[0][i];
+  }
+}
 
-template<typename Scalar, typename Packet, typename Index, const Index accRows, const Index remaining_rows>
+template<typename Scalar, typename Packet, typename Index, const Index accRows>
 EIGEN_ALWAYS_INLINE void MICRO_EXTRA_ROW(
   const Scalar* &lhs_ptr,
   const Scalar* &rhs_ptr,
-  PacketBlock<Packet,accRows> &accZero)
+  PacketBlock<Packet,4> &accZero,
+  Index remaining_rows)
 {
   Packet rhsV[4];
-  pbroadcastN<Packet,accRows>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  pger<accRows, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
+  pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
+  pger<4, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
   lhs_ptr += remaining_rows;
   rhs_ptr += accRows;
 }
 
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void gemm_unrolled_row_iteration(
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
+EIGEN_STRONG_INLINE void gemm_extra_row(
   const DataMapper& res,
   const Scalar* lhs_base,
   const Scalar* rhs_base,
@@ -1449,89 +1391,59 @@ EIGEN_ALWAYS_INLINE void gemm_unrolled_row_iteration(
   Index col,
   Index rows,
   Index cols,
+  Index remaining_rows,
   const Packet& pAlpha,
   const Packet& pMask)
 {
   const Scalar* rhs_ptr = rhs_base;
   const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
-  PacketBlock<Packet,accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7, acc;
+  PacketBlock<Packet,4> accZero, acc;
 
-  bsetzero<Scalar, Packet, accRows>(accZero0);
+  bsetzero<Scalar, Packet>(accZero);
 
-  Index remaining_depth = (col + quad_traits<Scalar>::rows < cols) ? depth : (depth & -quad_traits<Scalar>::rows);
+  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
   Index k = 0;
-  if (remaining_depth >= PEEL_ROW) {
-    MICRO_ZERO_PEEL_ROW
-    do
-    {
-      EIGEN_POWER_PREFETCH(rhs_ptr);
-      EIGEN_POWER_PREFETCH(lhs_ptr);
-      MICRO_WORK_PEEL_ROW
-    } while ((k += PEEL_ROW) + PEEL_ROW <= remaining_depth);
-    MICRO_ADD_PEEL_ROW
+  for(; k + PEEL <= remaining_depth; k+= PEEL)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr);
+    EIGEN_POWER_PREFETCH(lhs_ptr);
+    for (int l = 0; l < PEEL; l++) {
+      MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
+    }
   }
   for(; k < remaining_depth; k++)
   {
-    MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows, remaining_rows>(lhs_ptr, rhs_ptr, accZero0);
+    MICRO_EXTRA_ROW<Scalar, Packet, Index, accRows>(lhs_ptr, rhs_ptr, accZero, remaining_rows);
   }
 
   if ((remaining_depth == depth) && (rows >= accCols))
   {
-    bload<DataMapper, Packet, Index, 0, ColMajor, false, accRows>(acc, res, row, 0);
-    bscale<Packet,accRows>(acc, accZero0, pAlpha, pMask);
-    res.template storePacketBlock<Packet,accRows>(row, 0, acc);
+    for(Index j = 0; j < 4; j++) {
+      acc.packet[j] = res.template loadPacket<Packet>(row, col + j);
+    }
+    bscale<Packet>(acc, accZero, pAlpha, pMask);
+    res.template storePacketBlock<Packet,4>(row, col, acc);
   } else {
     for(; k < depth; k++)
     {
       Packet rhsV[4];
-      pbroadcastN<Packet,accRows>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      pger<accRows, Scalar, Packet, Index, false, remaining_rows>(&accZero0, lhs_ptr, rhsV);
+      pbroadcast4<Packet>(rhs_ptr, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
+      pger<4, Scalar, Packet, Index, false>(&accZero, lhs_ptr, rhsV, remaining_rows);
       lhs_ptr += remaining_rows;
       rhs_ptr += accRows;
     }
 
-    for(Index j = 0; j < accRows; j++) {
-      accZero0.packet[j] = vec_mul(pAlpha, accZero0.packet[j]);
+    for(Index j = 0; j < 4; j++) {
+      accZero.packet[j] = vec_mul(pAlpha, accZero.packet[j]);
+    }
+    for(Index j = 0; j < 4; j++) {
       for(Index i = 0; i < remaining_rows; i++) {
-        res(row + i, j) += accZero0.packet[j][i];
+        res(row + i, col + j) += accZero.packet[j][i];
       }
     }
   }
 }
 
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_ALWAYS_INLINE void gemm_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
-{
-  switch(remaining_rows) {
-    case 1:
-      gemm_unrolled_row_iteration<Scalar, Packet, DataMapper, Index, accRows, accCols, 1>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, pAlpha, pMask);
-      break;
-    case 2:
-      if (sizeof(Scalar) == sizeof(float)) {
-        gemm_unrolled_row_iteration<Scalar, Packet, DataMapper, Index, accRows, accCols, 2>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, pAlpha, pMask);
-      }
-      break;
-    default:
-      if (sizeof(Scalar) == sizeof(float)) {
-        gemm_unrolled_row_iteration<Scalar, Packet, DataMapper, Index, accRows, accCols, 3>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, pAlpha, pMask);
-      }
-      break;
-  }
-}
-
 #define MICRO_UNROLL(func) \
   func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
 
@@ -1550,24 +1462,34 @@ EIGEN_ALWAYS_INLINE void gemm_extra_row(
 
 #define MICRO_WORK_ONE(iter, peel) \
   if (unroll_factor > iter) { \
-    pger_common<Packet, false, accRows>(&accZero##iter, lhsV##iter, rhsV##peel); \
+    pger_common<Packet, false>(&accZero##iter, lhsV##iter, rhsV##peel); \
   }
 
 #define MICRO_TYPE_PEEL4(func, func2, peel) \
   if (PEEL > peel) { \
     Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
-    pbroadcastN<Packet,accRows>(rhs_ptr + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
+    pbroadcast4<Packet>(rhs_ptr + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
+    MICRO_UNROLL_WORK(func, func2, peel) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+  }
+
+#define MICRO_TYPE_PEEL1(func, func2, peel) \
+  if (PEEL > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
+    rhsV##peel[0] = pset1<Packet>(rhs_ptr[remaining_cols * peel]); \
     MICRO_UNROLL_WORK(func, func2, peel) \
   } else { \
     EIGEN_UNUSED_VARIABLE(rhsV##peel); \
   }
 
 #define MICRO_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M]; \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
   func(func1,func2,0); func(func1,func2,1); \
   func(func1,func2,2); func(func1,func2,3); \
   func(func1,func2,4); func(func1,func2,5); \
-  func(func1,func2,6); func(func1,func2,7);
+  func(func1,func2,6); func(func1,func2,7); \
+  func(func1,func2,8); func(func1,func2,9);
 
 #define MICRO_UNROLL_TYPE_ONE(M, func, func1, func2) \
   Packet rhsV0[M]; \
@@ -1581,9 +1503,17 @@ EIGEN_ALWAYS_INLINE void gemm_extra_row(
   MICRO_UNROLL_TYPE_ONE(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
   rhs_ptr += accRows;
 
+#define MICRO_ONE_PEEL1 \
+  MICRO_UNROLL_TYPE_PEEL(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
+  rhs_ptr += (remaining_cols * PEEL);
+
+#define MICRO_ONE1 \
+  MICRO_UNROLL_TYPE_ONE(1, MICRO_TYPE_PEEL1, MICRO_WORK_ONE, MICRO_LOAD_ONE); \
+  rhs_ptr += remaining_cols;
+
 #define MICRO_DST_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet, accRows>(accZero##iter); \
+    bsetzero<Scalar, Packet>(accZero##iter); \
   } else { \
     EIGEN_UNUSED_VARIABLE(accZero##iter); \
   }
@@ -1592,7 +1522,7 @@ EIGEN_ALWAYS_INLINE void gemm_extra_row(
 
 #define MICRO_SRC_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols; \
+    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
   }
@@ -1608,13 +1538,25 @@ EIGEN_ALWAYS_INLINE void gemm_extra_row(
 
 #define MICRO_STORE_ONE(iter) \
   if (unroll_factor > iter) { \
-    bload<DataMapper, Packet, Index, 0, ColMajor, false, accRows>(acc, res, row + iter*accCols, 0); \
-    bscale<Packet,accRows>(acc, accZero##iter, pAlpha); \
-    res.template storePacketBlock<Packet,accRows>(row + iter*accCols, 0, acc); \
+    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
+    acc.packet[1] = res.template loadPacket<Packet>(row + iter*accCols, col + 1); \
+    acc.packet[2] = res.template loadPacket<Packet>(row + iter*accCols, col + 2); \
+    acc.packet[3] = res.template loadPacket<Packet>(row + iter*accCols, col + 3); \
+    bscale<Packet>(acc, accZero##iter, pAlpha); \
+    res.template storePacketBlock<Packet,4>(row + iter*accCols, col, acc); \
   }
 
 #define MICRO_STORE MICRO_UNROLL(MICRO_STORE_ONE)
 
+#define MICRO_COL_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    acc.packet[0] = res.template loadPacket<Packet>(row + iter*accCols, col + 0); \
+    bscale<Packet>(acc, accZero##iter, pAlpha); \
+    res.template storePacketBlock<Packet,1>(row + iter*accCols, col, acc); \
+  }
+
+#define MICRO_COL_STORE MICRO_UNROLL(MICRO_COL_STORE_ONE)
+
 template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
 EIGEN_STRONG_INLINE void gemm_unrolled_iteration(
   const DataMapper& res,
@@ -1622,13 +1564,15 @@ EIGEN_STRONG_INLINE void gemm_unrolled_iteration(
   const Scalar* rhs_base,
   Index depth,
   Index strideA,
+  Index offsetA,
   Index& row,
+  Index col,
   const Packet& pAlpha)
 {
   const Scalar* rhs_ptr = rhs_base;
   const Scalar* lhs_ptr0 = NULL, *  lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
-  PacketBlock<Packet,accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
-  PacketBlock<Packet,accRows> acc;
+  PacketBlock<Packet,4> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+  PacketBlock<Packet,4> acc;
 
   MICRO_SRC_PTR
   MICRO_DST_PTR
@@ -1649,100 +1593,101 @@ EIGEN_STRONG_INLINE void gemm_unrolled_iteration(
   row += unroll_factor*accCols;
 }
 
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_ALWAYS_INLINE void gemm_cols(
+template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
+EIGEN_STRONG_INLINE void gemm_unrolled_col_iteration(
   const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
   Index depth,
   Index strideA,
   Index offsetA,
-  Index strideB,
-  Index offsetB,
+  Index& row,
   Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
+  Index remaining_cols,
+  const Packet& pAlpha)
 {
-  const DataMapper res3 = res.getSubMapper(0, col);
+  const Scalar* rhs_ptr = rhs_base;
+  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, *lhs_ptr7 = NULL;
+  PacketBlock<Packet,1> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+  PacketBlock<Packet,1> acc;
+
+  MICRO_SRC_PTR
+  MICRO_DST_PTR
 
-  const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-  const Scalar* lhs_base = blockA + accCols*offsetA;
-  Index row = 0;
+  Index k = 0;
+  for(; k + PEEL <= depth; k+= PEEL)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr);
+    MICRO_PREFETCH
+    MICRO_ONE_PEEL1
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_ONE1
+  }
+  MICRO_COL_STORE
+
+  row += unroll_factor*accCols;
+}
 
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
+EIGEN_STRONG_INLINE void gemm_unrolled_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index& row,
+  Index rows,
+  Index col,
+  Index remaining_cols,
+  const Packet& pAlpha)
+{
 #define MAX_UNROLL 6
   while(row + MAX_UNROLL*accCols <= rows) {
-    gemm_unrolled_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
+    gemm_unrolled_col_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
   }
   switch( (rows-row)/accCols ) {
 #if MAX_UNROLL > 7
     case 7:
-      gemm_unrolled_iteration<7, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
+      gemm_unrolled_col_iteration<7, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
       break;
 #endif
 #if MAX_UNROLL > 6
     case 6:
-      gemm_unrolled_iteration<6, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
+      gemm_unrolled_col_iteration<6, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
       break;
 #endif
 #if MAX_UNROLL > 5
-    case 5:
-      gemm_unrolled_iteration<5, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
+   case 5:
+      gemm_unrolled_col_iteration<5, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
       break;
 #endif
 #if MAX_UNROLL > 4
-    case 4:
-      gemm_unrolled_iteration<4, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
+   case 4:
+      gemm_unrolled_col_iteration<4, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
       break;
 #endif
 #if MAX_UNROLL > 3
-    case 3:
-      gemm_unrolled_iteration<3, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+   case 3:
+     gemm_unrolled_col_iteration<3, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
+     break;
 #endif
 #if MAX_UNROLL > 2
-    case 2:
-      gemm_unrolled_iteration<2, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+   case 2:
+     gemm_unrolled_col_iteration<2, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
+     break;
 #endif
 #if MAX_UNROLL > 1
-    case 1:
-      gemm_unrolled_iteration<1, Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+   case 1:
+     gemm_unrolled_col_iteration<1, Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_cols, pAlpha);
+     break;
 #endif
-    default:
-      break;
+   default:
+     break;
   }
 #undef MAX_UNROLL
-
-  if(remaining_rows > 0)
-  {
-    gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, blockA, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-  }
-}
-
-template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
-EIGEN_STRONG_INLINE void gemm_extra_cols(
-  const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index offsetB,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
-{
-  for (; col < cols; col++) {
-    gemm_cols<Scalar, Packet, DataMapper, Index, 1, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
-  }
 }
 
 /****************
@@ -1752,6 +1697,7 @@ template<typename Scalar, typename Index, typename Packet, typename RhsPacket, t
 EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
 {
       const Index remaining_rows = rows % accCols;
+      const Index remaining_cols = cols % accRows;
 
       if( strideA == -1 ) strideA = depth;
       if( strideB == -1 ) strideB = depth;
@@ -1762,10 +1708,79 @@ EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const
       Index col = 0;
       for(; col + accRows <= cols; col += accRows)
       {
-        gemm_cols<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
-      }
+        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
+        const Scalar* lhs_base = blockA;
+        Index row = 0;
+
+#define MAX_UNROLL 6
+        while(row + MAX_UNROLL*accCols <= rows) {
+          gemm_unrolled_iteration<MAX_UNROLL, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+        }
+        switch( (rows-row)/accCols ) {
+#if MAX_UNROLL > 7
+          case 7:
+            gemm_unrolled_iteration<7, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 6
+          case 6:
+            gemm_unrolled_iteration<6, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 5
+          case 5:
+            gemm_unrolled_iteration<5, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 4
+          case 4:
+            gemm_unrolled_iteration<4, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 3
+          case 3:
+            gemm_unrolled_iteration<3, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 2
+          case 2:
+            gemm_unrolled_iteration<2, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+#if MAX_UNROLL > 1
+          case 1:
+            gemm_unrolled_iteration<1, Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
+#endif
+          default:
+            break;
+        }
+#undef MAX_UNROLL
+
+        if(remaining_rows > 0)
+        {
+          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
+        }
+    }
 
-      gemm_extra_cols<Scalar, Packet, DataMapper, Index, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
+    if(remaining_cols > 0)
+    {
+      const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
+      const Scalar* lhs_base = blockA;
+
+      for(; col < cols; col++)
+      {
+        Index row = 0;
+
+        gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
+
+        if (remaining_rows > 0)
+        {
+          gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
+        }
+        rhs_base++;
+      }
+    }
 }
 
 #define accColsC (accCols / 2)
@@ -1774,66 +1789,117 @@ EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const
 
 // PEEL_COMPLEX loop factor.
 #define PEEL_COMPLEX 3
-#define PEEL_COMPLEX_ROW 3
 
-#define MICRO_COMPLEX_UNROLL_PEEL(func) \
-  func(0) func(1) func(2) func(3)
+template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_COL(
+  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
+  const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
+  PacketBlock<Packet,1> &accReal, PacketBlock<Packet,1> &accImag,
+  Index remaining_rows,
+  Index remaining_cols)
+{
+  Packet rhsV[1], rhsVi[1];
+  rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
+  if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
+  pgerc<1, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
+  lhs_ptr_real += remaining_rows;
+  if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
+  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+  rhs_ptr_real += remaining_cols;
+  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
+  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
+}
 
-#define MICRO_COMPLEX_ZERO_PEEL(peel) \
-  if ((PEEL_COMPLEX_ROW > peel) && (peel != 0)) { \
-    bsetzero<Scalar, Packet, accRows>(accReal##peel); \
-    bsetzero<Scalar, Packet, accRows>(accImag##peel); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(accReal##peel); \
-    EIGEN_UNUSED_VARIABLE(accImag##peel); \
-  }
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_extra_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index col,
+  Index remaining_rows,
+  Index remaining_cols,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag)
+{
+  const Scalar* rhs_ptr_real = rhs_base;
+  const Scalar* rhs_ptr_imag;
+  if(!RhsIsReal) rhs_ptr_imag = rhs_base + remaining_cols*strideB;
+  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
+  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
+  const Scalar* lhs_ptr_imag;
+  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
+  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+  PacketBlock<Packet,1> accReal, accImag;
+  PacketBlock<Packet,1> taccReal, taccImag;
+  PacketBlock<Packetc,1> acc0, acc1;
 
-#define MICRO_COMPLEX_ZERO_PEEL_ROW \
-  MICRO_COMPLEX_UNROLL_PEEL(MICRO_COMPLEX_ZERO_PEEL);
+  bsetzero<Scalar, Packet>(accReal);
+  bsetzero<Scalar, Packet>(accImag);
 
-#define MICRO_COMPLEX_WORK_PEEL(peel) \
-  if (PEEL_COMPLEX_ROW > peel) { \
-    pbroadcastN_old<Packet,accRows>(rhs_ptr_real + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
-    if(!RhsIsReal) pbroadcastN_old<Packet,accRows>(rhs_ptr_imag + (accRows * peel), rhsVi##peel[0], rhsVi##peel[1], rhsVi##peel[2], rhsVi##peel[3]); \
-    pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##peel, &accImag##peel, lhs_ptr_real + (remaining_rows * peel), lhs_ptr_imag + (remaining_rows * peel), rhsV##peel, rhsVi##peel); \
-  } else { \
-    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
-    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+  Index remaining_depth = (depth & -accRows);
+  Index k = 0;
+  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr_real);
+    if(!RhsIsReal) {
+      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
+    }
+    EIGEN_POWER_PREFETCH(lhs_ptr_real);
+    if(!LhsIsReal) {
+      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
+    }
+    for (int l = 0; l < PEEL_COMPLEX; l++) {
+      MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
+    }
+  }
+  for(; k < remaining_depth; k++)
+  {
+    MICRO_COMPLEX_EXTRA_COL<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows, remaining_cols);
   }
 
-#define MICRO_COMPLEX_WORK_PEEL_ROW \
-  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4]; \
-  Packet rhsVi0[4], rhsVi1[4], rhsVi2[4], rhsVi3[4]; \
-  MICRO_COMPLEX_UNROLL_PEEL(MICRO_COMPLEX_WORK_PEEL); \
-  lhs_ptr_real += (remaining_rows * PEEL_COMPLEX_ROW); \
-  if(!LhsIsReal) lhs_ptr_imag += (remaining_rows * PEEL_COMPLEX_ROW); \
-  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag); \
-  rhs_ptr_real += (accRows * PEEL_COMPLEX_ROW); \
-  if(!RhsIsReal) rhs_ptr_imag += (accRows * PEEL_COMPLEX_ROW); \
-  else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
-
-#define MICRO_COMPLEX_ADD_PEEL(peel, sum) \
-  if (PEEL_COMPLEX_ROW > peel) { \
-    for (Index i = 0; i < accRows; i++) { \
-      accReal##sum.packet[i] += accReal##peel.packet[i]; \
-      accImag##sum.packet[i] += accImag##peel.packet[i]; \
-    } \
+  for(; k < depth; k++)
+  {
+    Packet rhsV[1], rhsVi[1];
+    rhsV[0] = pset1<Packet>(rhs_ptr_real[0]);
+    if(!RhsIsReal) rhsVi[0] = pset1<Packet>(rhs_ptr_imag[0]);
+    pgerc<1, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
+    lhs_ptr_real += remaining_rows;
+    if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
+    rhs_ptr_real += remaining_cols;
+    if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
   }
 
-#define MICRO_COMPLEX_ADD_PEEL_ROW \
-  MICRO_COMPLEX_ADD_PEEL(2, 0) MICRO_COMPLEX_ADD_PEEL(3, 1) \
-  MICRO_COMPLEX_ADD_PEEL(1, 0)
+  bscalec<Packet,1>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
+  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
 
-template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index remaining_rows>
+  if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
+  {
+    res(row + 0, col + 0) += pfirst<Packetc>(acc0.packet[0]);
+  } else {
+    acc0.packet[0] += res.template loadPacket<Packetc>(row + 0, col + 0);
+    res.template storePacketBlock<Packetc,1>(row + 0, col + 0, acc0);
+    if(remaining_rows > accColsC) {
+      res(row + accColsC, col + 0) += pfirst<Packetc>(acc1.packet[0]);
+    }
+  }
+}
+
+template<typename Scalar, typename Packet, typename Index, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
 EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(
   const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
   const Scalar* &rhs_ptr_real, const Scalar* &rhs_ptr_imag,
-  PacketBlock<Packet,accRows> &accReal, PacketBlock<Packet,accRows> &accImag)
+  PacketBlock<Packet,4> &accReal, PacketBlock<Packet,4> &accImag,
+  Index remaining_rows)
 {
   Packet rhsV[4], rhsVi[4];
-  pbroadcastN_old<Packet,accRows>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-  if(!RhsIsReal) pbroadcastN_old<Packet,accRows>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-  pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
+  pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
+  if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
+  pgerc<4, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
   lhs_ptr_real += remaining_rows;
   if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
   else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
@@ -1842,8 +1908,8 @@ EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(
   else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
 }
 
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index remaining_rows>
-EIGEN_ALWAYS_INLINE void gemm_unrolled_complex_row_iteration(
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_extra_row(
   const DataMapper& res,
   const Scalar* lhs_base,
   const Scalar* rhs_base,
@@ -1855,141 +1921,106 @@ EIGEN_ALWAYS_INLINE void gemm_unrolled_complex_row_iteration(
   Index col,
   Index rows,
   Index cols,
+  Index remaining_rows,
   const Packet& pAlphaReal,
   const Packet& pAlphaImag,
   const Packet& pMask)
 {
   const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag = NULL;
+  const Scalar* rhs_ptr_imag;
   if(!RhsIsReal) rhs_ptr_imag = rhs_base + accRows*strideB;
   else EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
   const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
-  const Scalar* lhs_ptr_imag = NULL;
+  const Scalar* lhs_ptr_imag;
   if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
   else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
-  PacketBlock<Packet,accRows> accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,accRows> taccReal, taccImag;
-  PacketBlock<Packetc,accRows> acc0, acc1;
-  PacketBlock<Packetc,accRows*2> tRes;
+  PacketBlock<Packet,4> accReal, accImag;
+  PacketBlock<Packet,4> taccReal, taccImag;
+  PacketBlock<Packetc,4> acc0, acc1;
+  PacketBlock<Packetc,8> tRes;
 
-  bsetzero<Scalar, Packet, accRows>(accReal0);
-  bsetzero<Scalar, Packet, accRows>(accImag0);
+  bsetzero<Scalar, Packet>(accReal);
+  bsetzero<Scalar, Packet>(accImag);
 
-  Index remaining_depth = (col + quad_traits<Scalar>::rows < cols) ? depth : (depth & -quad_traits<Scalar>::rows);
+  Index remaining_depth = (col + accRows < cols) ? depth : (depth & -accRows);
   Index k = 0;
-  if (remaining_depth >= PEEL_COMPLEX_ROW) {
-    MICRO_COMPLEX_ZERO_PEEL_ROW
-    do
-    {
-      EIGEN_POWER_PREFETCH(rhs_ptr_real);
-      if(!RhsIsReal) {
-        EIGEN_POWER_PREFETCH(rhs_ptr_imag);
-      }
-      EIGEN_POWER_PREFETCH(lhs_ptr_real);
-      if(!LhsIsReal) {
-        EIGEN_POWER_PREFETCH(lhs_ptr_imag);
-      }
-      MICRO_COMPLEX_WORK_PEEL_ROW
-    } while ((k += PEEL_COMPLEX_ROW) + PEEL_COMPLEX_ROW <= remaining_depth);
-    MICRO_COMPLEX_ADD_PEEL_ROW
+  for(; k + PEEL_COMPLEX <= remaining_depth; k+= PEEL_COMPLEX)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr_real);
+    if(!RhsIsReal) {
+      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
+    }
+    EIGEN_POWER_PREFETCH(lhs_ptr_real);
+    if(!LhsIsReal) {
+      EIGEN_POWER_PREFETCH(lhs_ptr_imag);
+    }
+    for (int l = 0; l < PEEL_COMPLEX; l++) {
+      MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
+    }
   }
   for(; k < remaining_depth; k++)
   {
-    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, remaining_rows>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal0, accImag0);
+    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, Index, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real, rhs_ptr_imag, accReal, accImag, remaining_rows);
   }
 
   if ((remaining_depth == depth) && (rows >= accCols))
   {
-    bload<DataMapper, Packetc, Index, accColsC, ColMajor, true, accRows>(tRes, res, row, 0);
-    bscalec<Packet,accRows>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
-    bcouple<Packet, Packetc, accRows>(taccReal, taccImag, tRes, acc0, acc1);
-    res.template storePacketBlock<Packetc,accRows>(row + 0, 0, acc0);
-    res.template storePacketBlock<Packetc,accRows>(row + accColsC, 0, acc1);
+    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row, col);
+    bscalec<Packet>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
+    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1);
+    res.template storePacketBlock<Packetc,4>(row + 0, col, acc0);
+    res.template storePacketBlock<Packetc,4>(row + accColsC, col, acc1);
   } else {
     for(; k < depth; k++)
     {
       Packet rhsV[4], rhsVi[4];
-      pbroadcastN_old<Packet,accRows>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
-      if(!RhsIsReal) pbroadcastN_old<Packet,accRows>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
-      pgerc<accRows, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, remaining_rows>(&accReal0, &accImag0, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
+      pbroadcast4_old<Packet>(rhs_ptr_real, rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
+      if(!RhsIsReal) pbroadcast4_old<Packet>(rhs_ptr_imag, rhsVi[0], rhsVi[1], rhsVi[2], rhsVi[3]);
+      pgerc<4, Scalar, Packet, Index, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi, remaining_rows);
       lhs_ptr_real += remaining_rows;
       if(!LhsIsReal) lhs_ptr_imag += remaining_rows;
       rhs_ptr_real += accRows;
       if(!RhsIsReal) rhs_ptr_imag += accRows;
     }
 
-    bscalec<Packet,accRows>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag);
-    bcouple_common<Packet, Packetc, accRows>(taccReal, taccImag, acc0, acc1);
+    bscalec<Packet,4>(accReal, accImag, pAlphaReal, pAlphaImag, taccReal, taccImag);
+    bcouple_common<Packet, Packetc>(taccReal, taccImag, acc0, acc1);
 
     if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
     {
-      for(Index j = 0; j < accRows; j++) {
-        res(row + 0, j) += pfirst<Packetc>(acc0.packet[j]);
+      for(Index j = 0; j < 4; j++) {
+        res(row + 0, col + j) += pfirst<Packetc>(acc0.packet[j]);
       }
     } else {
-      for(Index j = 0; j < accRows; j++) {
+      for(Index j = 0; j < 4; j++) {
         PacketBlock<Packetc,1> acc2;
-        acc2.packet[0] = res.template loadPacket<Packetc>(row + 0, j) + acc0.packet[j];
-        res.template storePacketBlock<Packetc,1>(row + 0, j, acc2);
+        acc2.packet[0] = res.template loadPacket<Packetc>(row + 0, col + j) + acc0.packet[j];
+        res.template storePacketBlock<Packetc,1>(row + 0, col + j, acc2);
         if(remaining_rows > accColsC) {
-          res(row + accColsC, j) += pfirst<Packetc>(acc1.packet[j]);
+          res(row + accColsC, col + j) += pfirst<Packetc>(acc1.packet[j]);
         }
       }
     }
   }
 }
 
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
-  const DataMapper& res,
-  const Scalar* lhs_base,
-  const Scalar* rhs_base,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index row,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
-{
-  switch(remaining_rows) {
-    case 1:
-      gemm_unrolled_complex_row_iteration<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, 1>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, pAlphaReal, pAlphaImag, pMask);
-      break;
-    case 2:
-      if (sizeof(Scalar) == sizeof(float)) {
-        gemm_unrolled_complex_row_iteration<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, 2>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, pAlphaReal, pAlphaImag, pMask);
-      }
-      break;
-    default:
-      if (sizeof(Scalar) == sizeof(float)) {
-        gemm_unrolled_complex_row_iteration<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, 3>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, pAlphaReal, pAlphaImag, pMask);
-      }
-      break;
-  }
-}
-
 #define MICRO_COMPLEX_UNROLL(func) \
-  func(0) func(1) func(2) func(3)
+  func(0) func(1) func(2) func(3) func(4)
 
 #define MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
     MICRO_COMPLEX_UNROLL(func2); \
-    func(0,peel) func(1,peel) func(2,peel) func(3,peel)
+    func(0,peel) func(1,peel) func(2,peel) func(3,peel) func(4,peel)
 
 #define MICRO_COMPLEX_LOAD_ONE(iter) \
   if (unroll_factor > iter) { \
     lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
+    lhs_ptr_real##iter += accCols; \
     if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter + imag_delta); \
+      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
+      lhs_ptr_imag##iter += accCols; \
     } else { \
       EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
     } \
-    lhs_ptr_real##iter += accCols; \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhsV##iter); \
     EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
@@ -1997,16 +2028,37 @@ EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
 
 #define MICRO_COMPLEX_WORK_ONE4(iter, peel) \
   if (unroll_factor > iter) { \
-    pgerc_common<accRows, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
+    pgerc_common<4, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_WORK_ONE1(iter, peel) \
+  if (unroll_factor > iter) { \
+    pgerc_common<1, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
   }
 
 #define MICRO_COMPLEX_TYPE_PEEL4(func, func2, peel) \
   if (PEEL_COMPLEX > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3; \
-    pbroadcastN_old<Packet,accRows>(rhs_ptr_real + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
+    pbroadcast4_old<Packet>(rhs_ptr_real + (accRows * peel), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
     if(!RhsIsReal) { \
-      pbroadcastN_old<Packet,accRows>(rhs_ptr_imag + (accRows * peel), rhsVi##peel[0], rhsVi##peel[1], rhsVi##peel[2], rhsVi##peel[3]); \
+      pbroadcast4_old<Packet>(rhs_ptr_imag + (accRows * peel), rhsVi##peel[0], rhsVi##peel[1], rhsVi##peel[2], rhsVi##peel[3]); \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+    } \
+    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_TYPE_PEEL1(func, func2, peel) \
+  if (PEEL_COMPLEX > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
+    rhsV##peel[0] = pset1<Packet>(rhs_ptr_real[remaining_cols * peel]); \
+    if(!RhsIsReal) { \
+      rhsVi##peel[0] = pset1<Packet>(rhs_ptr_imag[remaining_cols * peel]); \
     } else { \
       EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
     } \
@@ -2017,10 +2069,13 @@ EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
   }
 
 #define MICRO_COMPLEX_UNROLL_TYPE_PEEL(M, func, func1, func2) \
-  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M]; \
-  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M]; \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M], rhsV8[M], rhsV9[M]; \
+  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M], rhsVi4[M], rhsVi5[M], rhsVi6[M], rhsVi7[M], rhsVi8[M], rhsVi9[M]; \
   func(func1,func2,0); func(func1,func2,1); \
-  func(func1,func2,2); func(func1,func2,3);
+  func(func1,func2,2); func(func1,func2,3); \
+  func(func1,func2,4); func(func1,func2,5); \
+  func(func1,func2,6); func(func1,func2,7); \
+  func(func1,func2,8); func(func1,func2,9);
 
 #define MICRO_COMPLEX_UNROLL_TYPE_ONE(M, func, func1, func2) \
   Packet rhsV0[M], rhsVi0[M];\
@@ -2036,10 +2091,20 @@ EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
   rhs_ptr_real += accRows; \
   if(!RhsIsReal) rhs_ptr_imag += accRows;
 
+#define MICRO_COMPLEX_ONE_PEEL1 \
+  MICRO_COMPLEX_UNROLL_TYPE_PEEL(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
+  rhs_ptr_real += (remaining_cols * PEEL_COMPLEX); \
+  if(!RhsIsReal) rhs_ptr_imag += (remaining_cols * PEEL_COMPLEX);
+
+#define MICRO_COMPLEX_ONE1 \
+  MICRO_COMPLEX_UNROLL_TYPE_ONE(1, MICRO_COMPLEX_TYPE_PEEL1, MICRO_COMPLEX_WORK_ONE1, MICRO_COMPLEX_LOAD_ONE); \
+  rhs_ptr_real += remaining_cols; \
+  if(!RhsIsReal) rhs_ptr_imag += remaining_cols;
+
 #define MICRO_COMPLEX_DST_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    bsetzero<Scalar, Packet, accRows>(accReal##iter); \
-    bsetzero<Scalar, Packet, accRows>(accImag##iter); \
+    bsetzero<Scalar, Packet>(accReal##iter); \
+    bsetzero<Scalar, Packet>(accImag##iter); \
   } else { \
     EIGEN_UNUSED_VARIABLE(accReal##iter); \
     EIGEN_UNUSED_VARIABLE(accImag##iter); \
@@ -2049,9 +2114,15 @@ EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
 
 #define MICRO_COMPLEX_SRC_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols; \
+    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
+    if(!LhsIsReal) { \
+      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
+    } \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
+    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
   }
 
 #define MICRO_COMPLEX_SRC_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
@@ -2059,21 +2130,35 @@ EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
 #define MICRO_COMPLEX_PREFETCH_ONE(iter) \
   if (unroll_factor > iter) { \
     EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
+    if(!LhsIsReal) { \
+      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
+    } \
   }
 
 #define MICRO_COMPLEX_PREFETCH MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
 
 #define MICRO_COMPLEX_STORE_ONE(iter) \
   if (unroll_factor > iter) { \
-    bload<DataMapper, Packetc, Index, accColsC, ColMajor, true, accRows>(tRes, res, row + iter*accCols, 0); \
-    bscalec<Packet,accRows>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
-    bcouple<Packet, Packetc, accRows>(taccReal, taccImag, tRes, acc0, acc1); \
-    res.template storePacketBlock<Packetc,accRows>(row + iter*accCols + 0, 0, acc0); \
-    res.template storePacketBlock<Packetc,accRows>(row + iter*accCols + accColsC, 0, acc1); \
+    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
+    bscalec<Packet,4>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
+    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
+    res.template storePacketBlock<Packetc,4>(row + iter*accCols + 0, col, acc0); \
+    res.template storePacketBlock<Packetc,4>(row + iter*accCols + accColsC, col, acc1); \
   }
 
 #define MICRO_COMPLEX_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_STORE_ONE)
 
+#define MICRO_COMPLEX_COL_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bload<DataMapper, Packetc, Index, accColsC, 0, ColMajor>(tRes, res, row + iter*accCols, col); \
+    bscalec<Packet,1>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag); \
+    bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc0, acc1); \
+    res.template storePacketBlock<Packetc,1>(row + iter*accCols + 0, col, acc0); \
+    res.template storePacketBlock<Packetc,1>(row + iter*accCols + accColsC, col, acc1); \
+  }
+
+#define MICRO_COMPLEX_COL_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_COL_STORE_ONE)
+
 template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
 EIGEN_STRONG_INLINE void gemm_complex_unrolled_iteration(
   const DataMapper& res,
@@ -2081,26 +2166,29 @@ EIGEN_STRONG_INLINE void gemm_complex_unrolled_iteration(
   const Scalar* rhs_base,
   Index depth,
   Index strideA,
+  Index offsetA,
   Index strideB,
   Index& row,
+  Index col,
   const Packet& pAlphaReal,
   const Packet& pAlphaImag)
 {
   const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag = NULL;
-  const Index imag_delta = accCols*strideA;
+  const Scalar* rhs_ptr_imag;
   if(!RhsIsReal) {
     rhs_ptr_imag = rhs_base + accRows*strideB;
   } else {
     EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
   }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_real1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_real3 = NULL;
-  PacketBlock<Packet,accRows> accReal0, accImag0, accReal1, accImag1;
-  PacketBlock<Packet,accRows> accReal2, accImag2, accReal3, accImag3;
-  PacketBlock<Packet,accRows> taccReal, taccImag;
-  PacketBlock<Packetc,accRows> acc0, acc1;
-  PacketBlock<Packetc,accRows*2> tRes;
+  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
+  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
+  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
+  PacketBlock<Packet,4> accReal0, accImag0, accReal1, accImag1;
+  PacketBlock<Packet,4> accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet,4> accReal4, accImag4;
+  PacketBlock<Packet,4> taccReal, taccImag;
+  PacketBlock<Packetc,4> acc0, acc1;
+  PacketBlock<Packetc,8> tRes;
 
   MICRO_COMPLEX_SRC_PTR
   MICRO_COMPLEX_DST_PTR
@@ -2124,93 +2212,112 @@ EIGEN_STRONG_INLINE void gemm_complex_unrolled_iteration(
   row += unroll_factor*accCols;
 }
 
-template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void gemm_complex_cols(
+template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_unrolled_col_iteration(
   const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
   Index depth,
   Index strideA,
   Index offsetA,
   Index strideB,
-  Index offsetB,
+  Index& row,
   Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
+  Index remaining_cols,
   const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
+  const Packet& pAlphaImag)
 {
-  const DataMapper res3 = res.getSubMapper(0, col);
+  const Scalar* rhs_ptr_real = rhs_base;
+  const Scalar* rhs_ptr_imag;
+  if(!RhsIsReal) {
+    rhs_ptr_imag = rhs_base + remaining_cols*strideB;
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
+  }
+  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
+  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
+  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
+  PacketBlock<Packet,1> accReal0, accImag0, accReal1, accImag1;
+  PacketBlock<Packet,1> accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet,1> accReal4, accImag4;
+  PacketBlock<Packet,1> taccReal, taccImag;
+  PacketBlock<Packetc,1> acc0, acc1;
+  PacketBlock<Packetc,2> tRes;
 
-  const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-  const Scalar* lhs_base = blockA + accCols*offsetA;
-  Index row = 0;
+  MICRO_COMPLEX_SRC_PTR
+  MICRO_COMPLEX_DST_PTR
 
-#define MAX_COMPLEX_UNROLL 3
-  while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
-    gemm_complex_unrolled_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_UNROLL > 4
-    case 4:
-      gemm_complex_unrolled_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_UNROLL > 3
-    case 3:
-      gemm_complex_unrolled_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_UNROLL > 2
-    case 2:
-      gemm_complex_unrolled_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_UNROLL > 1
-    case 1:
-      gemm_complex_unrolled_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-    default:
-      break;
+  Index k = 0;
+  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr_real);
+    if(!RhsIsReal) {
+      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
+    }
+    MICRO_COMPLEX_PREFETCH
+    MICRO_COMPLEX_ONE_PEEL1
   }
-#undef MAX_COMPLEX_UNROLL
-
-  if(remaining_rows > 0)
+  for(; k < depth; k++)
   {
-    gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+    MICRO_COMPLEX_ONE1
   }
+  MICRO_COMPLEX_COL_STORE
+
+  row += unroll_factor*accCols;
 }
 
 template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_STRONG_INLINE void gemm_complex_extra_cols(
+EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
   const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
   Index depth,
   Index strideA,
   Index offsetA,
   Index strideB,
-  Index offsetB,
-  Index col,
+  Index& row,
   Index rows,
-  Index cols,
-  Index remaining_rows,
+  Index col,
+  Index remaining_cols,
   const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
+  const Packet& pAlphaImag)
 {
-  for (; col < cols; col++) {
-    gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, Index, 1, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+#define MAX_COMPLEX_UNROLL 3
+  while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
+    gemm_complex_unrolled_col_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
   }
+  switch( (rows-row)/accCols ) {
+#if MAX_COMPLEX_UNROLL > 4
+   case 4:
+     gemm_complex_unrolled_col_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
+     break;
+#endif
+#if MAX_COMPLEX_UNROLL > 3
+   case 3:
+     gemm_complex_unrolled_col_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
+     break;
+#endif
+#if MAX_COMPLEX_UNROLL > 2
+   case 2:
+     gemm_complex_unrolled_col_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
+     break;
+#endif
+#if MAX_COMPLEX_UNROLL > 1
+   case 1:
+     gemm_complex_unrolled_col_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_cols, pAlphaReal, pAlphaImag);
+     break;
+#endif
+   default:
+     break;
+  }
+#undef MAX_COMPLEX_UNROLL
 }
 
 template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
 EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
 {
       const Index remaining_rows = rows % accCols;
+      const Index remaining_cols = cols % accRows;
 
       if( strideA == -1 ) strideA = depth;
       if( strideB == -1 ) strideB = depth;
@@ -2225,10 +2332,64 @@ EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* bl
       Index col = 0;
       for(; col + accRows <= cols; col += accRows)
       {
-        gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
+        const Scalar* lhs_base = blockA;
+        Index row = 0;
+
+#define MAX_COMPLEX_UNROLL 3
+        while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
+          gemm_complex_unrolled_iteration<MAX_COMPLEX_UNROLL, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+        }
+        switch( (rows-row)/accCols ) {
+#if MAX_COMPLEX_UNROLL > 4
+          case 4:
+            gemm_complex_unrolled_iteration<4, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_UNROLL > 3
+          case 3:
+            gemm_complex_unrolled_iteration<3, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_UNROLL > 2
+          case 2:
+            gemm_complex_unrolled_iteration<2, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_UNROLL > 1
+          case 1:
+            gemm_complex_unrolled_iteration<1, Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+          default:
+            break;
+        }
+#undef MAX_COMPLEX_UNROLL
+
+        if(remaining_rows > 0)
+        {
+          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+        }
       }
 
-      gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      if(remaining_cols > 0)
+      {
+        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
+        const Scalar* lhs_base = blockA;
+
+        for(; col < cols; col++)
+        {
+          Index row = 0;
+
+          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
+
+          if (remaining_rows > 0)
+          {
+            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
+          }
+          rhs_base++;
+        }
+      }
 }
 
 #undef accColsC
@@ -2488,10 +2649,10 @@ void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, Conjugat
     const Index accCols = quad_traits<float>::size;
     void (*gemm_function)(const DataMapper&, const float*, const float*, Index, Index, Index, float, Index, Index, Index, Index);
 
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
       //generate with MMA only
       gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
         gemm_function = &Eigen::internal::gemmMMA<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
       }
@@ -2501,7 +2662,7 @@ void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, Conjugat
     #else
       gemm_function = &Eigen::internal::gemm<float, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
     #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2527,20 +2688,20 @@ void gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr
     void (*gemm_function)(const DataMapper&, const std::complex<float>*, const std::complex<float>*,
           Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
 
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+     #endif
+      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2565,20 +2726,20 @@ void gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, Conjugat
     const Index accCols = quad_traits<float>::size;
     void (*gemm_function)(const DataMapper&, const float*, const std::complex<float>*,
           Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+     #endif
+       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2603,20 +2764,20 @@ void gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, Conjugat
     const Index accCols = quad_traits<float>::size;
     void (*gemm_function)(const DataMapper&, const std::complex<float>*, const float*,
           Index, Index, Index, std::complex<float>, Index, Index, Index, Index);
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+     #endif
+       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2640,10 +2801,10 @@ void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, Conjug
     const Index accCols = quad_traits<double>::size;
     void (*gemm_function)(const DataMapper&, const double*, const double*, Index, Index, Index, double, Index, Index, Index, Index);
 
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
       //generate with MMA only
       gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+    #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
         gemm_function = &Eigen::internal::gemmMMA<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
       }
@@ -2653,7 +2814,7 @@ void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, Conjug
     #else
       gemm_function = &Eigen::internal::gemm<double, Index, Packet, RhsPacket, DataMapper, accRows, accCols>;
     #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+      gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2678,20 +2839,20 @@ void gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper,
     const Index accCols = quad_traits<double>::size;
     void (*gemm_function)(const DataMapper&, const std::complex<double>*, const std::complex<double>*,
           Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+     #endif
+       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2716,20 +2877,20 @@ void gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, Conjug
     const Index accCols = quad_traits<double>::size;
     void (*gemm_function)(const DataMapper&, const std::complex<double>*, const double*,
           Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+     #endif
+       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 
 template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
@@ -2754,20 +2915,20 @@ void gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, Conjug
     const Index accCols = quad_traits<double>::size;
     void (*gemm_function)(const DataMapper&, const double*, const std::complex<double>*,
           Index, Index, Index, std::complex<double>, Index, Index, Index, Index);
-    #if defined(EIGEN_ALTIVEC_MMA_ONLY)
-      //generate with MMA only
-      gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-    #elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-      if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
-        gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-      }
-      else{
-        gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-      }
-    #else
-      gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
-    #endif
-    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+    #ifdef EIGEN_ALTIVEC_MMA_ONLY
+       //generate with MMA only
+       gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+     #elif defined(ALTIVEC_MMA_SUPPORT) && !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+       if (__builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma")){
+         gemm_function = &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+       }
+       else{
+         gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+       }
+     #else
+       gemm_function = &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Index, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+     #endif
+       gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
   }
 } // end namespace internal
 
diff --git a/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
new file mode 100644
index 0000000..33d5434
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
@@ -0,0 +1,221 @@
+//#define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
+#ifdef EIGEN_POWER_USE_PREFETCH
+#define EIGEN_POWER_PREFETCH(p)  prefetch(p)
+#else
+#define EIGEN_POWER_PREFETCH(p)
+#endif
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows>
+EIGEN_STRONG_INLINE void gemm_extra_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index row,
+  Index col,
+  Index remaining_rows,
+  Index remaining_cols,
+  const Packet& pAlpha);
+
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accRows, const Index accCols>
+EIGEN_STRONG_INLINE void gemm_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index row,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask);
+
+template<typename Scalar, typename Packet, typename DataMapper, typename Index, const Index accCols>
+EIGEN_STRONG_INLINE void gemm_unrolled_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index& row,
+  Index rows,
+  Index col,
+  Index remaining_cols,
+  const Packet& pAlpha);
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet bmask(const int remaining_rows);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_extra_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index col,
+  Index remaining_rows,
+  Index remaining_cols,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, typename Index, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex_unrolled_col(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index& row,
+  Index rows,
+  Index col,
+  Index remaining_cols,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag);
+
+template<typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadLhs(const Scalar* lhs);
+
+template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,4>& acc, const DataMapper& res, Index row, Index col);
+
+template<typename DataMapper, typename Packet, typename Index, const Index accCols, int N, int StorageOrder>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,8>& acc, const DataMapper& res, Index row, Index col);
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,4>& acc, PacketBlock<Packet,4>& accZ, const Packet& pAlpha);
+
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag);
+
+const static Packet16uc p16uc_SETCOMPLEX32_FIRST = {  0,  1,  2,  3,
+                                                     16, 17, 18, 19,
+                                                      4,  5,  6,  7,
+                                                     20, 21, 22, 23};
+
+const static Packet16uc p16uc_SETCOMPLEX32_SECOND = {  8,  9, 10, 11,
+                                                      24, 25, 26, 27,
+                                                      12, 13, 14, 15,
+                                                      28, 29, 30, 31};
+//[a,b],[ai,bi] = [a,ai] - This is equivalent to p16uc_GETREAL64
+const static Packet16uc p16uc_SETCOMPLEX64_FIRST = {  0,  1,  2,  3,  4,  5,  6,  7,
+                                                     16, 17, 18, 19, 20, 21, 22, 23};
+
+//[a,b],[ai,bi] = [b,bi] - This is equivalent to p16uc_GETIMAG64
+const static Packet16uc p16uc_SETCOMPLEX64_SECOND = {  8,  9, 10, 11, 12, 13, 14, 15,
+                                                      24, 25, 26, 27, 28, 29, 30, 31};
+
+
+// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
+template<typename Packet, typename Packetc>
+EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
+{
+  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
+  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_FIRST);
+  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_FIRST);
+  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_FIRST);
+
+  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
+  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX32_SECOND);
+  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX32_SECOND);
+  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX32_SECOND);
+}
+
+template<typename Packet, typename Packetc>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,4>& taccReal, PacketBlock<Packet,4>& taccImag, PacketBlock<Packetc,8>& tRes, PacketBlock<Packetc, 4>& acc1, PacketBlock<Packetc, 4>& acc2)
+{
+  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
+
+  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
+  acc1.packet[1] = padd<Packetc>(tRes.packet[1], acc1.packet[1]);
+  acc1.packet[2] = padd<Packetc>(tRes.packet[2], acc1.packet[2]);
+  acc1.packet[3] = padd<Packetc>(tRes.packet[3], acc1.packet[3]);
+
+  acc2.packet[0] = padd<Packetc>(tRes.packet[4], acc2.packet[0]);
+  acc2.packet[1] = padd<Packetc>(tRes.packet[5], acc2.packet[1]);
+  acc2.packet[2] = padd<Packetc>(tRes.packet[6], acc2.packet[2]);
+  acc2.packet[3] = padd<Packetc>(tRes.packet[7], acc2.packet[3]);
+}
+
+template<typename Packet, typename Packetc>
+EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
+{
+  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_FIRST);
+
+  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX32_SECOND);
+}
+
+template<typename Packet, typename Packetc>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,1>& taccReal, PacketBlock<Packet,1>& taccImag, PacketBlock<Packetc,2>& tRes, PacketBlock<Packetc, 1>& acc1, PacketBlock<Packetc, 1>& acc2)
+{
+  bcouple_common<Packet, Packetc>(taccReal, taccImag, acc1, acc2);
+
+  acc1.packet[0] = padd<Packetc>(tRes.packet[0], acc1.packet[0]);
+
+  acc2.packet[0] = padd<Packetc>(tRes.packet[1], acc2.packet[0]);
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,4>& taccReal, PacketBlock<Packet2d,4>& taccImag, PacketBlock<Packet1cd, 4>& acc1, PacketBlock<Packet1cd, 4>& acc2)
+{
+  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
+  acc1.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_FIRST);
+  acc1.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_FIRST);
+  acc1.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_FIRST);
+
+  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
+  acc2.packet[1].v = vec_perm(taccReal.packet[1], taccImag.packet[1], p16uc_SETCOMPLEX64_SECOND);
+  acc2.packet[2].v = vec_perm(taccReal.packet[2], taccImag.packet[2], p16uc_SETCOMPLEX64_SECOND);
+  acc2.packet[3].v = vec_perm(taccReal.packet[3], taccImag.packet[3], p16uc_SETCOMPLEX64_SECOND);
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void bcouple_common<Packet2d, Packet1cd>(PacketBlock<Packet2d,1>& taccReal, PacketBlock<Packet2d,1>& taccImag, PacketBlock<Packet1cd, 1>& acc1, PacketBlock<Packet1cd, 1>& acc2)
+{
+  acc1.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_FIRST);
+
+  acc2.packet[0].v = vec_perm(taccReal.packet[0], taccImag.packet[0], p16uc_SETCOMPLEX64_SECOND);
+}
+
+// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
+template<typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadRhs(const Scalar* rhs)
+{
+  return ploadu<Packet>(rhs);
+}
+
+} // end namespace internal
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
similarity index 64%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
index 7dda423..6540c6f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
@@ -11,11 +11,7 @@
 #ifndef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
 #define EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
 
-// If using dynamic dispatch, set the CPU target.
-#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-#pragma GCC push_options
-#pragma GCC target("cpu=power10,htm")
-#endif
+#pragma GCC target("cpu=power10")
 
 #ifdef __has_builtin
 #if !__has_builtin(__builtin_vsx_assemble_pair)
@@ -34,37 +30,37 @@ EIGEN_ALWAYS_INLINE void bsetzeroMMA(__vector_quad* acc)
 }
 
 template<typename DataMapper, typename Index, typename Packet, const Index accCols>
-EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, const DataMapper& data, const Packet& alpha, __vector_quad* acc)
+EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, Index j, const DataMapper& data, const Packet& alpha, __vector_quad* acc)
 {
   PacketBlock<Packet, 4> result;
   __builtin_mma_disassemble_acc(&result.packet, acc);
 
   PacketBlock<Packet, 4> tRes;
-  bload<DataMapper, Packet, Index, accCols, ColMajor, false, 4>(tRes, data, i, 0);
+  bload<DataMapper, Packet, Index, accCols, 0, ColMajor>(tRes, data, i, j);
 
-  bscale<Packet, 4>(tRes, result, alpha);
+  bscale<Packet>(tRes, result, alpha);
 
-  data.template storePacketBlock<Packet, 4>(i, 0, tRes);
+  data.template storePacketBlock<Packet, 4>(i, j, tRes);
 }
 
-template<typename DataMapper, typename Index, typename Packet, typename Packetc, const Index accColsC>
-EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, const DataMapper& data, const Packet& alphaReal, const Packet& alphaImag, __vector_quad* accReal, __vector_quad* accImag)
+template<typename DataMapper, typename Index, typename Packet, typename Packetc, const Index accColsC, int N>
+EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, Index j, const DataMapper& data, const Packet& alphaReal, const Packet& alphaImag, __vector_quad* accReal, __vector_quad* accImag)
 {
   PacketBlock<Packet, 4> resultReal, resultImag;
   __builtin_mma_disassemble_acc(&resultReal.packet, accReal);
   __builtin_mma_disassemble_acc(&resultImag.packet, accImag);
 
   PacketBlock<Packetc, 8> tRes;
-  bload<DataMapper, Packetc, Index, accColsC, ColMajor, true, 4>(tRes, data, i, 0);
+  bload<DataMapper, Packetc, Index, accColsC, N, ColMajor>(tRes, data, i, j);
 
   PacketBlock<Packet,4> taccReal, taccImag;
   bscalec<Packet,4>(resultReal, resultImag, alphaReal, alphaImag, taccReal, taccImag);
 
   PacketBlock<Packetc, 4> acc1, acc2;
-  bcouple<Packet, Packetc, 4>(taccReal, taccImag, tRes, acc1, acc2);
+  bcouple<Packet, Packetc>(taccReal, taccImag, tRes, acc1, acc2);
 
-  data.template storePacketBlock<Packetc, 4>(i, 0, acc1);
-  data.template storePacketBlock<Packetc, 4>(i + accColsC, 0, acc2);
+  data.template storePacketBlock<Packetc, 4>(i + N*accColsC, j, acc1);
+  data.template storePacketBlock<Packetc, 4>(i + (N+1)*accColsC, j, acc2);
 }
 
 // Defaults to float32, since Eigen still supports C++03 we can't use default template arguments
@@ -129,7 +125,7 @@ EIGEN_ALWAYS_INLINE void pgercMMA(__vector_quad* accReal, __vector_quad* accImag
 template<typename Scalar, typename Packet>
 EIGEN_ALWAYS_INLINE void ploadRhsMMA(const Scalar* rhs, Packet& rhsV)
 {
-  rhsV = ploadRhs<Scalar, Packet>(rhs);
+  rhsV = ploadRhs<Scalar, Packet>((const Scalar*)(rhs));
 } 
 
 template<>
@@ -188,11 +184,12 @@ EIGEN_ALWAYS_INLINE void ploadRhsMMA(const float*, __vector_pair&)
   }
 
 #define MICRO_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7; \
+  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
   MICRO_MMA_TYPE_PEEL(func,func2,type,0); MICRO_MMA_TYPE_PEEL(func,func2,type,1); \
   MICRO_MMA_TYPE_PEEL(func,func2,type,2); MICRO_MMA_TYPE_PEEL(func,func2,type,3); \
   MICRO_MMA_TYPE_PEEL(func,func2,type,4); MICRO_MMA_TYPE_PEEL(func,func2,type,5); \
-  MICRO_MMA_TYPE_PEEL(func,func2,type,6); MICRO_MMA_TYPE_PEEL(func,func2,type,7);
+  MICRO_MMA_TYPE_PEEL(func,func2,type,6); MICRO_MMA_TYPE_PEEL(func,func2,type,7); \
+  MICRO_MMA_TYPE_PEEL(func,func2,type,8); MICRO_MMA_TYPE_PEEL(func,func2,type,9);
 
 #define MICRO_MMA_UNROLL_TYPE_ONE(func, func2, type) \
   type rhsV0; \
@@ -225,7 +222,7 @@ EIGEN_ALWAYS_INLINE void ploadRhsMMA(const float*, __vector_pair&)
 
 #define MICRO_MMA_SRC_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols; \
+    lhs_ptr##iter = lhs_base + ( (row/accCols) + iter )*strideA*accCols + accCols*offsetA; \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
   }
@@ -241,19 +238,21 @@ EIGEN_ALWAYS_INLINE void ploadRhsMMA(const float*, __vector_pair&)
 
 #define MICRO_MMA_STORE_ONE(iter) \
   if (unroll_factor > iter) { \
-    storeAccumulator<DataMapper, Index, Packet, accCols>(row + iter*accCols, res, pAlpha, &accZero##iter); \
+    storeAccumulator<DataMapper, Index, Packet, accCols>(row + iter*accCols, col, res, pAlpha, &accZero##iter); \
   }
 
 #define MICRO_MMA_STORE MICRO_MMA_UNROLL(MICRO_MMA_STORE_ONE)
 
 template<int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_ALWAYS_INLINE void gemm_unrolled_MMA_iteration(
+EIGEN_STRONG_INLINE void gemm_unrolled_MMA_iteration(
   const DataMapper& res,
   const Scalar* lhs_base,
   const Scalar* rhs_base,
   Index depth,
   Index strideA,
+  Index offsetA,
   Index& row,
+  Index col,
   const Packet& pAlpha)
 {
   const Scalar* rhs_ptr = rhs_base;
@@ -279,98 +278,94 @@ EIGEN_ALWAYS_INLINE void gemm_unrolled_MMA_iteration(
   row += unroll_factor*accCols;
 }
 
-template<typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols>
-EIGEN_ALWAYS_INLINE void gemmMMA_cols(
-  const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index offsetB,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlpha,
-  const Packet& pMask)
+template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
+void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
 {
-  const DataMapper res3 = res.getSubMapper(0, col);
+      const Index remaining_rows = rows % accCols;
+      const Index remaining_cols = cols % accRows;
 
-  const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
-  const Scalar* lhs_base = blockA + accCols*offsetA;
-  Index row = 0;
+      if( strideA == -1 ) strideA = depth;
+      if( strideB == -1 ) strideB = depth;
+
+      const Packet pAlpha = pset1<Packet>(alpha);
+      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
+
+      Index col = 0;
+      for(; col + accRows <= cols; col += accRows)
+      {
+        const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
+        const Scalar* lhs_base = blockA;
 
+        Index row = 0;
 #define MAX_MMA_UNROLL 7
-  while(row + MAX_MMA_UNROLL*accCols <= rows) {
-    gemm_unrolled_MMA_iteration<MAX_MMA_UNROLL, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-  }
-  switch( (rows-row)/accCols ) {
+        while(row + MAX_MMA_UNROLL*accCols <= rows) {
+          gemm_unrolled_MMA_iteration<MAX_MMA_UNROLL, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+        }
+        switch( (rows-row)/accCols ) {
 #if MAX_MMA_UNROLL > 7
-    case 7:
-      gemm_unrolled_MMA_iteration<7, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 7:
+            gemm_unrolled_MMA_iteration<7, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 6
-    case 6:
-      gemm_unrolled_MMA_iteration<6, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 6:
+            gemm_unrolled_MMA_iteration<6, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 5
-    case 5:
-      gemm_unrolled_MMA_iteration<5, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 5:
+            gemm_unrolled_MMA_iteration<5, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 4
-    case 4:
-      gemm_unrolled_MMA_iteration<4, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 4:
+            gemm_unrolled_MMA_iteration<4, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 3
-    case 3:
-      gemm_unrolled_MMA_iteration<3, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 3:
+            gemm_unrolled_MMA_iteration<3, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 2
-    case 2:
-      gemm_unrolled_MMA_iteration<2, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 2:
+            gemm_unrolled_MMA_iteration<2, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
 #if MAX_MMA_UNROLL > 1
-    case 1:
-      gemm_unrolled_MMA_iteration<1, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res3, lhs_base, rhs_base, depth, strideA, row, pAlpha);
-      break;
+          case 1:
+            gemm_unrolled_MMA_iteration<1, Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, pAlpha);
+            break;
 #endif
-    default:
-      break;
-  }
+          default:
+            break;
+        }
 #undef MAX_MMA_UNROLL
 
-  if(remaining_rows > 0)
-  {
-    gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res3, blockA, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
-  }
-}
+        if(remaining_rows > 0)
+        {
+          gemm_extra_row<Scalar, Packet, DataMapper, Index, accRows, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, rows, cols, remaining_rows, pAlpha, pMask);
+        }
+      }
 
-template<typename Scalar, typename Index, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
-void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
-{
-      const Index remaining_rows = rows % accCols;
+      if(remaining_cols > 0)
+      {
+        const Scalar* rhs_base = blockB + col*strideB + remaining_cols*offsetB;
+        const Scalar* lhs_base = blockA;
 
-      if( strideA == -1 ) strideA = depth;
-      if( strideB == -1 ) strideB = depth;
+        for(; col < cols; col++)
+        {
+          Index row = 0;
 
-      const Packet pAlpha = pset1<Packet>(alpha);
-      const Packet pMask  = bmask<Packet>((const int)(remaining_rows));
+          gemm_unrolled_col<Scalar, Packet, DataMapper, Index, accCols>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, rows, col, remaining_cols, pAlpha);
 
-      Index col = 0;
-      for(; col + accRows <= cols; col += accRows)
-      {
-        gemmMMA_cols<Scalar, Packet, RhsPacket, DataMapper, Index, accRows, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
+          if (remaining_rows > 0)
+          {
+            gemm_extra_col<Scalar, Packet, DataMapper, Index, accRows>(res, lhs_base, rhs_base, depth, strideA, offsetA, row, col, remaining_rows, remaining_cols, pAlpha);
+          }
+          rhs_base++;
+        }
       }
-
-      gemm_extra_cols<Scalar, Packet, DataMapper, Index, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
 }
 
 #define accColsC (accCols / 2)
@@ -378,20 +373,21 @@ void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
 #define advanceCols ((RhsIsReal) ? 1 : 2)
 
 // PEEL_COMPLEX_MMA loop factor.
-#define PEEL_COMPLEX_MMA 3
+#define PEEL_COMPLEX_MMA 7
 
 #define MICRO_COMPLEX_MMA_UNROLL(func) \
-  func(0) func(1) func(2) func(3)
+  func(0) func(1) func(2) func(3) func(4)
 
 #define MICRO_COMPLEX_MMA_LOAD_ONE(iter) \
   if (unroll_factor > iter) { \
     lhsV##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter); \
+    lhs_ptr_real##iter += accCols; \
     if(!LhsIsReal) { \
-      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_real##iter + imag_delta); \
+      lhsVi##iter = ploadLhs<Scalar, Packet>(lhs_ptr_imag##iter); \
+      lhs_ptr_imag##iter += accCols; \
     } else { \
       EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
     } \
-    lhs_ptr_real##iter += accCols; \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhsV##iter); \
     EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
@@ -404,8 +400,8 @@ void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
 
 #define MICRO_COMPLEX_MMA_TYPE_PEEL(func, func2, type, peel) \
   if (PEEL_COMPLEX_MMA > peel) { \
-    Packet lhsV0, lhsV1, lhsV2, lhsV3; \
-    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3; \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4; \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3, lhsVi4; \
     ploadRhsMMA<Scalar, type>(rhs_ptr_real + (accRows * peel), rhsV##peel); \
     if(!RhsIsReal) { \
       ploadRhsMMA<Scalar, type>(rhs_ptr_imag + (accRows * peel), rhsVi##peel); \
@@ -413,17 +409,20 @@ void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
       EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
     } \
     MICRO_COMPLEX_MMA_UNROLL(func2); \
-    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) \
+    func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) func(4,type,peel) \
   } else { \
     EIGEN_UNUSED_VARIABLE(rhsV##peel); \
     EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
   }
 
 #define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(func, func2, type) \
-  type rhsV0, rhsV1, rhsV2, rhsV3; \
-  type rhsVi0, rhsVi1, rhsVi2, rhsVi3; \
+  type rhsV0, rhsV1, rhsV2, rhsV3, rhsV4, rhsV5, rhsV6, rhsV7, rhsV8, rhsV9; \
+  type rhsVi0, rhsVi1, rhsVi2, rhsVi3, rhsVi4, rhsVi5, rhsVi6, rhsVi7, rhsVi8, rhsVi9; \
   MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,0); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,1); \
-  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,2); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,3);
+  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,2); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,3); \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,4); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,5); \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,6); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,7); \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,8); MICRO_COMPLEX_MMA_TYPE_PEEL(func,func2,type,9);
 
 #define MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(func, func2, type) \
   type rhsV0, rhsVi0; \
@@ -460,9 +459,15 @@ void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
 
 #define MICRO_COMPLEX_MMA_SRC_PTR_ONE(iter) \
   if (unroll_factor > iter) { \
-    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols; \
+    lhs_ptr_real##iter = lhs_base + ( ((advanceRows*row)/accCols) + iter*advanceRows )*strideA*accCols + accCols*offsetA; \
+    if(!LhsIsReal) { \
+      lhs_ptr_imag##iter = lhs_ptr_real##iter + accCols*strideA; \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
+    } \
   } else { \
     EIGEN_UNUSED_VARIABLE(lhs_ptr_real##iter); \
+    EIGEN_UNUSED_VARIABLE(lhs_ptr_imag##iter); \
   }
 
 #define MICRO_COMPLEX_MMA_SRC_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_SRC_PTR_ONE)
@@ -470,40 +475,45 @@ void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB,
 #define MICRO_COMPLEX_MMA_PREFETCH_ONE(iter) \
   if (unroll_factor > iter) { \
     EIGEN_POWER_PREFETCH(lhs_ptr_real##iter); \
+    if(!LhsIsReal) { \
+      EIGEN_POWER_PREFETCH(lhs_ptr_imag##iter); \
+    } \
   }
 
 #define MICRO_COMPLEX_MMA_PREFETCH MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_PREFETCH_ONE)
 
 #define MICRO_COMPLEX_MMA_STORE_ONE(iter) \
   if (unroll_factor > iter) { \
-    storeComplexAccumulator<DataMapper, Index, Packet, Packetc, accColsC>(row + iter*accCols, res, pAlphaReal, pAlphaImag, &accReal##iter, &accImag##iter); \
+    storeComplexAccumulator<DataMapper, Index, Packet, Packetc, accColsC, 0>(row + iter*accCols, col, res, pAlphaReal, pAlphaImag, &accReal##iter, &accImag##iter); \
   }
 
 #define MICRO_COMPLEX_MMA_STORE MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_STORE_ONE)
 
 template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_MMA_iteration(
+EIGEN_STRONG_INLINE void gemm_complex_unrolled_MMA_iteration(
   const DataMapper& res,
   const Scalar* lhs_base,
   const Scalar* rhs_base,
   Index depth,
   Index strideA,
+  Index offsetA,
   Index strideB,
   Index& row,
+  Index col,
   const Packet& pAlphaReal,
   const Packet& pAlphaImag)
 {
   const Scalar* rhs_ptr_real = rhs_base;
-  const Scalar* rhs_ptr_imag = NULL;
-  const Index imag_delta = accCols*strideA;
+  const Scalar* rhs_ptr_imag;
   if(!RhsIsReal) {
     rhs_ptr_imag = rhs_base + accRows*strideB;
   } else {
     EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
   }
-  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_real1 = NULL;
-  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_real3 = NULL;
-  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
+  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_imag0 = NULL, * lhs_ptr_real1 = NULL, * lhs_ptr_imag1 = NULL;
+  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_imag2 = NULL, * lhs_ptr_real3 = NULL, * lhs_ptr_imag3 = NULL;
+  const Scalar* lhs_ptr_real4 = NULL, * lhs_ptr_imag4 = NULL;
+  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3, accReal4, accImag4;
 
   MICRO_COMPLEX_MMA_SRC_PTR
   MICRO_COMPLEX_MMA_DST_PTR
@@ -527,70 +537,11 @@ EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_MMA_iteration(
   row += unroll_factor*accCols;
 }
 
-template<typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, typename Index, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
-EIGEN_ALWAYS_INLINE void gemmMMA_complex_cols(
-  const DataMapper& res,
-  const Scalar* blockA,
-  const Scalar* blockB,
-  Index depth,
-  Index strideA,
-  Index offsetA,
-  Index strideB,
-  Index offsetB,
-  Index col,
-  Index rows,
-  Index cols,
-  Index remaining_rows,
-  const Packet& pAlphaReal,
-  const Packet& pAlphaImag,
-  const Packet& pMask)
-{
-  const DataMapper res3 = res.getSubMapper(0, col);
-
-  const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
-  const Scalar* lhs_base = blockA + accCols*offsetA;
-  Index row = 0;
-
-#define MAX_COMPLEX_MMA_UNROLL 4
-  while(row + MAX_COMPLEX_MMA_UNROLL*accCols <= rows) {
-    gemm_complex_unrolled_MMA_iteration<MAX_COMPLEX_MMA_UNROLL, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-  }
-  switch( (rows-row)/accCols ) {
-#if MAX_COMPLEX_MMA_UNROLL > 4
-    case 4:
-      gemm_complex_unrolled_MMA_iteration<4, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 3
-    case 3:
-      gemm_complex_unrolled_MMA_iteration<3, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 2
-    case 2:
-      gemm_complex_unrolled_MMA_iteration<2, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-#if MAX_COMPLEX_MMA_UNROLL > 1
-    case 1:
-      gemm_complex_unrolled_MMA_iteration<1, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, strideB, row, pAlphaReal, pAlphaImag);
-      break;
-#endif
-    default:
-      break;
-  }
-#undef MAX_COMPLEX_MMA_UNROLL
-
-  if(remaining_rows > 0)
-  {
-    gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
-  }
-}
-
 template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Index, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
 void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
 {
       const Index remaining_rows = rows % accCols;
+      const Index remaining_cols = cols % accRows;
 
       if( strideA == -1 ) strideA = depth;
       if( strideB == -1 ) strideB = depth;
@@ -605,23 +556,74 @@ void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsS
       Index col = 0;
       for(; col + accRows <= cols; col += accRows)
       {
-        gemmMMA_complex_cols<Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+        const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
+        const Scalar* lhs_base = blockA;
+        Index row = 0;
+
+#define MAX_COMPLEX_MMA_UNROLL 4
+        while(row + MAX_COMPLEX_MMA_UNROLL*accCols <= rows) {
+          gemm_complex_unrolled_MMA_iteration<MAX_COMPLEX_MMA_UNROLL, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+        }
+        switch( (rows-row)/accCols ) {
+#if MAX_COMPLEX_MMA_UNROLL > 4
+          case 4:
+            gemm_complex_unrolled_MMA_iteration<4, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 3
+          case 3:
+            gemm_complex_unrolled_MMA_iteration<3, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 2
+          case 2:
+            gemm_complex_unrolled_MMA_iteration<2, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 1
+          case 1:
+            gemm_complex_unrolled_MMA_iteration<1, Scalar, Packet, Packetc, RhsPacket, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, pAlphaReal, pAlphaImag);
+            break;
+#endif
+          default:
+            break;
+        }
+#undef MAX_COMPLEX_MMA_UNROLL
+
+        if(remaining_rows > 0)
+        {
+          gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+        }
       }
 
-      gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      if(remaining_cols > 0)
+      {
+        const Scalar* rhs_base = blockB + advanceCols*col*strideB + remaining_cols*offsetB;
+        const Scalar* lhs_base = blockA;
+
+        for(; col < cols; col++)
+        {
+          Index row = 0;
+
+          gemm_complex_unrolled_col<Scalar, Packet, Packetc, DataMapper, Index, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, col, remaining_cols, pAlphaReal, pAlphaImag);
+
+          if (remaining_rows > 0)
+          {
+            gemm_complex_extra_col<Scalar, Packet, Packetc, DataMapper, Index, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, col, remaining_rows, remaining_cols, pAlphaReal, pAlphaImag);
+          }
+          rhs_base++;
+        }
+      }
 }
 
 #undef accColsC
 #undef advanceRows
 #undef advanceCols
 
+#pragma GCC reset_options
 } // end namespace internal
 
 } // end namespace Eigen
 
-#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
-#pragma GCC pop_options
-#endif
-
 #endif // EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
index 5c1abe7..2a44054 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h
@@ -84,7 +84,7 @@ static _EIGEN_DECLARE_CONST_FAST_Packet4ui(PREV0DOT5, 0x3EFFFFFFu);
 static _EIGEN_DECLARE_CONST_FAST_Packet8us(ONE,1); //{ 1, 1, 1, 1, 1, 1, 1, 1}
 static _EIGEN_DECLARE_CONST_FAST_Packet16uc(ONE,1);
 static Packet4f p4f_MZERO = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
-#ifndef EIGEN_VECTORIZE_VSX
+#ifndef __VSX__
 static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
 #endif
 
@@ -114,7 +114,7 @@ static Packet16uc p16uc_QUADRUPLICATE16_HI = { 0,1,0,1,0,1,0,1, 2,3,2,3,2,3,2,3
 // Define global static constants:
 #ifdef _BIG_ENDIAN
 static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
 static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
 #endif
 static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
@@ -168,16 +168,13 @@ struct packet_traits<float> : default_packet_traits {
     HasCos = EIGEN_FAST_MATH,
     HasLog = 1,
     HasExp = 1,
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
     HasSqrt = 1,
 #if !EIGEN_COMP_CLANG
     HasRsqrt = 1,
 #else
     HasRsqrt = 0,
 #endif
-    HasTanh = EIGEN_FAST_MATH,
-    HasErf = EIGEN_FAST_MATH,
-    HasRint = 1,
 #else
     HasSqrt = 0,
     HasRsqrt = 0,
@@ -187,6 +184,7 @@ struct packet_traits<float> : default_packet_traits {
     HasRound = 1,
     HasFloor = 1,
     HasCeil = 1,
+    HasRint = 1,
     HasNegate = 1,
     HasBlend = 1
   };
@@ -212,24 +210,23 @@ struct packet_traits<bfloat16> : default_packet_traits {
     HasCos = EIGEN_FAST_MATH,
     HasLog = 1,
     HasExp = 1,
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
     HasSqrt = 1,
 #if !EIGEN_COMP_CLANG
     HasRsqrt = 1,
 #else
     HasRsqrt = 0,
 #endif
-    HasRint = 1,
 #else
     HasSqrt = 0,
     HasRsqrt = 0,
-    HasRint = 0,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
 #endif
-    HasTanh = 0,
-    HasErf = 0,
     HasRound = 1,
     HasFloor = 1,
     HasCeil = 1,
+    HasRint = 1,
     HasNegate = 1,
     HasBlend = 1
   };
@@ -435,7 +432,7 @@ EIGEN_STRONG_INLINE Packet pload_common(const __UNPACK_TYPE__(Packet)* from)
   // ignoring these warnings for now.
   EIGEN_UNUSED_VARIABLE(from);
   EIGEN_DEBUG_ALIGNED_LOAD
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
   return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
 #else
   return vec_ld(0, from);
@@ -484,7 +481,7 @@ EIGEN_STRONG_INLINE void pstore_common(__UNPACK_TYPE__(Packet)* to, const Packet
   // ignoring these warnings for now.
   EIGEN_UNUSED_VARIABLE(to);
   EIGEN_DEBUG_ALIGNED_STORE
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
   vec_xst(from, 0, to);
 #else
   vec_st(from, 0, to);
@@ -789,22 +786,8 @@ template<> EIGEN_STRONG_INLINE Packet8us  psub<Packet8us> (const Packet8us&  a,
 template<> EIGEN_STRONG_INLINE Packet16c  psub<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a - b; }
 template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a - b; }
 
-template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
-{
-#ifdef __POWER8_VECTOR__
-  return vec_neg(a);
-#else
-  return vec_xor(a, p4f_MZERO);
-#endif
-}
-template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
-{
-#ifdef __POWER8_VECTOR__
-  return vec_neg(a);
-#else
-  return p4i_ZERO - a;
-#endif
-}
+template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return p4f_ZERO - a; }
+template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return p4i_ZERO - a; }
 
 template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
 template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
@@ -819,7 +802,7 @@ template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a,
 
 template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
 {
-#ifndef EIGEN_VECTORIZE_VSX  // VSX actually provides a div instruction
+#ifndef __VSX__  // VSX actually provides a div instruction
   Packet4f t, y_0, y_1;
 
   // Altivec does not offer a divide instruction, we have to do a reciprocal approximation
@@ -848,7 +831,7 @@ template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8
 
 template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
 {
-  #ifdef EIGEN_VECTORIZE_VSX
+  #ifdef __VSX__
   // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
   Packet4f ret;
   __asm__ ("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
@@ -866,7 +849,7 @@ template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a,
 
 template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
 {
-  #ifdef EIGEN_VECTORIZE_VSX
+  #ifdef __VSX__
   // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
   Packet4f ret;
   __asm__ ("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
@@ -882,39 +865,26 @@ template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, con
 template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_max(a, b); }
 
 template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmple(a,b)); }
-// To fix bug with vec_cmplt on older versions
-#if defined(__POWER8_VECTOR__) || EIGEN_COMP_LLVM
 template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmplt(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmpeq(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
   Packet4f c = reinterpret_cast<Packet4f>(vec_cmpge(a,b));
   return vec_nor(c,c);
 }
 
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmple(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmplt(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmpeq(a,b)); }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet8s pcmp_le(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmple(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmplt(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmpeq(a,b)); }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet8us pcmp_le(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmple(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmplt(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmpeq(a,b)); }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet16c pcmp_le(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmple(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmplt(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmpeq(a,b)); }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmple(a,b)); }
-#endif
 template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmplt(a,b)); }
 template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmpeq(a,b)); }
 
@@ -953,7 +923,7 @@ template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
     Packet4f t = vec_add(reinterpret_cast<Packet4f>(vec_or(vec_and(reinterpret_cast<Packet4ui>(a), p4ui_SIGN), p4ui_PREV0DOT5)), a);
     Packet4f res;
 
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
     __asm__("xvrspiz %x0, %x1\n\t"
         : "=&wa" (res)
         : "wa" (t));
@@ -967,7 +937,6 @@ template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
 }
 template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a) { return vec_ceil(a); }
 template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return vec_floor(a); }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
 {
     Packet4f res;
@@ -978,19 +947,21 @@ template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
 
     return res;
 }
-#endif
 
 template<typename Packet> EIGEN_STRONG_INLINE Packet ploadu_common(const __UNPACK_TYPE__(Packet)* from)
 {
+  EIGEN_DEBUG_ALIGNED_LOAD
+#ifdef _BIG_ENDIAN
+  Packet16uc MSQ, LSQ;
+  Packet16uc mask;
+  MSQ = vec_ld(0, (unsigned char *)from);          // most significant quadword
+  LSQ = vec_ld(15, (unsigned char *)from);         // least significant quadword
+  mask = vec_lvsl(0, from);                        // create the permute mask
+  //TODO: Add static_cast here
+  return (Packet) vec_perm(MSQ, LSQ, mask);           // align the data
+#else
   EIGEN_DEBUG_UNALIGNED_LOAD
-#ifdef EIGEN_VECTORIZE_VSX
   return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
-#else
-  Packet16uc mask = vec_lvsl(0, from);                 // create the permute mask
-  Packet16uc MSQ = vec_ld(0, (unsigned char *)from);   // most significant quadword
-  Packet16uc LSQ = vec_ld(15, (unsigned char *)from);  // least significant quadword
-  //TODO: Add static_cast here
-  return (Packet) vec_perm(MSQ, LSQ, mask);            // align the data
 #endif
 }
 
@@ -1095,9 +1066,7 @@ template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const unsigned ch
 template<typename Packet> EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from)
 {
   EIGEN_DEBUG_UNALIGNED_STORE
-#ifdef EIGEN_VECTORIZE_VSX
-  vec_xst(from, 0, to);
-#else
+#ifdef _BIG_ENDIAN
   // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
   // Warning: not thread safe!
   Packet16uc MSQ, LSQ, edges;
@@ -1112,6 +1081,8 @@ template<typename Packet> EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE_
   LSQ = vec_perm((Packet16uc)from,edges,align);             // misalign the data (LSQ)
   vec_st( LSQ, 15, (unsigned char *)to );                   // Store the LSQ part first
   vec_st( MSQ, 0, (unsigned char *)to );                   // Store the MSQ part second
+#else
+  vec_xst(from, 0, to);
 #endif
 }
 template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const Packet4f& from)
@@ -1370,6 +1341,16 @@ template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, con
   BF16_TO_F32_BINARY_OP_WRAPPER(psub<Packet4f>, a, b);
 }
 
+template<> EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(vec_sqrt, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
+}
+
 template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
   return pldexp_generic(a,exponent);
 }
@@ -1409,11 +1390,9 @@ template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf> (const Packet8bf& a){
 template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf> (const Packet8bf& a){
   BF16_TO_F32_UNARY_OP_WRAPPER(pround<Packet4f>, a);
 }
-#ifdef __VSX__
 template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf> (const Packet8bf& a){
   BF16_TO_F32_UNARY_OP_WRAPPER(print<Packet4f>, a);
 }
-#endif
 template<> EIGEN_STRONG_INLINE Packet8bf pmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
   Packet4f a_even = Bf16ToF32Even(a);
   Packet4f a_odd = Bf16ToF32Odd(a);
@@ -2273,7 +2252,7 @@ template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Pa
 
 
 //---------- double ----------
-#ifdef EIGEN_VECTORIZE_VSX
+#ifdef __VSX__
 typedef __vector double              Packet2d;
 typedef __vector unsigned long long  Packet2ul;
 typedef __vector long long           Packet2l;
@@ -2325,11 +2304,7 @@ template<> struct packet_traits<double> : default_packet_traits
     HasLog  = 0,
     HasExp  = 1,
     HasSqrt = 1,
-#if !EIGEN_COMP_CLANG
     HasRsqrt = 1,
-#else
-    HasRsqrt = 0,
-#endif
     HasRound = 1,
     HasFloor = 1,
     HasCeil = 1,
@@ -2418,14 +2393,7 @@ template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const
 
 template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return a - b; }
 
-template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a)
-{
-#ifdef __POWER8_VECTOR__
-  return vec_neg(a);
-#else
-  return vec_xor(a, p2d_MZERO);
-#endif
-}
+template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return p2d_ZERO - a; }
 
 template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
 
@@ -2735,7 +2703,7 @@ template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, cons
 }
 
 
-#endif // EIGEN_VECTORIZE_VSX
+#endif // __VSX__
 } // end namespace internal
 
 } // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/CUDA/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/CUDA/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
index 45f6ddb..deb4c86 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/CUDA/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/CUDA/Complex.h
@@ -11,24 +11,13 @@
 #ifndef EIGEN_COMPLEX_CUDA_H
 #define EIGEN_COMPLEX_CUDA_H
 
+// clang-format off
 // Many std::complex methods such as operator+, operator-, operator* and
 // operator/ are not constexpr. Due to this, GCC and older versions of clang do
 // not treat them as device functions and thus Eigen functors making use of
 // these operators fail to compile. Here, we manually specialize these
 // operators and functors for complex types when building for CUDA to enable
 // their use on-device.
-//
-// NOTES:
-//  - Compound assignment operators +=,-=,*=,/=(Scalar) will not work on device,
-//    since they are already specialized in the standard. Using them will result
-//    in silent kernel failures.
-//  - Compiling with MSVC and using +=,-=,*=,/=(std::complex<Scalar>) will lead
-//    to duplicate definition errors, since these are already specialized in
-//    Visual Studio's <complex> header (contrary to the standard).  This is
-//    preferable to removing such definitions, which will lead to silent kernel
-//    failures.
-//  - Compiling with ICC requires defining _USE_COMPLEX_SPECIALIZATION_ prior
-//    to the first inclusion of <complex>.
 
 #if defined(EIGEN_CUDACC) && defined(EIGEN_GPU_COMPILE_PHASE)
     
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/BFloat16.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/BFloat16.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
index f21d1a0..1c28f4f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/BFloat16.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/BFloat16.h
@@ -251,7 +251,12 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw truncate_to_bfloat16(const
     output.value = std::signbit(v) ? 0xFFC0: 0x7FC0;
     return output;
   }
-  output.value = static_cast<numext::uint16_t>(numext::bit_cast<numext::uint32_t>(v) >> 16);
+  const uint16_t* p = reinterpret_cast<const uint16_t*>(&v);
+#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+  output.value = p[0];
+#else
+  output.value = p[1];
+#endif
   return output;
 }
 
@@ -457,7 +462,14 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true
 }
 
 EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h) {
-    return numext::bit_cast<float>(static_cast<numext::uint32_t>(h.value) << 16);
+    float result = 0;
+    unsigned short* q = reinterpret_cast<unsigned short*>(&result);
+#if defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+    q[0] = h.value;
+#else
+    q[1] = h.value;
+#endif
+    return result;
 }
 // --- standard functions ---
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/ConjHelper.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/ConjHelper.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/ConjHelper.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
index a76ea0f..c9fbaf6 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
@@ -642,10 +642,10 @@ Packet psincos_float(const Packet& _x)
   PacketI y_int = preinterpret<PacketI>(y_round); // last 23 digits represent integer (if abs(x)<2^24)
   y = psub(y_round, cst_rounding_magic); // nearest integer to x*4/pi
 
-  // Subtract y * Pi/2 to reduce x to the interval -Pi/4 <= x <= +Pi/4
+  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4
   // using "Extended precision modular arithmetic"
-  #if defined(EIGEN_VECTORIZE_FMA)
-  // This version requires true FMA for high accuracy.
+  #if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD)
+  // This version requires true FMA for high accuracy
   // It provides a max error of 1ULP up to (with absolute_error < 5.9605e-08):
   const float huge_th = ComputeSine ? 117435.992f : 71476.0625f;
   x = pmadd(y, pset1<Packet>(-1.57079601287841796875f), x);
@@ -757,26 +757,6 @@ Packet pcos_float(const Packet& x)
   return psincos_float<false>(x);
 }
 
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED Packet pdiv_complex(const Packet& x, const Packet& y) {
-  typedef typename unpacket_traits<Packet>::as_real RealPacket;
-  // In the following we annotate the code for the case where the inputs
-  // are a pair length-2 SIMD vectors representing a single pair of complex
-  // numbers x = a + i*b, y = c + i*d.
-  const RealPacket y_abs = pabs(y.v);  // |c|, |d|
-  const RealPacket y_abs_flip = pcplxflip(Packet(y_abs)).v; // |d|, |c|
-  const RealPacket y_max = pmax(y_abs, y_abs_flip); // max(|c|, |d|), max(|c|, |d|)
-  const RealPacket y_scaled = pdiv(y.v, y_max);  // c / max(|c|, |d|), d / max(|c|, |d|)
-  // Compute scaled denominator.
-  const RealPacket y_scaled_sq = pmul(y_scaled, y_scaled); // c'**2, d'**2
-  const RealPacket denom = padd(y_scaled_sq, pcplxflip(Packet(y_scaled_sq)).v);
-  Packet result_scaled = pmul(x, pconj(Packet(y_scaled)));  // a * c' + b * d', -a * d + b * c
-  // Divide elementwise by denom.
-  result_scaled = Packet(pdiv(result_scaled.v, denom));
-  // Rescale result
-  return Packet(pdiv(result_scaled.v, y_max));
-}
 
 template<typename Packet>
 EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
@@ -915,7 +895,7 @@ void fast_twosum(const Packet& x, const Packet& y, Packet& s_hi, Packet& s_lo) {
   s_lo = psub(y, t);
 }
 
-#ifdef EIGEN_VECTORIZE_FMA
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
 // This function implements the extended precision product of
 // a pair of floating point numbers. Given {x, y}, it computes the pair
 // {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
@@ -966,7 +946,7 @@ void twoprod(const Packet& x, const Packet& y,
   p_lo = pmadd(x_lo, y_lo, p_lo);
 }
 
-#endif  // EIGEN_VECTORIZE_FMA
+#endif  // EIGEN_HAS_SINGLE_INSTRUCTION_MADD
 
 
 // This function implements Dekker's algorithm for the addition
@@ -1463,40 +1443,39 @@ EIGEN_STRONG_INLINE Packet generic_pow_impl(const Packet& x, const Packet& y) {
 }
 
 // Generic implementation of pow(x,y).
-template <typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_pow(const Packet& x, const Packet& y) {
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+EIGEN_UNUSED
+Packet generic_pow(const Packet& x, const Packet& y) {
   typedef typename unpacket_traits<Packet>::type Scalar;
 
   const Packet cst_pos_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
-  const Packet cst_neg_inf = pset1<Packet>(-NumTraits<Scalar>::infinity());
   const Packet cst_zero = pset1<Packet>(Scalar(0));
   const Packet cst_one = pset1<Packet>(Scalar(1));
   const Packet cst_nan = pset1<Packet>(NumTraits<Scalar>::quiet_NaN());
 
   const Packet abs_x = pabs(x);
   // Predicates for sign and magnitude of x.
-  const Packet abs_x_is_zero = pcmp_eq(abs_x, cst_zero);
-  const Packet x_has_signbit = pcmp_eq(por(pand(x, cst_neg_inf), cst_pos_inf), cst_neg_inf);
-  const Packet x_is_neg = pandnot(x_has_signbit, abs_x_is_zero);
-  const Packet x_is_neg_zero = pand(x_has_signbit, abs_x_is_zero);
+  const Packet x_is_zero = pcmp_eq(x, cst_zero);
+  const Packet x_is_neg = pcmp_lt(x, cst_zero);
   const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
-  const Packet abs_x_is_one = pcmp_eq(abs_x, cst_one);
+  const Packet abs_x_is_one =  pcmp_eq(abs_x, cst_one);
   const Packet abs_x_is_gt_one = pcmp_lt(cst_one, abs_x);
   const Packet abs_x_is_lt_one = pcmp_lt(abs_x, cst_one);
-  const Packet x_is_one = pandnot(abs_x_is_one, x_is_neg);
-  const Packet x_is_neg_one = pand(abs_x_is_one, x_is_neg);
+  const Packet x_is_one =  pandnot(abs_x_is_one, x_is_neg);
+  const Packet x_is_neg_one =  pand(abs_x_is_one, x_is_neg);
   const Packet x_is_nan = pandnot(ptrue(x), pcmp_eq(x, x));
 
   // Predicates for sign and magnitude of y.
-  const Packet abs_y = pabs(y);
   const Packet y_is_one = pcmp_eq(y, cst_one);
-  const Packet abs_y_is_zero = pcmp_eq(abs_y, cst_zero);
+  const Packet y_is_zero = pcmp_eq(y, cst_zero);
   const Packet y_is_neg = pcmp_lt(y, cst_zero);
-  const Packet y_is_pos = pandnot(ptrue(y), por(abs_y_is_zero, y_is_neg));
+  const Packet y_is_pos = pandnot(ptrue(y), por(y_is_zero, y_is_neg));
   const Packet y_is_nan = pandnot(ptrue(y), pcmp_eq(y, y));
-  const Packet abs_y_is_inf = pcmp_eq(abs_y, cst_pos_inf);
+  const Packet abs_y_is_inf = pcmp_eq(pabs(y), cst_pos_inf);
   EIGEN_CONSTEXPR Scalar huge_exponent =
-      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) / NumTraits<Scalar>::epsilon();
+      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) /
+       NumTraits<Scalar>::epsilon();
   const Packet abs_y_is_huge = pcmp_le(pset1<Packet>(huge_exponent), pabs(y));
 
   // Predicates for whether y is integer and/or even.
@@ -1505,31 +1484,39 @@ EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_pow(const Pac
   const Packet y_is_even = pcmp_eq(pround(y_div_2), y_div_2);
 
   // Predicates encoding special cases for the value of pow(x,y)
-  const Packet invalid_negative_x = pandnot(pandnot(pandnot(x_is_neg, abs_x_is_inf), y_is_int), abs_y_is_inf);
+  const Packet invalid_negative_x = pandnot(pandnot(pandnot(x_is_neg, abs_x_is_inf),
+                                                    y_is_int),
+                                            abs_y_is_inf);
+  const Packet pow_is_one = por(por(x_is_one, y_is_zero),
+                                pand(x_is_neg_one,
+                                     por(abs_y_is_inf, pandnot(y_is_even, invalid_negative_x))));
   const Packet pow_is_nan = por(invalid_negative_x, por(x_is_nan, y_is_nan));
-  const Packet pow_is_one =
-      por(por(x_is_one, abs_y_is_zero), pand(x_is_neg_one, por(abs_y_is_inf, pandnot(y_is_even, invalid_negative_x))));
-  const Packet pow_is_zero = por(por(por(pand(abs_x_is_zero, y_is_pos), pand(abs_x_is_inf, y_is_neg)),
-                                     pand(pand(abs_x_is_lt_one, abs_y_is_huge), y_is_pos)),
-                                 pand(pand(abs_x_is_gt_one, abs_y_is_huge), y_is_neg));
-  const Packet pow_is_inf = por(por(por(pand(abs_x_is_zero, y_is_neg), pand(abs_x_is_inf, y_is_pos)),
-                                    pand(pand(abs_x_is_lt_one, abs_y_is_huge), y_is_neg)),
-                                pand(pand(abs_x_is_gt_one, abs_y_is_huge), y_is_pos));
-  const Packet inf_val =
-      pselect(pandnot(pand(por(pand(abs_x_is_inf, x_is_neg), pand(x_is_neg_zero, y_is_neg)), y_is_int), y_is_even),
-              cst_neg_inf, cst_pos_inf);
+  const Packet pow_is_zero = por(por(por(pand(x_is_zero, y_is_pos),
+                                         pand(abs_x_is_inf, y_is_neg)),
+                                     pand(pand(abs_x_is_lt_one, abs_y_is_huge),
+                                          y_is_pos)),
+                                 pand(pand(abs_x_is_gt_one, abs_y_is_huge),
+                                      y_is_neg));
+  const Packet pow_is_inf = por(por(por(pand(x_is_zero, y_is_neg),
+                                        pand(abs_x_is_inf, y_is_pos)),
+                                    pand(pand(abs_x_is_lt_one, abs_y_is_huge),
+                                         y_is_neg)),
+                                pand(pand(abs_x_is_gt_one, abs_y_is_huge),
+                                     y_is_pos));
 
   // General computation of pow(x,y) for positive x or negative x and integer y.
   const Packet negate_pow_abs = pandnot(x_is_neg, y_is_even);
   const Packet pow_abs = generic_pow_impl(abs_x, y);
-  return pselect(
-      y_is_one, x,
-      pselect(pow_is_one, cst_one,
-              pselect(pow_is_nan, cst_nan,
-                      pselect(pow_is_inf, inf_val,
-                              pselect(pow_is_zero, cst_zero, pselect(negate_pow_abs, pnegate(pow_abs), pow_abs))))));
+  return pselect(y_is_one, x,
+                 pselect(pow_is_one, cst_one,
+                         pselect(pow_is_nan, cst_nan,
+                                 pselect(pow_is_inf, cst_pos_inf,
+                                         pselect(pow_is_zero, cst_zero,
+                                                 pselect(negate_pow_abs, pnegate(pow_abs), pow_abs))))));
 }
 
+
+
 /* polevl (modified for Eigen)
  *
  *      Evaluate polynomial
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
index 730cc73..177a04e 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
@@ -101,12 +101,6 @@ EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
 EIGEN_UNUSED
 Packet psqrt_complex(const Packet& a);
 
-/** \internal \returns x / y for complex types */
-template<typename Packet>
-EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
-EIGEN_UNUSED
-Packet pdiv_complex(const Packet& x, const Packet& y);
-
 template <typename Packet, int N> struct ppolevl;
 
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/Half.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/Half.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/Half.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/Half.h
index 6e2b31f..9f8e8cc 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/Half.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/Half.h
@@ -36,6 +36,8 @@
 #ifndef EIGEN_HALF_H
 #define EIGEN_HALF_H
 
+#include <sstream>
+
 #if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
 // When compiling with GPU support, the "__half_raw" base class as well as
 // some other routines are defined in the GPU compiler header files
@@ -332,7 +334,7 @@ EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
 }
 #endif
 
-#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
 EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
   return half(vaddh_f16(a.x, b.x));
 }
@@ -532,12 +534,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
 
 #elif defined(EIGEN_HAS_FP16_C)
   __half_raw h;
-  #if EIGEN_COMP_MSVC
-    // MSVC does not have scalar instructions.
-    h.x =_mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(ff), 0), 0);
-  #else
-    h.x = _cvtss_sh(ff, 0);
-  #endif
+  h.x = _cvtss_sh(ff, 0);
   return h;
 
 #elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
@@ -598,12 +595,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
   (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
   return __half2float(h);
 #elif defined(EIGEN_HAS_FP16_C)
-  #if EIGEN_COMP_MSVC
-    // MSVC does not have scalar instructions.
-    return _mm_cvtss_f32(_mm_cvtph_ps(_mm_set1_epi16(h.x)));
-  #else
-    return _cvtsh_ss(h.x);
-  #endif
+  return _cvtsh_ss(h.x);
 #elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
   return static_cast<float>(h.x);
 #else
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/Settings.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/Settings.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/Settings.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/Default/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/Default/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
similarity index 90%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
index bfc11ef..689110d 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/PacketMath.h
@@ -121,6 +121,7 @@ template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const do
 // invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
 // of the functions, while the latter can only deal with one of them.
 #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
+namespace {
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_and(const float& a,
                                                         const float& b) {
@@ -179,6 +180,8 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double lt_mask(const double& a,
   return __longlong_as_double(a < b ? 0xffffffffffffffffull : 0ull);
 }
 
+}  // namespace
+
 template <>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pand<float4>(const float4& a,
                                                           const float4& b) {
@@ -490,10 +493,9 @@ ptranspose(PacketBlock<double2,2>& kernel) {
 
 #endif // defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
 
-// Half-packet functions are not available on the host for CUDA 9.0-9.2, only
-// on device. There is no benefit to using them on the host anyways, since they are
-// emulated.
-#if (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+// Packet4h2 must be defined in the macro without EIGEN_CUDA_ARCH, meaning
+// its corresponding packet_traits<Eigen::half> must be visible on host.
+#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
 
 typedef ulonglong2 Packet4h2;
 template<> struct unpacket_traits<Packet4h2> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h2 half; };
@@ -524,9 +526,42 @@ template<> struct packet_traits<Eigen::half> : default_packet_traits
   };
 };
 
+namespace {
+// This is equivalent to make_half2, which is undocumented and doesn't seem to always exist.
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 combine_half(const __half& a, const __half& b) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  return __halves2half2(a, b);
+#else
+  // Round-about way since __halves2half2 is a __device__ function.
+  return __floats2half2_rn(__half2float(a), __half2float(b));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_low(const half2& a) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  return __low2half(a);
+#else
+  return __float2half(__low2float(a));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE __half get_half2_high(const half2& a) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  return __high2half(a);
+#else
+  return __float2half(__high2float(a));
+#endif
+}
+} // namespace
+
 template<>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
   return __half2half2(from);
+#else
+  const float f = __half2float(from);
+  return __floats2half2_rn(f, f);
+#endif
 }
 
 template <>
@@ -541,6 +576,8 @@ pset1<Packet4h2>(const Eigen::half& from) {
   return r;
 }
 
+// We now need this visible on both host and device.
+// #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
 namespace {
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
@@ -548,11 +585,11 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu(const Eigen::half* from) {
-  return __halves2half2(from[0], from[1]);
+  return combine_half(from[0], from[1]);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploaddup(const Eigen::half*  from) {
-  return __halves2half2(from[0], from[0]);
+  return combine_half(from[0], from[0]);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to,
@@ -562,8 +599,8 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to,
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu(Eigen::half* to,
                                                    const half2& from) {
-  to[0] = __low2half(from);
-  to[1] = __high2half(from);
+  to[0] = get_half2_low(from);
+  to[1] = get_half2_high(from);
 }
 
 
@@ -573,7 +610,7 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_aligned(
   // Input is guaranteed to be properly aligned.
   return __ldg(reinterpret_cast<const half2*>(from));
 #else
-  return __halves2half2(*(from+0), *(from+1));
+  return combine_half(*(from+0), *(from+1));
 #endif
 }
 
@@ -582,31 +619,31 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_unaligned(
 #if defined(EIGEN_GPU_HAS_LDG)
   return __halves2half2(__ldg(from+0), __ldg(from+1));
 #else
-  return __halves2half2(*(from+0), *(from+1));
+  return combine_half(*(from+0), *(from+1));
 #endif
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pgather(const Eigen::half* from,
                                                     Index stride) {
-  return __halves2half2(from[0*stride], from[1*stride]);
+  return combine_half(from[0*stride], from[1*stride]);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter(
     Eigen::half* to, const half2& from, Index stride) {
-  to[stride*0] = __low2half(from);
-  to[stride*1] = __high2half(from);
+  to[stride*0] = get_half2_low(from);
+  to[stride*1] = get_half2_high(from);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst(const half2& a) {
-  return __low2half(a);
+  return get_half2_low(a);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pabs(const half2& a) {
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
   half result1 = half_impl::raw_uint16_to_half(a1.x & 0x7FFF);
   half result2 = half_impl::raw_uint16_to_half(a2.x & 0x7FFF);
-  return __halves2half2(result1, result2);
+  return combine_half(result1, result2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ptrue(const half2& /*a*/) {
@@ -621,12 +658,12 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pzero(const half2& /*a*/) {
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
 ptranspose(PacketBlock<half2,2>& kernel) {
-  __half a1 = __low2half(kernel.packet[0]);
-  __half a2 = __high2half(kernel.packet[0]);
-  __half b1 = __low2half(kernel.packet[1]);
-  __half b2 = __high2half(kernel.packet[1]);
-  kernel.packet[0] = __halves2half2(a1, b1);
-  kernel.packet[1] = __halves2half2(a2, b2);
+  __half a1 = get_half2_low(kernel.packet[0]);
+  __half a2 = get_half2_high(kernel.packet[0]);
+  __half b1 = get_half2_low(kernel.packet[1]);
+  __half b2 = get_half2_high(kernel.packet[1]);
+  kernel.packet[0] = combine_half(a1, b1);
+  kernel.packet[1] = combine_half(a2, b2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
@@ -634,88 +671,88 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
   return __halves2half2(a, __hadd(a, __float2half(1.0f)));
 #else
   float f = __half2float(a) + 1.0f;
-  return __halves2half2(a, __float2half(f));
+  return combine_half(a, __float2half(f));
 #endif
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pselect(const half2& mask,
                                                     const half2& a,
                                                     const half2& b) {
-  half mask_low = __low2half(mask);
-  half mask_high = __high2half(mask);
-  half result_low = mask_low == half(0) ? __low2half(b) : __low2half(a);
-  half result_high = mask_high == half(0) ? __high2half(b) : __high2half(a);
-  return __halves2half2(result_low, result_high);
+  half mask_low = get_half2_low(mask);
+  half mask_high = get_half2_high(mask);
+  half result_low = mask_low == half(0) ? get_half2_low(b) : get_half2_low(a);
+  half result_high = mask_high == half(0) ? get_half2_high(b) : get_half2_high(a);
+  return combine_half(result_low, result_high);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_eq(const half2& a,
                                                     const half2& b) {
   half true_half = half_impl::raw_uint16_to_half(0xffffu);
   half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half eq1 = __half2float(a1) == __half2float(b1) ? true_half : false_half;
   half eq2 = __half2float(a2) == __half2float(b2) ? true_half : false_half;
-  return __halves2half2(eq1, eq2);
+  return combine_half(eq1, eq2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_lt(const half2& a,
                                                     const half2& b) {
   half true_half = half_impl::raw_uint16_to_half(0xffffu);
   half false_half = half_impl::raw_uint16_to_half(0x0000u);
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half eq1 = __half2float(a1) < __half2float(b1) ? true_half : false_half;
   half eq2 = __half2float(a2) < __half2float(b2) ? true_half : false_half;
-  return __halves2half2(eq1, eq2);
+  return combine_half(eq1, eq2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pand(const half2& a,
                                                  const half2& b) {
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half result1 = half_impl::raw_uint16_to_half(a1.x & b1.x);
   half result2 = half_impl::raw_uint16_to_half(a2.x & b2.x);
-  return __halves2half2(result1, result2);
+  return combine_half(result1, result2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 por(const half2& a,
                                                 const half2& b) {
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half result1 = half_impl::raw_uint16_to_half(a1.x | b1.x);
   half result2 = half_impl::raw_uint16_to_half(a2.x | b2.x);
-  return __halves2half2(result1, result2);
+  return combine_half(result1, result2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pxor(const half2& a,
                                                  const half2& b) {
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half result1 = half_impl::raw_uint16_to_half(a1.x ^ b1.x);
   half result2 = half_impl::raw_uint16_to_half(a2.x ^ b2.x);
-  return __halves2half2(result1, result2);
+  return combine_half(result1, result2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pandnot(const half2& a,
                                                     const half2& b) {
-  half a1 = __low2half(a);
-  half a2 = __high2half(a);
-  half b1 = __low2half(b);
-  half b2 = __high2half(b);
+  half a1 = get_half2_low(a);
+  half a2 = get_half2_high(a);
+  half b1 = get_half2_low(b);
+  half b2 = get_half2_high(b);
   half result1 = half_impl::raw_uint16_to_half(a1.x & ~b1.x);
   half result2 = half_impl::raw_uint16_to_half(a2.x & ~b2.x);
-  return __halves2half2(result1, result2);
+  return combine_half(result1, result2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd(const half2& a,
@@ -814,9 +851,9 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin(const half2& a,
   float a2 = __high2float(a);
   float b1 = __low2float(b);
   float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
-  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
-  return __halves2half2(r1, r2);
+  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
+  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
+  return combine_half(r1, r2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a,
@@ -825,9 +862,9 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a,
   float a2 = __high2float(a);
   float b1 = __low2float(b);
   float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
-  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
-  return __halves2half2(r1, r2);
+  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
+  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
+  return combine_half(r1, r2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux(const half2& a) {
@@ -848,7 +885,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max(const half2& a) {
 #else
   float a1 = __low2float(a);
   float a2 = __high2float(a);
-  return a1 > a2 ? __low2half(a) : __high2half(a);
+  return a1 > a2 ? get_half2_low(a) : get_half2_high(a);
 #endif
 }
 
@@ -860,7 +897,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min(const half2& a) {
 #else
   float a1 = __low2float(a);
   float a2 = __high2float(a);
-  return a1 < a2 ? __low2half(a) : __high2half(a);
+  return a1 < a2 ? get_half2_low(a) : get_half2_high(a);
 #endif
 }
 
@@ -1031,10 +1068,10 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
 pgather<Eigen::half, Packet4h2>(const Eigen::half* from, Index stride) {
   Packet4h2 r;
   half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = __halves2half2(from[0 * stride], from[1 * stride]);
-  p_alias[1] = __halves2half2(from[2 * stride], from[3 * stride]);
-  p_alias[2] = __halves2half2(from[4 * stride], from[5 * stride]);
-  p_alias[3] = __halves2half2(from[6 * stride], from[7 * stride]);
+  p_alias[0] = combine_half(from[0 * stride], from[1 * stride]);
+  p_alias[1] = combine_half(from[2 * stride], from[3 * stride]);
+  p_alias[2] = combine_half(from[4 * stride], from[5 * stride]);
+  p_alias[3] = combine_half(from[6 * stride], from[7 * stride]);
   return r;
 }
 
@@ -1115,12 +1152,12 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half2(
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
 ptranspose_half(half2& f0, half2& f1) {
-  __half a1 = __low2half(f0);
-  __half a2 = __high2half(f0);
-  __half b1 = __low2half(f1);
-  __half b2 = __high2half(f1);
-  f0 = __halves2half2(a1, b1);
-  f1 = __halves2half2(a2, b2);
+  __half a1 = get_half2_low(f0);
+  __half a2 = get_half2_high(f0);
+  __half b1 = get_half2_low(f1);
+  __half b2 = get_half2_high(f1);
+  f0 = combine_half(a1, b1);
+  f1 = combine_half(a2, b2);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
@@ -1217,10 +1254,10 @@ plset<Packet4h2>(const Eigen::half& a) {
   float f = __half2float(a);
   Packet4h2 r;
   half2* p_alias = reinterpret_cast<half2*>(&r);
-  p_alias[0] = __halves2half2(a, __float2half(f + 1.0f));
-  p_alias[1] = __halves2half2(__float2half(f + 2.0f), __float2half(f + 3.0f));
-  p_alias[2] = __halves2half2(__float2half(f + 4.0f), __float2half(f + 5.0f));
-  p_alias[3] = __halves2half2(__float2half(f + 6.0f), __float2half(f + 7.0f));
+  p_alias[0] = combine_half(a, __float2half(f + 1.0f));
+  p_alias[1] = combine_half(__float2half(f + 2.0f), __float2half(f + 3.0f));
+  p_alias[2] = combine_half(__float2half(f + 4.0f), __float2half(f + 5.0f));
+  p_alias[3] = combine_half(__float2half(f + 6.0f), __float2half(f + 7.0f));
   return r;
 #endif
 }
@@ -1440,9 +1477,9 @@ template <>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4h2>(
     const Packet4h2& a) {
   const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = __halves2half2(predux_max(a_alias[0]),
+  half2 m0 = combine_half(predux_max(a_alias[0]),
                             predux_max(a_alias[1]));
-  half2 m1 = __halves2half2(predux_max(a_alias[2]),
+  half2 m1 = combine_half(predux_max(a_alias[2]),
                             predux_max(a_alias[3]));
   __half first  = predux_max(m0);
   __half second = predux_max(m1);
@@ -1459,9 +1496,9 @@ template <>
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4h2>(
     const Packet4h2& a) {
   const half2* a_alias = reinterpret_cast<const half2*>(&a);
-  half2 m0 = __halves2half2(predux_min(a_alias[0]),
+  half2 m0 = combine_half(predux_min(a_alias[0]),
                             predux_min(a_alias[1]));
-  half2 m1 = __halves2half2(predux_min(a_alias[2]),
+  half2 m1 = combine_half(predux_min(a_alias[2]),
                             predux_min(a_alias[3]));
   __half first  = predux_min(m0);
   __half second = predux_min(m1);
@@ -1615,9 +1652,9 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a,
   float a2 = __high2float(a);
   float b1 = __low2float(b);
   float b2 = __high2float(b);
-  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
-  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
-  return __halves2half2(r1, r2);
+  __half r1 = a1 < b1 ? get_half2_low(a) : get_half2_low(b);
+  __half r2 = a2 < b2 ? get_half2_high(a) : get_half2_high(b);
+  return combine_half(r1, r2);
 }
 
 template<>
@@ -1627,12 +1664,14 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a,
   float a2 = __high2float(a);
   float b1 = __low2float(b);
   float b2 = __high2float(b);
-  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
-  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
-  return __halves2half2(r1, r2);
+  __half r1 = a1 > b1 ? get_half2_low(a) : get_half2_low(b);
+  __half r2 = a2 > b2 ? get_half2_high(a) : get_half2_high(b);
+  return combine_half(r1, r2);
 }
 
-#endif // (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+// #endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
+
+#endif // defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
 
 #undef EIGEN_GPU_HAS_LDG
 #undef EIGEN_CUDA_HAS_FP16_ARITHMETIC
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
index c8195bb..7545462 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/GPU/TypeCasting.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/GPU/TypeCasting.h
@@ -15,7 +15,8 @@ namespace Eigen {
 namespace internal {
 
 #if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
-    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+
 
 template <>
 struct type_casting_traits<Eigen::half, float> {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/HIP/hcc/math_constants.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
index 76e9f7c..53dacfa 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/Complex.h
@@ -75,13 +75,16 @@ struct Packet2cf {
   EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
     return Packet2cf(*this) -= b;
   }
-  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const {
-    return pdiv_complex(Packet2cf(*this), b);
-  }
   EIGEN_STRONG_INLINE Packet2cf& operator/=(const Packet2cf& b) {
-    *this = Packet2cf(*this) / b;
+    *this *= b.conjugate();
+    Packet4f s = pmul<Packet4f>(b.v, b.v);
+    s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+    v = pdiv(v, s);
     return *this;
   }
+  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const {
+    return Packet2cf(*this) /= b;
+  }
   EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
     return Packet2cf(pnegate(v));
   }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/MSA/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/MSA/PacketMath.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
similarity index 94%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
index 6cfe867..f40af7f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/Complex.h
@@ -129,12 +129,12 @@ template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Pa
 
 template<> EIGEN_STRONG_INLINE Packet1cf pconj(const Packet1cf& a)
 {
-  const Packet2ui b = Packet2ui(vreinterpret_u32_f32(a.v));
+  const Packet2ui b = vreinterpret_u32_f32(a.v);
   return Packet1cf(vreinterpret_f32_u32(veor_u32(b, p2ui_CONJ_XOR())));
 }
 template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
 {
-  const Packet4ui b = Packet4ui(vreinterpretq_u32_f32(a.v));
+  const Packet4ui b = vreinterpretq_u32_f32(a.v);
   return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
 }
 
@@ -347,11 +347,27 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
 
 template<> EIGEN_STRONG_INLINE Packet1cf pdiv<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for NEON
+  Packet1cf res = pmul(a, pconj(b));
+  Packet2f s, rev_s;
+
+  // this computes the norm
+  s = vmul_f32(b.v, b.v);
+  rev_s = vrev64_f32(s);
+
+  return Packet1cf(pdiv<Packet2f>(res.v, vadd_f32(s, rev_s)));
 }
 template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for NEON
+  Packet2cf res = pmul(a,pconj(b));
+  Packet4f s, rev_s;
+
+  // this computes the norm
+  s = vmulq_f32(b.v, b.v);
+  rev_s = vrev64q_f32(s);
+
+  return Packet2cf(pdiv<Packet4f>(res.v, vaddq_f32(s, rev_s)));
 }
 
 EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1cf, 1>& /*kernel*/) {}
@@ -373,10 +389,13 @@ template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
 //---------- double ----------
 #if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
 
-inline uint64x2_t p2ul_CONJ_XOR() {
-  static const uint64_t p2ul_conj_XOR_DATA[] = {0x0, 0x8000000000000000};
-  return vld1q_u64(p2ul_conj_XOR_DATA);
-}
+// See bug 1325, clang fails to call vld1q_u64.
+#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
+  static uint64x2_t p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
+#else
+  const uint64_t  p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
+  static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
+#endif
 
 struct Packet1cd
 {
@@ -446,7 +465,7 @@ template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a)
 { return Packet1cd(pnegate<Packet2d>(a.v)); }
 
 template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
-{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR()))); }
+{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); }
 
 template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
 {
@@ -461,7 +480,7 @@ template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, con
   // Multiply the imag a with b
   v2 = vmulq_f64(v2, b.v);
   // Conjugate v2
-  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR()));
+  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR));
   // Swap real/imag elements in v2.
   v2 = preverse<Packet2d>(v2);
   // Add and return the result
@@ -534,7 +553,12 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
 
 template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for NEON
+  Packet1cd res = pmul(a,pconj(b));
+  Packet2d s = pmul<Packet2d>(b.v, b.v);
+  Packet2d rev_s = preverse<Packet2d>(s);
+
+  return Packet1cd(pdiv(res.v, padd<Packet2d>(s,rev_s)));
 }
 
 EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
index 0963b0f..3481f33 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
@@ -24,7 +24,7 @@ struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
 
   template <typename LaneIdType>
   EIGEN_STRONG_INLINE void madd(const Packet4f& a, const Packet4f& b,
-                                Packet4f& c, Packet4f&,
+                                Packet4f& c, Packet4f& tmp,
                                 const LaneIdType&) const {
     acc(a, b, c);
   }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
index f24fdfb..d2aeef4 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
@@ -57,16 +57,6 @@ typedef eigen_packet_wrapper<uint32x4_t ,15> Packet4ui;
 typedef eigen_packet_wrapper<int64x2_t  ,16> Packet2l;
 typedef eigen_packet_wrapper<uint64x2_t ,17> Packet2ul;
 
-EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) {
-  float from[4] = {a, b, c, d};
-  return vld1q_f32(from);
-}
-
-EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) {
-  float from[2] = {a, b};
-  return vld1_f32(from);
-}
-
 #else
 
 typedef float32x2_t                          Packet2f;
@@ -88,22 +78,11 @@ typedef uint32x4_t                           Packet4ui;
 typedef int64x2_t                            Packet2l;
 typedef uint64x2_t                           Packet2ul;
 
-EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) {
-  const Packet2f low = {a, b};
-  const Packet2f high = {c, d};
-  return vcombine_f32(low, high);
-}
-
-EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) {
-  const Packet2f result = {a, b};
-  return result;
-}
-
 #endif // EIGEN_COMP_MSVC_STRICT
 
 EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask){
   const float* a = reinterpret_cast<const float*>(&m);
-  Packet4f res = make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3 )), *(a + ((mask >> 6) & 3)));
+  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3 )), *(a + ((mask >> 6) & 3))};
   return res;
 }
 
@@ -116,7 +95,7 @@ EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f &m, const Packet4f &n, int
 {
   const float* a = reinterpret_cast<const float*>(&m);
   const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  Packet4f res = {*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
   return res;
 }
 
@@ -125,7 +104,7 @@ EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f &m, const Packet4f &n
 {
   const float* a = reinterpret_cast<const float*>(&m);
   const float* b = reinterpret_cast<const float*>(&n);
-  Packet4f res = make_packet4f(*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  Packet4f res = {*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3))};
   return res;
 }
 
@@ -156,7 +135,7 @@ EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b
   return shuffle2<true>(a,b,eigen_neon_shuffle_mask(2, 2, 3, 3));
 }
 #define vec4f_duplane(a, p) \
-  Packet4f(vdupq_lane_f32(vget_low_f32(a), p))
+  vdupq_lane_f32(vget_low_f32(a), p)
 
 #define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
   const Packet4f p4f_##NAME = pset1<Packet4f>(X)
@@ -167,7 +146,7 @@ EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b
 #define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
   const Packet4i p4i_##NAME = pset1<Packet4i>(X)
 
-#if EIGEN_ARCH_ARM64 && EIGEN_COMP_GNUC
+#if EIGEN_ARCH_ARM64
   // __builtin_prefetch tends to do nothing on ARM64 compilers because the
   // prefetch instructions there are too detailed for __builtin_prefetch to map
   // meaningfully to them.
@@ -176,7 +155,7 @@ EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b
   #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
 #elif defined __pld
   #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
-#elif EIGEN_ARCH_ARM
+#elif EIGEN_ARCH_ARM32
   #define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ("pld [%[addr]]\n" :: [addr] "r" (ADDR) : );
 #else
   // by default no explicit prefetching
@@ -883,12 +862,12 @@ template<> EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, con
 
 template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b);
 template<> EIGEN_STRONG_INLINE Packet2f paddsub<Packet2f>(const Packet2f& a, const Packet2f & b) {
-  Packet2f mask = make_packet2f(numext::bit_cast<float>(0x80000000u), 0.0f);
+  Packet2f mask = {numext::bit_cast<float>(0x80000000u), 0.0f};
   return padd(a, pxor(mask, b));
 }
 template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
 template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  Packet4f mask = make_packet4f(numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f);
+  Packet4f mask = {numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f};
   return padd(a, pxor(mask, b));
 }
 
@@ -968,6 +947,57 @@ template<> EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, con
     vdup_n_u64(vgetq_lane_u64(a, 1)*vgetq_lane_u64(b, 1)));
 }
 
+template<> EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vdiv_f32(a,b);
+#else
+  Packet2f inv, restep, div;
+
+  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
+  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
+  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
+  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
+  // Newton-Raphson and vrecpsq_f32()
+  inv = vrecpe_f32(b);
+
+  // This returns a differential, by which we will have to multiply inv to get a better
+  // approximation of 1/b.
+  restep = vrecps_f32(b, inv);
+  inv = vmul_f32(restep, inv);
+
+  // Finally, multiply a by 1/b and get the wanted result of the division.
+  div = vmul_f32(a, inv);
+
+  return div;
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vdivq_f32(a,b);
+#else
+  Packet4f inv, restep, div;
+
+  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
+  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
+  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
+  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
+  // Newton-Raphson and vrecpsq_f32()
+  inv = vrecpeq_f32(b);
+
+  // This returns a differential, by which we will have to multiply inv to get a better
+  // approximation of 1/b.
+  restep = vrecpsq_f32(b, inv);
+  inv = vmulq_f32(restep, inv);
+
+  // Finally, multiply a by 1/b and get the wanted result of the division.
+  div = vmulq_f32(a, inv);
+
+  return div;
+#endif
+}
+
 template<> EIGEN_STRONG_INLINE Packet4c pdiv<Packet4c>(const Packet4c& /*a*/, const Packet4c& /*b*/)
 {
   eigen_assert(false && "packet integer division are not supported by NEON");
@@ -1049,15 +1079,12 @@ template<> EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& /*a*/,
   return pset1<Packet2ul>(0ULL);
 }
 
-#ifdef EIGEN_VECTORIZE_FMA
-template <>
-EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
-  return vfmaq_f32(c, a, b);
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c) {
-  return vfma_f32(c, a, b);
-}
+
+#ifdef __ARM_FEATURE_FMA
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
+{ return vfmaq_f32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
+{ return vfma_f32(c,a,b); }
 #else
 template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
 {
@@ -2472,7 +2499,7 @@ template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us predux_half_dowto4(co
 template<> EIGEN_STRONG_INLINE float predux_mul<Packet2f>(const Packet2f& a)
 { return vget_lane_f32(a, 0) * vget_lane_f32(a, 1); }
 template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
-{ return predux_mul<Packet2f>(vmul_f32(vget_low_f32(a), vget_high_f32(a))); }
+{ return predux_mul(vmul_f32(vget_low_f32(a), vget_high_f32(a))); }
 template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet4c>(const Packet4c& a)
 {
   int8x8_t prod = vreinterpret_s8_s32(vdup_n_s32(a));
@@ -2486,7 +2513,7 @@ template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet8c>(const Packet8c& a)
   return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 4);
 }
 template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a)
-{ return predux_mul<Packet8c>(vmul_s8(vget_low_s8(a), vget_high_s8(a))); }
+{ return predux_mul(vmul_s8(vget_low_s8(a), vget_high_s8(a))); }
 template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet4uc>(const Packet4uc& a)
 {
   uint8x8_t prod = vreinterpret_u8_u32(vdup_n_u32(a));
@@ -2500,7 +2527,7 @@ template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet8uc>(const Packet8uc& a)
   return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 4);
 }
 template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a)
-{ return predux_mul<Packet8uc>(vmul_u8(vget_low_u8(a), vget_high_u8(a))); }
+{ return predux_mul(vmul_u8(vget_low_u8(a), vget_high_u8(a))); }
 template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet4s>(const Packet4s& a)
 {
   const int16x4_t prod = vmul_s16(a, vrev32_s16(a));
@@ -2536,11 +2563,11 @@ template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a
 template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet2i>(const Packet2i& a)
 { return vget_lane_s32(a, 0) * vget_lane_s32(a, 1); }
 template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a)
-{ return predux_mul<Packet2i>(vmul_s32(vget_low_s32(a), vget_high_s32(a))); }
+{ return predux_mul(vmul_s32(vget_low_s32(a), vget_high_s32(a))); }
 template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet2ui>(const Packet2ui& a)
 { return vget_lane_u32(a, 0) * vget_lane_u32(a, 1); }
 template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a)
-{ return predux_mul<Packet2ui>(vmul_u32(vget_low_u32(a), vget_high_u32(a))); }
+{ return predux_mul(vmul_u32(vget_low_u32(a), vget_high_u32(a))); }
 template<> EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a)
 { return vgetq_lane_s64(a, 0) * vgetq_lane_s64(a, 1); }
 template<> EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a)
@@ -3153,7 +3180,7 @@ template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
   return padd(tmp, mask);
 }
 
-#endif  // EIGEN_ARCH_ARMV8
+#endif
 
 /**
  * Computes the integer square root
@@ -3246,115 +3273,40 @@ template<> EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
   return res;
 }
 
-EIGEN_STRONG_INLINE Packet4f prsqrt_float_unsafe(const Packet4f& a) {
-  // Compute approximate reciprocal sqrt.
-  // Does not correctly handle +/- 0 or +inf
-  float32x4_t result = vrsqrteq_f32(a);
-  result = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, result), result), result);
-  result = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, result), result), result);
-  return result;
-}
-
-EIGEN_STRONG_INLINE Packet2f prsqrt_float_unsafe(const Packet2f& a) {
-  // Compute approximate reciprocal sqrt.
-  // Does not correctly handle +/- 0 or +inf
-  float32x2_t result = vrsqrte_f32(a);
-  result = vmul_f32(vrsqrts_f32(vmul_f32(a, result), result), result);
-  result = vmul_f32(vrsqrts_f32(vmul_f32(a, result), result), result);
-  return result;
-}
-
-template<typename Packet> Packet prsqrt_float_common(const Packet& a) {
-  const Packet cst_zero = pzero(a);
-  const Packet cst_inf = pset1<Packet>(NumTraits<float>::infinity());
-  Packet return_zero = pcmp_eq(a, cst_inf);
-  Packet return_inf = pcmp_eq(a, cst_zero);
-  Packet result = prsqrt_float_unsafe(a);
-  result = pselect(return_inf, por(cst_inf, a), result);
-  result = pandnot(result, return_zero);
-  return result;
-}
-
 template<> EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
-  return prsqrt_float_common(a);
+  // Compute approximate reciprocal sqrt.
+  Packet4f x = vrsqrteq_f32(a);
+  // Do Newton iterations for 1/sqrt(x).
+  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
+  x = vmulq_f32(vrsqrtsq_f32(vmulq_f32(a, x), x), x);
+  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
+  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
 }
 
 template<> EIGEN_STRONG_INLINE Packet2f prsqrt(const Packet2f& a) {
-  return prsqrt_float_common(a);
-}
-
-EIGEN_STRONG_INLINE Packet4f preciprocal(const Packet4f& a)
-{
-  // Compute approximate reciprocal.
-  float32x4_t result = vrecpeq_f32(a);
-  result = vmulq_f32(vrecpsq_f32(a, result), result);
-  result = vmulq_f32(vrecpsq_f32(a, result), result);
-  return result;
-}
-
-EIGEN_STRONG_INLINE Packet2f preciprocal(const Packet2f& a)
-{
-  // Compute approximate reciprocal.
-  float32x2_t result = vrecpe_f32(a);
-  result = vmul_f32(vrecps_f32(a, result), result);
-  result = vmul_f32(vrecps_f32(a, result), result);
-  return result;
+  // Compute approximate reciprocal sqrt.
+  Packet2f x = vrsqrte_f32(a);
+  // Do Newton iterations for 1/sqrt(x).
+  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
+  x = vmul_f32(vrsqrts_f32(vmul_f32(a, x), x), x);
+  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
+  return pselect(pcmp_eq(a, pzero(a)), infinity, x);
 }
 
 // Unfortunately vsqrt_f32 is only available for A64.
 #if EIGEN_ARCH_ARM64
-template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) { return vsqrtq_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) { return vsqrt_f32(a); }
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv(const Packet4f& a, const Packet4f& b) { return vdivq_f32(a, b); }
-
-template<> EIGEN_STRONG_INLINE Packet2f pdiv(const Packet2f& a, const Packet2f& b) { return vdiv_f32(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& _x){return vsqrtq_f32(_x);}
+template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& _x){return vsqrt_f32(_x); }
 #else
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet psqrt_float_common(const Packet& a) {
-  const Packet cst_zero = pzero(a);
-  const Packet cst_inf = pset1<Packet>(NumTraits<float>::infinity());
-  
-  Packet result = pmul(a, prsqrt_float_unsafe(a));  
-  Packet a_is_zero = pcmp_eq(a, cst_zero);
-  Packet a_is_inf = pcmp_eq(a, cst_inf);
-  Packet return_a = por(a_is_zero, a_is_inf);
-  
-  result = pselect(return_a, a, result);
-  return result;
-}
-
 template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
-  return psqrt_float_common(a);
+  const Packet4f infinity = pset1<Packet4f>(NumTraits<float>::infinity());
+  const Packet4f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
+  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
 }
-
 template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
-  return psqrt_float_common(a);
-}
-
-template<typename Packet>
-EIGEN_STRONG_INLINE Packet pdiv_float_common(const Packet& a, const Packet& b) {
-  // if b is large, NEON intrinsics will flush preciprocal(b) to zero
-  // avoid underflow with the following manipulation:
-  // a / b = f * (a * reciprocal(f * b))
-
-  const Packet cst_one = pset1<Packet>(1.0f);
-  const Packet cst_quarter = pset1<Packet>(0.25f);
-  const Packet cst_thresh = pset1<Packet>(NumTraits<float>::highest() / 4.0f);
-  
-  Packet b_will_underflow = pcmp_le(cst_thresh, pabs(b));
-  Packet f = pselect(b_will_underflow, cst_quarter, cst_one);
-  Packet result = pmul(f, pmul(a, preciprocal(pmul(b, f))));
-  return result;
-}
-
-template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
-  return pdiv_float_common(a, b);
-}
-
-template<> EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b) {
-  return pdiv_float_common(a, b);
+  const Packet2f infinity = pset1<Packet2f>(NumTraits<float>::infinity());
+  const Packet2f is_zero_or_inf = por(pcmp_eq(a, pzero(a)), pcmp_eq(a, infinity));
+  return pselect(is_zero_or_inf, a, pmul(a, prsqrt(a)));
 }
 #endif
 
@@ -3436,7 +3388,7 @@ EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p)
 {
   // See the scalar implemention in BFloat16.h for a comprehensible explanation
   // of this fast rounding algorithm
-  Packet4ui input = Packet4ui(vreinterpretq_u32_f32(p));
+  Packet4ui input = reinterpret_cast<Packet4ui>(p);
 
   // lsb = (input >> 16) & 1
   Packet4ui lsb =  vandq_u32(vshrq_n_u32(input, 16), vdupq_n_u32(1));
@@ -3461,7 +3413,7 @@ EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p)
 
 EIGEN_STRONG_INLINE Packet4f Bf16ToF32(const Packet4bf& p)
 {
-  return Packet4f(vreinterpretq_f32_u32(vshlq_n_u32(vmovl_u16(p), 16)));
+  return reinterpret_cast<Packet4f>(vshlq_n_u32(vmovl_u16(p), 16));
 }
 
 EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) {
@@ -3469,21 +3421,21 @@ EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) {
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf pset1<Packet4bf>(const bfloat16& from) {
-  return Packet4bf(pset1<Packet4us>(from.value));
+  return pset1<Packet4us>(from.value);
 }
 
 template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet4bf>(const Packet4bf& from) {
-  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(Packet4us(from))));
+  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(from)));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf pload<Packet4bf>(const bfloat16* from)
 {
-  return Packet4bf(pload<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+  return pload<Packet4us>(reinterpret_cast<const uint16_t*>(from));
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf ploadu<Packet4bf>(const bfloat16* from)
 {
-  return Packet4bf(ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+  return ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from));
 }
 
 template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet4bf& from)
@@ -3498,7 +3450,7 @@ template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet
 
 template<> EIGEN_STRONG_INLINE Packet4bf ploaddup<Packet4bf>(const bfloat16* from)
 {
-  return Packet4bf(ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+  return ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from));
 }
 
 template <> EIGEN_STRONG_INLINE Packet4bf pabs(const Packet4bf& a) {
@@ -3545,25 +3497,25 @@ template<> EIGEN_STRONG_INLINE Packet4bf plset<Packet4bf>(const bfloat16& a)
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf por(const Packet4bf& a,const Packet4bf& b) {
-  return Packet4bf(por<Packet4us>(Packet4us(a), Packet4us(b)));
+  return por<Packet4us>(a, b);
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf pxor(const Packet4bf& a,const Packet4bf& b) {
-  return Packet4bf(pxor<Packet4us>(Packet4us(a), Packet4us(b)));
+  return pxor<Packet4us>(a, b);
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf pand(const Packet4bf& a,const Packet4bf& b) {
-  return Packet4bf(pand<Packet4us>(Packet4us(a), Packet4us(b)));
+  return pand<Packet4us>(a, b);
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf pandnot(const Packet4bf& a,const Packet4bf& b) {
-  return Packet4bf(pandnot<Packet4us>(Packet4us(a), Packet4us(b)));
+  return pandnot<Packet4us>(a, b);
 }
 
 template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4bf pselect(const Packet4bf& mask, const Packet4bf& a,
                                                       const Packet4bf& b)
 {
-  return Packet4bf(pselect<Packet4us>(Packet4us(mask), Packet4us(a), Packet4us(b)));
+  return pselect<Packet4us>(mask, a, b);
 }
 
 template<> EIGEN_STRONG_INLINE Packet4bf print<Packet4bf>(const Packet4bf& a)
@@ -3602,13 +3554,13 @@ template<> EIGEN_STRONG_INLINE Packet4bf pdiv<Packet4bf>(const Packet4bf& a, con
 template<>
 EIGEN_STRONG_INLINE Packet4bf pgather<bfloat16, Packet4bf>(const bfloat16* from, Index stride)
 {
-  return Packet4bf(pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride));
+  return pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride);
 }
 
 template<>
 EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet4bf>(bfloat16* to, const Packet4bf& from, Index stride)
 {
-  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), Packet4us(from), stride);
+  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), from, stride);
 }
 
 template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet4bf>(const Packet4bf& a)
@@ -3633,7 +3585,7 @@ template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet4bf>(const Packet4bf& a
 
 template<> EIGEN_STRONG_INLINE Packet4bf preverse<Packet4bf>(const Packet4bf& a)
 {
-  return Packet4bf(preverse<Packet4us>(Packet4us(a)));
+  return preverse<Packet4us>(a);
 }
 
 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4bf, 4>& kernel)
@@ -3668,7 +3620,7 @@ template<> EIGEN_STRONG_INLINE Packet4bf pcmp_le<Packet4bf>(const Packet4bf& a,
 
 template<> EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a)
 {
-  return Packet4bf(pxor<Packet4us>(Packet4us(a), pset1<Packet4us>(static_cast<uint16_t>(0x8000))));
+  return pxor<Packet4us>(a, pset1<Packet4us>(static_cast<uint16_t>(0x8000)));
 }
 
 //---------- double ----------
@@ -3686,35 +3638,17 @@ template<> EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a)
 
 #if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
 
-#if EIGEN_COMP_GNUC
 // Bug 907: workaround missing declarations of the following two functions in the ADK
 // Defining these functions as templates ensures that if these intrinsics are
 // already defined in arm_neon.h, then our workaround doesn't cause a conflict
 // and has lower priority in overload resolution.
-// This doesn't work with MSVC though, since the function names are macros.
 template <typename T> uint64x2_t vreinterpretq_u64_f64(T a) { return (uint64x2_t) a; }
-template <typename T> float64x2_t vreinterpretq_f64_u64(T a) { return (float64x2_t) a; }
-#endif
 
-#if EIGEN_COMP_MSVC_STRICT
-typedef eigen_packet_wrapper<float64x2_t, 18> Packet2d;
-typedef eigen_packet_wrapper<float64x1_t, 19> Packet1d;
-
-EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) {
-  double from[2] = {a, b};
-  return vld1q_f64(from);
-}
+template <typename T> float64x2_t vreinterpretq_f64_u64(T a) { return (float64x2_t) a; }
 
-#else
 typedef float64x2_t Packet2d;
 typedef float64x1_t Packet1d;
 
-EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) {
-  double from[2] = {a, b};
-  return vld1q_f64(from);
-}
-#endif
-
 // fuctionally equivalent to _mm_shuffle_pd in SSE (i.e. shuffle(m, n, mask) equals _mm_shuffle_pd(m,n,mask))
 // Currently used in LU/arch/InverseSize4.h to enable a shared implementation
 // for fast inversion of matrices of size 4.
@@ -3722,7 +3656,7 @@ EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int m
 {
   const double* a = reinterpret_cast<const double*>(&m);
   const double* b = reinterpret_cast<const double*>(&n);
-  Packet2d res = make_packet2d(*(a + (mask & 1)), *(b + ((mask >> 1) & 1)));
+  Packet2d res = {*(a + (mask & 1)), *(b + ((mask >> 1) & 1))};
   return res;
 }
 
@@ -3739,7 +3673,7 @@ EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a,const Packet2d& b)
   return shuffle(a, b, 3);
 }
 #define vec2d_duplane(a, p) \
-  Packet2d(vdupq_laneq_f64(a, p))
+  vdupq_laneq_f64(a, p)
 
 template<> struct packet_traits<double>  : default_packet_traits
 {
@@ -3813,7 +3747,7 @@ template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const
 
 template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
 template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b){
-  const Packet2d mask = make_packet2d(numext::bit_cast<double>(0x8000000000000000ull), 0.0);
+  const Packet2d mask = {numext::bit_cast<double>(0x8000000000000000ull),0.0};
   return padd(a, pxor(mask, b));
 }
 
@@ -3825,7 +3759,7 @@ template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const
 
 template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); }
 
-#ifdef EIGEN_VECTORIZE_FMA
+#ifdef __ARM_FEATURE_FMA
 // See bug 936. See above comment about FMA for float.
 template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
 { return vfmaq_f64(c,a,b); }
@@ -3928,7 +3862,7 @@ template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
 { return (vget_low_f64(a) * vget_high_f64(a))[0]; }
 #else
 template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
-{ return vget_lane_f64(vmul_f64(vget_low_f64(a), vget_high_f64(a)), 0); }
+{ return vget_lane_f64(vget_low_f64(a) * vget_high_f64(a), 0); }
 #endif
 
 // min
@@ -3984,7 +3918,7 @@ template<> EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
 
 template<> EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& _x){ return vsqrtq_f64(_x); }
 
-#endif // EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+#endif // EIGEN_ARCH_ARM64
 
 // Do we have an fp16 types and supporting Neon intrinsics?
 #if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
similarity index 92%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
index c546466..54f9733 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/NEON/TypeCasting.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/NEON/TypeCasting.h
@@ -15,113 +15,6 @@ namespace Eigen {
 
 namespace internal {
 
-//==============================================================================
-// preinterpret
-//==============================================================================
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
-  return Packet2f(vreinterpret_f32_s32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
-  return Packet2f(vreinterpret_f32_u32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
-  return Packet4f(vreinterpretq_f32_s32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
-  return Packet4f(vreinterpretq_f32_u32(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
-  return static_cast<Packet4c>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
-  return Packet8c(vreinterpret_s8_u8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
-  return Packet16c(vreinterpretq_s8_u8(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
-  return static_cast<Packet4uc>(a);
-}
-template <>
-EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
-  return Packet8uc(vreinterpret_u8_s8(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
-  return Packet16uc(vreinterpretq_u8_s8(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
-  return Packet4s(vreinterpret_s16_u16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
-  return Packet8s(vreinterpretq_s16_u16(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
-  return Packet4us(vreinterpret_u16_s16(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
-  return Packet8us(vreinterpretq_u16_s16(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
-  return Packet2i(vreinterpret_s32_f32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
-  return Packet2i(vreinterpret_s32_u32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
-  return Packet4i(vreinterpretq_s32_f32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
-  return Packet4i(vreinterpretq_s32_u32(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
-  return Packet2ui(vreinterpret_u32_f32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
-  return Packet2ui(vreinterpret_u32_s32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
-  return Packet4ui(vreinterpretq_u32_f32(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
-  return Packet4ui(vreinterpretq_u32_s32(a));
-}
-
-template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
-  return Packet2l(vreinterpretq_s64_u64(a));
-}
-template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
-  return Packet2ul(vreinterpretq_u64_s64(a));
-}
-
 //==============================================================================
 // pcast, SrcType = float
 //==============================================================================
@@ -295,7 +188,7 @@ struct type_casting_traits<numext::int8_t, numext::uint64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
-  return preinterpret<Packet2ul>(pcast<Packet16c, Packet2l>(a));
+  return vreinterpretq_u64_s64(pcast<Packet16c, Packet2l>(a));
 }
 
 template <>
@@ -319,11 +212,11 @@ struct type_casting_traits<numext::int8_t, numext::uint32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
-  return preinterpret<Packet4ui>(pcast<Packet16c, Packet4i>(a));
+  return vreinterpretq_u32_s32(pcast<Packet16c, Packet4i>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet2ui pcast<Packet8c, Packet2ui>(const Packet8c& a) {
-  return preinterpret<Packet2ui>(pcast<Packet8c, Packet2i>(a));
+  return vreinterpret_u32_s32(pcast<Packet8c, Packet2i>(a));
 }
 
 template <>
@@ -347,11 +240,11 @@ struct type_casting_traits<numext::int8_t, numext::uint16_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
-  return preinterpret<Packet8us>(pcast<Packet16c, Packet8s>(a));
+  return vreinterpretq_u16_s16(pcast<Packet16c, Packet8s>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet4us pcast<Packet8c, Packet4us>(const Packet8c& a) {
-  return preinterpret<Packet4us>(pcast<Packet8c, Packet4s>(a));
+  return vreinterpret_u16_s16(pcast<Packet8c, Packet4s>(a));
 }
 
 template <>
@@ -377,11 +270,11 @@ struct type_casting_traits<numext::int8_t, numext::uint8_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet16uc pcast<Packet16c, Packet16uc>(const Packet16c& a) {
-  return preinterpret<Packet16uc>(a);
+  return vreinterpretq_u8_s8(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet8uc pcast<Packet8c, Packet8uc>(const Packet8c& a) {
-  return preinterpret<Packet8uc>(a);
+  return vreinterpret_u8_s8(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet4uc pcast<Packet4c, Packet4uc>(const Packet4c& a) {
@@ -422,7 +315,7 @@ struct type_casting_traits<numext::uint8_t, numext::int64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
-  return preinterpret<Packet2l>(pcast<Packet16uc, Packet2ul>(a));
+  return vreinterpretq_s64_u64(pcast<Packet16uc, Packet2ul>(a));
 }
 
 template <>
@@ -446,11 +339,11 @@ struct type_casting_traits<numext::uint8_t, numext::int32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
-  return preinterpret<Packet4i>(pcast<Packet16uc, Packet4ui>(a));
+  return vreinterpretq_s32_u32(pcast<Packet16uc, Packet4ui>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet2i pcast<Packet8uc, Packet2i>(const Packet8uc& a) {
-  return preinterpret<Packet2i>(pcast<Packet8uc, Packet2ui>(a));
+  return vreinterpret_s32_u32(pcast<Packet8uc, Packet2ui>(a));
 }
 
 template <>
@@ -474,11 +367,11 @@ struct type_casting_traits<numext::uint8_t, numext::int16_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
-  return preinterpret<Packet8s>(pcast<Packet16uc, Packet8us>(a));
+  return vreinterpretq_s16_u16(pcast<Packet16uc, Packet8us>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet4s pcast<Packet8uc, Packet4s>(const Packet8uc& a) {
-  return preinterpret<Packet4s>(pcast<Packet8uc, Packet4us>(a));
+  return vreinterpret_s16_u16(pcast<Packet8uc, Packet4us>(a));
 }
 
 template <>
@@ -504,11 +397,11 @@ struct type_casting_traits<numext::uint8_t, numext::int8_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet16c pcast<Packet16uc, Packet16c>(const Packet16uc& a) {
-  return preinterpret<Packet16c>(a);
+  return vreinterpretq_s8_u8(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet8c pcast<Packet8uc, Packet8c>(const Packet8uc& a) {
-  return preinterpret<Packet8c>(a);
+  return vreinterpret_s8_u8(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet4c pcast<Packet4uc, Packet4c>(const Packet4uc& a) {
@@ -549,7 +442,7 @@ struct type_casting_traits<numext::int16_t, numext::uint64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
-  return preinterpret<Packet2ul>(pcast<Packet8s, Packet2l>(a));
+  return vreinterpretq_u64_s64(pcast<Packet8s, Packet2l>(a));
 }
 
 template <>
@@ -573,11 +466,11 @@ struct type_casting_traits<numext::int16_t, numext::uint32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
-  return preinterpret<Packet4ui>(pcast<Packet8s, Packet4i>(a));
+  return vreinterpretq_u32_s32(pcast<Packet8s, Packet4i>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet2ui pcast<Packet4s, Packet2ui>(const Packet4s& a) {
-  return preinterpret<Packet2ui>(pcast<Packet4s, Packet2i>(a));
+  return vreinterpret_u32_s32(pcast<Packet4s, Packet2i>(a));
 }
 
 template <>
@@ -599,11 +492,11 @@ struct type_casting_traits<numext::int16_t, numext::uint16_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet8us pcast<Packet8s, Packet8us>(const Packet8s& a) {
-  return preinterpret<Packet8us>(a);
+  return vreinterpretq_u16_s16(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet4us pcast<Packet4s, Packet4us>(const Packet4s& a) {
-  return preinterpret<Packet4us>(a);
+  return vreinterpret_u16_s16(a);
 }
 
 template <>
@@ -666,7 +559,7 @@ struct type_casting_traits<numext::uint16_t, numext::int64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
-  return preinterpret<Packet2l>(pcast<Packet8us, Packet2ul>(a));
+  return vreinterpretq_s64_u64(pcast<Packet8us, Packet2ul>(a));
 }
 
 template <>
@@ -690,11 +583,11 @@ struct type_casting_traits<numext::uint16_t, numext::int32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
-  return preinterpret<Packet4i>(pcast<Packet8us, Packet4ui>(a));
+  return vreinterpretq_s32_u32(pcast<Packet8us, Packet4ui>(a));
 }
 template <>
 EIGEN_STRONG_INLINE Packet2i pcast<Packet4us, Packet2i>(const Packet4us& a) {
-  return preinterpret<Packet2i>(pcast<Packet4us, Packet2ui>(a));
+  return vreinterpret_s32_u32(pcast<Packet4us, Packet2ui>(a));
 }
 
 template <>
@@ -716,11 +609,11 @@ struct type_casting_traits<numext::uint16_t, numext::int16_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet8s pcast<Packet8us, Packet8s>(const Packet8us& a) {
-  return preinterpret<Packet8s>(a);
+  return vreinterpretq_s16_u16(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet4s pcast<Packet4us, Packet4s>(const Packet4us& a) {
-  return preinterpret<Packet4s>(a);
+  return vreinterpret_s16_u16(a);
 }
 
 template <>
@@ -742,11 +635,11 @@ struct type_casting_traits<numext::uint16_t, numext::int8_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
-  return preinterpret<Packet16c>(pcast<Packet8us, Packet16uc>(a, b));
+  return vreinterpretq_s8_u8(pcast<Packet8us, Packet16uc>(a, b));
 }
 template <>
 EIGEN_STRONG_INLINE Packet8c pcast<Packet4us, Packet8c>(const Packet4us& a, const Packet4us& b) {
-  return preinterpret<Packet8c>(pcast<Packet4us, Packet8uc>(a, b));
+  return vreinterpret_s8_u8(pcast<Packet4us, Packet8uc>(a, b));
 }
 
 //==============================================================================
@@ -781,7 +674,7 @@ struct type_casting_traits<numext::int32_t, numext::uint64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
-  return preinterpret<Packet2ul>(pcast<Packet4i, Packet2l>(a));
+  return vreinterpretq_u64_s64(pcast<Packet4i, Packet2l>(a));
 }
 
 template <>
@@ -803,11 +696,11 @@ struct type_casting_traits<numext::int32_t, numext::uint32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4ui pcast<Packet4i, Packet4ui>(const Packet4i& a) {
-  return preinterpret<Packet4ui>(a);
+  return vreinterpretq_u32_s32(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet2ui pcast<Packet2i, Packet2ui>(const Packet2i& a) {
-  return preinterpret<Packet2ui>(a);
+  return vreinterpret_u32_s32(a);
 }
 
 template <>
@@ -906,7 +799,7 @@ struct type_casting_traits<numext::uint32_t, numext::int64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
-  return preinterpret<Packet2l>(pcast<Packet4ui, Packet2ul>(a));
+  return vreinterpretq_s64_u64(pcast<Packet4ui, Packet2ul>(a));
 }
 
 template <>
@@ -928,11 +821,11 @@ struct type_casting_traits<numext::uint32_t, numext::int32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4i pcast<Packet4ui, Packet4i>(const Packet4ui& a) {
-  return preinterpret<Packet4i>(a);
+  return vreinterpretq_s32_u32(a);
 }
 template <>
 EIGEN_STRONG_INLINE Packet2i pcast<Packet2ui, Packet2i>(const Packet2ui& a) {
-  return preinterpret<Packet2i>(a);
+  return vreinterpret_s32_u32(a);
 }
 
 template <>
@@ -954,11 +847,11 @@ struct type_casting_traits<numext::uint32_t, numext::int16_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
-  return preinterpret<Packet8s>(pcast<Packet4ui, Packet8us>(a, b));
+  return vreinterpretq_s16_u16(pcast<Packet4ui, Packet8us>(a, b));
 }
 template <>
 EIGEN_STRONG_INLINE Packet4s pcast<Packet2ui, Packet4s>(const Packet2ui& a, const Packet2ui& b) {
-  return preinterpret<Packet4s>(pcast<Packet2ui, Packet4us>(a, b));
+  return vreinterpret_s16_u16(pcast<Packet2ui, Packet4us>(a, b));
 }
 
 template <>
@@ -987,12 +880,12 @@ struct type_casting_traits<numext::uint32_t, numext::int8_t> {
 template <>
 EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
                                                           const Packet4ui& d) {
-  return preinterpret<Packet16c>(pcast<Packet4ui, Packet16uc>(a, b, c, d));
+  return vreinterpretq_s8_u8(pcast<Packet4ui, Packet16uc>(a, b, c, d));
 }
 template <>
 EIGEN_STRONG_INLINE Packet8c pcast<Packet2ui, Packet8c>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
                                                         const Packet2ui& d) {
-  return preinterpret<Packet8c>(pcast<Packet2ui, Packet8uc>(a, b, c, d));
+  return vreinterpret_s8_u8(pcast<Packet2ui, Packet8uc>(a, b, c, d));
 }
 
 //==============================================================================
@@ -1022,7 +915,7 @@ struct type_casting_traits<numext::int64_t, numext::uint64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2ul pcast<Packet2l, Packet2ul>(const Packet2l& a) {
-  return preinterpret<Packet2ul>(a);
+  return vreinterpretq_u64_s64(a);
 }
 
 template <>
@@ -1120,7 +1013,7 @@ struct type_casting_traits<numext::uint64_t, numext::int64_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet2l pcast<Packet2ul, Packet2l>(const Packet2ul& a) {
-  return preinterpret<Packet2l>(a);
+  return vreinterpretq_s64_u64(a);
 }
 
 template <>
@@ -1138,7 +1031,7 @@ struct type_casting_traits<numext::uint64_t, numext::int32_t> {
 };
 template <>
 EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
-  return preinterpret<Packet4i>(pcast<Packet2ul, Packet4ui>(a, b));
+  return vreinterpretq_s32_u32(pcast<Packet2ul, Packet4ui>(a, b));
 }
 
 template <>
@@ -1160,7 +1053,7 @@ struct type_casting_traits<numext::uint64_t, numext::int16_t> {
 template <>
 EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
                                                         const Packet2ul& d) {
-  return preinterpret<Packet8s>(pcast<Packet2ul, Packet8us>(a, b, c, d));
+  return vreinterpretq_s16_u16(pcast<Packet2ul, Packet8us>(a, b, c, d));
 }
 
 template <>
@@ -1184,40 +1077,122 @@ template <>
 EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
                                                           const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
                                                           const Packet2ul& g, const Packet2ul& h) {
-  return preinterpret<Packet16c>(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
+  return vreinterpretq_s8_u8(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
 }
 
-#if EIGEN_ARCH_ARM64
-
 //==============================================================================
-// pcast/preinterpret, Double
+// preinterpret
 //==============================================================================
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
+  return vreinterpret_f32_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
+  return vreinterpret_f32_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return vreinterpretq_f32_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
+  return vreinterpretq_f32_u32(a);
+}
 
 template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
-  return Packet2d(vreinterpretq_f64_s64(a));
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
+  return static_cast<Packet4c>(a);
 }
 template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
-  return Packet2d(vreinterpretq_f64_u64(a));
+EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
+  return vreinterpret_s8_u8(a);
 }
 template <>
-EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
-  return Packet2l(vreinterpretq_s64_f64(a));
+EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
+  return vreinterpretq_s8_u8(a);
 }
+
 template <>
-EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
-  return Packet2ul(vreinterpretq_u64_f64(a));
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
+  return static_cast<Packet4uc>(a);
 }
 template <>
-EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
-  return Packet2d(vreinterpretq_f64_s32(a));
+EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
+  return vreinterpret_u8_s8(a);
 }
 template <>
-EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
-  return Packet4i(vreinterpretq_s32_f64(a));
+EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
+  return vreinterpretq_u8_s8(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
+  return vreinterpret_s16_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
+  return vreinterpretq_s16_u16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
+  return vreinterpret_u16_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
+  return vreinterpretq_u16_s16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
+  return vreinterpret_s32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
+  return vreinterpret_s32_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return vreinterpretq_s32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return vreinterpretq_s32_u32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
+  return vreinterpret_u32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
+  return vreinterpret_u32_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
+  return vreinterpretq_u32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return vreinterpretq_u32_s32(a);
 }
 
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
+  return vreinterpretq_s64_u64(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
+  return vreinterpretq_u64_s64(a);
+}
+
+#if EIGEN_ARCH_ARM64
+
+//==============================================================================
+// pcast/preinterpret, Double
+//==============================================================================
+
 template <>
 struct type_casting_traits<double, double> {
   enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
@@ -1339,9 +1314,7 @@ struct type_casting_traits<numext::int8_t, double> {
 template <>
 EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
   // Discard all but first two values.
-  // MSVC defines most intrinsics as macros, so we need to do this in two lines for portability.
-  Packet2f tmp = pcast<Packet8c, Packet2f>(vget_low_s8(a));
-  return vcvt_f64_f32(tmp);
+  return vcvt_f64_f32(pcast<Packet8c, Packet2f>(vget_low_s8(a)));
 }
 
 template <>
@@ -1351,8 +1324,7 @@ struct type_casting_traits<numext::uint8_t, double> {
 template <>
 EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
   // Discard all but first two values.
-  Packet2f tmp = pcast<Packet8uc, Packet2f>(vget_low_u8(a));
-  return vcvt_f64_f32(tmp);
+  return vcvt_f64_f32(pcast<Packet8uc, Packet2f>(vget_low_u8(a)));
 }
 
 template <>
@@ -1362,8 +1334,7 @@ struct type_casting_traits<numext::int16_t, double> {
 template <>
 EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
   // Discard all but first two values.
-  Packet2f tmp = pcast<Packet4s, Packet2f>(vget_low_s16(a));
-  return vcvt_f64_f32(tmp);
+  return vcvt_f64_f32(pcast<Packet4s, Packet2f>(vget_low_s16(a)));
 }
 
 template <>
@@ -1373,8 +1344,7 @@ struct type_casting_traits<numext::uint16_t, double> {
 template <>
 EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
   // Discard all but first two values.
-  Packet2f tmp = pcast<Packet4us, Packet2f>(vget_low_u16(a));
-  return vcvt_f64_f32(tmp);
+  return vcvt_f64_f32(pcast<Packet4us, Packet2f>(vget_low_u16(a)));
 }
 
 template <>
@@ -1415,6 +1385,31 @@ EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
   return vcvtq_f64_u64(a);
 }
 
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
+  return vreinterpretq_f64_s64(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
+  return vreinterpretq_f64_u64(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
+  return vreinterpretq_s64_f64(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
+  return vreinterpretq_u64_f64(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
+  return vreinterpretq_f64_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
+  return vreinterpretq_s32_f64(a);
+}
+
 #endif  // EIGEN_ARCH_ARM64
 
 }  // end namespace internal
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
index e9ab2c4..8fe22da 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/Complex.h
@@ -106,9 +106,14 @@ template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<fl
 
 template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
 {
-  const float re = std::real(from);
-  const float im = std::imag(from);
-  return Packet2cf(_mm_set_ps(im, re, im, re));
+  Packet2cf res;
+#ifdef EIGEN_VECTORIZE_SSE3
+  res.v = _mm_castpd_ps(_mm_loaddup_pd(reinterpret_cast<double const*>(&from)));
+#else
+  res.v = _mm_castpd_ps(_mm_load_sd(reinterpret_cast<double const*>(&from)));
+  res.v = _mm_movelh_ps(res.v, res.v);
+#endif
+  return res;
 }
 
 template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
@@ -169,9 +174,14 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
 
 template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for SSE3 and 4
+  Packet2cf res = pmul(a, pconj(b));
+  __m128 s = _mm_mul_ps(b.v,b.v);
+  return Packet2cf(_mm_div_ps(res.v,_mm_add_ps(s,vec4f_swizzle1(s, 1, 0, 3, 2))));
 }
 
+
+
 //---------- double ----------
 struct Packet1cd
 {
@@ -289,7 +299,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
 
 template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for SSE3 and 4
+  Packet1cd res = pmul(a,pconj(b));
+  __m128d s = _mm_mul_pd(b.v,b.v);
+  return Packet1cd(_mm_div_pd(res.v, _mm_add_pd(s,_mm_shuffle_pd(s, s, 0x1))));
 }
 
 EIGEN_STRONG_INLINE Packet1cd pcplxflip/* <Packet1cd> */(const Packet1cd& x)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
index b485e0d..db102c7 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h
@@ -444,7 +444,7 @@ template<> EIGEN_STRONG_INLINE Packet16b pcmp_eq(const Packet16b& a, const Packe
 template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return por(pcmp_lt(a,b), pcmp_eq(a,b)); }
 
 template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 63
+#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
   // There appears to be a bug in GCC, by which the optimizer may
   // flip the argument order in calls to _mm_min_ps, so we have to
   // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
@@ -463,7 +463,7 @@ template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const
 #endif
 }
 template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 63
+#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
   // There appears to be a bug in GCC, by which the optimizer may
   // flip the argument order in calls to _mm_min_pd, so we have to
   // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
@@ -494,7 +494,7 @@ template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const
 
 
 template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
-#if EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 63
+#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
   // There appears to be a bug in GCC, by which the optimizer may
   // flip the argument order in calls to _mm_max_ps, so we have to
   // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
@@ -513,7 +513,7 @@ template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const
 #endif
 }
 template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
-#if EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 63
+#if EIGEN_COMP_GNUC && EIGEN_COMP_GNUC < 63
   // There appears to be a bug in GCC, by which the optimizer may
   // flip the argument order in calls to _mm_max_pd, so we have to
   // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SSE/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/PacketMath.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SVE/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SVE/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/InteropHeaders.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/PacketMath.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/SyclMemoryModel.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/TypeCasting.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/SYCL/TypeCasting.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/SYCL/TypeCasting.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/Complex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
similarity index 91%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/Complex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
index 3c38066..0b9b33d 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/Complex.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/Complex.h
@@ -16,9 +16,7 @@ namespace Eigen {
 namespace internal {
 
 #if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
-inline Packet4ui  p4ui_CONJ_XOR() {
-  return { 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; //vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
-}
+static Packet4ui  p4ui_CONJ_XOR = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; //vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
 #endif
 
 static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
@@ -93,18 +91,8 @@ template<> struct packet_traits<std::complex<double> >  : default_packet_traits
   };
 };
 
-template<> struct unpacket_traits<Packet2cf> {
-  typedef std::complex<float>  type;
-  enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-  typedef Packet2cf half;
-  typedef Packet4f as_real;
-};
-template<> struct unpacket_traits<Packet1cd> {
-  typedef std::complex<double> type;
-  enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
-  typedef Packet1cd half;
-  typedef Packet2d as_real;
-};
+template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float>  type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; };
+template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; };
 
 /* Forward declaration */
 EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel);
@@ -162,7 +150,7 @@ template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::c
 
 template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
 {
-  EIGEN_ALIGN16 std::complex<double> res;
+  std::complex<double> EIGEN_ALIGN16 res;
   pstore<std::complex<double> >(&res, a);
 
   return res;
@@ -181,7 +169,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
 
 template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for AltiVec
+  Packet1cd res = pmul(a,pconj(b));
+  Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_);
+  return Packet1cd(pdiv(res.v, s + vec_perm(s, s, p16uc_REVERSE64)));
 }
 
 EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
@@ -204,7 +195,7 @@ template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<f
 
 template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
 {
-  EIGEN_ALIGN16 std::complex<float> res[2];
+  std::complex<float> EIGEN_ALIGN16 res[2];
   pstore<std::complex<float> >(res, a);
 
   return res[0];
@@ -234,14 +225,14 @@ template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<flo
 
 template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
 {
-  EIGEN_ALIGN16 std::complex<float> af[2];
+  std::complex<float> EIGEN_ALIGN16 af[2];
   af[0] = from[0*stride];
   af[1] = from[1*stride];
   return pload<Packet2cf>(af);
 }
 template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
 {
-  EIGEN_ALIGN16 std::complex<float> af[2];
+  std::complex<float> EIGEN_ALIGN16 af[2];
   pstore<std::complex<float> >((std::complex<float> *) af, from);
   to[0*stride] = af[0];
   to[1*stride] = af[1];
@@ -317,7 +308,11 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
 
 template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for AltiVec
+  Packet2cf res;
+  res.cd[0] = pdiv<Packet1cd>(a.cd[0], b.cd[0]);
+  res.cd[1] = pdiv<Packet1cd>(a.cd[1], b.cd[1]);
+  return res;
 }
 
 EIGEN_STRONG_INLINE Packet2cf pcplxflip/*<Packet2cf>*/(const Packet2cf& x)
@@ -347,7 +342,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packe
   Packet4f tmp = { eq[1], eq[0], eq[3], eq[2] };
   return (Packet2cf)pand<Packet4f>(eq, tmp);
 }
-template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR()))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
 template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
   Packet4f a_re, a_im, prod, prod_im;
@@ -360,7 +355,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
 
   // multiply a_im * b and get the conjugate result
   prod_im = a_im * b.v;
-  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR()));
+  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR));
   // permute back to a proper order
   prod_im = vec_perm(prod_im, prod_im, p16uc_COMPLEX32_REV);
 
@@ -399,7 +394,10 @@ EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
 
 template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
 {
-  return pdiv_complex(a, b);
+  // TODO optimize it for AltiVec
+  Packet2cf res = pmul(a, pconj(b));
+  Packet4f s = pmul<Packet4f>(b.v, b.v);
+  return Packet2cf(pdiv(res.v, padd<Packet4f>(s, vec_perm(s, s, p16uc_COMPLEX32_REV))));
 }
 
 template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/MathFunctions.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/PacketMath.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/PacketMath.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
index a7b59c8..1f55a90 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/arch/ZVector/PacketMath.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/arch/ZVector/PacketMath.h
@@ -91,8 +91,8 @@ static _EIGEN_DECLARE_CONST_FAST_Packet2l(ZERO, 0);
 static _EIGEN_DECLARE_CONST_FAST_Packet2l(ONE, 1);
 
 static Packet2d p2d_ONE = { 1.0, 1.0 };
-static Packet2d p2d_ZERO_ = { numext::bit_cast<double>(0x8000000000000000ull),
-                              numext::bit_cast<double>(0x8000000000000000ull) };
+static Packet2d p2d_ZERO_ = { numext::bit_cast<double>0x8000000000000000ull),
+                              numext::bit_cast<double>0x8000000000000000ull) };
 
 #if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
 #define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
@@ -358,7 +358,7 @@ pbroadcast4<Packet2d>(const double *a,
 
 template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
 {
-  EIGEN_ALIGN16 int ai[4];
+  int EIGEN_ALIGN16 ai[4];
   ai[0] = from[0*stride];
   ai[1] = from[1*stride];
   ai[2] = from[2*stride];
@@ -368,7 +368,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* f
 
 template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
 {
-  EIGEN_ALIGN16 double af[2];
+  double EIGEN_ALIGN16 af[2];
   af[0] = from[0*stride];
   af[1] = from[1*stride];
  return pload<Packet2d>(af);
@@ -376,7 +376,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const dou
 
 template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
 {
-  EIGEN_ALIGN16 int ai[4];
+  int EIGEN_ALIGN16 ai[4];
   pstore<int>((int *)ai, from);
   to[0*stride] = ai[0];
   to[1*stride] = ai[1];
@@ -386,7 +386,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const
 
 template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
 {
-  EIGEN_ALIGN16 double af[2];
+  double EIGEN_ALIGN16 af[2];
   pstore<double>(af, from);
   to[0*stride] = af[0];
   to[1*stride] = af[1];
@@ -460,8 +460,8 @@ template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d&
 template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
 template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
 
-template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { EIGEN_ALIGN16 int    x[4]; pstore(x, a); return x[0]; }
-template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { EIGEN_ALIGN16 double x[2]; pstore(x, a); return x[0]; }
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int    EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; }
 
 template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
 {
@@ -639,7 +639,7 @@ pbroadcast4<Packet4f>(const float *a,
 
 template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
 {
-  EIGEN_ALIGN16 float ai[4];
+  float EIGEN_ALIGN16 ai[4];
   ai[0] = from[0*stride];
   ai[1] = from[1*stride];
   ai[2] = from[2*stride];
@@ -649,7 +649,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const floa
 
 template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
 {
-  EIGEN_ALIGN16 float ai[4];
+  float EIGEN_ALIGN16 ai[4];
   pstore<float>((float *)ai, from);
   to[0*stride] = ai[0];
   to[1*stride] = ai[1];
@@ -785,7 +785,7 @@ template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*    from)
   return p;
 }
 
-template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x[2]; vec_st2f(a.v4f[0], &x[0]); return x[0]; }
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float  EIGEN_ALIGN16 x[2]; vec_st2f(a.v4f[0], &x[0]); return x[0]; }
 
 template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
 {
@@ -943,7 +943,7 @@ pbroadcast4<Packet4f>(const float *a,
 
 template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
 {
-  EIGEN_ALIGN16 float af[4];
+  float EIGEN_ALIGN16 af[4];
   af[0] = from[0*stride];
   af[1] = from[1*stride];
   af[2] = from[2*stride];
@@ -953,7 +953,7 @@ template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const floa
 
 template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
 {
-  EIGEN_ALIGN16 float af[4];
+  float EIGEN_ALIGN16 af[4];
   pstore<float>((float*)af, from);
   to[0*stride] = af[0];
   to[1*stride] = af[1];
@@ -978,7 +978,7 @@ template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f> (const Packet4f& a) { r
 template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>  (const Packet4f& a) { return vec_ceil(a); }
 template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f> (const Packet4f& a) { return vec_floor(a); }
 template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>   (const Packet4f& a) { return vec_abs(a); }
-template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x[4]; pstore(x, a); return x[0]; }
+template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; pstore(x, a); return x[0]; }
 
 template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
 {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/AssignmentFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/AssignmentFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/BinaryFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/BinaryFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/BinaryFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/NullaryFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/NullaryFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/NullaryFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/StlFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/StlFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/StlFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/TernaryFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/TernaryFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/TernaryFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/functors/UnaryFunctors.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/functors/UnaryFunctors.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/functors/UnaryFunctors.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
index 4c649a2..f35b760 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h
@@ -2269,8 +2269,8 @@ EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Pa
   bool gone_half = false, gone_quarter = false, gone_last = false;
 
   Index i = 0;
-  Index pack = Pack1;
-  Index psize = PacketSize;
+  int pack = Pack1;
+  int psize = PacketSize;
   while(pack>0)
   {
     Index remaining_rows = rows-i;
@@ -2290,21 +2290,21 @@ EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Pa
           {
             if (psize == PacketSize) {
               PacketBlock<Packet> kernel;
-              for (Index p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i+p+m, k);
+              for (int p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i+p+m, k);
               ptranspose(kernel);
-              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel.packet[p]));
+              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel.packet[p]));
             } else if (HasHalf && psize == HalfPacketSize) {
               gone_half = true;
               PacketBlock<HalfPacket> kernel_half;
-              for (Index p = 0; p < psize; ++p) kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i+p+m, k);
+              for (int p = 0; p < psize; ++p) kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i+p+m, k);
               ptranspose(kernel_half);
-              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_half.packet[p]));
+              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_half.packet[p]));
             } else if (HasQuarter && psize == QuarterPacketSize) {
               gone_quarter = true;
               PacketBlock<QuarterPacket> kernel_quarter;
-              for (Index p = 0; p < psize; ++p) kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i+p+m, k);
+              for (int p = 0; p < psize; ++p) kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i+p+m, k);
               ptranspose(kernel_quarter);
-              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_quarter.packet[p]));
+              for (int p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_quarter.packet[p]));
 	    }
           }
           count += psize*pack;
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
index 73ddd26..caa65fc 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h
@@ -59,9 +59,9 @@ typedef gebp_traits<LhsScalar,RhsScalar> Traits;
 
 typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
 static void run(Index rows, Index cols, Index depth,
-  const LhsScalar* lhs_, Index lhsStride,
-  const RhsScalar* rhs_, Index rhsStride,
-  ResScalar* res_, Index resIncr, Index resStride,
+  const LhsScalar* _lhs, Index lhsStride,
+  const RhsScalar* _rhs, Index rhsStride,
+  ResScalar* _res, Index resIncr, Index resStride,
   ResScalar alpha,
   level3_blocking<LhsScalar,RhsScalar>& blocking,
   GemmParallelInfo<Index>* info = 0)
@@ -69,9 +69,9 @@ static void run(Index rows, Index cols, Index depth,
   typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
   typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
   typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor,Unaligned,ResInnerStride> ResMapper;
-  LhsMapper lhs(lhs_, lhsStride);
-  RhsMapper rhs(rhs_, rhsStride);
-  ResMapper res(res_, resStride, resIncr);
+  LhsMapper lhs(_lhs, lhsStride);
+  RhsMapper rhs(_rhs, rhsStride);
+  ResMapper res(_res, resStride, resIncr);
 
   Index kc = blocking.kc();                   // cache block size along the K direction
   Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
similarity index 91%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
index dddba6f..6ba0d9b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
@@ -60,9 +60,9 @@ template <typename Index, typename LhsScalar, int LhsStorageOrder, bool Conjugat
 struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,UpLo,Version>
 {
   typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
-  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs_, Index lhsStride,
-                                      const RhsScalar* rhs_, Index rhsStride,
-                                      ResScalar* res_, Index resIncr, Index resStride,
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
+                                      const RhsScalar* _rhs, Index rhsStride,
+                                      ResScalar* _res, Index resIncr, Index resStride,
                                       const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
   {
     typedef gebp_traits<LhsScalar,RhsScalar> Traits;
@@ -70,9 +70,9 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
     typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
     typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
     typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(lhs_,lhsStride);
-    RhsMapper rhs(rhs_,rhsStride);
-    ResMapper res(res_, resStride, resIncr);
+    LhsMapper lhs(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
 
     Index kc = blocking.kc();
     Index mc = (std::min)(size,blocking.mc());
@@ -113,7 +113,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
           gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc,
                (std::min)(size,i2), alpha, -1, -1, 0, 0);
 
-        sybb(res_+resStride*i2 + resIncr*i2, resIncr, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha);
+        sybb(_res+resStride*i2 + resIncr*i2, resIncr, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha);
 
         if (UpLo==Upper)
         {
@@ -144,11 +144,11 @@ struct tribb_kernel
   enum {
     BlockSize  = meta_least_common_multiple<EIGEN_PLAIN_ENUM_MAX(mr,nr),EIGEN_PLAIN_ENUM_MIN(mr,nr)>::ret
   };
-  void operator()(ResScalar* res_, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
+  void operator()(ResScalar* _res, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
   {
     typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
     typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
-    ResMapper res(res_, resStride, resIncr);
+    ResMapper res(_res, resStride, resIncr);
     gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
     gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;
 
@@ -300,19 +300,14 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
   }
 };
 
-template <typename _MatrixType, unsigned int _Mode>
-template <typename ProductType>
-EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename TriangularViewImpl<_MatrixType, _Mode, Dense>::TriangularViewType&
-TriangularViewImpl<_MatrixType, _Mode, Dense>::_assignProduct(
-    const ProductType& prod, const typename TriangularViewImpl<_MatrixType, _Mode, Dense>::Scalar& alpha, bool beta) {
-  EIGEN_STATIC_ASSERT((_Mode & UnitDiag) == 0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
+template<typename MatrixType, unsigned int UpLo>
+template<typename ProductType>
+EIGEN_DEVICE_FUNC TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
+{
+  EIGEN_STATIC_ASSERT((UpLo&UnitDiag)==0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
   eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
 
-  general_product_to_triangular_selector<_MatrixType, ProductType, _Mode,
-                                         internal::traits<ProductType>::InnerSize == 1>::run(derived()
-                                                                                                 .nestedExpression()
-                                                                                                 .const_cast_derived(),
-                                                                                             prod, alpha, beta);
+  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);
 
   return derived();
 }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
index 974a047..dfb6aeb 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixVector.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h
@@ -359,11 +359,6 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
          HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
   };
 
-  typedef typename make_unsigned<Index>::type UnsignedIndex;
-  const Index fullColBlockEnd = LhsPacketSize * (UnsignedIndex(cols) / LhsPacketSize);
-  const Index halfColBlockEnd = LhsPacketSizeHalf * (UnsignedIndex(cols) / LhsPacketSizeHalf);
-  const Index quarterColBlockEnd = LhsPacketSizeQuarter * (UnsignedIndex(cols) / LhsPacketSizeQuarter);
-
   Index i=0;
   for(; i<n8; i+=8)
   {
@@ -376,7 +371,8 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
               c6 = pset1<ResPacket>(ResScalar(0)),
               c7 = pset1<ResPacket>(ResScalar(0));
 
-    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    Index j=0;
+    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
     {
       RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
 
@@ -397,8 +393,7 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
     ResScalar cc5 = predux(c5);
     ResScalar cc6 = predux(c6);
     ResScalar cc7 = predux(c7);
-
-    for (Index j = fullColBlockEnd; j < cols; ++j)
+    for(; j<cols; ++j)
     {
       RhsScalar b0 = rhs(j,0);
 
@@ -427,7 +422,8 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
               c2 = pset1<ResPacket>(ResScalar(0)),
               c3 = pset1<ResPacket>(ResScalar(0));
 
-    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    Index j=0;
+    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
     {
       RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
 
@@ -440,8 +436,7 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
     ResScalar cc1 = predux(c1);
     ResScalar cc2 = predux(c2);
     ResScalar cc3 = predux(c3);
-
-    for(Index j = fullColBlockEnd; j < cols; ++j)
+    for(; j<cols; ++j)
     {
       RhsScalar b0 = rhs(j,0);
 
@@ -460,7 +455,8 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
     ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
               c1 = pset1<ResPacket>(ResScalar(0));
 
-    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    Index j=0;
+    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
     {
       RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
 
@@ -469,8 +465,7 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
     }
     ResScalar cc0 = predux(c0);
     ResScalar cc1 = predux(c1);
-
-    for(Index j = fullColBlockEnd; j < cols; ++j)
+    for(; j<cols; ++j)
     {
       RhsScalar b0 = rhs(j,0);
 
@@ -485,15 +480,15 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
     ResPacket c0 = pset1<ResPacket>(ResScalar(0));
     ResPacketHalf c0_h = pset1<ResPacketHalf>(ResScalar(0));
     ResPacketQuarter c0_q = pset1<ResPacketQuarter>(ResScalar(0));
-
-    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    Index j=0;
+    for(; j+LhsPacketSize<=cols; j+=LhsPacketSize)
     {
       RhsPacket b0 = rhs.template load<RhsPacket,Unaligned>(j,0);
       c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i,j),b0,c0);
     }
     ResScalar cc0 = predux(c0);
     if (HasHalf) {
-      for (Index j = fullColBlockEnd; j < halfColBlockEnd; j += LhsPacketSizeHalf)
+      for(; j+LhsPacketSizeHalf<=cols; j+=LhsPacketSizeHalf)
         {
           RhsPacketHalf b0 = rhs.template load<RhsPacketHalf,Unaligned>(j,0);
           c0_h = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i,j),b0,c0_h);
@@ -501,14 +496,14 @@ EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,Lhs
       cc0 += predux(c0_h);
     }
     if (HasQuarter) {
-      for (Index j = halfColBlockEnd; j < quarterColBlockEnd; j += LhsPacketSizeQuarter)
+      for(; j+LhsPacketSizeQuarter<=cols; j+=LhsPacketSizeQuarter)
         {
           RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter,Unaligned>(j,0);
           c0_q = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i,j),b0,c0_q);
         }
       cc0 += predux(c0_q);
     }
-    for (Index j = quarterColBlockEnd; j < cols; ++j)
+    for(; j<cols; ++j)
     {
       cc0 += cj.pmul(lhs(i,j), rhs(j,0));
     }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/Parallelizer.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/Parallelizer.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/Parallelizer.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/Parallelizer.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
index 86c8f3e..33ecf10 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
@@ -43,7 +43,7 @@ struct symm_pack_lhs
       for(Index w=0; w<BlockRows; w++)
         blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
   }
-  void operator()(Scalar* blockA, const Scalar* lhs_, Index lhsStride, Index cols, Index rows)
+  void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
   {
     typedef typename unpacket_traits<typename packet_traits<Scalar>::type>::half HalfPacket;
     typedef typename unpacket_traits<typename unpacket_traits<typename packet_traits<Scalar>::type>::half>::half QuarterPacket;
@@ -53,7 +53,7 @@ struct symm_pack_lhs
            HasHalf = (int)HalfPacketSize < (int)PacketSize,
            HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
 
-    const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(lhs_,lhsStride);
+    const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(_lhs,lhsStride);
     Index count = 0;
     //Index peeled_mc3 = (rows/Pack1)*Pack1;
     
@@ -101,11 +101,11 @@ template<typename Scalar, typename Index, int nr, int StorageOrder>
 struct symm_pack_rhs
 {
   enum { PacketSize = packet_traits<Scalar>::size };
-  void operator()(Scalar* blockB, const Scalar* rhs_, Index rhsStride, Index rows, Index cols, Index k2)
+  void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
   {
     Index end_k = k2 + rows;
     Index count = 0;
-    const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(rhs_,rhsStride);
+    const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(_rhs,rhsStride);
     Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
     Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
 
@@ -330,8 +330,8 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
 
   static EIGEN_DONT_INLINE void run(
     Index rows, Index cols,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
     Scalar* res,        Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
 };
@@ -342,9 +342,9 @@ template <typename Scalar, typename Index,
           int ResInnerStride>
 EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>::run(
     Index rows, Index cols,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
-    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
   {
     Index size = rows;
@@ -355,10 +355,10 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,t
     typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper;
     typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
     typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(lhs_,lhsStride);
-    LhsTransposeMapper lhs_transpose(lhs_,lhsStride);
-    RhsMapper rhs(rhs_,rhsStride);
-    ResMapper res(res_, resStride, resIncr);
+    LhsMapper lhs(_lhs,lhsStride);
+    LhsTransposeMapper lhs_transpose(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
 
     Index kc = blocking.kc();                   // cache block size along the K direction
     Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
@@ -425,8 +425,8 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
 
   static EIGEN_DONT_INLINE void run(
     Index rows, Index cols,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
     Scalar* res,        Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
 };
@@ -437,9 +437,9 @@ template <typename Scalar, typename Index,
           int ResInnerStride>
 EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>::run(
     Index rows, Index cols,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
-    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
   {
     Index size = cols;
@@ -448,8 +448,8 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,f
 
     typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
     typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(lhs_,lhsStride);
-    ResMapper res(res_,resStride, resIncr);
+    LhsMapper lhs(_lhs,lhsStride);
+    ResMapper res(_res,resStride, resIncr);
 
     Index kc = blocking.kc();                   // cache block size along the K direction
     Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
@@ -466,7 +466,7 @@ EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,f
     {
       const Index actual_kc = (std::min)(k2+kc,size)-k2;
 
-      pack_rhs(blockB, rhs_, rhsStride, actual_kc, cols, k2);
+      pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
 
       // => GEPP
       for(Index i2=0; i2<rows; i2+=mc)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointRank2Update.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/SelfadjointRank2Update.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
index ba605a1..f0c6050 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h
@@ -18,10 +18,10 @@ namespace internal {
 // struct gemm_pack_lhs_triangular
 // {
 //   Matrix<Scalar,mr,mr,
-//   void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* lhs_, int lhsStride, int depth, int rows)
+//   void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* _lhs, int lhsStride, int depth, int rows)
 //   {
 //     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
-//     const_blas_data_mapper<Scalar, StorageOrder> lhs(lhs_,lhsStride);
+//     const_blas_data_mapper<Scalar, StorageOrder> lhs(_lhs,lhsStride);
 //     int count = 0;
 //     const int peeled_mc = (rows/mr)*mr;
 //     for(int i=0; i<peeled_mc; i+=mr)
@@ -96,8 +96,8 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
 
   static EIGEN_DONT_INLINE void run(
     Index _rows, Index _cols, Index _depth,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
     Scalar* res,        Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
 };
@@ -110,9 +110,9 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
                                                         LhsStorageOrder,ConjugateLhs,
                                                         RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
     Index _rows, Index _cols, Index _depth,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
-    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
   {
     // strip zeros
@@ -124,9 +124,9 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
     typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
     typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
     typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(lhs_,lhsStride);
-    RhsMapper rhs(rhs_,rhsStride);
-    ResMapper res(res_, resStride, resIncr);
+    LhsMapper lhs(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
 
     Index kc = blocking.kc();                   // cache block size along the K direction
     Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
@@ -254,8 +254,8 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
 
   static EIGEN_DONT_INLINE void run(
     Index _rows, Index _cols, Index _depth,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
     Scalar* res,        Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
 };
@@ -268,9 +268,9 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
                                                         LhsStorageOrder,ConjugateLhs,
                                                         RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
     Index _rows, Index _cols, Index _depth,
-    const Scalar* lhs_, Index lhsStride,
-    const Scalar* rhs_, Index rhsStride,
-    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
     const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
   {
     const Index PacketBytes = packet_traits<Scalar>::size*sizeof(Scalar);
@@ -283,9 +283,9 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
     typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
     typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
     typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
-    LhsMapper lhs(lhs_,lhsStride);
-    RhsMapper rhs(rhs_,rhsStride);
-    ResMapper res(res_, resStride, resIncr);
+    LhsMapper lhs(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
 
     Index kc = blocking.kc();                   // cache block size along the K direction
     Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
similarity index 90%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
index 0a25748..76bfa15 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h
@@ -26,30 +26,30 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
     HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
     HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
   };
-  static EIGEN_DONT_INLINE  void run(Index rows_, Index cols_, const LhsScalar* lhs_, Index lhsStride,
-                                     const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr, const RhsScalar& alpha);
+  static EIGEN_DONT_INLINE  void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+                                     const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha);
 };
 
 template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
 EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
-  ::run(Index rows_, Index cols_, const LhsScalar* lhs_, Index lhsStride,
-        const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr, const RhsScalar& alpha)
+  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha)
   {
     static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index size = (std::min)(rows_,cols_);
-    Index rows = IsLower ? rows_ : (std::min)(rows_,cols_);
-    Index cols = IsLower ? (std::min)(rows_,cols_) : cols_;
+    Index size = (std::min)(_rows,_cols);
+    Index rows = IsLower ? _rows : (std::min)(_rows,_cols);
+    Index cols = IsLower ? (std::min)(_rows,_cols) : _cols;
 
     typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(lhs_,rows,cols,OuterStride<>(lhsStride));
+    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
     typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
 
     typedef Map<const Matrix<RhsScalar,Dynamic,1>, 0, InnerStride<> > RhsMap;
-    const RhsMap rhs(rhs_,cols,InnerStride<>(rhsIncr));
+    const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr));
     typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
 
     typedef Map<Matrix<ResScalar,Dynamic,1> > ResMap;
-    ResMap res(res_,rows);
+    ResMap res(_res,rows);
 
     typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
     typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
@@ -84,7 +84,7 @@ EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,Con
           rows, cols-size,
           LhsMapper(&lhs.coeffRef(0,size), lhsStride),
           RhsMapper(&rhs.coeffRef(size), rhsIncr),
-          res_, resIncr, alpha);
+          _res, resIncr, alpha);
     }
   }
 
@@ -97,30 +97,30 @@ struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
     HasUnitDiag = (Mode & UnitDiag)==UnitDiag,
     HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag
   };
-  static EIGEN_DONT_INLINE void run(Index rows_, Index cols_, const LhsScalar* lhs_, Index lhsStride,
-                                    const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr, const ResScalar& alpha);
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+                                    const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha);
 };
 
 template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
 EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
-  ::run(Index rows_, Index cols_, const LhsScalar* lhs_, Index lhsStride,
-        const RhsScalar* rhs_, Index rhsIncr, ResScalar* res_, Index resIncr, const ResScalar& alpha)
+  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
   {
     static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
-    Index diagSize = (std::min)(rows_,cols_);
-    Index rows = IsLower ? rows_ : diagSize;
-    Index cols = IsLower ? diagSize : cols_;
+    Index diagSize = (std::min)(_rows,_cols);
+    Index rows = IsLower ? _rows : diagSize;
+    Index cols = IsLower ? diagSize : _cols;
 
     typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
-    const LhsMap lhs(lhs_,rows,cols,OuterStride<>(lhsStride));
+    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
     typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
 
     typedef Map<const Matrix<RhsScalar,Dynamic,1> > RhsMap;
-    const RhsMap rhs(rhs_,cols);
+    const RhsMap rhs(_rhs,cols);
     typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
 
     typedef Map<Matrix<ResScalar,Dynamic,1>, 0, InnerStride<> > ResMap;
-    ResMap res(res_,rows,InnerStride<>(resIncr));
+    ResMap res(_res,rows,InnerStride<>(resIncr));
 
     typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
     typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
similarity index 83%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
index 0f8d3a1..3d47a2b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
@@ -50,18 +50,18 @@ struct triangular_matrix_vector_product_trmv :
 #define EIGEN_BLAS_TRMV_SPECIALIZE(Scalar) \
 template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
 struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
- static void run(Index rows_, Index cols_, const Scalar* lhs_, Index lhsStride, \
-                                     const Scalar* rhs_, Index rhsIncr, Scalar* res_, Index resIncr, Scalar alpha) { \
+ static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
       triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
-        rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha); \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
   } \
 }; \
 template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
 struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
- static void run(Index rows_, Index cols_, const Scalar* lhs_, Index lhsStride, \
-                                     const Scalar* rhs_, Index rhsIncr, Scalar* res_, Index resIncr, Scalar alpha) { \
+ static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
       triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
-        rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha); \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
   } \
 };
 
@@ -81,23 +81,23 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
     IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
     LowUp = IsLower ? Lower : Upper \
   }; \
- static void run(Index rows_, Index cols_, const EIGTYPE* lhs_, Index lhsStride, \
-                 const EIGTYPE* rhs_, Index rhsIncr, EIGTYPE* res_, Index resIncr, EIGTYPE alpha) \
+ static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
  { \
    if (ConjLhs || IsZeroDiag) { \
      triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
-       rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha); \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
      return; \
    }\
-   Index size = (std::min)(rows_,cols_); \
-   Index rows = IsLower ? rows_ : size; \
-   Index cols = IsLower ? size : cols_; \
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
 \
    typedef VectorX##EIGPREFIX VectorRhs; \
    EIGTYPE *x, *y;\
 \
 /* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(rhs_,cols,InnerStride<>(rhsIncr)); \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
    VectorRhs x_tmp; \
    if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
    x = x_tmp.data(); \
@@ -121,24 +121,24 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
    diag = IsUnitDiag ? 'U' : 'N'; \
 \
 /* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)lhs_, &lda, (BLASTYPE*)x, &incx); \
+   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
 \
 /* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)res_, &incy); \
+   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
 /* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
    if (size<(std::max)(rows,cols)) { \
      if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
      x = x_tmp.data(); \
      if (size<rows) { \
-       y = res_ + size*resIncr; \
-       a = lhs_ + size; \
+       y = _res + size*resIncr; \
+       a = _lhs + size; \
        m = convert_index<BlasIndex>(rows-size); \
        n = convert_index<BlasIndex>(size); \
      } \
      else { \
        x += size; \
-       y = res_; \
-       a = lhs_ + size*lda; \
+       y = _res; \
+       a = _lhs + size*lda; \
        m = convert_index<BlasIndex>(size); \
        n = convert_index<BlasIndex>(cols-size); \
      } \
@@ -170,23 +170,23 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
     IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
     LowUp = IsLower ? Lower : Upper \
   }; \
- static void run(Index rows_, Index cols_, const EIGTYPE* lhs_, Index lhsStride, \
-                 const EIGTYPE* rhs_, Index rhsIncr, EIGTYPE* res_, Index resIncr, EIGTYPE alpha) \
+ static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
  { \
    if (IsZeroDiag) { \
      triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
-       rows_, cols_, lhs_, lhsStride, rhs_, rhsIncr, res_, resIncr, alpha); \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
      return; \
    }\
-   Index size = (std::min)(rows_,cols_); \
-   Index rows = IsLower ? rows_ : size; \
-   Index cols = IsLower ? size : cols_; \
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
 \
    typedef VectorX##EIGPREFIX VectorRhs; \
    EIGTYPE *x, *y;\
 \
 /* Set x*/ \
-   Map<const VectorRhs, 0, InnerStride<> > rhs(rhs_,cols,InnerStride<>(rhsIncr)); \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
    VectorRhs x_tmp; \
    if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
    x = x_tmp.data(); \
@@ -210,24 +210,24 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
    diag = IsUnitDiag ? 'U' : 'N'; \
 \
 /* call ?TRMV*/ \
-   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)lhs_, &lda, (BLASTYPE*)x, &incx); \
+   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
 \
 /* Add op(a_tr)rhs into res*/ \
-   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)res_, &incy); \
+   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
 /* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
    if (size<(std::max)(rows,cols)) { \
      if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
      x = x_tmp.data(); \
      if (size<rows) { \
-       y = res_ + size*resIncr; \
-       a = lhs_ + size*lda; \
+       y = _res + size*resIncr; \
+       a = _lhs + size*lda; \
        m = convert_index<BlasIndex>(rows-size); \
        n = convert_index<BlasIndex>(size); \
      } \
      else { \
        x += size; \
-       y = res_; \
-       a = lhs_ + size; \
+       y = _res; \
+       a = _lhs + size; \
        m = convert_index<BlasIndex>(size); \
        n = convert_index<BlasIndex>(cols-size); \
      } \
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/products/TriangularSolverVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/products/TriangularSolverVector.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/BlasUtil.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/BlasUtil.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/BlasUtil.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/BlasUtil.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/ConfigureVectorization.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/ConfigureVectorization.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
index 7d51426..af4e696 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/ConfigureVectorization.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ConfigureVectorization.h
@@ -339,7 +339,7 @@
     extern "C" {
       // In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
       // Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
-      #if EIGEN_COMP_ICC >= 1110 || EIGEN_COMP_EMSCRIPTEN
+      #if EIGEN_COMP_ICC >= 1110
         #include <immintrin.h>
       #else
         #include <mmintrin.h>
@@ -363,11 +363,10 @@
       #endif
     } // end extern "C"
 
-  #elif defined(__VSX__) && !defined(__APPLE__)
+  #elif defined __VSX__
 
     #define EIGEN_VECTORIZE
-    #define EIGEN_VECTORIZE_VSX 1
-    #define EIGEN_VECTORIZE_FMA
+    #define EIGEN_VECTORIZE_VSX
     #include <altivec.h>
     // We need to #undef all these ugly tokens defined in <altivec.h>
     // => use __vector instead of vector
@@ -379,7 +378,6 @@
 
     #define EIGEN_VECTORIZE
     #define EIGEN_VECTORIZE_ALTIVEC
-    #define EIGEN_VECTORIZE_FMA
     #include <altivec.h>
     // We need to #undef all these ugly tokens defined in <altivec.h>
     // => use __vector instead of vector
@@ -440,20 +438,13 @@
   #include <arm_fp16.h>
 #endif
 
-// Enable FMA for ARM.
-#if defined(__ARM_FEATURE_FMA)
-#define EIGEN_VECTORIZE_FMA
-#endif
-
-#if defined(__F16C__) && !defined(EIGEN_GPUCC) && (!EIGEN_COMP_CLANG_STRICT || EIGEN_COMP_CLANG>=380)
+#if defined(__F16C__) && (!defined(EIGEN_GPUCC) && (!defined(EIGEN_COMP_CLANG) || EIGEN_COMP_CLANG>=380))
   // We can use the optimized fp16 to float and float to fp16 conversion routines
   #define EIGEN_HAS_FP16_C
 
-  #if EIGEN_COMP_GNUC
-    // Make sure immintrin.h is included, even if e.g. vectorization is
-    // explicitly disabled (see also issue #2395).
-    // Note that FP16C intrinsics for gcc and clang are included by immintrin.h,
-    // as opposed to emmintrin.h as suggested by Intel:
+  #if defined(EIGEN_COMP_CLANG)
+    // Workaround for clang: The FP16C intrinsics for clang are included by
+    // immintrin.h, as opposed to emmintrin.h as suggested by Intel:
     // https://software.intel.com/sites/landingpage/IntrinsicsGuide/#othertechs=FP16C&expand=1711
     #include <immintrin.h>
   #endif
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Constants.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Constants.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/Constants.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Constants.h
index 0667b1c..35dcaa7 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Constants.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Constants.h
@@ -134,7 +134,7 @@ const unsigned int LinearAccessBit = 0x10;
   * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable.
   * This rules out read-only expressions.
   *
-  * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but not
+  * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but note
   * the other:
   *   \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit
   *   \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit but not LvalueBit
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/DisableStupidWarnings.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
similarity index 56%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/DisableStupidWarnings.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
index 0865fb6..fe0cfec 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/DisableStupidWarnings.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h
@@ -1,10 +1,9 @@
 #ifndef EIGEN_WARNINGS_DISABLED
 #define EIGEN_WARNINGS_DISABLED
 
-#if defined(_MSC_VER)
+#ifdef _MSC_VER
   // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p))
   // 4101 - unreferenced local variable
-  // 4127 - conditional expression is constant
   // 4181 - qualifier applied to reference type ignored
   // 4211 - nonstandard extension used : redefined extern to static
   // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data
@@ -20,7 +19,7 @@
   #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
     #pragma warning( push )
   #endif
-  #pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
+  #pragma warning( disable : 4100 4101 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
 
 #elif defined __INTEL_COMPILER
   // 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
@@ -36,28 +35,25 @@
   #pragma warning disable 2196 279 1684 2259
 
 #elif defined __clang__
+  // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
+  //     this is really a stupid warning as it warns on compile-time expressions involving enums
   #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
     #pragma clang diagnostic push
   #endif
-  #if defined(__has_warning)
-    // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
-    //     this is really a stupid warning as it warns on compile-time expressions involving enums
-    #if __has_warning("-Wconstant-logical-operand")
-      #pragma clang diagnostic ignored "-Wconstant-logical-operand"
-    #endif
-    #if __has_warning("-Wimplicit-int-float-conversion")
-      #pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
-    #endif
-    #if ( defined(__ALTIVEC__) || defined(__VSX__) ) && __cplusplus < 201103L
-      // warning: generic selections are a C11-specific feature
-      // ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
-      #if __has_warning("-Wc11-extensions")
-        #pragma clang diagnostic ignored "-Wc11-extensions"
-      #endif
-    #endif
+  #pragma clang diagnostic ignored "-Wconstant-logical-operand"
+  #if __clang_major__ >= 3 && __clang_minor__ >= 5
+    #pragma clang diagnostic ignored "-Wabsolute-value"
+  #endif
+  #if __clang_major__ >= 10
+    #pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
+  #endif
+  #if ( defined(__ALTIVEC__) || defined(__VSX__) ) && __cplusplus < 201103L
+    // warning: generic selections are a C11-specific feature
+    // ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
+    #pragma clang diagnostic ignored "-Wc11-extensions"
   #endif
 
-#elif defined __GNUC__ && !defined(__FUJITSU)
+#elif defined __GNUC__
 
   #if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
     #pragma GCC diagnostic push
@@ -78,53 +74,25 @@
 #endif
 
 #if defined __NVCC__
-  // MSVC 14.16 (required by CUDA 9.*) does not support the _Pragma keyword, so
-  // we instead use Microsoft's __pragma extension.
-  #if defined _MSC_VER
-    #define EIGEN_MAKE_PRAGMA(X) __pragma(#X)
-  #else
-    #define EIGEN_MAKE_PRAGMA(X) _Pragma(#X)
-  #endif
-  #if defined __NVCC_DIAG_PRAGMA_SUPPORT__
-    #define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(nv_diag_suppress X)
-  #else
-    #define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(diag_suppress X)
-  #endif
-
-  EIGEN_NV_DIAG_SUPPRESS(boolean_controlling_expr_is_constant)
+  #pragma diag_suppress boolean_controlling_expr_is_constant
   // Disable the "statement is unreachable" message
-  EIGEN_NV_DIAG_SUPPRESS(code_is_unreachable)
+  #pragma diag_suppress code_is_unreachable
   // Disable the "dynamic initialization in unreachable code" message
-  EIGEN_NV_DIAG_SUPPRESS(initialization_not_reachable)
+  #pragma diag_suppress initialization_not_reachable
   // Disable the "invalid error number" message that we get with older versions of nvcc
-  EIGEN_NV_DIAG_SUPPRESS(1222)
+  #pragma diag_suppress 1222
   // Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are many of them and they seem to change with every version of the compiler)
-  EIGEN_NV_DIAG_SUPPRESS(2527)
-  EIGEN_NV_DIAG_SUPPRESS(2529)
-  EIGEN_NV_DIAG_SUPPRESS(2651)
-  EIGEN_NV_DIAG_SUPPRESS(2653)
-  EIGEN_NV_DIAG_SUPPRESS(2668)
-  EIGEN_NV_DIAG_SUPPRESS(2669)
-  EIGEN_NV_DIAG_SUPPRESS(2670)
-  EIGEN_NV_DIAG_SUPPRESS(2671)
-  EIGEN_NV_DIAG_SUPPRESS(2735)
-  EIGEN_NV_DIAG_SUPPRESS(2737)
-  EIGEN_NV_DIAG_SUPPRESS(2739)
-  EIGEN_NV_DIAG_SUPPRESS(2885)
-  EIGEN_NV_DIAG_SUPPRESS(2888)
-  EIGEN_NV_DIAG_SUPPRESS(2976)
-  EIGEN_NV_DIAG_SUPPRESS(2979)
-  EIGEN_NV_DIAG_SUPPRESS(20011)
-  EIGEN_NV_DIAG_SUPPRESS(20014)
-  // Disable the "// __device__ annotation is ignored on a function(...) that is
-  //              explicitly defaulted on its first declaration" message.
-  // The __device__ annotation seems to actually be needed in some cases,
-  // otherwise resulting in kernel runtime errors.
-  EIGEN_NV_DIAG_SUPPRESS(2886)
-  EIGEN_NV_DIAG_SUPPRESS(2977)
-  EIGEN_NV_DIAG_SUPPRESS(20012)
-  #undef EIGEN_NV_DIAG_SUPPRESS
-  #undef EIGEN_MAKE_PRAGMA
+  #pragma diag_suppress 2527
+  #pragma diag_suppress 2529
+  #pragma diag_suppress 2651
+  #pragma diag_suppress 2653
+  #pragma diag_suppress 2668
+  #pragma diag_suppress 2669
+  #pragma diag_suppress 2670
+  #pragma diag_suppress 2671
+  #pragma diag_suppress 2735
+  #pragma diag_suppress 2737
+  #pragma diag_suppress 2739
 #endif
 
 #else
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/ForwardDeclarations.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/ForwardDeclarations.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ForwardDeclarations.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/IndexedViewHelper.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/IndexedViewHelper.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
index 0d64d5e..f85de30 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/IndexedViewHelper.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IndexedViewHelper.h
@@ -168,7 +168,7 @@ template<int Size> struct get_compile_time_incr<AllRange<Size> > {
   * \ingroup Core_Module
   * Can be used as a parameter to DenseBase::operator()(const RowIndices&, const ColIndices&) to index all rows or columns
   */
-static const Eigen::internal::all_t all;
+static const Eigen::internal::all_t all; // PLEASE use Eigen::all instead of Eigen::placeholders::all
 
 
 namespace placeholders {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/IntegralConstant.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/IntegralConstant.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
index e0092f6..945d426 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/IntegralConstant.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/IntegralConstant.h
@@ -138,7 +138,7 @@ template<int N,int Default> struct get_fixed_value<FixedInt<N>,Default> {
   static const int value = N;
 };
 
-#if !EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
+#if !EIGEN_HAS_CXX14
 template<int N,int Default> struct get_fixed_value<FixedInt<N> (*)(),Default> {
   static const int value = N;
 };
@@ -154,7 +154,7 @@ struct get_fixed_value<variable_if_dynamic<T,N>,Default> {
 };
 
 template<typename T> EIGEN_DEVICE_FUNC Index get_runtime_value(const T &x) { return x; }
-#if !EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
+#if !EIGEN_HAS_CXX14
 template<int N> EIGEN_DEVICE_FUNC Index get_runtime_value(FixedInt<N> (*)()) { return N; }
 #endif
 
@@ -166,7 +166,7 @@ template<typename T, int DynamicKey=Dynamic, typename EnableIf=void> struct clea
 // Convert any integral type (e.g., short, int, unsigned int, etc.) to Eigen::Index
 template<typename T, int DynamicKey> struct cleanup_index_type<T,DynamicKey,typename internal::enable_if<internal::is_integral<T>::value>::type> { typedef Index type; };
 
-#if !EIGEN_HAS_CXX14_VARIABLE_TEMPLATES
+#if !EIGEN_HAS_CXX14
 // In c++98/c++11, fix<N> is a pointer to function that we better cleanup to a true FixedInt<N>:
 template<int N, int DynamicKey> struct cleanup_index_type<FixedInt<N> (*)(), DynamicKey> { typedef FixedInt<N> type; };
 #endif
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/MKL_support.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/MKL_support.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/MKL_support.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/MKL_support.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Macros.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Macros.h
similarity index 94%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/Macros.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Macros.h
index eb88e5f..986c3d4 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Macros.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Macros.h
@@ -17,7 +17,7 @@
 
 #define EIGEN_WORLD_VERSION 3
 #define EIGEN_MAJOR_VERSION 4
-#define EIGEN_MINOR_VERSION 1
+#define EIGEN_MINOR_VERSION 0
 
 #define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
                                       (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@@ -179,13 +179,6 @@
   #define EIGEN_COMP_PGI 0
 #endif
 
-/// \internal EIGEN_COMP_NVHPC set to NVHPC version if the compiler is nvc++
-#if defined(__NVCOMPILER)
-#define EIGEN_COMP_NVHPC (__NVCOMPILER_MAJOR__ * 100 + __NVCOMPILER_MINOR__)
-#else
-#define EIGEN_COMP_NVHPC 0
-#endif
-
 /// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler
 #if defined(__CC_ARM) || defined(__ARMCC_VERSION)
   #define EIGEN_COMP_ARM 1
@@ -282,7 +275,7 @@
 
 /// \internal EIGEN_HAS_ARM64_FP16 set to 1 if the architecture provides an IEEE
 /// compliant Arm fp16 type
-#if EIGEN_ARCH_ARM_OR_ARM64
+#if EIGEN_ARCH_ARM64
   #ifndef EIGEN_HAS_ARM64_FP16
     #if defined(__ARM_FP16_FORMAT_IEEE)
       #define EIGEN_HAS_ARM64_FP16 1
@@ -294,7 +287,7 @@
 
 /// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
 /// supports Neon vector intrinsics for fp16.
-#if EIGEN_ARCH_ARM_OR_ARM64
+#if EIGEN_ARCH_ARM64
   #ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
     #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
       #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
@@ -306,7 +299,7 @@
 
 /// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
 /// supports Neon scalar intrinsics for fp16.
-#if EIGEN_ARCH_ARM_OR_ARM64
+#if EIGEN_ARCH_ARM64
   #ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
     #if defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC)
       #define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
@@ -336,7 +329,7 @@
 #endif
 
 /// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC
-#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC) || defined(__POWERPC__)
+#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC)
   #define EIGEN_ARCH_PPC 1
 #else
   #define EIGEN_ARCH_PPC 0
@@ -572,32 +565,6 @@
 //
 #endif
 
-/// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
-/// supports Neon vector intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
-    // Clang only supports FP16 on aarch64, and not all intrinsics are available
-    // on A32 anyways even in GCC (e.g. vdiv_f16, vsqrt_f16).
-    #if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
-    #else
-      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 0
-    #endif
-  #endif
-#endif
-
-/// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
-/// supports Neon scalar intrinsics for fp16.
-#if EIGEN_ARCH_ARM64
-  #ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
-    // Clang only supports FP16 on aarch64, and not all intrinsics are available
-    // on A32 anyways, even in GCC (e.g. vceqh_f16).
-    #if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
-      #define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
-    #endif
-  #endif
-#endif
-
 #if defined(EIGEN_USE_SYCL) && defined(__SYCL_DEVICE_ONLY__)
 // EIGEN_USE_SYCL is a user-defined macro while __SYCL_DEVICE_ONLY__ is a compiler-defined macro.
 // In most cases we want to check if both macros are defined which can be done using the define below.
@@ -1157,28 +1124,14 @@ namespace Eigen {
     // directly for std::complex<T>, Eigen::half, Eigen::bfloat16. For these,
     // you will need to apply to the underlying POD type.
     #if EIGEN_ARCH_PPC && EIGEN_COMP_GNUC_STRICT
-      // This seems to be broken on clang. Packet4f is loaded into a single
-      //   register rather than a vector, zeroing out some entries. Integer
+      // This seems to be broken on clang.  Packet4f is loaded into a single
+      //   register rather than a vector, zeroing out some entries.  Integer
       //   types also generate a compile error.
-      #if EIGEN_OS_MAC
-        // General, Altivec for Apple (VSX were added in ISA v2.06):
-        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v" (X));
-      #else
-        // General, Altivec, VSX otherwise:
-        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v,wa" (X));
-      #endif
+      // General, Altivec, VSX.
+      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v,wa" (X));
     #elif EIGEN_ARCH_ARM_OR_ARM64
       // General, NEON.
-      // Clang doesn't like "r",
-      //    error: non-trivial scalar-to-vector conversion, possible invalid
-      //           constraint for vector type
-      // GCC < 5 doesn't like "g",
-      //    error: 'asm' operand requires impossible reload
-      #if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_MOST(5, 0)
-        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,w" (X));
-      #else
-        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,w" (X));
-      #endif
+      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,w" (X));
     #elif EIGEN_ARCH_i386_OR_x86_64
       // General, SSE.
       #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,x" (X));
@@ -1232,7 +1185,7 @@ namespace Eigen {
   #define EIGEN_USING_STD(FUNC) using std::FUNC;
 #endif
 
-#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1916 || (EIGEN_COMP_MSVC == 1916 && EIGEN_COMP_NVCC))
+#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1900 || (EIGEN_COMP_MSVC == 1900 && EIGEN_COMP_NVCC))
   // For older MSVC versions, as well as 1900 && CUDA 8, using the base operator is necessary,
   //   otherwise we get duplicate definition errors
   // For later MSVC versions, we require explicit operator= definition, otherwise we get
@@ -1263,7 +1216,7 @@ namespace Eigen {
  * This is necessary, because the implicit definition is deprecated if the copy-assignment is overridden.
  */
 #if EIGEN_HAS_CXX11
-#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) EIGEN_DEVICE_FUNC CLASS(const CLASS&) = default;
+#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) CLASS(const CLASS&) = default;
 #else
 #define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS)
 #endif
@@ -1288,12 +1241,12 @@ namespace Eigen {
  */
 #if EIGEN_HAS_CXX11
 #define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    EIGEN_DEVICE_FUNC Derived() = default; \
-    EIGEN_DEVICE_FUNC ~Derived() = default;
+    Derived() = default; \
+    ~Derived() = default;
 #else
 #define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
-    EIGEN_DEVICE_FUNC Derived() {}; \
-    /* EIGEN_DEVICE_FUNC ~Derived() {}; */
+    Derived() {}; \
+    /* ~Derived() {}; */
 #endif
 
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Memory.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Memory.h
similarity index 93%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/Memory.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Memory.h
index 3aea7df..875318c 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Memory.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Memory.h
@@ -292,59 +292,20 @@ template<typename T> EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T
 /** \internal Constructs the elements of an array.
   * The \a size parameter tells on how many objects to call the constructor of T.
   */
-template<typename T> EIGEN_DEVICE_FUNC inline T* default_construct_elements_of_array(T *ptr, std::size_t size)
+template<typename T> EIGEN_DEVICE_FUNC inline T* construct_elements_of_array(T *ptr, std::size_t size)
 {
-  std::size_t i=0;
+  std::size_t i;
   EIGEN_TRY
   {
-    for (i = 0; i < size; ++i) ::new (ptr + i) T;
+      for (i = 0; i < size; ++i) ::new (ptr + i) T;
+      return ptr;
   }
   EIGEN_CATCH(...)
   {
     destruct_elements_of_array(ptr, i);
     EIGEN_THROW;
   }
-  return ptr;
-}
-
-/** \internal Copy-constructs the elements of an array.
-  * The \a size parameter tells on how many objects to copy.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* copy_construct_elements_of_array(T *ptr, const T* src, std::size_t size)
-{
-  std::size_t i=0;
-  EIGEN_TRY
-  {
-      for (i = 0; i < size; ++i) ::new (ptr + i) T(*(src + i));
-  }
-  EIGEN_CATCH(...)
-  {
-    destruct_elements_of_array(ptr, i);
-    EIGEN_THROW;
-  }
-  return ptr;
-}
-
-/** \internal Move-constructs the elements of an array.
-  * The \a size parameter tells on how many objects to move.
-  */
-template<typename T> EIGEN_DEVICE_FUNC inline T* move_construct_elements_of_array(T *ptr, T* src, std::size_t size)
-{
-  std::size_t i=0;
-  EIGEN_TRY
-  {
-#if EIGEN_HAS_RVALUE_REFERENCES
-    for (i = 0; i < size; ++i) ::new (ptr + i) T(std::move(*(src + i)));
-#else
-    for (i = 0; i < size; ++i) ::new (ptr + i) T(*(src + i));
-#endif
-  }
-  EIGEN_CATCH(...)
-  {
-    destruct_elements_of_array(ptr, i);
-    EIGEN_THROW;
-  }
-  return ptr;
+  return NULL;
 }
 
 /*****************************************************************************
@@ -365,10 +326,10 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(std::size_t s
 template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size)
 {
   check_size_for_overflow<T>(size);
-  T *result = static_cast<T*>(aligned_malloc(sizeof(T)*size));
+  T *result = reinterpret_cast<T*>(aligned_malloc(sizeof(T)*size));
   EIGEN_TRY
   {
-    return default_construct_elements_of_array(result, size);
+    return construct_elements_of_array(result, size);
   }
   EIGEN_CATCH(...)
   {
@@ -381,10 +342,10 @@ template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size)
 template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(std::size_t size)
 {
   check_size_for_overflow<T>(size);
-  T *result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
+  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
   EIGEN_TRY
   {
-    return default_construct_elements_of_array(result, size);
+    return construct_elements_of_array(result, size);
   }
   EIGEN_CATCH(...)
   {
@@ -416,32 +377,21 @@ template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned
 {
   check_size_for_overflow<T>(new_size);
   check_size_for_overflow<T>(old_size);
-  
-  // If elements need to be explicitly initialized, we cannot simply realloc
-  // (or memcpy) the memory block - each element needs to be reconstructed.
-  // Otherwise, objects that contain internal pointers like mpfr or
-  // AnnoyingScalar can be pointing to the wrong thing.
-  T* result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*new_size));
-  EIGEN_TRY
+  if(new_size < old_size)
+    destruct_elements_of_array(pts+new_size, old_size-new_size);
+  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
+  if(new_size > old_size)
   {
-    // Move-construct initial elements.
-    std::size_t copy_size = (std::min)(old_size, new_size);
-    move_construct_elements_of_array(result, pts, copy_size);
-    
-    // Default-construct remaining elements.
-    if (new_size > old_size) {
-      default_construct_elements_of_array(result + copy_size, new_size - old_size);
+    EIGEN_TRY
+    {
+      construct_elements_of_array(result+old_size, new_size-old_size);
+    }
+    EIGEN_CATCH(...)
+    {
+      conditional_aligned_free<Align>(result);
+      EIGEN_THROW;
     }
-    
-    // Delete old elements.
-    conditional_aligned_delete<T, Align>(pts, old_size);      
-  }
-  EIGEN_CATCH(...)
-  {
-    conditional_aligned_free<Align>(result);
-    EIGEN_THROW;
   }
-
   return result;
 }
 
@@ -451,12 +401,12 @@ template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned
   if(size==0)
     return 0; // short-cut. Also fixes Bug 884
   check_size_for_overflow<T>(size);
-  T *result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
+  T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
   if(NumTraits<T>::RequireInitialization)
   {
     EIGEN_TRY
     {
-      default_construct_elements_of_array(result, size);
+      construct_elements_of_array(result, size);
     }
     EIGEN_CATCH(...)
     {
@@ -469,13 +419,24 @@ template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned
 
 template<typename T, bool Align> inline T* conditional_aligned_realloc_new_auto(T* pts, std::size_t new_size, std::size_t old_size)
 {
-  if (NumTraits<T>::RequireInitialization) {
-    return conditional_aligned_realloc_new<T, Align>(pts, new_size, old_size);
-  }
-  
   check_size_for_overflow<T>(new_size);
   check_size_for_overflow<T>(old_size);
-  return static_cast<T*>(conditional_aligned_realloc<Align>(static_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
+  if(NumTraits<T>::RequireInitialization && (new_size < old_size))
+    destruct_elements_of_array(pts+new_size, old_size-new_size);
+  T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
+  if(NumTraits<T>::RequireInitialization && (new_size > old_size))
+  {
+    EIGEN_TRY
+    {
+      construct_elements_of_array(result+old_size, new_size-old_size);
+    }
+    EIGEN_CATCH(...)
+    {
+      conditional_aligned_free<Align>(result);
+      EIGEN_THROW;
+    }
+  }
+  return result;
 }
 
 template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T *ptr, std::size_t size)
@@ -656,7 +617,7 @@ template<typename T> class aligned_stack_memory_handler : noncopyable
       : m_ptr(ptr), m_size(size), m_deallocate(dealloc)
     {
       if(NumTraits<T>::RequireInitialization && m_ptr)
-        Eigen::internal::default_construct_elements_of_array(m_ptr, size);
+        Eigen::internal::construct_elements_of_array(m_ptr, size);
     }
     EIGEN_DEVICE_FUNC
     ~aligned_stack_memory_handler()
@@ -707,7 +668,7 @@ struct local_nested_eval_wrapper<Xpr,NbEvaluations,true>
       m_deallocate(ptr==0)
   {
     if(NumTraits<Scalar>::RequireInitialization && object.data())
-      Eigen::internal::default_construct_elements_of_array(object.data(), object.size());
+      Eigen::internal::construct_elements_of_array(object.data(), object.size());
     object = xpr;
   }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Meta.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Meta.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/Meta.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Meta.h
index de1a638..81ae2a3 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/Meta.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/Meta.h
@@ -133,10 +133,7 @@ template<typename T> struct remove_all<T*>        { typedef typename remove_all<
 template<typename T> struct is_arithmetic      { enum { value = false }; };
 template<> struct is_arithmetic<float>         { enum { value = true }; };
 template<> struct is_arithmetic<double>        { enum { value = true }; };
-// GPU devices treat `long double` as `double`.
-#ifndef EIGEN_GPU_COMPILE_PHASE
 template<> struct is_arithmetic<long double>   { enum { value = true }; };
-#endif
 template<> struct is_arithmetic<bool>          { enum { value = true }; };
 template<> struct is_arithmetic<char>          { enum { value = true }; };
 template<> struct is_arithmetic<signed char>   { enum { value = true }; };
@@ -170,8 +167,10 @@ template<> struct is_integral<signed int>             { enum { value = true }; }
 template<> struct is_integral<unsigned int>           { enum { value = true }; };
 template<> struct is_integral<signed long>            { enum { value = true }; };
 template<> struct is_integral<unsigned long>          { enum { value = true }; };
-template<> struct is_integral<signed long long>       { enum { value = true }; };
-template<> struct is_integral<unsigned long long>     { enum { value = true }; };
+#if EIGEN_COMP_MSVC
+template<> struct is_integral<signed __int64>         { enum { value = true }; };
+template<> struct is_integral<unsigned __int64>       { enum { value = true }; };
+#endif
 #endif
 
 #if EIGEN_HAS_CXX11
@@ -190,9 +189,21 @@ template<> struct make_unsigned<signed int>       { typedef unsigned int type; }
 template<> struct make_unsigned<unsigned int>     { typedef unsigned int type; };
 template<> struct make_unsigned<signed long>      { typedef unsigned long type; };
 template<> struct make_unsigned<unsigned long>    { typedef unsigned long type; };
+#if EIGEN_COMP_MSVC
+template<> struct make_unsigned<signed __int64>   { typedef unsigned __int64 type; };
+template<> struct make_unsigned<unsigned __int64> { typedef unsigned __int64 type; };
+#endif
+
+// Some platforms define int64_t as `long long` even for C++03, where
+// `long long` is not guaranteed by the standard. In this case we are missing
+// the definition for make_unsigned. If we just define it, we run into issues
+// where `long long` doesn't exist in some compilers for C++03. We therefore add
+// the specialization for these platforms only.
+#if EIGEN_OS_MAC || EIGEN_COMP_MINGW
 template<> struct make_unsigned<unsigned long long> { typedef unsigned long long type; };
 template<> struct make_unsigned<long long>          { typedef unsigned long long type; };
 #endif
+#endif
 
 template <typename T> struct add_const { typedef const T type; };
 template <typename T> struct add_const<T&> { typedef T& type; };
@@ -455,32 +466,20 @@ template<typename T, std::size_t N> struct array_size<std::array<T,N> > {
 };
 #endif
 
-
 /** \internal
-  * Analogue of the std::ssize free function.
-  * It returns the signed size of the container or view \a x of type \c T
+  * Analogue of the std::size free function.
+  * It returns the size of the container or view \a x of type \c T
   *
   * It currently supports:
   *  - any types T defining a member T::size() const
   *  - plain C arrays as T[N]
   *
-  * For C++20, this function just forwards to `std::ssize`, or any ADL discoverable `ssize` function.
   */
-#if EIGEN_COMP_CXXVER < 20
-template <typename T>
-EIGEN_CONSTEXPR std::ptrdiff_t index_list_size(const T& x) {
-  return static_cast<std::ptrdiff_t>(x.size());
-}
+template<typename T>
+EIGEN_CONSTEXPR Index size(const T& x) { return x.size(); }
 
-template<typename T, std::ptrdiff_t N>
-EIGEN_CONSTEXPR std::ptrdiff_t index_list_size(const T (&)[N]) { return N; }
-#else
-template <typename T>
-EIGEN_CONSTEXPR auto index_list_size(T&& x) {
-  using std::ssize;
-  return ssize(std::forward<T>(x));
-}
-#endif // EIGEN_COMP_CXXVER
+template<typename T,std::size_t N>
+EIGEN_CONSTEXPR Index size(const T (&) [N]) { return N; }
 
 /** \internal
   * Convenient struct to get the result type of a nullary, unary, binary, or
@@ -697,7 +696,8 @@ struct has_binary_operator
 template<int Y,
          int InfX = 0,
          int SupX = ((Y==1) ? 1 : Y/2),
-         bool Done = ((SupX - InfX) <= 1 || ((SupX * SupX <= Y) && ((SupX + 1) * (SupX + 1) > Y)))>
+         bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) >
+                                // use ?: instead of || just to shut up a stupid gcc 4.3 warning
 class meta_sqrt
 {
     enum {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/NonMPL2.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/NonMPL2.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/NonMPL2.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/NonMPL2.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/ReenableStupidWarnings.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/ReenableStupidWarnings.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/ReshapedHelper.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/ReshapedHelper.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/ReshapedHelper.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/StaticAssert.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/StaticAssert.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/StaticAssert.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/StaticAssert.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/SymbolicIndex.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/SymbolicIndex.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/SymbolicIndex.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Core/util/XprHelper.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/XprHelper.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Core/util/XprHelper.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Core/util/XprHelper.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
index 6a99f4c..081e918 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h
@@ -316,8 +316,9 @@ void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm
   // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
   m_eivec.noalias() = m_schur.matrixU() * m_matX;
   // .. and normalize the eigenvectors
-  for (Index k = 0; k < n; k++) {
-    m_eivec.col(k).stableNormalize();
+  for(Index k=0 ; k<n ; k++)
+  {
+    m_eivec.col(k).normalize();
   }
 }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexSchur.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexSchur.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/EigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/EigenSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/EigenSolver.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
similarity index 94%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
index 26324ce..87d789b 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
@@ -119,8 +119,8 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       : m_eivec(),
         m_alphas(),
         m_betas(),
-        m_computeEigenvectors(false),
-        m_isInitialized(false),
+        m_valuesOkay(false),
+        m_vectorsOkay(false),
         m_realQZ()
     {}
 
@@ -134,8 +134,8 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       : m_eivec(size, size),
         m_alphas(size),
         m_betas(size),
-        m_computeEigenvectors(false),
-        m_isInitialized(false),
+        m_valuesOkay(false),
+        m_vectorsOkay(false),
         m_realQZ(size),
         m_tmp(size)
     {}
@@ -156,8 +156,8 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       : m_eivec(A.rows(), A.cols()),
         m_alphas(A.cols()),
         m_betas(A.cols()),
-        m_computeEigenvectors(false),
-        m_isInitialized(false),
+        m_valuesOkay(false),
+        m_vectorsOkay(false),
         m_realQZ(A.cols()),
         m_tmp(A.cols())
     {
@@ -177,8 +177,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       * \sa eigenvalues()
       */
     EigenvectorsType eigenvectors() const {
-      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvectors");
-      eigen_assert(m_computeEigenvectors && "Eigenvectors for GeneralizedEigenSolver were not calculated");
+      eigen_assert(m_vectorsOkay && "Eigenvectors for GeneralizedEigenSolver were not calculated.");
       return m_eivec;
     }
 
@@ -202,7 +201,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       */
     EigenvalueType eigenvalues() const
     {
-      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvalues.");
+      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
       return EigenvalueType(m_alphas,m_betas);
     }
 
@@ -213,7 +212,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       * \sa betas(), eigenvalues() */
     ComplexVectorType alphas() const
     {
-      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute alphas.");
+      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
       return m_alphas;
     }
 
@@ -224,7 +223,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
       * \sa alphas(), eigenvalues() */
     VectorType betas() const
     {
-      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute betas.");
+      eigen_assert(m_valuesOkay && "GeneralizedEigenSolver is not initialized.");
       return m_betas;
     }
 
@@ -255,7 +254,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
 
     ComputationInfo info() const
     {
-      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+      eigen_assert(m_valuesOkay && "EigenSolver is not initialized.");
       return m_realQZ.info();
     }
 
@@ -278,8 +277,7 @@ template<typename _MatrixType> class GeneralizedEigenSolver
     EigenvectorsType m_eivec;
     ComplexVectorType m_alphas;
     VectorType m_betas;
-    bool m_computeEigenvectors;
-    bool m_isInitialized;
+    bool m_valuesOkay, m_vectorsOkay;
     RealQZ<MatrixType> m_realQZ;
     ComplexVectorType m_tmp;
 };
@@ -294,6 +292,8 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
   using std::abs;
   eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows());
   Index size = A.cols();
+  m_valuesOkay = false;
+  m_vectorsOkay = false;
   // Reduce to generalized real Schur form:
   // A = Q S Z and B = Q T Z
   m_realQZ.compute(A, B, computeEigenvectors);
@@ -406,9 +406,10 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
         i += 2;
       }
     }
+
+    m_valuesOkay = true;
+    m_vectorsOkay = computeEigenvectors;
   }
-  m_computeEigenvectors = computeEigenvectors;
-  m_isInitialized = true;
   return *this;
 }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealQZ.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealQZ.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealQZ.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealSchur.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealSchur.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
index 37394e1..7304ef3 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealSchur.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealSchur.h
@@ -435,33 +435,34 @@ inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& ex
   shiftInfo.coeffRef(1) = m_matT.coeff(iu-1,iu-1);
   shiftInfo.coeffRef(2) = m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);
 
-  // Alternate exceptional shifting strategy every 16 iterations.
-  if (iter % 16 == 0) {
-    // Wilkinson's original ad hoc shift
-    if (iter % 32 != 0) {
-      exshift += shiftInfo.coeff(0);
+  // Wilkinson's original ad hoc shift
+  if (iter == 10)
+  {
+    exshift += shiftInfo.coeff(0);
+    for (Index i = 0; i <= iu; ++i)
+      m_matT.coeffRef(i,i) -= shiftInfo.coeff(0);
+    Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2));
+    shiftInfo.coeffRef(0) = Scalar(0.75) * s;
+    shiftInfo.coeffRef(1) = Scalar(0.75) * s;
+    shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
+  }
+
+  // MATLAB's new ad hoc shift
+  if (iter == 30)
+  {
+    Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+    s = s * s + shiftInfo.coeff(2);
+    if (s > Scalar(0))
+    {
+      s = sqrt(s);
+      if (shiftInfo.coeff(1) < shiftInfo.coeff(0))
+        s = -s;
+      s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+      s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
+      exshift += s;
       for (Index i = 0; i <= iu; ++i)
-        m_matT.coeffRef(i,i) -= shiftInfo.coeff(0);
-      Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2));
-      shiftInfo.coeffRef(0) = Scalar(0.75) * s;
-      shiftInfo.coeffRef(1) = Scalar(0.75) * s;
-      shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
-    } else {
-      // MATLAB's new ad hoc shift
-      Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-      s = s * s + shiftInfo.coeff(2);
-      if (s > Scalar(0))
-      {
-        s = sqrt(s);
-        if (shiftInfo.coeff(1) < shiftInfo.coeff(0))
-          s = -s;
-        s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
-        s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
-        exshift += s;
-        for (Index i = 0; i <= iu; ++i)
-          m_matT.coeffRef(i,i) -= s;
-        shiftInfo.setConstant(Scalar(0.964));
-      }
+        m_matT.coeffRef(i,i) -= s;
+      shiftInfo.setConstant(Scalar(0.964));
     }
   }
 }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/Tridiagonalization.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/Tridiagonalization.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
index eda8279..674c92a 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Eigenvalues/Tridiagonalization.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h
@@ -440,8 +440,9 @@ void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonal
 template<typename MatrixType, int Size, bool IsComplex>
 struct tridiagonalization_inplace_selector
 {
+  typedef typename Tridiagonalization<MatrixType>::CoeffVectorType CoeffVectorType;
   typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
-  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType>
+  template<typename DiagonalType, typename SubDiagonalType>
   static EIGEN_DEVICE_FUNC
       void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType& hCoeffs, bool extractQ)
   {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/AlignedBox.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/AlignedBox.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/AlignedBox.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/AlignedBox.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/AngleAxis.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/AngleAxis.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/AngleAxis.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/AngleAxis.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/EulerAngles.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/EulerAngles.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/EulerAngles.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/EulerAngles.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Homogeneous.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Homogeneous.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Homogeneous.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Homogeneous.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Hyperplane.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Hyperplane.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Hyperplane.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Hyperplane.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/OrthoMethods.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/OrthoMethods.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/OrthoMethods.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/ParametrizedLine.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/ParametrizedLine.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/ParametrizedLine.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Quaternion.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Quaternion.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Quaternion.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Quaternion.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Rotation2D.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Rotation2D.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Rotation2D.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Rotation2D.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/RotationBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/RotationBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/RotationBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/RotationBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Scaling.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Scaling.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Scaling.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Scaling.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Transform.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Transform.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Transform.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Transform.h
index a7756be..52b8c2a 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Transform.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Transform.h
@@ -985,10 +985,7 @@ Transform<Scalar,Dim,Mode,Options>::preshear(const Scalar& sx, const Scalar& sy)
 {
   EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
   EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
-  LinearMatrixType shear = LinearMatrixType::Identity(2, 2);
-  shear.coeffRef(0, 1) = sy;
-  shear.coeffRef(1, 0) = sx;
-  m_matrix.template block<Dim, HDim>(0, 0) = shear * m_matrix.template block<Dim, HDim>(0, 0);
+  m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
   return *this;
 }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Translation.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Translation.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Translation.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Translation.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Umeyama.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Umeyama.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/Umeyama.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Umeyama.h
index 2a5c395..6b75500 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/Umeyama.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/Umeyama.h
@@ -136,10 +136,8 @@ umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, boo
   // Eq. (39)
   VectorType S = VectorType::Ones(m);
 
-  if  ( svd.matrixU().determinant() * svd.matrixV().determinant() < 0 ) {
-    Index tmp = m - 1;  
-    S(tmp) = -1;
-  }
+  if  ( svd.matrixU().determinant() * svd.matrixV().determinant() < 0 )
+    S(m-1) = -1;
 
   // Eq. (40) and (43)
   Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Geometry/arch/Geometry_SIMD.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Geometry/arch/Geometry_SIMD.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Geometry/arch/Geometry_SIMD.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Householder/BlockHouseholder.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Householder/BlockHouseholder.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/BlockHouseholder.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Householder/Householder.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/Householder.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Householder/Householder.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/Householder.h
index d8984a3..5bc037f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Householder/Householder.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/Householder.h
@@ -69,7 +69,7 @@ void MatrixBase<Derived>::makeHouseholder(
   Scalar& tau,
   RealScalar& beta) const
 {
-  using numext::sqrt;
+  using std::sqrt;
   using numext::conj;
   
   EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Householder/HouseholderSequence.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Householder/HouseholderSequence.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
index b9c4aee..022f6c3 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/Householder/HouseholderSequence.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Householder/HouseholderSequence.h
@@ -14,9 +14,7 @@
 namespace Eigen {
 
 /** \ingroup Householder_Module
-  *
   * \householder_module
-  *
   * \class HouseholderSequence
   * \brief Sequence of Householder reflections acting on subspaces with decreasing size
   * \tparam VectorsType type of matrix containing the Householder vectors
@@ -520,10 +518,7 @@ typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,Ot
   return res;
 }
 
-/** \ingroup Householder_Module
-  * 
-  * \householder_module
-  *
+/** \ingroup Householder_Module \householder_module
   * \brief Convenience function for constructing a Householder sequence.
   * \returns A HouseholderSequence constructed from the specified arguments.
   */
@@ -533,10 +528,7 @@ HouseholderSequence<VectorsType,CoeffsType> householderSequence(const VectorsTyp
   return HouseholderSequence<VectorsType,CoeffsType,OnTheLeft>(v, h);
 }
 
-/** \ingroup Householder_Module
-  *
-  * \householder_module
-  *
+/** \ingroup Householder_Module \householder_module
   * \brief Convenience function for constructing a Householder sequence.
   * \returns A HouseholderSequence constructed from the specified arguments.
   * \details This function differs from householderSequence() in that the template argument \p OnTheSide of
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
index 1c9ade5..153acef 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -49,9 +49,9 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
     x.setZero();
     return true;
   }
-  Scalar rho    (1);
-  Scalar alpha  (1);
-  Scalar w      (1);
+  Scalar rho    = 1;
+  Scalar alpha  = 1;
+  Scalar w      = 1;
   
   VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
   VectorType y(n),  z(n);
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
index c3ca0ad..5d8c6b4 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -29,6 +29,8 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
                         const Preconditioner& precond, Index& iters,
                         typename Dest::RealScalar& tol_error)
 {
+  using std::sqrt;
+  using std::abs;
   typedef typename Dest::RealScalar RealScalar;
   typedef typename Dest::Scalar Scalar;
   typedef Matrix<Scalar,Dynamic,1> VectorType;
@@ -54,7 +56,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
   if (residualNorm2 < threshold)
   {
     iters = 0;
-    tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+    tol_error = sqrt(residualNorm2 / rhsNorm2);
     return;
   }
 
@@ -84,7 +86,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
     p = z + beta * p;                           // update search direction
     i++;
   }
-  tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+  tol_error = sqrt(residualNorm2 / rhsNorm2);
   iters = i;
 }
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
index 5e632c4..7803fd8 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
@@ -160,13 +160,13 @@ class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,_Up
     }
 
     /** \returns the sparse lower triangular factor L */
-    const FactorType& matrixL() const { eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_L; }
+    const FactorType& matrixL() const { eigen_assert("m_factorizationIsOk"); return m_L; }
 
     /** \returns a vector representing the scaling factor S */
-    const VectorRx& scalingS() const { eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_scale; }
+    const VectorRx& scalingS() const { eigen_assert("m_factorizationIsOk"); return m_scale; }
 
     /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
-    const PermutationType& permutationP() const { eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); return m_perm; }
+    const PermutationType& permutationP() const { eigen_assert("m_analysisIsOk"); return m_perm; }
 
   protected:
     FactorType m_L;              // The lower part stored in CSC
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/Jacobi/Jacobi.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/Jacobi/Jacobi.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/Jacobi/Jacobi.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/Jacobi/Jacobi.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/KLUSupport/KLUSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/KLUSupport/KLUSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/KLUSupport/KLUSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/Determinant.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/Determinant.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/Determinant.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/Determinant.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/FullPivLU.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/FullPivLU.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/FullPivLU.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/FullPivLU.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/InverseImpl.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/InverseImpl.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/InverseImpl.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/InverseImpl.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/PartialPivLU.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/PartialPivLU.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/PartialPivLU.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/PartialPivLU.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/PartialPivLU_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/PartialPivLU_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/LU/arch/InverseSize4.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
similarity index 95%
rename from 3rdParty/ceres_2.2/include/Eigen/src/LU/arch/InverseSize4.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
index 22ae38a..a232ffc 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/LU/arch/InverseSize4.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/LU/arch/InverseSize4.h
@@ -35,13 +35,6 @@
 #ifndef EIGEN_INVERSE_SIZE_4_H
 #define EIGEN_INVERSE_SIZE_4_H
 
-#if EIGEN_COMP_GNUC_STRICT
-// These routines requires bit manipulation of the sign, which is not compatible
-// with fastmath.
-#pragma GCC push_options
-#pragma GCC optimize ("no-fast-math")
-#endif
-
 namespace Eigen
 {
 namespace internal
@@ -150,8 +143,8 @@ struct compute_inverse_size4<Architecture::Target, float, MatrixType, ResultType
     iC = psub(iC, pmul(vec4f_swizzle2(A, A, 1, 0, 3, 2), vec4f_swizzle2(DC, DC, 2, 1, 2, 1)));
     iC = psub(pmul(B, vec4f_duplane(dC, 0)), iC);
 
-    EIGEN_ALIGN_MAX const float sign_mask[4] = {0.0f, -0.0f, -0.0f, 0.0f};
-    const Packet4f p4f_sign_PNNP = pload<Packet4f>(sign_mask);
+    const float sign_mask[4] = {0.0f, numext::bit_cast<float>(0x80000000u), numext::bit_cast<float>(0x80000000u), 0.0f};
+    const Packet4f p4f_sign_PNNP = ploadu<Packet4f>(sign_mask);
     rd = pxor(rd, p4f_sign_PNNP);
     iA = pmul(iA, rd);
     iB = pmul(iB, rd);
@@ -333,10 +326,10 @@ struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultTyp
     iC1 = psub(pmul(B1, dC), iC1);
     iC2 = psub(pmul(B2, dC), iC2);
 
-    EIGEN_ALIGN_MAX const double sign_mask1[2] = {0.0, -0.0};
-    EIGEN_ALIGN_MAX const double sign_mask2[2] = {-0.0, 0.0};
-    const Packet2d sign_PN = pload<Packet2d>(sign_mask1);
-    const Packet2d sign_NP = pload<Packet2d>(sign_mask2);
+    const double sign_mask1[2] = {0.0, numext::bit_cast<double>(0x8000000000000000ull)};
+    const double sign_mask2[2] = {numext::bit_cast<double>(0x8000000000000000ull), 0.0};
+    const Packet2d sign_PN = ploadu<Packet2d>(sign_mask1);
+    const Packet2d sign_NP = ploadu<Packet2d>(sign_mask2);
     d1 = pxor(rd, sign_PN);
     d2 = pxor(rd, sign_NP);
 
@@ -355,9 +348,4 @@ struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultTyp
 #endif
 } // namespace internal
 } // namespace Eigen
-
-#if EIGEN_COMP_GNUC_STRICT
-#pragma GCC pop_options
-#endif
-
 #endif
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/MetisSupport/MetisSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/MetisSupport/MetisSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/MetisSupport/MetisSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Amd.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Amd.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Amd.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Amd.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Eigen_Colamd.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Ordering.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/OrderingMethods/Ordering.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/OrderingMethods/Ordering.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/PaStiXSupport/PaStiXSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/PardisoSupport/PardisoSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/PardisoSupport/PardisoSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/ColPivHouseholderQR.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/ColPivHouseholderQR.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/ColPivHouseholderQR.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/CompleteOrthogonalDecomposition.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/FullPivHouseholderQR.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/FullPivHouseholderQR.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/HouseholderQR.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/HouseholderQR.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/HouseholderQR.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/HouseholderQR.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/QR/HouseholderQR_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/QR/HouseholderQR_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
similarity index 96%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
index 3ceac5a..013c7ae 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
@@ -258,12 +258,12 @@ class SPQR : public SparseSolverBase<SPQR<_MatrixType> >
     int m_ordering; // Ordering method to use, see SPQR's manual
     int m_allow_tol; // Allow to use some tolerance during numerical factorization.
     RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero
-    mutable cholmod_sparse *m_cR = nullptr; // The sparse R factor in cholmod format
+    mutable cholmod_sparse *m_cR; // The sparse R factor in cholmod format
     mutable MatrixType m_R; // The sparse matrix R in Eigen format
-    mutable StorageIndex *m_E = nullptr; // The permutation applied to columns
-    mutable cholmod_sparse *m_H = nullptr;  //The householder vectors
-    mutable StorageIndex *m_HPinv = nullptr; // The row permutation of H
-    mutable cholmod_dense *m_HTau = nullptr; // The Householder coefficients
+    mutable StorageIndex *m_E; // The permutation applied to columns
+    mutable cholmod_sparse *m_H;  //The householder vectors
+    mutable StorageIndex *m_HPinv; // The row permutation of H
+    mutable cholmod_dense *m_HTau; // The Householder coefficients
     mutable Index m_rank; // The rank of the matrix
     mutable cholmod_common m_cc; // Workspace and parameters
     bool m_useDefaultThreshold;     // Use default threshold
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SVD/BDCSVD.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/BDCSVD.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SVD/BDCSVD.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/BDCSVD.h
index 79a6562..17f8e44 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SVD/BDCSVD.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/BDCSVD.h
@@ -27,10 +27,6 @@
 #define eigen_internal_assert(X) assert(X);
 #endif
 
-#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
-#include <iostream>
-#endif
-
 namespace Eigen {
 
 #ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
@@ -176,7 +172,7 @@ public:
 
   void setSwitchSize(int s) 
   {
-    eigen_assert(s>=3 && "BDCSVD the size of the algo switch has to be at least 3.");
+    eigen_assert(s>3 && "BDCSVD the size of the algo switch has to be greater than 3");
     m_algoswap = s;
   }
  
@@ -408,7 +404,7 @@ void BDCSVD<MatrixType>::structured_update(Block<MatrixXr,Dynamic,Dynamic> A, co
 //@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU; 
 // lastCol + 1 - firstCol is the size of the submatrix.
 //@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section 1 for more information on W)
-//@param firstColW : Same as firstRowW with the column.
+//@param firstRowW : Same as firstRowW with the column.
 //@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix 
 // to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper.
 template<typename MatrixType>
@@ -903,7 +899,7 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
       RealScalar fLeft = secularEq(leftShifted, col0, diag, perm, diagShifted, shift);
       eigen_internal_assert(fLeft<Literal(0));
 
-#if defined EIGEN_BDCSVD_DEBUG_VERBOSE || defined EIGEN_BDCSVD_SANITY_CHECKS || defined EIGEN_INTERNAL_DEBUGGING
+#if defined EIGEN_INTERNAL_DEBUGGING || defined EIGEN_BDCSVD_SANITY_CHECKS
       RealScalar fRight = secularEq(rightShifted, col0, diag, perm, diagShifted, shift);
 #endif
 
@@ -978,8 +974,8 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
     // perturb singular value slightly if it equals diagonal entry to avoid division by zero later
     // (deflation is supposed to avoid this from happening)
     // - this does no seem to be necessary anymore -
-    // if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
-    // if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
+//     if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
+//     if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
   }
 }
 
@@ -1033,14 +1029,7 @@ void BDCSVD<MatrixType>::perturbCol0
             std::cout << "  " << "j=" << j << "\n";
           }
 #endif
-          // Avoid index out of bounds.
-          // Will end up setting zhat(k) = 0.
-          if (i >= k && l == 0) {
-            m_info = NumericalIssue;
-            prod = 0;
-            break;
-          }
-          Index j = i<k ? i : l > 0 ? perm(l-1) : i;
+          Index j = i<k ? i : perm(l-1);
 #ifdef EIGEN_BDCSVD_SANITY_CHECKS
           if(!(dk!=Literal(0) || diag(i)!=Literal(0)))
           {
@@ -1253,8 +1242,8 @@ void BDCSVD<MatrixType>::deflation(Eigen::Index firstCol, Eigen::Index lastCol,
 #endif
   {
     // Check for total deflation
-    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting.
-    const bool total_deflation = (col0.tail(length-1).array().abs()<considerZero).all();
+    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting
+    bool total_deflation = (col0.tail(length-1).array()<considerZero).all();
     
     // Sort the diagonal entries, since diag(1:k-1) and diag(k:length) are already sorted, let's do a sorted merge.
     // First, compute the respective permutation.
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SVD/JacobiSVD.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/JacobiSVD.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SVD/JacobiSVD.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/JacobiSVD.h
index 4b002ad..9d95acd 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SVD/JacobiSVD.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/JacobiSVD.h
@@ -680,7 +680,6 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
   if (!(numext::isfinite)(scale)) {
     m_isInitialized = true;
     m_info = InvalidInput;
-    m_nonzeroSingularValues = 0;
     return *this;
   }
   if(scale==RealScalar(0)) scale = RealScalar(1);
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/JacobiSVD_LAPACKE.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SVD/SVDBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/SVDBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SVD/SVDBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/SVDBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SVD/UpperBidiagonalization.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SVD/UpperBidiagonalization.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
index a5b2f60..997defc 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SVD/UpperBidiagonalization.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SVD/UpperBidiagonalization.h
@@ -161,14 +161,13 @@ void upperbidiagonalization_blocked_helper(MatrixType& A,
   typedef typename MatrixType::Scalar Scalar;
   typedef typename MatrixType::RealScalar RealScalar;
   typedef typename NumTraits<RealScalar>::Literal Literal;
-  static const int StorageOrder =
-      (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor;
-  typedef InnerStride<StorageOrder == ColMajor ? 1 : Dynamic> ColInnerStride;
-  typedef InnerStride<StorageOrder == ColMajor ? Dynamic : 1> RowInnerStride;
+  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
+  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? 1 : Dynamic> ColInnerStride;
+  typedef InnerStride<int(StorageOrder) == int(ColMajor) ? Dynamic : 1> RowInnerStride;
   typedef Ref<Matrix<Scalar, Dynamic, 1>, 0, ColInnerStride>    SubColumnType;
   typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, RowInnerStride>    SubRowType;
   typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder > > SubMatType;
-
+  
   Index brows = A.rows();
   Index bcols = A.cols();
 
@@ -294,7 +293,7 @@ void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagona
   Index size = (std::min)(rows, cols);
 
   // X and Y are work space
-  enum { StorageOrder = (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor };
+  enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
   Matrix<Scalar,
          MatrixType::RowsAtCompileTime,
          Dynamic,
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCholesky/SimplicialCholesky.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/AmbiVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/AmbiVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/AmbiVector.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/CompressedStorage.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/CompressedStorage.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/CompressedStorage.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/MappedSparseMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/MappedSparseMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseAssign.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseAssign.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseAssign.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseBlock.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseBlock.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
index c16caec..5b4f6cc 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseBlock.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseBlock.h
@@ -429,7 +429,12 @@ struct unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBa
 
     enum {
       IsRowMajor = XprType::IsRowMajor,
-      OuterVector = (BlockCols == 1 && ArgType::IsRowMajor) || (BlockRows == 1 && !ArgType::IsRowMajor),
+
+      OuterVector =  (BlockCols==1 && ArgType::IsRowMajor)
+                    | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
+                      // revert to || as soon as not needed anymore.
+                     (BlockRows==1 && !ArgType::IsRowMajor),
+
       CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
       Flags = XprType::Flags
     };
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseColEtree.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseColEtree.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseColEtree.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCompressedBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCompressedBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCompressedBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDenseProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDenseProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDiagonalProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDot.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDot.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseDot.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseDot.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseFuzzy.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseFuzzy.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseFuzzy.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMap.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMap.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMap.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMap.h
index 6247d79..f99be33 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMap.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMap.h
@@ -237,7 +237,6 @@ class Map<SparseMatrixType>
     /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
       * stored as a sparse format as defined by the pointers \a outerIndexPtr, \a innerIndexPtr, and \a valuePtr.
       * If the optional parameter \a innerNonZerosPtr is the null pointer, then a standard compressed format is assumed.
-      * The inner indices must be sorted appropriately.
       *
       * This constructor is available only if \c SparseMatrixType is non-const.
       *
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
index 5522769..616b4a0 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrix.h
@@ -781,17 +781,18 @@ class SparseMatrix
       return *this;
     }
 
+#ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename OtherDerived>
     inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
     { return Base::operator=(other.derived()); }
 
     template<typename Lhs, typename Rhs>
     inline SparseMatrix& operator=(const Product<Lhs,Rhs,AliasFreeProduct>& other);
+#endif // EIGEN_PARSED_BY_DOXYGEN
 
     template<typename OtherDerived>
     EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
 
-#ifndef EIGEN_NO_IO
     friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
     {
       EIGEN_DBG_SPARSE(
@@ -836,7 +837,6 @@ class SparseMatrix
       s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
       return s;
     }
-#endif
 
     /** Destructor */
     inline ~SparseMatrix()
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrixBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrixBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
index 417a236..229449f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseMatrixBase.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
@@ -113,7 +113,7 @@ template<typename Derived> class SparseMatrixBase
                         Transpose<const Derived>
                      >::type AdjointReturnType;
     typedef Transpose<Derived> TransposeReturnType;
-    typedef Transpose<const Derived> ConstTransposeReturnType;
+    typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
 
     // FIXME storage order do not match evaluator storage order
     typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
@@ -214,7 +214,7 @@ template<typename Derived> class SparseMatrixBase
     inline void assignGeneric(const OtherDerived& other);
 
   public:
-#ifndef EIGEN_NO_IO
+
     friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
     {
       typedef typename Derived::Nested Nested;
@@ -263,7 +263,6 @@ template<typename Derived> class SparseMatrixBase
       }
       return s;
     }
-#endif
 
     template<typename OtherDerived>
     Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparsePermutation.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparsePermutation.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparsePermutation.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseProduct.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseProduct.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
index f55e8ff..af8a774 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseProduct.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseProduct.h
@@ -165,7 +165,6 @@ protected:
 } // end namespace internal
 
 // sparse matrix = sparse-product (can be sparse*sparse, sparse*perm, etc.)
-
 template<typename Scalar, int _Options, typename _StorageIndex>
 template<typename Lhs, typename Rhs>
 SparseMatrix<Scalar,_Options,_StorageIndex>& SparseMatrix<Scalar,_Options,_StorageIndex>::operator=(const Product<Lhs,Rhs,AliasFreeProduct>& src)
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseRedux.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseRedux.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseRedux.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseRef.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseRef.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseRef.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseRef.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSelfAdjointView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSolverBase.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSolverBase.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSolverBase.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
similarity index 94%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
index 25ce404..88820a4 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
@@ -90,9 +90,9 @@ struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,C
 
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
   {
-    typename remove_all<ResultType>::type res_(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, res_, tolerance);
-    res.swap(res_);
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
+    res.swap(_res);
   }
 };
 
@@ -104,9 +104,9 @@ struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,C
   {
     // we need a col-major matrix to hold the result
     typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> SparseTemporaryType;
-    SparseTemporaryType res_(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, res_, tolerance);
-    res = res_;
+    SparseTemporaryType _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
+    res = _res;
   }
 };
 
@@ -117,9 +117,9 @@ struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,R
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
   {
     // let's transpose the product to get a column x column product
-    typename remove_all<ResultType>::type res_(res.rows(), res.cols());
-    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, res_, tolerance);
-    res.swap(res_);
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
+    res.swap(_res);
   }
 };
 
@@ -137,9 +137,9 @@ struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,R
 
     // let's transpose the product to get a column x column product
 //     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
-//     SparseTemporaryType res_(res.cols(), res.rows());
-//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, res_);
-//     res = res_.transpose();
+//     SparseTemporaryType _res(res.cols(), res.rows());
+//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
+//     res = _res.transpose();
   }
 };
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseTranspose.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseTranspose.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseTranspose.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseTriangularView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseTriangularView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseTriangularView.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseUtil.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseUtil.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseUtil.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseVector.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseVector.h
index 106925b..05779be 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseVector.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseVector.h
@@ -329,7 +329,6 @@ class SparseVector
     }
     #endif
 
-#ifndef EIGEN_NO_IO
     friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
     {
       for (Index i=0; i<m.nonZeros(); ++i)
@@ -337,7 +336,6 @@ class SparseVector
       s << std::endl;
       return s;
     }
-#endif
 
     /** Destructor */
     inline ~SparseVector() {}
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseView.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseView.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/SparseView.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/SparseView.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/TriangularSolver.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseCore/TriangularSolver.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
index 7cb2c26..f9c56ba 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseCore/TriangularSolver.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseCore/TriangularSolver.h
@@ -270,11 +270,11 @@ struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
       }
 
 
-//       Index count = 0;
+      Index count = 0;
       // FIXME compute a reference value to filter zeros
       for (typename AmbiVector<Scalar,StorageIndex>::Iterator it(tempVector/*,1e-12*/); it; ++it)
       {
-//         ++ count;
+        ++ count;
 //         std::cerr << "fill " << it.index() << ", " << col << "\n";
 //         std::cout << it.value() << "  ";
         // FIXME use insertBack
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU.h
index 6eb7950..0c8d893 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU.h
@@ -35,10 +35,9 @@ public:
     MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
   };
 
-  SparseLUTransposeView() : APIBase(), m_sparseLU(NULL) {}
-  SparseLUTransposeView(const SparseLUTransposeView& view) : APIBase() {
+  SparseLUTransposeView() : m_sparseLU(NULL) {}
+  SparseLUTransposeView(const SparseLUTransposeView& view) {
     this->m_sparseLU = view.m_sparseLU;
-    this->m_isInitialized = view.m_isInitialized;
   }
   void setIsInitialized(const bool isInitialized) {this->m_isInitialized = isInitialized;}
   void setSparseLU(SparseLUType* sparseLU) {m_sparseLU = sparseLU;}
@@ -753,13 +752,10 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
       info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu);
       if ( info ) 
       {
-        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR";
-#ifndef EIGEN_NO_IO
+        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR ... ZERO COLUMN AT ";
         std::ostringstream returnInfo;
-        returnInfo << " ... ZERO COLUMN AT ";
-        returnInfo << info;
+        returnInfo << info; 
         m_lastError += returnInfo.str();
-#endif
         m_info = NumericalIssue; 
         m_factorizationIsOk = false; 
         return; 
@@ -834,6 +830,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
   template<typename Dest>   void solveInPlace(MatrixBase<Dest> &X) const
   {
     Index nrhs = X.cols();
+    Index n    = X.rows();
     // Backward solve with U
     for (Index k = m_mapL.nsuper(); k >= 0; k--)
     {
@@ -853,7 +850,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
       {
         // FIXME: the following lines should use Block expressions and not Map!
         Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X.coeffRef(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
         U = A.template triangularView<Upper>().solve(U);
       }
 
@@ -876,6 +873,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
   {
     using numext::conj;
     Index nrhs = X.cols();
+    Index n    = X.rows();
     // Forward solve with U
     for (Index k = 0; k <=  m_mapL.nsuper(); k++)
     {
@@ -906,7 +904,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator
       else
       {
         Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
         if(Conjugate)
           U = A.adjoint().template triangularView<Lower>().solve(U);
         else
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLUImpl.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLUImpl.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLUImpl.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Memory.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Memory.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Structs.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Structs.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
index 16a0c41..cf5ec44 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Structs.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h
@@ -70,8 +70,8 @@
 #define EIGEN_LU_STRUCTS
 namespace Eigen {
 namespace internal {
-
-enum MemType {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL};
+  
+typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} MemType; 
 
 template <typename IndexVector, typename ScalarVector>
 struct LU_GlobalLU_t {
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
index fd5e9fa..0be293d 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
@@ -274,8 +274,9 @@ void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) co
         
         // Triangular solve 
         Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
-        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
-        U = A.template triangularView<UnitLower>().solve(U);        
+        Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
+        U = A.template triangularView<UnitLower>().solve(U); 
+        
         // Matrix-vector product 
         new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
         work.topRows(nrow).noalias() = A * U;
@@ -348,7 +349,7 @@ void MappedSuperNodalMatrix<Scalar,Index_>::solveTransposedInPlace( MatrixBase<D
 
       // Matrix-vector product with transposed submatrix
       Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
-      typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+      Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
       if(Conjugate)
         U = U - A.adjoint() * work.topRows(nrow);
       else
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Utils.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_Utils.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_column_bmod.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_column_dfs.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
diff --git a/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
new file mode 100644
index 0000000..e37c2fe
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h
@@ -0,0 +1,280 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_GEMM_KERNEL_H
+#define EIGEN_SPARSELU_GEMM_KERNEL_H
+
+namespace Eigen {
+
+namespace internal {
+
+
+/** \internal
+  * A general matrix-matrix product kernel optimized for the SparseLU factorization.
+  *  - A, B, and C must be column major
+  *  - lda and ldc must be multiples of the respective packet size
+  *  - C must have the same alignment as A
+  */
+template<typename Scalar>
+EIGEN_DONT_INLINE
+void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const Scalar* B, Index ldb, Scalar* C, Index ldc)
+{
+  using namespace Eigen::internal;
+  
+  typedef typename packet_traits<Scalar>::type Packet;
+  enum {
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+    PacketSize = packet_traits<Scalar>::size,
+    PM = 8,                             // peeling in M
+    RN = 2,                             // register blocking
+    RK = NumberOfRegisters>=16 ? 4 : 2, // register blocking
+    BM = 4096/sizeof(Scalar),           // number of rows of A-C per chunk
+    SM = PM*PacketSize                  // step along M
+  };
+  Index d_end = (d/RK)*RK;    // number of columns of A (rows of B) suitable for full register blocking
+  Index n_end = (n/RN)*RN;    // number of columns of B-C suitable for processing RN columns at once
+  Index i0 = internal::first_default_aligned(A,m);
+  
+  eigen_internal_assert(((lda%PacketSize)==0) && ((ldc%PacketSize)==0) && (i0==internal::first_default_aligned(C,m)));
+  
+  // handle the non aligned rows of A and C without any optimization:
+  for(Index i=0; i<i0; ++i)
+  {
+    for(Index j=0; j<n; ++j)
+    {
+      Scalar c = C[i+j*ldc];
+      for(Index k=0; k<d; ++k)
+        c += B[k+j*ldb] * A[i+k*lda];
+      C[i+j*ldc] = c;
+    }
+  }
+  // process the remaining rows per chunk of BM rows
+  for(Index ib=i0; ib<m; ib+=BM)
+  {
+    Index actual_b = std::min<Index>(BM, m-ib);                 // actual number of rows
+    Index actual_b_end1 = (actual_b/SM)*SM;                   // actual number of rows suitable for peeling
+    Index actual_b_end2 = (actual_b/PacketSize)*PacketSize;   // actual number of rows suitable for vectorization
+    
+    // Let's process two columns of B-C at once
+    for(Index j=0; j<n_end; j+=RN)
+    {
+      const Scalar* Bc0 = B+(j+0)*ldb;
+      const Scalar* Bc1 = B+(j+1)*ldb;
+      
+      for(Index k=0; k<d_end; k+=RK)
+      {
+        
+        // load and expand a RN x RK block of B
+        Packet b00, b10, b20, b30, b01, b11, b21, b31;
+                  { b00 = pset1<Packet>(Bc0[0]); }
+                  { b10 = pset1<Packet>(Bc0[1]); }
+        if(RK==4) { b20 = pset1<Packet>(Bc0[2]); }
+        if(RK==4) { b30 = pset1<Packet>(Bc0[3]); }
+                  { b01 = pset1<Packet>(Bc1[0]); }
+                  { b11 = pset1<Packet>(Bc1[1]); }
+        if(RK==4) { b21 = pset1<Packet>(Bc1[2]); }
+        if(RK==4) { b31 = pset1<Packet>(Bc1[3]); }
+        
+        Packet a0, a1, a2, a3, c0, c1, t0, t1;
+        
+        const Scalar* A0 = A+ib+(k+0)*lda;
+        const Scalar* A1 = A+ib+(k+1)*lda;
+        const Scalar* A2 = A+ib+(k+2)*lda;
+        const Scalar* A3 = A+ib+(k+3)*lda;
+        
+        Scalar* C0 = C+ib+(j+0)*ldc;
+        Scalar* C1 = C+ib+(j+1)*ldc;
+        
+                  a0 = pload<Packet>(A0);
+                  a1 = pload<Packet>(A1);
+        if(RK==4)
+        {
+          a2 = pload<Packet>(A2);
+          a3 = pload<Packet>(A3);
+        }
+        else
+        {
+          // workaround "may be used uninitialized in this function" warning
+          a2 = a3 = a0;
+        }
+        
+#define KMADD(c, a, b, tmp) {tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);}
+#define WORK(I)  \
+                     c0 = pload<Packet>(C0+i+(I)*PacketSize);    \
+                     c1 = pload<Packet>(C1+i+(I)*PacketSize);    \
+                     KMADD(c0, a0, b00, t0)                      \
+                     KMADD(c1, a0, b01, t1)                      \
+                     a0 = pload<Packet>(A0+i+(I+1)*PacketSize);  \
+                     KMADD(c0, a1, b10, t0)                      \
+                     KMADD(c1, a1, b11, t1)                      \
+                     a1 = pload<Packet>(A1+i+(I+1)*PacketSize);  \
+          if(RK==4){ KMADD(c0, a2, b20, t0)                     }\
+          if(RK==4){ KMADD(c1, a2, b21, t1)                     }\
+          if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize); }\
+          if(RK==4){ KMADD(c0, a3, b30, t0)                     }\
+          if(RK==4){ KMADD(c1, a3, b31, t1)                     }\
+          if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize); }\
+                     pstore(C0+i+(I)*PacketSize, c0);            \
+                     pstore(C1+i+(I)*PacketSize, c1)
+        
+        // process rows of A' - C' with aggressive vectorization and peeling 
+        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
+        {
+          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL1");
+                    prefetch((A0+i+(5)*PacketSize));
+                    prefetch((A1+i+(5)*PacketSize));
+          if(RK==4) prefetch((A2+i+(5)*PacketSize));
+          if(RK==4) prefetch((A3+i+(5)*PacketSize));
+
+          WORK(0);
+          WORK(1);
+          WORK(2);
+          WORK(3);
+          WORK(4);
+          WORK(5);
+          WORK(6);
+          WORK(7);
+        }
+        // process the remaining rows with vectorization only
+        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
+        {
+          WORK(0);
+        }
+#undef WORK
+        // process the remaining rows without vectorization
+        for(Index i=actual_b_end2; i<actual_b; ++i)
+        {
+          if(RK==4)
+          {
+            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
+            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]+A2[i]*Bc1[2]+A3[i]*Bc1[3];
+          }
+          else
+          {
+            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
+            C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1];
+          }
+        }
+        
+        Bc0 += RK;
+        Bc1 += RK;
+      } // peeled loop on k
+    } // peeled loop on the columns j
+    // process the last column (we now perform a matrix-vector product)
+    if((n-n_end)>0)
+    {
+      const Scalar* Bc0 = B+(n-1)*ldb;
+      
+      for(Index k=0; k<d_end; k+=RK)
+      {
+        
+        // load and expand a 1 x RK block of B
+        Packet b00, b10, b20, b30;
+                  b00 = pset1<Packet>(Bc0[0]);
+                  b10 = pset1<Packet>(Bc0[1]);
+        if(RK==4) b20 = pset1<Packet>(Bc0[2]);
+        if(RK==4) b30 = pset1<Packet>(Bc0[3]);
+        
+        Packet a0, a1, a2, a3, c0, t0/*, t1*/;
+        
+        const Scalar* A0 = A+ib+(k+0)*lda;
+        const Scalar* A1 = A+ib+(k+1)*lda;
+        const Scalar* A2 = A+ib+(k+2)*lda;
+        const Scalar* A3 = A+ib+(k+3)*lda;
+        
+        Scalar* C0 = C+ib+(n_end)*ldc;
+        
+                  a0 = pload<Packet>(A0);
+                  a1 = pload<Packet>(A1);
+        if(RK==4)
+        {
+          a2 = pload<Packet>(A2);
+          a3 = pload<Packet>(A3);
+        }
+        else
+        {
+          // workaround "may be used uninitialized in this function" warning
+          a2 = a3 = a0;
+        }
+        
+#define WORK(I) \
+                   c0 = pload<Packet>(C0+i+(I)*PacketSize);     \
+                   KMADD(c0, a0, b00, t0)                       \
+                   a0 = pload<Packet>(A0+i+(I+1)*PacketSize);   \
+                   KMADD(c0, a1, b10, t0)                       \
+                   a1 = pload<Packet>(A1+i+(I+1)*PacketSize);   \
+        if(RK==4){ KMADD(c0, a2, b20, t0)                      }\
+        if(RK==4){ a2 = pload<Packet>(A2+i+(I+1)*PacketSize);  }\
+        if(RK==4){ KMADD(c0, a3, b30, t0)                      }\
+        if(RK==4){ a3 = pload<Packet>(A3+i+(I+1)*PacketSize);  }\
+                   pstore(C0+i+(I)*PacketSize, c0);
+        
+        // aggressive vectorization and peeling
+        for(Index i=0; i<actual_b_end1; i+=PacketSize*8)
+        {
+          EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2");
+          WORK(0);
+          WORK(1);
+          WORK(2);
+          WORK(3);
+          WORK(4);
+          WORK(5);
+          WORK(6);
+          WORK(7);
+        }
+        // vectorization only
+        for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize)
+        {
+          WORK(0);
+        }
+        // remaining scalars
+        for(Index i=actual_b_end2; i<actual_b; ++i)
+        {
+          if(RK==4) 
+            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3];
+          else
+            C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1];
+        }
+        
+        Bc0 += RK;
+#undef WORK
+      }
+    }
+    
+    // process the last columns of A, corresponding to the last rows of B
+    Index rd = d-d_end;
+    if(rd>0)
+    {
+      for(Index j=0; j<n; ++j)
+      {
+        enum {
+          Alignment = PacketSize>1 ? Aligned : 0
+        };
+        typedef Map<Matrix<Scalar,Dynamic,1>, Alignment > MapVector;
+        typedef Map<const Matrix<Scalar,Dynamic,1>, Alignment > ConstMapVector;
+        if(rd==1)       MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b);
+        
+        else if(rd==2)  MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
+                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b);
+        
+        else            MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b)
+                                                        + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b)
+                                                        + B[2+d_end+j*ldb] * ConstMapVector(A+(d_end+2)*lda+ib, actual_b);
+      }
+    }
+  
+  } // blocking on the rows of A and C
+}
+#undef KMADD
+
+} // namespace internal
+
+} // namespace Eigen
+
+#endif // EIGEN_SPARSELU_GEMM_KERNEL_H
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
similarity index 94%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
index 7aecbca..6f75d50 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
@@ -75,6 +75,8 @@ void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVe
   // Identify the relaxed supernodes by postorder traversal of the etree
   Index snode_start; // beginning of a snode 
   StorageIndex k;
+  Index nsuper_et_post = 0; // Number of relaxed snodes in postordered etree 
+  Index nsuper_et = 0; // Number of relaxed snodes in the original etree 
   StorageIndex l; 
   for (j = 0; j < n; )
   {
@@ -86,6 +88,7 @@ void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVe
       parent = et(j);
     }
     // Found a supernode in postordered etree, j is the last column 
+    ++nsuper_et_post;
     k = StorageIndex(n);
     for (Index i = snode_start; i <= j; ++i)
       k = (std::min)(k, inv_post(i));
@@ -94,6 +97,7 @@ void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVe
     {
       // This is also a supernode in the original etree
       relax_end(k) = l; // Record last column 
+      ++nsuper_et; 
     }
     else 
     {
@@ -103,6 +107,7 @@ void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVe
         if (descendants(i) == 0) 
         {
           relax_end(l) = l;
+          ++nsuper_et;
         }
       }
     }
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
similarity index 97%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
index 7a101ea..8c1b3e8 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
@@ -69,7 +69,8 @@ EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index seg
   Index aligned_with_B_offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize))%PacketSize;
   Map<Matrix<Scalar,Dynamic,1>, 0, OuterStride<> > l(tempv.data()+segsize+aligned_offset+aligned_with_B_offset, nrow, OuterStride<>(ldl) );
   
-  l.noalias() = B * u;
+  l.setZero();
+  internal::sparselu_gemm<Scalar>(l.rows(), l.cols(), B.cols(), B.data(), B.outerStride(), u.data(), u.outerStride(), l.data(), l.outerStride());
   
   // Scatter tempv[] into SPA dense[] as a temporary storage 
   isub = lptr + no_zeros;
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
index 92cdb0e..f052001 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h
@@ -148,7 +148,8 @@ void SparseLUImpl<Scalar,StorageIndex>::panel_bmod(const Index m, const Index w,
       Index offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
       MappedMatrixBlock L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
       
-      L.noalias() = B * U;
+      L.setZero();
+      internal::sparselu_gemm<Scalar>(L.rows(), L.cols(), B.cols(), B.data(), B.outerStride(), U.data(), U.outerStride(), L.data(), L.outerStride());
       
       // scatter U and L
       u_col = 0;
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_pivotL.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_pivotL.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_pruneL.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_pruneL.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_relax_snode.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SparseQR/SparseQR.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseQR/SparseQR.h
similarity index 99%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SparseQR/SparseQR.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseQR/SparseQR.h
index 07802f4..d1fb96f 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/SparseQR/SparseQR.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SparseQR/SparseQR.h
@@ -69,7 +69,7 @@ namespace internal {
   * detailed in the following paper:
   * <i>
   * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
-  * Sparse QR Factorization", ACM Trans. on Math. Soft. 38(1), 2011.
+  * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011.
   * </i>
   * Even though it is qualified as "rank-revealing", this strategy might fail for some 
   * rank deficient problems. When this class is used to solve linear or least-square problems
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdDeque.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdDeque.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdDeque.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdDeque.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdList.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdList.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdList.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdList.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdVector.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdVector.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/StlSupport/StdVector.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/StdVector.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/StlSupport/details.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/details.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/StlSupport/details.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/StlSupport/details.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/SuperLUSupport/SuperLUSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/UmfPackSupport/UmfPackSupport.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/Image.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/Image.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/Image.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/Image.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/Kernel.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/Kernel.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/Kernel.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/Kernel.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/RealSvd2x2.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/RealSvd2x2.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/RealSvd2x2.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/RealSvd2x2.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/blas.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/blas.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/blas.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/blas.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/lapack.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapack.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/lapack.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapack.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/lapacke.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapacke.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/lapacke.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapacke.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/misc/lapacke_mangling.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapacke_mangling.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/misc/lapacke_mangling.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/misc/lapacke_mangling.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
similarity index 83%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
index 6a79563..0e5d544 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h
@@ -30,53 +30,15 @@ operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
   *
   * \sa max()
   */
-template <int NaNPropagation, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-min
-#else
-(min)
-#endif
-(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,PropagateFast>, const Derived, const OtherDerived>
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-min
-#else
-(min)
-#endif
-(const OtherDerived &other) const
-{
-  return (min<PropagateFast>)(other);
-}
+EIGEN_MAKE_CWISE_BINARY_OP(min,min)
 
 /** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
   *
   * \sa max()
   */
-template <int NaNPropagation>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived,
-    const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-min
-#else
-(min)
-#endif
-(const Scalar &other) const
-{
-  return (min<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
 EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,PropagateFast>, const Derived,
-    const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived,
+                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
 #ifdef EIGEN_PARSED_BY_DOXYGEN
 min
 #else
@@ -84,7 +46,7 @@ min
 #endif
 (const Scalar &other) const
 {
-  return (min<PropagateFast>)(Derived::PlainObject::Constant(rows(), cols(), other));
+  return (min)(Derived::PlainObject::Constant(rows(), cols(), other));
 }
 
 /** \returns an expression of the coefficient-wise max of \c *this and \a other
@@ -94,52 +56,14 @@ min
   *
   * \sa min()
   */
-template <int NaNPropagation, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-max
-#else
-(max)
-#endif
-(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
-template <typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,PropagateFast>, const Derived, const OtherDerived>
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-max
-#else
-(max)
-#endif
-(const OtherDerived &other) const
-{
-  return (max<PropagateFast>)(other);
-}
+EIGEN_MAKE_CWISE_BINARY_OP(max,max)
 
 /** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
   *
   * \sa min()
   */
-template <int NaNPropagation>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived,
-                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
-#ifdef EIGEN_PARSED_BY_DOXYGEN
-max
-#else
-(max)
-#endif
-(const Scalar &other) const
-{
-  return (max<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
-}
-
 EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,PropagateFast>, const Derived,
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived,
                                         const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
 #ifdef EIGEN_PARSED_BY_DOXYGEN
 max
@@ -148,10 +72,13 @@ max
 #endif
 (const Scalar &other) const
 {
-  return (max<PropagateFast>)(Derived::PlainObject::Constant(rows(), cols(), other));
+  return (max)(Derived::PlainObject::Constant(rows(), cols(), other));
 }
 
 /** \returns an expression of the coefficient-wise absdiff of \c *this and \a other
+  *
+  * Example: \include Cwise_absolute_difference.cpp
+  * Output: \verbinclude Cwise_absolute_difference.out
   *
   * \sa absolute_difference()
   */
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/BlockMethods.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/BlockMethods.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/BlockMethods.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/BlockMethods.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/CommonCwiseBinaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/CommonCwiseUnaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/IndexedViewMethods.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
similarity index 98%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/IndexedViewMethods.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
index 15c35b0..5bfb19a 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/plugins/IndexedViewMethods.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/IndexedViewMethods.h
@@ -90,8 +90,8 @@ operator()(const RowIndices& rowIndices, const ColIndices& colIndices) EIGEN_IND
   return BlockType(derived(),
                    internal::first(actualRowIndices),
                    internal::first(actualColIndices),
-                   internal::index_list_size(actualRowIndices),
-                   internal::index_list_size(actualColIndices));
+                   internal::size(actualRowIndices),
+                   internal::size(actualColIndices));
 }
 
 // The following overload returns a Scalar
@@ -168,7 +168,7 @@ operator()(const Indices& indices) EIGEN_INDEXED_VIEW_METHOD_CONST
   EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
   typename IvcType<Indices>::type actualIndices = ivcSize(indices);
   return VectorBlock<EIGEN_INDEXED_VIEW_METHOD_CONST Derived,internal::array_size<Indices>::value>
-            (derived(), internal::first(actualIndices), internal::index_list_size(actualIndices));
+            (derived(), internal::first(actualIndices), internal::size(actualIndices));
 }
 
 template<typename IndexType>
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
similarity index 74%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
index 514d83a..a0feef8 100644
--- a/3rdParty/ceres_2.2/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
+++ b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h
@@ -72,39 +72,23 @@ cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
   *
   * \sa class CwiseBinaryOp, max()
   */
-template<int NaNPropagation, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
-cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
 template<typename OtherDerived>
 EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,PropagateFast>, const Derived, const OtherDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>
 cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
 {
-  return cwiseMin<PropagateFast>(other);
+  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
 }
 
 /** \returns an expression of the coefficient-wise min of *this and scalar \a other
   *
   * \sa class CwiseBinaryOp, min()
   */
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const ConstantReturnType>
-cwiseMin(const Scalar &other) const
-{
-  return cwiseMin<NaNPropagation>(Derived::Constant(rows(), cols(), other));
-}
-
 EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,PropagateFast>, const Derived, const ConstantReturnType>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
 cwiseMin(const Scalar &other) const
 {
-  return cwiseMin<PropagateFast>(Derived::Constant(rows(), cols(), other));
+  return cwiseMin(Derived::Constant(rows(), cols(), other));
 }
 
 /** \returns an expression of the coefficient-wise max of *this and \a other
@@ -114,39 +98,23 @@ cwiseMin(const Scalar &other) const
   *
   * \sa class CwiseBinaryOp, min()
   */
-template<int NaNPropagation, typename OtherDerived>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
-cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
-{
-  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
-}
-
 template<typename OtherDerived>
 EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,PropagateFast>, const Derived, const OtherDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>
 cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
 {
-  return cwiseMax<PropagateFast>(other);
+  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
 }
 
 /** \returns an expression of the coefficient-wise max of *this and scalar \a other
   *
   * \sa class CwiseBinaryOp, min()
   */
-template<int NaNPropagation>
-EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const ConstantReturnType>
-cwiseMax(const Scalar &other) const
-{
-  return cwiseMax<NaNPropagation>(Derived::Constant(rows(), cols(), other));
-}
-
 EIGEN_DEVICE_FUNC
-EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,PropagateFast>, const Derived, const ConstantReturnType>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar>, const Derived, const ConstantReturnType>
 cwiseMax(const Scalar &other) const
 {
-  return cwiseMax<PropagateFast>(Derived::Constant(rows(), cols(), other));
+  return cwiseMax(Derived::Constant(rows(), cols(), other));
 }
 
 
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h
diff --git a/3rdParty/ceres_2.2/include/Eigen/src/plugins/ReshapedMethods.h b/3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
similarity index 100%
rename from 3rdParty/ceres_2.2/include/Eigen/src/plugins/ReshapedMethods.h
rename to 3rdParty/ceres_2.2/include/eigen3/Eigen/src/plugins/ReshapedMethods.h
diff --git a/3rdParty/ceres_2.2/include/eigen3/signature_of_eigen3_matrix_library b/3rdParty/ceres_2.2/include/eigen3/signature_of_eigen3_matrix_library
new file mode 100644
index 0000000..80aaf46
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/signature_of_eigen3_matrix_library
@@ -0,0 +1 @@
+This file is just there as a signature to help identify directories containing Eigen3. When writing a script looking for Eigen3, just look for this file. This is especially useful to help disambiguate with Eigen2...
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AdolcForward b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AdolcForward
new file mode 100644
index 0000000..56caeae
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AdolcForward
@@ -0,0 +1,159 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ADLOC_FORWARD
+#define EIGEN_ADLOC_FORWARD
+
+//--------------------------------------------------------------------------------
+//
+// This file provides support for adolc's adouble type in forward mode.
+// ADOL-C is a C++ automatic differentiation library,
+// see https://projects.coin-or.org/ADOL-C for more information.
+//
+// Note that the maximal number of directions is controlled by
+// the preprocessor token NUMBER_DIRECTIONS. The default is 2.
+//
+//--------------------------------------------------------------------------------
+
+#define ADOLC_TAPELESS
+#ifndef NUMBER_DIRECTIONS
+# define NUMBER_DIRECTIONS 2
+#endif
+#include <adolc/adtl.h>
+
+// adolc defines some very stupid macros:
+#if defined(malloc)
+# undef malloc
+#endif
+
+#if defined(calloc)
+# undef calloc
+#endif
+
+#if defined(realloc)
+# undef realloc
+#endif
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+  * \defgroup AdolcForward_Module Adolc forward module
+  * This module provides support for adolc's adouble type in forward mode.
+  * ADOL-C is a C++ automatic differentiation library,
+  * see https://projects.coin-or.org/ADOL-C for more information.
+  * It mainly consists in:
+  *  - a struct Eigen::NumTraits<adtl::adouble> specialization
+  *  - overloads of internal::* math function for adtl::adouble type.
+  *
+  * Note that the maximal number of directions is controlled by
+  * the preprocessor token NUMBER_DIRECTIONS. The default is 2.
+  *
+  * \code
+  * #include <unsupported/Eigen/AdolcSupport>
+  * \endcode
+  */
+  //@{
+
+} // namespace Eigen
+
+// Eigen's require a few additional functions which must be defined in the same namespace
+// than the custom scalar type own namespace
+namespace adtl {
+
+inline const adouble& conj(const adouble& x)  { return x; }
+inline const adouble& real(const adouble& x)  { return x; }
+inline adouble imag(const adouble&)    { return 0.; }
+inline adouble abs(const adouble&  x)  { return fabs(x); }
+inline adouble abs2(const adouble& x)  { return x*x; }
+
+inline bool (isinf)(const adouble& x) { return (Eigen::numext::isinf)(x.getValue()); }
+inline bool (isnan)(const adouble& x) { return (Eigen::numext::isnan)(x.getValue()); }
+
+}
+
+namespace Eigen {
+
+template<> struct NumTraits<adtl::adouble>
+    : NumTraits<double>
+{
+  typedef adtl::adouble Real;
+  typedef adtl::adouble NonInteger;
+  typedef adtl::adouble Nested;
+  enum {
+    IsComplex = 0,
+    IsInteger = 0,
+    IsSigned = 1,
+    RequireInitialization = 1,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+};
+
+template<typename Functor> class AdolcForwardJacobian : public Functor
+{
+  typedef adtl::adouble ActiveScalar;
+public:
+
+  AdolcForwardJacobian() : Functor() {}
+  AdolcForwardJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+  template<typename T0>
+  AdolcForwardJacobian(const T0& a0) : Functor(a0) {}
+  template<typename T0, typename T1>
+  AdolcForwardJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
+  template<typename T0, typename T1, typename T2>
+  AdolcForwardJacobian(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2) {}
+
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename Functor::JacobianType JacobianType;
+
+  typedef Matrix<ActiveScalar, InputType::SizeAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValueType::SizeAtCompileTime, 1> ActiveValue;
+
+  void operator() (const InputType& x, ValueType* v, JacobianType* _jac) const
+  {
+    eigen_assert(v!=0);
+    if (!_jac)
+    {
+      Functor::operator()(x, v);
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+
+    for (int j=0; j<jac.cols(); j++)
+      for (int i=0; i<jac.cols(); i++)
+        ax[i].setADValue(j, i==j ? 1 : 0);
+
+    Functor::operator()(ax, &av);
+
+    for (int i=0; i<jac.rows(); i++)
+    {
+      (*v)[i] = av[i].getValue();
+      for (int j=0; j<jac.cols(); j++)
+        jac.coeffRef(i,j) = av[i].getADValue(j);
+    }
+  }
+protected:
+
+};
+
+//@}
+
+}
+
+#endif // EIGEN_ADLOC_FORWARD
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AlignedVector3 b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AlignedVector3
new file mode 100644
index 0000000..4fa1842
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AlignedVector3
@@ -0,0 +1,234 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ALIGNED_VECTOR3
+#define EIGEN_ALIGNED_VECTOR3
+
+#include "../../Eigen/Geometry"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+  * \defgroup AlignedVector3_Module Aligned vector3 module
+  *
+  * \code
+  * #include <unsupported/Eigen/AlignedVector3>
+  * \endcode
+  */
+  //@{
+
+
+/** \class AlignedVector3
+  *
+  * \brief A vectorization friendly 3D vector
+  *
+  * This class represents a 3D vector internally using a 4D vector
+  * such that vectorization can be seamlessly enabled. Of course,
+  * the same result can be achieved by directly using a 4D vector.
+  * This class makes this process simpler.
+  *
+  */
+// TODO specialize Cwise
+template<typename _Scalar> class AlignedVector3;
+
+namespace internal {
+template<typename _Scalar> struct traits<AlignedVector3<_Scalar> >
+  : traits<Matrix<_Scalar,3,1,0,4,1> >
+{
+};
+}
+
+template<typename _Scalar> class AlignedVector3
+  : public MatrixBase<AlignedVector3<_Scalar> >
+{
+    typedef Matrix<_Scalar,4,1> CoeffType;
+    CoeffType m_coeffs;
+  public:
+
+    typedef MatrixBase<AlignedVector3<_Scalar> > Base;	
+    EIGEN_DENSE_PUBLIC_INTERFACE(AlignedVector3)
+    using Base::operator*;
+
+    inline Index rows() const { return 3; }
+    inline Index cols() const { return 1; }
+    
+    Scalar* data() { return m_coeffs.data(); }
+    const Scalar* data() const { return m_coeffs.data(); }
+    Index innerStride() const { return 1; }
+    Index outerStride() const { return 3; }
+
+    inline const Scalar& coeff(Index row, Index col) const
+    { return m_coeffs.coeff(row, col); }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    { return m_coeffs.coeffRef(row, col); }
+
+    inline const Scalar& coeff(Index index) const
+    { return m_coeffs.coeff(index); }
+
+    inline Scalar& coeffRef(Index index)
+    { return m_coeffs.coeffRef(index);}
+
+
+    inline AlignedVector3()
+    {}
+
+    inline AlignedVector3(const Scalar& x, const Scalar& y, const Scalar& z)
+      : m_coeffs(x, y, z, Scalar(0))
+    {}
+
+    inline AlignedVector3(const AlignedVector3& other)
+      : Base(), m_coeffs(other.m_coeffs)
+    {}
+
+    template<typename XprType, int Size=XprType::SizeAtCompileTime>
+    struct generic_assign_selector {};
+
+    template<typename XprType> struct generic_assign_selector<XprType,4>
+    {
+      inline static void run(AlignedVector3& dest, const XprType& src)
+      {
+        dest.m_coeffs = src;
+      }
+    };
+
+    template<typename XprType> struct generic_assign_selector<XprType,3>
+    {
+      inline static void run(AlignedVector3& dest, const XprType& src)
+      {
+        dest.m_coeffs.template head<3>() = src;
+        dest.m_coeffs.w() = Scalar(0);
+      }
+    };
+
+    template<typename Derived>
+    inline AlignedVector3(const MatrixBase<Derived>& other)
+    {
+      generic_assign_selector<Derived>::run(*this,other.derived());
+    }
+
+    inline AlignedVector3& operator=(const AlignedVector3& other)
+    { m_coeffs = other.m_coeffs; return *this; }
+
+    template <typename Derived>
+    inline AlignedVector3& operator=(const MatrixBase<Derived>& other)
+    {
+      generic_assign_selector<Derived>::run(*this,other.derived());
+      return *this;
+    }
+
+    inline AlignedVector3 operator+(const AlignedVector3& other) const
+    { return AlignedVector3(m_coeffs + other.m_coeffs); }
+
+    inline AlignedVector3& operator+=(const AlignedVector3& other)
+    { m_coeffs += other.m_coeffs; return *this; }
+
+    inline AlignedVector3 operator-(const AlignedVector3& other) const
+    { return AlignedVector3(m_coeffs - other.m_coeffs); }
+
+    inline AlignedVector3 operator-() const
+    { return AlignedVector3(-m_coeffs); }
+
+    inline AlignedVector3 operator-=(const AlignedVector3& other)
+    { m_coeffs -= other.m_coeffs; return *this; }
+
+    inline AlignedVector3 operator*(const Scalar& s) const
+    { return AlignedVector3(m_coeffs * s); }
+
+    inline friend AlignedVector3 operator*(const Scalar& s,const AlignedVector3& vec)
+    { return AlignedVector3(s * vec.m_coeffs); }
+
+    inline AlignedVector3& operator*=(const Scalar& s)
+    { m_coeffs *= s; return *this; }
+
+    inline AlignedVector3 operator/(const Scalar& s) const
+    { return AlignedVector3(m_coeffs / s); }
+
+    inline AlignedVector3& operator/=(const Scalar& s)
+    { m_coeffs /= s; return *this; }
+
+    inline Scalar dot(const AlignedVector3& other) const
+    {
+      eigen_assert(m_coeffs.w()==Scalar(0));
+      eigen_assert(other.m_coeffs.w()==Scalar(0));
+      return m_coeffs.dot(other.m_coeffs);
+    }
+
+    inline void normalize()
+    {
+      m_coeffs /= norm();
+    }
+
+    inline AlignedVector3 normalized() const
+    {
+      return AlignedVector3(m_coeffs / norm());
+    }
+
+    inline Scalar sum() const
+    {
+      eigen_assert(m_coeffs.w()==Scalar(0));
+      return m_coeffs.sum();
+    }
+
+    inline Scalar squaredNorm() const
+    {
+      eigen_assert(m_coeffs.w()==Scalar(0));
+      return m_coeffs.squaredNorm();
+    }
+
+    inline Scalar norm() const
+    {
+      using std::sqrt;
+      return sqrt(squaredNorm());
+    }
+
+    inline AlignedVector3 cross(const AlignedVector3& other) const
+    {
+      return AlignedVector3(m_coeffs.cross3(other.m_coeffs));
+    }
+
+    template<typename Derived>
+    inline bool isApprox(const MatrixBase<Derived>& other, const RealScalar& eps=NumTraits<Scalar>::dummy_precision()) const
+    {
+      return m_coeffs.template head<3>().isApprox(other,eps);
+    }
+    
+    CoeffType& coeffs() { return m_coeffs; }
+    const CoeffType& coeffs() const { return m_coeffs; }
+};
+
+namespace internal {
+
+template<typename _Scalar>
+struct eval<AlignedVector3<_Scalar>, Dense>
+{
+ typedef const AlignedVector3<_Scalar>& type;
+};
+
+template<typename Scalar>
+struct evaluator<AlignedVector3<Scalar> >
+  : evaluator<Matrix<Scalar,4,1> >
+{
+  typedef AlignedVector3<Scalar> XprType;
+  typedef evaluator<Matrix<Scalar,4,1> > Base;
+  
+  evaluator(const XprType &m) : Base(m.coeffs()) {}  
+};
+
+}
+
+//@}
+
+}
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ALIGNED_VECTOR3
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/ArpackSupport b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/ArpackSupport
new file mode 100644
index 0000000..67c4ac8
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/ArpackSupport
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARPACKSUPPORT_MODULE_H
+#define EIGEN_ARPACKSUPPORT_MODULE_H
+
+#include "../../Eigen/Core"
+
+/** \defgroup ArpackSupport_Module Arpack support module
+  *
+  * This module provides a wrapper to Arpack, a library for sparse eigenvalue decomposition.
+  *
+  * \code
+  * #include <Eigen/ArpackSupport>
+  * \endcode
+  */
+
+#include "../../Eigen/SparseCholesky"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+#include "src/Eigenvalues/ArpackSelfAdjointEigenSolver.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ARPACKSUPPORT_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AutoDiff b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AutoDiff
new file mode 100644
index 0000000..7a4ff46
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/AutoDiff
@@ -0,0 +1,46 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_MODULE
+#define EIGEN_AUTODIFF_MODULE
+
+namespace Eigen {
+
+/**
+  * \defgroup AutoDiff_Module Auto Diff module
+  *
+  * This module features forward automatic differentation via a simple
+  * templated scalar type wrapper AutoDiffScalar.
+  *
+  * Warning : this should NOT be confused with numerical differentiation, which
+  * is a different method and has its own module in Eigen : \ref NumericalDiff_Module.
+  *
+  * \code
+  * #include <unsupported/Eigen/AutoDiff>
+  * \endcode
+  */
+//@{
+
+}
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+
+#include "src/AutoDiff/AutoDiffScalar.h"
+// #include "src/AutoDiff/AutoDiffVector.h"
+#include "src/AutoDiff/AutoDiffJacobian.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+
+
+namespace Eigen {
+//@}
+}
+
+#endif // EIGEN_AUTODIFF_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/BVH b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/BVH
new file mode 100644
index 0000000..666c983
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/BVH
@@ -0,0 +1,95 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BVH_MODULE_H
+#define EIGEN_BVH_MODULE_H
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Geometry"
+#include "../../Eigen/StdVector"
+#include <algorithm>
+#include <queue>
+
+namespace Eigen {
+
+/**
+  * \defgroup BVH_Module BVH module
+  * \brief This module provides generic bounding volume hierarchy algorithms
+  * and reference tree implementations.
+  *
+  *
+  * \code
+  * #include <unsupported/Eigen/BVH>
+  * \endcode
+  *
+  * A bounding volume hierarchy (BVH) can accelerate many geometric queries.  This module provides a generic implementation
+  * of the two basic algorithms over a BVH: intersection of a query object against all objects in the hierarchy and minimization
+  * of a function over the objects in the hierarchy.  It also provides intersection and minimization over a cartesian product of
+  * two BVH's.  A BVH accelerates intersection by using the fact that if a query object does not intersect a volume, then it cannot
+  * intersect any object contained in that volume.  Similarly, a BVH accelerates minimization because the minimum of a function
+  * over a volume is no greater than the minimum of a function over any object contained in it.
+  *
+  * Some sample queries that can be written in terms of intersection are:
+  *   - Determine all points where a ray intersects a triangle mesh
+  *   - Given a set of points, determine which are contained in a query sphere
+  *   - Given a set of spheres, determine which contain the query point
+  *   - Given a set of disks, determine if any is completely contained in a query rectangle (represent each 2D disk as a point \f$(x,y,r)\f$
+  *     in 3D and represent the rectangle as a pyramid based on the original rectangle and shrinking in the \f$r\f$ direction)
+  *   - Given a set of points, count how many pairs are \f$d\pm\epsilon\f$ apart (done by looking at the cartesian product of the set
+  *     of points with itself)
+  *
+  * Some sample queries that can be written in terms of function minimization over a set of objects are:
+  *   - Find the intersection between a ray and a triangle mesh closest to the ray origin (function is infinite off the ray)
+  *   - Given a polyline and a query point, determine the closest point on the polyline to the query
+  *   - Find the diameter of a point cloud (done by looking at the cartesian product and using negative distance as the function)
+  *   - Determine how far two meshes are from colliding (this is also a cartesian product query)
+  *
+  * This implementation decouples the basic algorithms both from the type of hierarchy (and the types of the bounding volumes) and
+  * from the particulars of the query.  To enable abstraction from the BVH, the BVH is required to implement a generic mechanism
+  * for traversal.  To abstract from the query, the query is responsible for keeping track of results.
+  *
+  * To be used in the algorithms, a hierarchy must implement the following traversal mechanism (see KdBVH for a sample implementation): \code
+      typedef Volume  //the type of bounding volume
+      typedef Object  //the type of object in the hierarchy
+      typedef Index   //a reference to a node in the hierarchy--typically an int or a pointer
+      typedef VolumeIterator //an iterator type over node children--returns Index
+      typedef ObjectIterator //an iterator over object (leaf) children--returns const Object &
+      Index getRootIndex() const //returns the index of the hierarchy root
+      const Volume &getVolume(Index index) const //returns the bounding volume of the node at given index
+      void getChildren(Index index, VolumeIterator &outVBegin, VolumeIterator &outVEnd,
+                      ObjectIterator &outOBegin, ObjectIterator &outOEnd) const
+      //getChildren takes a node index and makes [outVBegin, outVEnd) range over its node children
+      //and [outOBegin, outOEnd) range over its object children
+    \endcode
+  *
+  * To use the hierarchy, call BVIntersect or BVMinimize, passing it a BVH (or two, for cartesian product) and a minimizer or intersector.
+  * For an intersection query on a single BVH, the intersector encapsulates the query and must provide two functions:
+  * \code
+      bool intersectVolume(const Volume &volume) //returns true if the query intersects the volume
+      bool intersectObject(const Object &object) //returns true if the intersection search should terminate immediately
+    \endcode
+  * The guarantee that BVIntersect provides is that intersectObject will be called on every object whose bounding volume
+  * intersects the query (but possibly on other objects too) unless the search is terminated prematurely.  It is the
+  * responsibility of the intersectObject function to keep track of the results in whatever manner is appropriate.
+  * The cartesian product intersection and the BVMinimize queries are similar--see their individual documentation.
+  *
+  * The following is a simple but complete example for how to use the BVH to accelerate the search for a closest red-blue point pair:
+  * \include BVH_Example.cpp
+  * Output: \verbinclude BVH_Example.out
+  */
+}
+
+//@{
+
+#include "src/BVH/BVAlgorithms.h"
+#include "src/BVH/KdBVH.h"
+
+//@}
+
+#endif // EIGEN_BVH_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/Tensor b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/Tensor
new file mode 100644
index 0000000..0938bb5
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/Tensor
@@ -0,0 +1,137 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+//#ifndef EIGEN_CXX11_TENSOR_MODULE
+//#define EIGEN_CXX11_TENSOR_MODULE
+
+#include "../../../Eigen/Core"
+
+#if EIGEN_HAS_CXX11
+
+#include "../SpecialFunctions"
+
+#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
+#include "src/util/CXX11Meta.h"
+#include "src/util/MaxSizeVector.h"
+
+/** \defgroup CXX11_Tensor_Module Tensor Module
+  *
+  * This module provides a Tensor class for storing arbitrarily indexed
+  * objects.
+  *
+  * \code
+  * #include <Eigen/CXX11/Tensor>
+  * \endcode
+  *
+  * Much of the documentation can be found \ref eigen_tensors "here".
+  */
+
+#include <atomic>
+#include <chrono>
+#include <cmath>
+#include <cstddef>
+#include <cstring>
+#include <random>
+#include <thread>
+
+#if defined(EIGEN_USE_THREADS) || defined(EIGEN_USE_SYCL)
+#include "ThreadPool"
+#endif
+
+#ifdef EIGEN_USE_GPU
+  #include <iostream>
+  #if defined(EIGEN_USE_HIP)
+    #include <hip/hip_runtime.h>
+  #else
+    #include <cuda_runtime.h>
+  #endif
+#endif
+
+#include "src/Tensor/TensorMacros.h"
+#include "src/Tensor/TensorForwardDeclarations.h"
+#include "src/Tensor/TensorMeta.h"
+#include "src/Tensor/TensorFunctors.h"
+#include "src/Tensor/TensorCostModel.h"
+#include "src/Tensor/TensorDeviceDefault.h"
+#include "src/Tensor/TensorDeviceThreadPool.h"
+#include "src/Tensor/TensorDeviceGpu.h"
+#ifndef gpu_assert
+#define gpu_assert(x)
+#endif
+#include "src/Tensor/TensorDeviceSycl.h"
+#include "src/Tensor/TensorIndexList.h"
+#include "src/Tensor/TensorDimensionList.h"
+#include "src/Tensor/TensorDimensions.h"
+#include "src/Tensor/TensorInitializer.h"
+#include "src/Tensor/TensorTraits.h"
+#include "src/Tensor/TensorRandom.h"
+#include "src/Tensor/TensorUInt128.h"
+#include "src/Tensor/TensorIntDiv.h"
+#include "src/Tensor/TensorGlobalFunctions.h"
+
+#include "src/Tensor/TensorBase.h"
+#include "src/Tensor/TensorBlock.h"
+
+#include "src/Tensor/TensorEvaluator.h"
+#include "src/Tensor/TensorExpr.h"
+#include "src/Tensor/TensorReduction.h"
+#include "src/Tensor/TensorReductionGpu.h"
+#include "src/Tensor/TensorArgMax.h"
+#include "src/Tensor/TensorConcatenation.h"
+#include "src/Tensor/TensorContractionMapper.h"
+#include "src/Tensor/TensorContractionBlocking.h"
+#include "src/Tensor/TensorContraction.h"
+#include "src/Tensor/TensorContractionThreadPool.h"
+#include "src/Tensor/TensorContractionGpu.h"
+#include "src/Tensor/TensorConversion.h"
+#include "src/Tensor/TensorConvolution.h"
+#include "src/Tensor/TensorFFT.h"
+#include "src/Tensor/TensorPatch.h"
+#include "src/Tensor/TensorImagePatch.h"
+#include "src/Tensor/TensorVolumePatch.h"
+#include "src/Tensor/TensorBroadcasting.h"
+#include "src/Tensor/TensorChipping.h"
+#include "src/Tensor/TensorInflation.h"
+#include "src/Tensor/TensorLayoutSwap.h"
+#include "src/Tensor/TensorMorphing.h"
+#include "src/Tensor/TensorPadding.h"
+#include "src/Tensor/TensorReverse.h"
+#include "src/Tensor/TensorShuffling.h"
+#include "src/Tensor/TensorStriding.h"
+#include "src/Tensor/TensorCustomOp.h"
+#include "src/Tensor/TensorEvalTo.h"
+#include "src/Tensor/TensorForcedEval.h"
+#include "src/Tensor/TensorGenerator.h"
+#include "src/Tensor/TensorAssign.h"
+#include "src/Tensor/TensorScan.h"
+#include "src/Tensor/TensorTrace.h"
+
+#ifdef EIGEN_USE_SYCL
+#include "src/Tensor/TensorReductionSycl.h"
+#include "src/Tensor/TensorConvolutionSycl.h"
+#include "src/Tensor/TensorContractionSycl.h"
+#include "src/Tensor/TensorScanSycl.h"
+#endif
+
+#include "src/Tensor/TensorExecutor.h"
+#include "src/Tensor/TensorDevice.h"
+
+#include "src/Tensor/TensorStorage.h"
+#include "src/Tensor/Tensor.h"
+#include "src/Tensor/TensorFixedSize.h"
+#include "src/Tensor/TensorMap.h"
+#include "src/Tensor/TensorRef.h"
+
+#include "src/Tensor/TensorIO.h"
+
+#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_HAS_CXX11
+//#endif // EIGEN_CXX11_TENSOR_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
new file mode 100644
index 0000000..b09c5e4
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/TensorSymmetry
@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_MODULE
+#define EIGEN_CXX11_TENSORSYMMETRY_MODULE
+
+#include "Tensor"
+
+#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include "src/util/CXX11Meta.h"
+
+/** \defgroup CXX11_TensorSymmetry_Module Tensor Symmetry Module
+  *
+  * This module provides a classes that allow for the definition of
+  * symmetries w.r.t. tensor indices.
+  *
+  * Including this module will implicitly include the Tensor module.
+  *
+  * \code
+  * #include <Eigen/TensorSymmetry>
+  * \endcode
+  */
+
+#include "src/TensorSymmetry/util/TemplateGroupTheory.h"
+#include "src/TensorSymmetry/Symmetry.h"
+#include "src/TensorSymmetry/StaticSymmetry.h"
+#include "src/TensorSymmetry/DynamicSymmetry.h"
+
+#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CXX11_TENSORSYMMETRY_MODULE
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/ThreadPool b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
new file mode 100644
index 0000000..c5cafb2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/ThreadPool
@@ -0,0 +1,74 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_MODULE
+#define EIGEN_CXX11_THREADPOOL_MODULE
+
+#include "../../../Eigen/Core"
+
+#include "../../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup CXX11_ThreadPool_Module C++11 ThreadPool Module
+  *
+  * This module provides 2 threadpool implementations
+  *  - a simple reference implementation
+  *  - a faster non blocking implementation
+  *
+  * This module requires C++11.
+  *
+  * \code
+  * #include <Eigen/CXX11/ThreadPool>
+  * \endcode
+  */
+
+
+// The code depends on CXX11, so only include the module if the
+// compiler supports it.
+#if (EIGEN_COMP_CXXVER >= 11)
+#include <cstddef>
+#include <cstring>
+#include <time.h>
+
+#include <vector>
+#include <atomic>
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+#include <thread>
+#include <functional>
+#include <memory>
+#include <utility>
+
+// There are non-parenthesized calls to "max" in the  <unordered_map> header,
+// which trigger a check in test/main.h causing compilation to fail.
+// We work around the check here by removing the check for max in
+// the case where we have to emulate thread_local.
+#ifdef max
+#undef max
+#endif
+#include <unordered_map>
+
+#include "src/util/CXX11Meta.h"
+#include "src/util/MaxSizeVector.h"
+
+#include "src/ThreadPool/ThreadLocal.h"
+#include "src/ThreadPool/ThreadYield.h"
+#include "src/ThreadPool/ThreadCancel.h"
+#include "src/ThreadPool/EventCount.h"
+#include "src/ThreadPool/RunQueue.h"
+#include "src/ThreadPool/ThreadPoolInterface.h"
+#include "src/ThreadPool/ThreadEnvironment.h"
+#include "src/ThreadPool/Barrier.h"
+#include "src/ThreadPool/NonBlockingThreadPool.h"
+
+#endif
+
+#include "../../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CXX11_THREADPOOL_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
new file mode 100644
index 0000000..8cac2bb
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.h
@@ -0,0 +1,554 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_H
+
+namespace Eigen {
+
+/** \class Tensor
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor class.
+  *
+  * The %Tensor class is the work-horse for all \em dense tensors within Eigen.
+  *
+  * The %Tensor class encompasses only dynamic-size objects so far.
+  *
+  * The first two template parameters are required:
+  * \tparam Scalar_  Numeric type, e.g. float, double, int or `std::complex<float>`.
+  *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
+  * \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
+  *
+  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
+  * \tparam Options_  A combination of either \b #RowMajor or \b #ColMajor, and of either
+  *                 \b #AutoAlign or \b #DontAlign.
+  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
+  *                 for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization.
+  *                 Support for such operations (i.e. adding two tensors etc.) is planned.
+  *
+  * You can access elements of tensors using normal subscripting:
+  *
+  * \code
+  * Eigen::Tensor<double, 4> t(10, 10, 10, 10);
+  * t(0, 1, 2, 3) = 42.0;
+  * \endcode
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN.
+  *
+  * <i><b>Some notes:</b></i>
+  *
+  * <dl>
+  * <dt><b>Relation to other parts of Eigen:</b></dt>
+  * <dd>The midterm development goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
+  * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
+  * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor
+  * class does not provide any of these features and is only available as a stand-alone class that just allows for
+  * coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
+  * change dramatically.</dd>
+  * </dl>
+  *
+  * \ref TopicStorageOrders
+  */
+
+template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
+{
+  public:
+    typedef Tensor<Scalar_, NumIndices_, Options_, IndexType_> Self;
+    typedef TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> > Base;
+    typedef typename Eigen::internal::nested<Self>::type Nested;
+    typedef typename internal::traits<Self>::StorageKind StorageKind;
+    typedef typename internal::traits<Self>::Index Index;
+    typedef Scalar_ Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+    enum {
+      IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0) & !(Options_&DontAlign),
+      Layout = Options_ & RowMajor ? RowMajor : ColMajor,
+      CoordAccess = true,
+      RawAccess = true
+    };
+
+    static const int Options = Options_;
+    static const int NumIndices = NumIndices_;
+    typedef DSizes<Index, NumIndices_> Dimensions;
+
+  protected:
+    TensorStorage<Scalar, Dimensions, Options> m_storage;
+
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomIndices>
+    struct isOfNormalIndex{
+      static const bool is_array = internal::is_base_of<array<Index, NumIndices>, CustomIndices>::value;
+      static const bool is_int = NumTraits<CustomIndices>::IsInteger;
+      static const bool value = is_array | is_int;
+    };
+#endif
+
+  public:
+    // Metadata
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         rank()                   const { return NumIndices; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions&             dimensions()             const { return m_storage.dimensions(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                         size()                   const { return m_storage.size(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar                        *data()                        { return m_storage.data(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar                  *data()                  const { return m_storage.data(); }
+
+    // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    // work, because that uses base().coeffRef() - and we don't yet
+    // implement a similar class hierarchy
+    inline Self& base()             { return *this; }
+    inline const Self& base() const { return *this; }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+    }
+#endif
+
+    // normal indices
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
+    {
+      eigen_internal_assert(checkIndexRange(indices));
+      return m_storage.data()[linearizedIndex(indices)];
+    }
+
+    // custom indices
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomIndices,
+             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
+    >
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(CustomIndices& indices) const
+    {
+        return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff() const
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return m_storage.data()[0];
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_storage.data()[index];
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+    }
+#endif
+
+    // normal indices
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
+    {
+      eigen_internal_assert(checkIndexRange(indices));
+      return m_storage.data()[linearizedIndex(indices)];
+    }
+
+    // custom indices
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomIndices,
+             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
+             >
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(CustomIndices& indices)
+    {
+        return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return m_storage.data()[0];
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_storage.data()[index];
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
+    {
+      return coeff(array<Index, 2>(i0, i1));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
+    {
+      return coeff(array<Index, 3>(i0, i1, i2));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
+    {
+      return coeff(array<Index, 4>(i0, i1, i2, i3));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
+    {
+      return coeff(array<Index, 5>(i0, i1, i2, i3, i4));
+    }
+#endif
+
+    // custom indices
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomIndices,
+             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
+    >
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(CustomIndices& indices) const
+    {
+        return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
+    }
+#endif
+
+    // normal indices
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
+    {
+      return coeff(indices);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return coeff(index);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()() const
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeff();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
+    {
+      // The bracket operator is only for vectors, use the parenthesis operator instead.
+      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeff(index);
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
+    {
+      return coeffRef(array<Index, 2>(i0, i1));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
+    {
+      return coeffRef(array<Index, 3>(i0, i1, i2));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
+    {
+      return coeffRef(array<Index, 4>(i0, i1, i2, i3));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
+    {
+      return coeffRef(array<Index, 5>(i0, i1, i2, i3, i4));
+    }
+#endif
+
+    // normal indices
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
+    {
+      return coeffRef(indices);
+    }
+
+    // custom indices
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomIndices,
+             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
+    >
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(CustomIndices& indices)
+    {
+      return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index index)
+    {
+      eigen_assert(index >= 0 && index < size());
+      return coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeffRef();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator[](Index index)
+    {
+      // The bracket operator is only for vectors, use the parenthesis operator instead
+      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor()
+      : m_storage()
+    {
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor(const Self& other)
+      : m_storage(other.m_storage)
+    {
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index firstDimension, IndexTypes... otherDimensions)
+        : m_storage(firstDimension, otherDimensions...)
+    {
+      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+#else
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(Index dim1)
+      : m_storage(dim1, array<Index, 1>(dim1))
+    {
+      EIGEN_STATIC_ASSERT(1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2)
+      : m_storage(dim1*dim2, array<Index, 2>(dim1, dim2))
+    {
+      EIGEN_STATIC_ASSERT(2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3)
+      : m_storage(dim1*dim2*dim3, array<Index, 3>(dim1, dim2, dim3))
+    {
+      EIGEN_STATIC_ASSERT(3 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4)
+      : m_storage(dim1*dim2*dim3*dim4, array<Index, 4>(dim1, dim2, dim3, dim4))
+    {
+      EIGEN_STATIC_ASSERT(4 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4, Index dim5)
+      : m_storage(dim1*dim2*dim3*dim4*dim5, array<Index, 5>(dim1, dim2, dim3, dim4, dim5))
+    {
+      EIGEN_STATIC_ASSERT(5 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+#endif
+
+    /** Normal Dimension */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(const array<Index, NumIndices>& dimensions)
+        : m_storage(internal::array_prod(dimensions), dimensions)
+    {
+      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
+    {
+      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+      Assign assign(*this, other.derived());
+      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other)
+    {
+      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+      Assign assign(*this, other.derived());
+      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    }
+
+    #if EIGEN_HAS_RVALUE_REFERENCES
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor(Self&& other)
+      : m_storage(std::move(other.m_storage))
+    {
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor& operator=(Self&& other)
+    {
+      m_storage = std::move(other.m_storage);
+      return *this;
+    }
+    #endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other)
+    {
+      typedef TensorAssignOp<Tensor, const Tensor> Assign;
+      Assign assign(*this, other);
+      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+      return *this;
+    }
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other)
+    {
+      typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
+      Assign assign(*this, other);
+      resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+      return *this;
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    void resize(Index firstDimension, IndexTypes... otherDimensions)
+    {
+      // The number of dimensions used to resize a tensor must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}});
+    }
+#endif
+
+    /** Normal Dimension */
+    EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions)
+    {
+      int i;
+      Index size = Index(1);
+      for (i = 0; i < NumIndices; i++) {
+        internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]);
+        size *= dimensions[i];
+      }
+      #ifdef EIGEN_INITIALIZE_COEFFS
+        bool size_changed = size != this->size();
+        m_storage.resize(size, dimensions);
+        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+      #else
+        m_storage.resize(size, dimensions);
+      #endif
+    }
+
+    // Why this overload, DSizes is derived from array ??? //
+    EIGEN_DEVICE_FUNC void resize(const DSizes<Index, NumIndices>& dimensions) {
+      array<Index, NumIndices> dims;
+      for (int i = 0; i < NumIndices; ++i) {
+        dims[i] = dimensions[i];
+      }
+      resize(dims);
+    }
+
+    EIGEN_DEVICE_FUNC
+    void resize()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      // Nothing to do: rank 0 tensors have fixed size
+    }
+
+#ifdef EIGEN_HAS_INDEX_LIST
+    template <typename FirstType, typename... OtherTypes>
+    EIGEN_DEVICE_FUNC
+    void resize(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
+      array<Index, NumIndices> dims;
+      for (int i = 0; i < NumIndices; ++i) {
+        dims[i] = static_cast<Index>(dimensions[i]);
+      }
+      resize(dims);
+    }
+#endif
+
+    /** Custom Dimension */
+#ifdef EIGEN_HAS_SFINAE
+    template<typename CustomDimension,
+             EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomDimension>::value) )
+    >
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(CustomDimension& dimensions)
+    {
+      resize(internal::customIndices2Array<Index,NumIndices>(dimensions));
+    }
+#endif
+
+#ifndef EIGEN_EMULATE_CXX11_META_H
+    template <typename std::ptrdiff_t... Indices>
+    EIGEN_DEVICE_FUNC
+    void resize(const Sizes<Indices...>& dimensions) {
+      array<Index, NumIndices> dims;
+      for (int i = 0; i < NumIndices; ++i) {
+        dims[i] = static_cast<Index>(dimensions[i]);
+      }
+      resize(dims);
+    }
+#else
+    template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
+    EIGEN_DEVICE_FUNC
+    void resize(const Sizes<V1, V2, V3, V4, V5>& dimensions) {
+      array<Index, NumIndices> dims;
+      for (int i = 0; i < NumIndices; ++i) {
+        dims[i] = static_cast<Index>(dimensions[i]);
+      }
+      resize(dims);
+    }
+#endif
+
+  protected:
+
+    bool checkIndexRange(const array<Index, NumIndices>& indices) const
+    {
+      using internal::array_apply_and_reduce;
+      using internal::array_zip_and_reduce;
+      using internal::greater_equal_zero_op;
+      using internal::logical_and_op;
+      using internal::lesser_op;
+
+      return
+        // check whether the indices are all >= 0
+        array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
+        // check whether the indices fit in the dimensions
+        array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
+    {
+      if (Options&RowMajor) {
+        return m_storage.dimensions().IndexOfRowMajor(indices);
+      } else {
+        return m_storage.dimensions().IndexOfColMajor(indices);
+      }
+    }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
new file mode 100644
index 0000000..8b8fb92
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
@@ -0,0 +1,329 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
+
+namespace Eigen {
+namespace internal {
+
+/** \class TensorIndexTuple
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor + Index Tuple class.
+  *
+  *
+  */
+template<typename XprType>
+struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+};
+
+template<typename XprType>
+struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
+{
+  typedef const TensorIndexTupleOp<XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename XprType>
+struct nested<TensorIndexTupleOp<XprType>, 1,
+              typename eval<TensorIndexTupleOp<XprType> >::type>
+{
+  typedef TensorIndexTupleOp<XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename XprType>
+class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
+  typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
+      : m_xpr(expr) {}
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type&
+  expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+};
+
+// Eval as rvalue
+template<typename ArgType, typename Device>
+struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
+{
+  typedef TensorIndexTupleOp<ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  static const int NumDims = internal::array_size<Dimensions>::value;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_impl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return CoeffReturnType(index, m_impl.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+namespace internal {
+
+/** \class TensorTupleIndex
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>.
+  *
+  */
+template<typename ReduceOp, typename Dims, typename XprType>
+struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef Index Scalar;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static const int Layout = XprTraits::Layout;
+};
+
+template<typename ReduceOp, typename Dims, typename XprType>
+struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
+{
+  typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename ReduceOp, typename Dims, typename XprType>
+struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
+              typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
+{
+  typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename ReduceOp, typename Dims, typename XprType>
+class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
+  typedef Index CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
+                                                          const ReduceOp& reduce_op,
+                                                          const Index return_dim,
+                                                          const Dims& reduce_dims)
+      : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type&
+  expression() const { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC
+  const ReduceOp& reduce_op() const { return m_reduce_op; }
+
+  EIGEN_DEVICE_FUNC
+  const Dims& reduce_dims() const { return m_reduce_dims; }
+
+  EIGEN_DEVICE_FUNC
+  Index return_dim() const { return m_return_dim; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const ReduceOp m_reduce_op;
+    const Index m_return_dim;
+    const Dims m_reduce_dims;
+};
+
+// Eval as rvalue
+template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
+{
+  typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
+  typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
+  typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
+  static const int NumDims = internal::array_size<InputDimensions>::value;
+  typedef array<Index, NumDims> StrideDims;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<TupleType, Device> TupleStorageMem;
+
+  enum {
+    IsAligned         = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess      = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
+    BlockAccess       = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_orig_impl(op.expression(), device),
+        m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
+        m_return_dim(op.return_dim())
+  {
+    gen_strides(m_orig_impl.dimensions(), m_strides);
+    if (Layout == static_cast<int>(ColMajor)) {
+      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
+      m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size;
+    } else {
+      const Index total_size = internal::array_prod(m_orig_impl.dimensions());
+      m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size;
+    }    
+    // If m_return_dim is not a valid index, returns 1 or this can crash on Windows.
+    m_stride_div = ((m_return_dim >= 0) &&
+                    (m_return_dim < static_cast<Index>(m_strides.size())))
+                   ? m_strides[m_return_dim] : 1;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_impl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    const TupleType v = m_impl.coeff(index);
+    return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+#ifdef EIGEN_USE_SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+    m_orig_impl.bind(cgh);
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double compute_cost = 1.0 +
+        (m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>()));
+    return m_orig_impl.costPerCoeff(vectorized) +
+           m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
+  }
+
+ private:
+  EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) {
+    if (m_return_dim < 0) {
+      return;  // Won't be using the strides.
+    }
+    eigen_assert(m_return_dim < NumDims &&
+                 "Asking to convert index to a dimension outside of the rank");
+
+    // Calculate m_stride_div and m_stride_mod, which are used to
+    // calculate the value of an index w.r.t. the m_return_dim.
+    if (Layout == static_cast<int>(ColMajor)) {
+      strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        strides[i] = strides[i-1] * dims[i-1];
+      }
+    } else {
+      strides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        strides[i] = strides[i+1] * dims[i+1];
+      }
+    }
+  }
+
+ protected:
+  TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
+  TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
+  const Index m_return_dim;
+  StrideDims m_strides;
+  Index m_stride_mod;
+  Index m_stride_div;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
new file mode 100644
index 0000000..e5811d6
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
@@ -0,0 +1,247 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
+
+namespace Eigen {
+
+/** \class TensorAssign
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor assignment class.
+  *
+  * This class is represents the assignment of the values resulting from the evaluation of
+  * the rhs expression to the memory locations denoted by the lhs expression.
+  */
+namespace internal {
+template<typename LhsXprType, typename RhsXprType>
+struct traits<TensorAssignOp<LhsXprType, RhsXprType> >
+{
+  typedef typename LhsXprType::Scalar Scalar;
+  typedef typename traits<LhsXprType>::StorageKind StorageKind;
+  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
+                                      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
+  static const int Layout = internal::traits<LhsXprType>::Layout;
+  typedef typename traits<LhsXprType>::PointerType PointerType;
+
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename LhsXprType, typename RhsXprType>
+struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense>
+{
+  typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
+};
+
+template<typename LhsXprType, typename RhsXprType>
+struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type>
+{
+  typedef TensorAssignOp<LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename LhsXprType, typename RhsXprType>
+class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
+
+  static const int NumDims = Eigen::internal::traits<TensorAssignOp>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC
+    typename internal::remove_all<typename LhsXprType::Nested>::type&
+    lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename RhsXprType::Nested>::type&
+    rhsExpression() const { return m_rhs_xpr; }
+
+  protected:
+    typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr;
+    const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr;
+};
+
+
+template<typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
+{
+  typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static const int NumDims = XprType::NumDims;
+
+  enum {
+    IsAligned         = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
+                        int(TensorEvaluator<RightArgType, Device>::IsAligned),
+    PacketAccess      = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
+                        int(TensorEvaluator<RightArgType, Device>::PacketAccess),
+    BlockAccess       = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
+                        int(TensorEvaluator<RightArgType, Device>::BlockAccess),
+    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
+                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
+    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
+    RawAccess         = TensorEvaluator<LeftArgType, Device>::RawAccess
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
+      RightTensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const Device& device) :
+      m_leftImpl(op.lhsExpression(), device),
+      m_rightImpl(op.rhsExpression(), device)
+  {
+    EIGEN_STATIC_ASSERT(
+        (static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
+        YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
+  {
+    // The dimensions of the lhs and the rhs tensors should be equal to prevent
+    // overflows and ensure the result is fully initialized.
+    // TODO: use left impl instead if right impl dimensions are known at compile time.
+    return m_rightImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
+    // null value), attempt to evaluate the rhs expression in place. Returns true iff in place
+    // evaluation isn't supported and the caller still needs to manually assign the values generated
+    // by the rhs to the lhs.
+    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
+      m_rightImpl.evalSubExprsIfNeededAsync(
+          m_leftImpl.data(), [done](bool need_assign) { done(need_assign); });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
+    m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
+
+    const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
+    const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
+    m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
+  }
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_leftImpl.coeff(index);
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+    return m_leftImpl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here, but reduce left
+    // cost by one load because we are using m_leftImpl.coeffRef.
+    TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
+    return m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(
+               numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)),
+               left.bytes_stored(), left.compute_cycles()) +
+           TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::merge(
+        m_leftImpl.getResourceRequirements(),
+        m_rightImpl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
+      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    if (TensorEvaluator<LeftArgType, Device>::RawAccess &&
+        m_leftImpl.data() != NULL) {
+      // If destination has raw data access, we pass it as a potential
+      // destination for a block descriptor evaluation.
+      desc.template AddDestinationBuffer<Layout>(
+          /*dst_base=*/m_leftImpl.data() + desc.offset(),
+          /*dst_strides=*/internal::strides<Layout>(m_leftImpl.dimensions()));
+    }
+
+    RightTensorBlock block = m_rightImpl.block(desc, scratch, /*root_of_expr_ast=*/true);
+    // If block was evaluated into a destination, there is no need to do assignment.
+    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
+      m_leftImpl.writeBlock(desc, block);
+    }
+    block.cleanup();
+  }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_leftImpl.bind(cgh);
+    m_rightImpl.bind(cgh);
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_leftImpl.data(); }
+
+ private:
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+};
+
+}
+
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
new file mode 100644
index 0000000..35b6458
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
@@ -0,0 +1,1176 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BASE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BASE_H
+
+// clang-format off
+
+namespace Eigen {
+
+/** \class TensorBase
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor base class.
+  *
+  * This class is the common parent of the Tensor and TensorMap class, thus
+  * making it possible to use either class interchangeably in expressions.
+  */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+// FIXME Doxygen does not like the inheritance with different template parameters
+// Since there is no doxygen documentation inside, we disable it for now
+template<typename Derived>
+class TensorBase<Derived, ReadOnlyAccessors>
+{
+  public:
+    typedef internal::traits<Derived> DerivedTraits;
+    typedef typename DerivedTraits::Scalar Scalar;
+    typedef typename DerivedTraits::Index Index;
+    typedef typename internal::remove_const<Scalar>::type CoeffReturnType;
+    static const int NumDimensions = DerivedTraits::NumDimensions;
+
+    // Generic nullary operation support.
+    template <typename CustomNullaryOp> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<CustomNullaryOp, const Derived>
+    nullaryExpr(const CustomNullaryOp& func) const {
+      return TensorCwiseNullaryOp<CustomNullaryOp, const Derived>(derived(), func);
+    }
+
+    // Coefficient-wise nullary operators
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
+    constant(const Scalar& value) const {
+      return nullaryExpr(internal::scalar_constant_op<Scalar>(value));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::UniformRandomGenerator<Scalar>, const Derived>
+    random() const {
+      return nullaryExpr(internal::UniformRandomGenerator<Scalar>());
+    }
+    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<RandomGenerator, const Derived>
+    random(const RandomGenerator& gen = RandomGenerator()) const {
+      return nullaryExpr(gen);
+    }
+
+    // Tensor generation
+    template <typename Generator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorGeneratorOp<Generator, const Derived>
+    generate(const Generator& generator) const {
+      return TensorGeneratorOp<Generator, const Derived>(derived(), generator);
+    }
+
+    // Generic unary operation support.
+    template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived>
+    unaryExpr(const CustomUnaryOp& func) const {
+      return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
+    }
+
+    // Coefficient-wise unary operators
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived>
+    operator-() const {
+      return unaryExpr(internal::scalar_opposite_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived>
+    sqrt() const {
+      return unaryExpr(internal::scalar_sqrt_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived>
+    sign() const {
+      return unaryExpr(internal::scalar_sign_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived>
+    rsqrt() const {
+      return unaryExpr(internal::scalar_rsqrt_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived>
+    square() const {
+      return unaryExpr(internal::scalar_square_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived>
+    cube() const {
+      return unaryExpr(internal::scalar_cube_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived>
+    inverse() const {
+      return unaryExpr(internal::scalar_inverse_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived>
+    tanh() const {
+      return unaryExpr(internal::scalar_tanh_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived>
+    lgamma() const {
+      return unaryExpr(internal::scalar_lgamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived>
+    digamma() const {
+      return unaryExpr(internal::scalar_digamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0_op<Scalar>, const Derived>
+    bessel_i0() const {
+      return unaryExpr(internal::scalar_bessel_i0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i0e_op<Scalar>, const Derived>
+    bessel_i0e() const {
+      return unaryExpr(internal::scalar_bessel_i0e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1_op<Scalar>, const Derived>
+    bessel_i1() const {
+      return unaryExpr(internal::scalar_bessel_i1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_i1e_op<Scalar>, const Derived>
+    bessel_i1e() const {
+      return unaryExpr(internal::scalar_bessel_i1e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j0_op<Scalar>, const Derived>
+    bessel_j0() const {
+      return unaryExpr(internal::scalar_bessel_j0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y0_op<Scalar>, const Derived>
+    bessel_y0() const {
+      return unaryExpr(internal::scalar_bessel_y0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_j1_op<Scalar>, const Derived>
+    bessel_j1() const {
+      return unaryExpr(internal::scalar_bessel_j1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_y1_op<Scalar>, const Derived>
+    bessel_y1() const {
+      return unaryExpr(internal::scalar_bessel_y1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0_op<Scalar>, const Derived>
+    bessel_k0() const {
+      return unaryExpr(internal::scalar_bessel_k0_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k0e_op<Scalar>, const Derived>
+    bessel_k0e() const {
+      return unaryExpr(internal::scalar_bessel_k0e_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1_op<Scalar>, const Derived>
+    bessel_k1() const {
+      return unaryExpr(internal::scalar_bessel_k1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_bessel_k1e_op<Scalar>, const Derived>
+    bessel_k1e() const {
+      return unaryExpr(internal::scalar_bessel_k1e_op<Scalar>());
+    }
+
+    // igamma(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igamma_op<Scalar>, const Derived, const OtherDerived>
+    igamma(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igamma_op<Scalar>());
+    }
+
+    // igamma_der_a(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igamma_der_a_op<Scalar>, const Derived, const OtherDerived>
+    igamma_der_a(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igamma_der_a_op<Scalar>());
+    }
+
+    // gamma_sample_der_alpha(alpha = this, sample = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_gamma_sample_der_alpha_op<Scalar>, const Derived, const OtherDerived>
+    gamma_sample_der_alpha(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_gamma_sample_der_alpha_op<Scalar>());
+    }
+
+    // igammac(a = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_igammac_op<Scalar>, const Derived, const OtherDerived>
+    igammac(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_igammac_op<Scalar>());
+    }
+
+    // zeta(x = this, q = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const OtherDerived>
+    zeta(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_zeta_op<Scalar>());
+    }
+
+    // polygamma(n = this, x = other)
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const Derived, const OtherDerived>
+    polygamma(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_polygamma_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived>
+    erf() const {
+      return unaryExpr(internal::scalar_erf_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived>
+    erfc() const {
+      return unaryExpr(internal::scalar_erfc_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived>
+    ndtri() const {
+      return unaryExpr(internal::scalar_ndtri_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived>
+    sigmoid() const {
+      return unaryExpr(internal::scalar_logistic_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived>
+    exp() const {
+      return unaryExpr(internal::scalar_exp_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived>
+    expm1() const {
+      return unaryExpr(internal::scalar_expm1_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived>
+    log() const {
+      return unaryExpr(internal::scalar_log_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived>
+    log1p() const {
+      return unaryExpr(internal::scalar_log1p_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived>
+    log2() const {
+      return unaryExpr(internal::scalar_log2_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived>
+    abs() const {
+      return unaryExpr(internal::scalar_abs_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_clamp_op<Scalar>, const Derived>
+    clip(Scalar min, Scalar max) const {
+      return unaryExpr(internal::scalar_clamp_op<Scalar>(min, max));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const typename internal::conditional<NumTraits<CoeffReturnType>::IsComplex,
+                                                             TensorCwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
+                                                             Derived>::type
+    conjugate() const {
+      return choose(Cond<NumTraits<CoeffReturnType>::IsComplex>(), unaryExpr(internal::scalar_conjugate_op<Scalar>()), derived());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >, const Derived>
+    pow(Scalar exponent) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_pow_op<Scalar,Scalar> >(exponent));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>
+    real() const {
+      return unaryExpr(internal::scalar_real_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived>
+    imag() const {
+      return unaryExpr(internal::scalar_imag_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >, const Derived>
+    operator+ (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_sum_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_sum_op<Scalar> >, const Derived>
+    operator+ (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_sum_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >, const Derived>
+    operator- (Scalar rhs) const {
+      EIGEN_STATIC_ASSERT((NumTraits<Scalar>::IsSigned || internal::is_same<Scalar, const std::complex<float> >::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return unaryExpr(internal::bind2nd_op<internal::scalar_difference_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_difference_op<Scalar> >, const Derived>
+    operator- (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_difference_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >, const Derived>
+    operator* (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_product_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_product_op<Scalar> >, const Derived>
+    operator* (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_product_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >, const Derived>
+    operator/ (Scalar rhs) const {
+      return unaryExpr(internal::bind2nd_op<internal::scalar_quotient_op<Scalar,Scalar> >(rhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE friend
+    const TensorCwiseUnaryOp<internal::bind1st_op<internal::scalar_quotient_op<Scalar> >, const Derived>
+    operator/ (Scalar lhs, const Derived& rhs) {
+      return rhs.unaryExpr(internal::bind1st_op<internal::scalar_quotient_op<Scalar> >(lhs));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_mod_op<Scalar>, const Derived>
+    operator% (Scalar rhs) const {
+      EIGEN_STATIC_ASSERT(NumTraits<Scalar>::IsInteger, YOU_MADE_A_PROGRAMMING_MISTAKE_TRY_MOD);
+      return unaryExpr(internal::scalar_mod_op<Scalar>(rhs));
+    }
+
+    template <int NanPropagation=PropagateFast>
+    EIGEN_DEVICE_FUNC
+        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMax(Scalar threshold) const {
+      return cwiseMax<NanPropagation>(constant(threshold));
+    }
+
+    template <int NanPropagation=PropagateFast>
+    EIGEN_DEVICE_FUNC
+        EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NanPropagation>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMin(Scalar threshold) const {
+      return cwiseMin<NanPropagation>(constant(threshold));
+    }
+
+    template<typename NewType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const typename internal::conditional<internal::is_same<NewType, CoeffReturnType>::value,
+                                                             Derived,
+                                                             TensorConversionOp<NewType, const Derived> >::type
+    cast() const {
+      return choose(Cond<internal::is_same<NewType, CoeffReturnType>::value>(), derived(), TensorConversionOp<NewType, const Derived>(derived()));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived>
+    round() const {
+      return unaryExpr(internal::scalar_round_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived>
+    rint() const {
+      return unaryExpr(internal::scalar_rint_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived>
+    ceil() const {
+      return unaryExpr(internal::scalar_ceil_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived>
+    floor() const {
+      return unaryExpr(internal::scalar_floor_op<Scalar>());
+    }
+
+    // Generic binary operation support.
+    template <typename CustomBinaryOp, typename OtherDerived> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
+    binaryExpr(const OtherDerived& other, const CustomBinaryOp& func) const {
+      return TensorCwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other, func);
+    }
+
+    // Coefficient-wise binary operators.
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived>
+    operator+(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_sum_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived>
+    operator-(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_difference_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>
+    operator*(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_product_op<Scalar>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
+    operator/(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_quotient_op<Scalar>());
+    }
+
+  template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMax(const OtherDerived& other) const {
+    return binaryExpr(other.derived(), internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
+    }
+
+  template<int NaNPropagation=PropagateFast, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar, NaNPropagation>, const Derived, const OtherDerived>
+    cwiseMin(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>
+    operator&&(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_boolean_and_op());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>
+    operator||(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_boolean_or_op());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>
+    operator^(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_boolean_xor_op());
+    }
+
+    // Comparisons and tests.
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>
+    operator<(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>
+    operator<=(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>
+    operator>(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>
+    operator>=(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>
+    operator==(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>
+    operator!=(const OtherDerived& other) const {
+      return binaryExpr(other.derived(), internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>());
+    }
+
+    // comparisons and tests for Scalars
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator<(Scalar threshold) const {
+      return operator<(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator<=(Scalar threshold) const {
+      return operator<=(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator>(Scalar threshold) const {
+      return operator>(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator>=(Scalar threshold) const {
+      return operator>=(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator==(Scalar threshold) const {
+      return operator==(constant(threshold));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    operator!=(Scalar threshold) const {
+      return operator!=(constant(threshold));
+    }
+
+    // Checks
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived>
+    (isnan)() const {
+      return unaryExpr(internal::scalar_isnan_op<Scalar>());
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived>
+    (isinf)() const {
+      return unaryExpr(internal::scalar_isinf_op<Scalar>());
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived>
+    (isfinite)() const {
+      return unaryExpr(internal::scalar_isfinite_op<Scalar>());
+    }
+
+    // Coefficient-wise ternary operators.
+    template<typename ThenDerived, typename ElseDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
+    select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const {
+      return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
+    }
+
+    // Contractions.
+    typedef Eigen::IndexPair<Index> DimensionPair;
+
+    template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>
+    contract(const OtherDerived& other, const Dimensions& dims) const {
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>(derived(), other.derived(), dims);
+    }
+
+    template<typename OtherDerived, typename Dimensions, typename OutputKernel> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>
+    contract(const OtherDerived& other, const Dimensions& dims, const OutputKernel& output_kernel) const {
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>(derived(), other.derived(), dims, output_kernel);
+    }
+
+    // Convolutions.
+    template<typename KernelDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>
+    convolve(const KernelDerived& kernel, const Dimensions& dims) const {
+      return TensorConvolutionOp<const Dimensions, const Derived, const KernelDerived>(derived(), kernel.derived(), dims);
+    }
+
+    // Fourier transforms
+    template <int FFTDataType, int FFTDirection, typename FFT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>
+    fft(const FFT& dims) const {
+      return TensorFFTOp<const FFT, const Derived, FFTDataType, FFTDirection>(derived(), dims);
+    }
+
+    // Scan.
+    typedef TensorScanOp<internal::SumReducer<CoeffReturnType>, const Derived> TensorScanSumOp;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanSumOp
+    cumsum(const Index& axis, bool exclusive = false) const {
+      return TensorScanSumOp(derived(), axis, exclusive);
+    }
+
+    typedef TensorScanOp<internal::ProdReducer<CoeffReturnType>, const Derived> TensorScanProdOp;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanProdOp
+    cumprod(const Index& axis, bool exclusive = false) const {
+      return TensorScanProdOp(derived(), axis, exclusive);
+    }
+
+    template <typename Reducer>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorScanOp<Reducer, const Derived>
+    scan(const Index& axis, const Reducer& reducer, bool exclusive = false) const {
+      return TensorScanOp<Reducer, const Derived>(derived(), axis, exclusive, reducer);
+    }
+
+    // Reductions.
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>
+    sum(const Dims& dims) const {
+      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::SumReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    sum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::SumReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::SumReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>
+    mean(const Dims& dims) const {
+      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::MeanReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    mean() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MeanReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MeanReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>
+    prod(const Dims& dims) const {
+      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const Dims, const Derived>(derived(), dims, internal::ProdReducer<CoeffReturnType>());
+    }
+
+    const TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>
+    prod() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::ProdReducer<CoeffReturnType>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::ProdReducer<CoeffReturnType>());
+    }
+
+    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
+    maximum(const Dims& dims) const {
+      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <int NanPropagation=PropagateFast>
+    const TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
+    maximum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MaxReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MaxReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <typename Dims,int NanPropagation=PropagateFast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>
+    minimum(const Dims& dims) const {
+      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const Dims, const Derived>(derived(), dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <int NanPropagation=PropagateFast>
+    const TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>
+    minimum() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorReductionOp<internal::MinReducer<CoeffReturnType,NanPropagation>, const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims, internal::MinReducer<CoeffReturnType,NanPropagation>());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::AndReducer, const Dims, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
+    all(const Dims& dims) const {
+      return cast<bool>().reduce(dims, internal::AndReducer());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::AndReducer, const DimensionList<Index, NumDimensions>, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
+    all() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return cast<bool>().reduce(in_dims, internal::AndReducer());
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::OrReducer, const Dims, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
+    any(const Dims& dims) const {
+      return cast<bool>().reduce(dims, internal::OrReducer());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<internal::OrReducer, const DimensionList<Index, NumDimensions>, const typename internal::conditional<internal::is_same<bool, CoeffReturnType>::value, Derived, TensorConversionOp<bool, const Derived> >::type >
+    any() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return cast<bool>().reduce(in_dims, internal::OrReducer());
+    }
+
+   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTupleReducerOp<
+      internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
+      const array<Index, NumDimensions>, const Derived>
+    argmax() const {
+      array<Index, NumDimensions> in_dims;
+      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
+      return TensorTupleReducerOp<
+        internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
+        const array<Index, NumDimensions>,
+        const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTupleReducerOp<
+      internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
+      const array<Index, NumDimensions>, const Derived>
+    argmin() const {
+      array<Index, NumDimensions> in_dims;
+      for (Index d = 0; d < NumDimensions; ++d) in_dims[d] = d;
+      return TensorTupleReducerOp<
+        internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
+        const array<Index, NumDimensions>,
+        const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), -1, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTupleReducerOp<
+      internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
+      const array<Index, 1>, const Derived>
+    argmax(const Index return_dim) const {
+      array<Index, 1> in_dims;
+      in_dims[0] = return_dim;
+      return TensorTupleReducerOp<
+        internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >,
+        const array<Index, 1>,
+        const Derived>(derived(), internal::ArgMaxTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTupleReducerOp<
+      internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
+      const array<Index, 1>, const Derived>
+    argmin(const Index return_dim) const {
+      array<Index, 1> in_dims;
+      in_dims[0] = return_dim;
+      return TensorTupleReducerOp<
+        internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >,
+        const array<Index, 1>,
+        const Derived>(derived(), internal::ArgMinTupleReducer<Tuple<Index, CoeffReturnType> >(), return_dim, in_dims);
+    }
+
+    template <typename Reducer, typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReductionOp<Reducer, const Dims, const Derived>
+    reduce(const Dims& dims, const Reducer& reducer) const {
+      return TensorReductionOp<Reducer, const Dims, const Derived>(derived(), dims, reducer);
+    }
+
+    template <typename Dims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorTraceOp<const Dims, const Derived>
+    trace(const Dims& dims) const {
+      return TensorTraceOp<const Dims, const Derived>(derived(), dims);
+    }
+
+    const TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>
+    trace() const {
+      DimensionList<Index, NumDimensions> in_dims;
+      return TensorTraceOp<const DimensionList<Index, NumDimensions>, const Derived>(derived(), in_dims);
+    }
+
+    template <typename Broadcast> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorBroadcastingOp<const Broadcast, const Derived>
+    broadcast(const Broadcast& bcast) const {
+      return TensorBroadcastingOp<const Broadcast, const Derived>(derived(), bcast);
+    }
+
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConcatenationOp<Axis, const Derived, const OtherDerived>
+    concatenate(const OtherDerived& other, Axis axis) const {
+      return TensorConcatenationOp<Axis, const Derived, const OtherDerived>(derived(), other.derived(), axis);
+    }
+
+    template <typename PatchDims> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPatchOp<const PatchDims, const Derived>
+    extract_patches(const PatchDims& patch_dims) const {
+      return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
+    extract_image_patches(const Index patch_rows = 1, const Index patch_cols = 1,
+                          const Index row_stride = 1, const Index col_stride = 1,
+                          const Index in_row_stride = 1, const Index in_col_stride = 1,
+                          const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
+      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
+                                                                 in_row_stride, in_col_stride, 1, 1, padding_type, padding_value);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
+    extract_image_patches(const Index patch_rows, const Index patch_cols,
+                          const Index row_stride, const Index col_stride,
+                          const Index in_row_stride, const Index in_col_stride,
+                          const Index row_inflate_stride, const Index col_inflate_stride,
+                          const Index padding_top, const Index padding_bottom,
+                          const Index padding_left,const Index padding_right,
+                          const Scalar padding_value) const {
+      return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride,
+                                                                 in_row_stride, in_col_stride, row_inflate_stride, col_inflate_stride,
+                                                                 padding_top, padding_bottom, padding_left, padding_right, padding_value);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
+    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
+                           const Index plane_stride = 1, const Index row_stride = 1, const Index col_stride = 1,
+                           const PaddingType padding_type = PADDING_SAME, const Scalar padding_value = Scalar(0)) const {
+      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, 1, 1, 1, padding_type, padding_value);
+    }
+
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>
+    extract_volume_patches(const Index patch_planes, const Index patch_rows, const Index patch_cols,
+                           const Index plane_stride, const Index row_stride, const Index col_stride,
+                           const Index plane_inflate_stride, const Index row_inflate_stride, const Index col_inflate_stride,
+                           const Index padding_top_z, const Index padding_bottom_z,
+                           const Index padding_top, const Index padding_bottom,
+                           const Index padding_left, const Index padding_right, const Scalar padding_value = Scalar(0)) const {
+      return TensorVolumePatchOp<Dynamic, Dynamic, Dynamic, const Derived>(derived(), patch_planes, patch_rows, patch_cols, plane_stride, row_stride, col_stride, 1, 1, 1, plane_inflate_stride, row_inflate_stride, col_inflate_stride, padding_top_z, padding_bottom_z, padding_top, padding_bottom, padding_left, padding_right, padding_value);
+    }
+
+    // Morphing operators.
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorLayoutSwapOp<const Derived>
+    swap_layout() const {
+      return TensorLayoutSwapOp<const Derived>(derived());
+    }
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReshapingOp<const NewDimensions, const Derived>
+    reshape(const NewDimensions& newDimensions) const {
+      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
+    }
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) const {
+      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
+    }
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                const Derived>(derived(), startIndices, stopIndices, strides);
+    }
+    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<DimId, const Derived>
+    chip(const Index offset) const {
+      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<Dynamic, const Derived>
+    chip(const Index offset, const Index dim) const {
+      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
+    }
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReverseOp<const ReverseDimensions, const Derived>
+    reverse(const ReverseDimensions& rev) const {
+      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
+    }
+    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPaddingOp<const PaddingDimensions, const Derived>
+    pad(const PaddingDimensions& padding) const {
+      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, internal::scalar_cast_op<int, Scalar>()(0));
+    }
+    template <typename PaddingDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorPaddingOp<const PaddingDimensions, const Derived>
+    pad(const PaddingDimensions& padding, const Scalar padding_value) const {
+      return TensorPaddingOp<const PaddingDimensions, const Derived>(derived(), padding, padding_value);
+    }
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorShufflingOp<const Shuffle, const Derived>
+    shuffle(const Shuffle& shfl) const {
+      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingOp<const Strides, const Derived>
+    stride(const Strides& strides) const {
+      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorInflationOp<const Strides, const Derived>
+    inflate(const Strides& strides) const {
+      return TensorInflationOp<const Strides, const Derived>(derived(), strides);
+    }
+
+    // Returns a tensor containing index/value tuples
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorIndexTupleOp<const Derived>
+    index_tuples() const {
+      return TensorIndexTupleOp<const Derived>(derived());
+    }
+
+    // Support for custom unary and binary operations
+    template <typename CustomUnaryFunc>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCustomUnaryOp<const CustomUnaryFunc, const Derived> customOp(const CustomUnaryFunc& op) const {
+      return TensorCustomUnaryOp<const CustomUnaryFunc, const Derived>(derived(), op);
+    }
+    template <typename OtherDerived, typename CustomBinaryFunc>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived> customOp(const OtherDerived& other, const CustomBinaryFunc& op) const {
+      return TensorCustomBinaryOp<const CustomBinaryFunc, const Derived, const OtherDerived>(derived(), other, op);
+    }
+
+    // Force the evaluation of the expression.
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorForcedEvalOp<const Derived> eval() const {
+      return TensorForcedEvalOp<const Derived>(derived());
+    }
+
+  protected:
+    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
+    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
+    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
+    template <typename OtherDerived, int AccessLevel> friend class Eigen::TensorBase;
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
+};
+
+template<typename Derived, int AccessLevel = internal::accessors_level<Derived>::value>
+class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> {
+ public:
+    typedef TensorBase<Derived, ReadOnlyAccessors> Base;
+    typedef internal::traits<Derived> DerivedTraits;
+    typedef typename DerivedTraits::Scalar Scalar;
+    typedef typename DerivedTraits::Index Index;
+    typedef Scalar CoeffReturnType;
+    static const int NumDimensions = DerivedTraits::NumDimensions;
+
+    template <typename Scalar, int NumIndices, int Options, typename IndexType> friend class Tensor;
+    template <typename Scalar, typename Dimensions, int Option, typename IndexTypes> friend class TensorFixedSize;
+    // the Eigen:: prefix is required to workaround a compilation issue with nvcc 9.0
+    template <typename OtherDerived, int OtherAccessLevel> friend class Eigen::TensorBase;
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setZero() {
+      return setConstant(Scalar(0));
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setConstant(const Scalar& val) {
+      return derived() = this->constant(val);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setRandom() {
+      return derived() = this->random();
+    }
+    template <typename RandomGenerator> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setRandom() {
+      return derived() = this->template random<RandomGenerator>();
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& setValues(
+        const typename internal::Initializer<Derived, NumDimensions>::InitList& vals) {
+      TensorEvaluator<Derived, DefaultDevice> eval(derived(), DefaultDevice());
+      internal::initialize_tensor<Derived, NumDimensions>(eval, vals);
+      return derived();
+    }
+#endif  // EIGEN_HAS_VARIADIC_TEMPLATES
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator+=(const OtherDerived& other) {
+      return derived() = derived() + other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator-=(const OtherDerived& other) {
+      return derived() = derived() - other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator*=(const OtherDerived& other) {
+      return derived() = derived() * other.derived();
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator/=(const OtherDerived& other) {
+      return derived() = derived() / other.derived();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorLayoutSwapOp<const Derived>
+    swap_layout() const {
+      return TensorLayoutSwapOp<const Derived>(derived());
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorLayoutSwapOp<Derived>
+    swap_layout() {
+      return TensorLayoutSwapOp<Derived>(derived());
+    }
+
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorConcatenationOp<const Axis, const Derived, const OtherDerived>
+    concatenate(const OtherDerived& other, const Axis& axis) const {
+      return TensorConcatenationOp<const Axis, const Derived, const OtherDerived>(derived(), other, axis);
+    }
+    template <typename Axis, typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorConcatenationOp<const Axis, Derived, OtherDerived>
+    concatenate(const OtherDerived& other, const Axis& axis) {
+      return TensorConcatenationOp<const Axis, Derived, OtherDerived>(derived(), other, axis);
+    }
+
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReshapingOp<const NewDimensions, const Derived>
+    reshape(const NewDimensions& newDimensions) const {
+      return TensorReshapingOp<const NewDimensions, const Derived>(derived(), newDimensions);
+    }
+    template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReshapingOp<const NewDimensions, Derived>
+    reshape(const NewDimensions& newDimensions) {
+      return TensorReshapingOp<const NewDimensions, Derived>(derived(), newDimensions);
+    }
+
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorSlicingOp<const StartIndices, const Sizes, const Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) const {
+      return TensorSlicingOp<const StartIndices, const Sizes, const Derived>(derived(), startIndices, sizes);
+    }
+    template <typename StartIndices, typename Sizes> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorSlicingOp<const StartIndices, const Sizes, Derived>
+    slice(const StartIndices& startIndices, const Sizes& sizes) {
+      return TensorSlicingOp<const StartIndices, const Sizes, Derived>(derived(), startIndices, sizes);
+    }
+
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, const Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) const {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                const Derived>(derived(), startIndices, stopIndices, strides);
+    }
+    template <typename StartIndices, typename StopIndices, typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides, Derived>
+    stridedSlice(const StartIndices& startIndices, const StopIndices& stopIndices, const Strides& strides) {
+      return TensorStridingSlicingOp<const StartIndices, const StopIndices, const Strides,
+                                Derived>(derived(), startIndices, stopIndices, strides);
+    }
+
+    template <DenseIndex DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<DimId, const Derived>
+    chip(const Index offset) const {
+      return TensorChippingOp<DimId, const Derived>(derived(), offset, DimId);
+    }
+    template <Index DimId> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorChippingOp<DimId, Derived>
+    chip(const Index offset) {
+      return TensorChippingOp<DimId, Derived>(derived(), offset, DimId);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorChippingOp<Dynamic, const Derived>
+    chip(const Index offset, const Index dim) const {
+      return TensorChippingOp<Dynamic, const Derived>(derived(), offset, dim);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorChippingOp<Dynamic, Derived>
+    chip(const Index offset, const Index dim) {
+      return TensorChippingOp<Dynamic, Derived>(derived(), offset, dim);
+    }
+
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorReverseOp<const ReverseDimensions, const Derived>
+    reverse(const ReverseDimensions& rev) const {
+      return TensorReverseOp<const ReverseDimensions, const Derived>(derived(), rev);
+    }
+    template <typename ReverseDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReverseOp<const ReverseDimensions, Derived>
+    reverse(const ReverseDimensions& rev) {
+      return TensorReverseOp<const ReverseDimensions, Derived>(derived(), rev);
+    }
+
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorShufflingOp<const Shuffle, const Derived>
+    shuffle(const Shuffle& shfl) const {
+      return TensorShufflingOp<const Shuffle, const Derived>(derived(), shfl);
+    }
+    template <typename Shuffle> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorShufflingOp<const Shuffle, Derived>
+    shuffle(const Shuffle& shfl) {
+      return TensorShufflingOp<const Shuffle, Derived>(derived(), shfl);
+    }
+
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorStridingOp<const Strides, const Derived>
+    stride(const Strides& strides) const {
+      return TensorStridingOp<const Strides, const Derived>(derived(), strides);
+    }
+    template <typename Strides> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorStridingOp<const Strides, Derived>
+    stride(const Strides& strides) {
+      return TensorStridingOp<const Strides, Derived>(derived(), strides);
+    }
+
+    // Select the device on which to evaluate the expression.
+    template <typename DeviceType>
+    TensorDevice<Derived, DeviceType> device(const DeviceType& dev) {
+      return TensorDevice<Derived, DeviceType>(dev, derived());
+    }
+
+    // Select the async device on which to evaluate the expression.
+    template <typename DeviceType, typename DoneCallback>
+    TensorAsyncDevice<Derived, DeviceType, DoneCallback> device(const DeviceType& dev, DoneCallback done) {
+      return TensorAsyncDevice<Derived, DeviceType, DoneCallback>(dev, derived(), std::move(done));
+    }
+
+ protected:
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TensorBase)
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorBase)
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other)
+    {
+      typedef TensorAssignOp<Derived, const OtherDerived> Assign;
+      Assign assign(derived(), other.derived());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+      return derived();
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& derived() { return *static_cast<Derived*>(this); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); }
+};
+#endif // EIGEN_PARSED_BY_DOXYGEN
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
new file mode 100644
index 0000000..1e55d12
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -0,0 +1,1559 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+
+namespace Eigen {
+namespace internal {
+
+// -------------------------------------------------------------------------- //
+// Forward declarations for templates defined below.
+template <typename Scalar, typename IndexType, int NumDims, int Layout>
+class TensorBlockIO;
+
+// -------------------------------------------------------------------------- //
+// Helper function to compute strides for densely stored buffer of given
+// dimensions.
+
+// TODO(ezhulenev): We compute strides 1000 times in different evaluators, use
+// this function instead everywhere.
+template <int Layout, typename IndexType, int NumDims>
+EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
+    const DSizes<IndexType, NumDims>& dimensions) {
+  DSizes<IndexType, NumDims> strides;
+  if (NumDims == 0) return strides;
+
+  // TODO(ezhulenev): Use templates to unroll this loop (similar to
+  // h_array_reduce in CXX11meta.h)? Benchmark it.
+  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+    strides[0] = 1;
+    for (int i = 1; i < NumDims; ++i) {
+      strides[i] = strides[i - 1] * dimensions[i - 1];
+    }
+  } else {
+    strides[NumDims - 1] = 1;
+    for (int i = NumDims - 2; i >= 0; --i) {
+      strides[i] = strides[i + 1] * dimensions[i + 1];
+    }
+  }
+
+  return strides;
+}
+
+template <int Layout, typename IndexType, size_t NumDims>
+EIGEN_ALWAYS_INLINE DSizes<IndexType, NumDims> strides(
+    const Eigen::array<IndexType, NumDims>& dimensions) {
+  return strides<Layout>(DSizes<IndexType, NumDims>(dimensions));
+}
+
+template <int Layout, std::ptrdiff_t... Indices>
+EIGEN_STRONG_INLINE DSizes<std::ptrdiff_t, sizeof...(Indices)> strides(
+    const Sizes<Indices...>& sizes) {
+  return strides<Layout>(DSizes<std::ptrdiff_t, sizeof...(Indices)>(sizes));
+}
+
+// -------------------------------------------------------------------------- //
+
+// Tensor block shape type defines what are the shape preference for the blocks
+// extracted from the larger tensor.
+//
+// Example: blocks of 100 elements from the large 100x100 tensor:
+// - tensor: 100x100
+// - target_block_size: 100
+//
+// TensorBlockShapeType:
+//  - kUniformAllDims: 100 blocks of size 10x10
+//  - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column
+//                      or row major layout)
+enum class TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };
+
+struct TensorBlockResourceRequirements {
+  TensorBlockShapeType shape_type;  // target block shape
+  size_t size;                      // target block size
+  TensorOpCost cost_per_coeff;      // cost of computing a single block element
+
+#ifdef EIGEN_HIPCC
+  // For HIPCC, we need to explicitly declare as a "device fun", the constructor
+  // which is implicitly invoked in the "merge" / "any" routines. else HIPCC
+  // errors out complaining about the lack of a matching constructor
+  EIGEN_DEVICE_FUNC
+  TensorBlockResourceRequirements(TensorBlockShapeType shape_type_, size_t size_,
+				  TensorOpCost cost_)
+    : shape_type(shape_type_), size(size_), cost_per_coeff(cost_)
+  {}
+#endif
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
+      TensorBlockShapeType shape_type, size_t size_in_bytes,
+      TensorOpCost cost) {
+    const size_t size = numext::maxi(size_t(1), size_in_bytes / sizeof(Scalar));
+    return {shape_type, size, cost};
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
+      TensorBlockShapeType shape_type, size_t size_in_bytes) {
+    // This default cost per coefficient is valid for most materialized tensor
+    // block evaluation implementations, because they typically just read
+    // coefficients from the underlying tensor storage, and write to the tensor
+    // block buffer (scratch or destination memory, reads and writes have linear
+    // access pattern). We ignore the fixed cost of block evaluation, because in
+    // practice it should negligible.
+    //
+    // Lazy block evaluation adds the cost of calling a functor for each
+    // coefficient.
+    //
+    // All non-trivial block evaluation implementations must provide their own
+    // cost approximation (e.g. shuffling inner dimension has a much higher cost
+    // because it reads memory randomly, although the total number of moved
+    // bytes is the same).
+    return withShapeAndSize<Scalar>(shape_type, size_in_bytes,
+                                    {/*bytes_loaded=*/sizeof(Scalar),
+                                     /*bytes_stored=*/sizeof(Scalar),
+                                     /*compute_cycles=*/0});
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements skewed(
+      size_t size_in_bytes) {
+    return withShapeAndSize<Scalar>(TensorBlockShapeType::kSkewedInnerDims,
+                                    size_in_bytes);
+  }
+
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements uniform(
+      size_t size_in_bytes) {
+    return withShapeAndSize<Scalar>(TensorBlockShapeType::kUniformAllDims,
+                                    size_in_bytes);
+  }
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE TensorBlockResourceRequirements
+  merge(const TensorBlockResourceRequirements& lhs,
+        const TensorBlockResourceRequirements& rhs) {
+    return {merge(lhs.shape_type, rhs.shape_type),           // shape_type
+            merge(lhs.size, rhs.size),                       // size
+            merge(lhs.cost_per_coeff, rhs.cost_per_coeff)};  // cost_per_coeff
+  }
+
+  EIGEN_DEVICE_FUNC TensorBlockResourceRequirements& addCostPerCoeff(
+      TensorOpCost cost) {
+    cost_per_coeff += cost;
+    return *this;
+  }
+
+  // This is a resource requirement that should be returned from expressions
+  // that do not have any block evaluation preference (e.g. default tensor
+  // expression with raw buffer access).
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {
+    return {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};
+  }
+
+ private:
+  using Requirements = TensorBlockResourceRequirements;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE size_t merge(size_t lhs_size, size_t rhs_size) {
+    return numext::maxi(lhs_size, rhs_size);
+  }
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE TensorBlockShapeType
+  merge(TensorBlockShapeType lhs, TensorBlockShapeType rhs) {
+    return (lhs == TensorBlockShapeType::kSkewedInnerDims ||
+            rhs == TensorBlockShapeType::kSkewedInnerDims)
+               ? TensorBlockShapeType::kSkewedInnerDims
+               : TensorBlockShapeType::kUniformAllDims;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost,
+                                                TensorOpCost rhs_cost) {
+    return lhs_cost + rhs_cost;
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockDescriptor specifies a block offset within a tensor and the block
+// sizes along each of the tensor dimensions.
+
+template <int NumDims, typename IndexType = Eigen::Index>
+class TensorBlockDescriptor {
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  // If we evaluate a Tensor assignment, and expression on the left, already has
+  // a memory buffer, then we might do performance optimization, and evaluate
+  // the root expression directly into the final output memory. Some time it's
+  // possible to reuse it for materializing subexpressions inside an expression
+  // tree, to to avoid dynamic memory allocation.
+  //
+  // The pointer type of the underlying storage is erased, because passing
+  // Scalar type through all the expression evaluation layers is way too many
+  // templates. In practice destination buffer type should always match the
+  // evaluated expression scalar type.
+  class DestinationBuffer {
+   public:
+    enum DestinationBufferKind : int {
+      // The above explicit specification of "int" as the enum basetype is
+      // needed to get around a HIPCC link error ("the field type is not
+      // amp-compatible")
+      // which is issued for class members with the enum type.
+      // TODO(rocm):
+      // remove the "int" basetype once HIPCC has been fixed to not error out
+      // in the above scenario.
+
+      // Destination buffer is not defined (`m_data` == nullptr).
+      kEmpty,
+
+      // Tensor block defined by an owning tensor block descriptor can fit
+      // contiguously into the destination buffer. In this case it's safe to
+      // materialize tensor block in the destination buffer, wrap it in a
+      // TensorMap, and use to build Eigen expression on top of it.
+      kContiguous,
+
+      // Destination buffer strides do not match strides of the contiguously
+      // stored block, and it's impossible to define a TensorMap over this
+      // buffer. However if we are evaluating a root of an expression tree, we
+      // still can materialize an output into this destination, because we can
+      // guarantee that no one will ever access it through block API.
+      //
+      // In theory it is possible to build valid TensorStriding<TensorMap>
+      // expression on top of this destination buffer, however it has
+      // inefficient coeff/packet access, and defeats the purpose of fast block
+      // evaluation API.
+      kStrided
+    };
+
+    template <typename Scalar>
+    Scalar* data() const {
+      eigen_assert(m_data_type_size == sizeof(Scalar));
+      return static_cast<Scalar*>(m_data);
+    }
+
+    const Dimensions& strides() const { return m_strides; }
+    const DestinationBufferKind& kind() const { return m_kind; }
+
+   private:
+    friend class TensorBlockDescriptor;
+
+    DestinationBuffer() : m_data(NULL), m_data_type_size(0), m_kind(kEmpty) {}
+
+    template <typename Scalar>
+    DestinationBuffer(Scalar* data, const Dimensions& strides,
+                      DestinationBufferKind kind)
+        : m_data(static_cast<void*>(data)),
+          m_data_type_size(sizeof(Scalar)),
+          m_strides(strides),
+          m_kind(kind) {}
+
+    template <int Layout, typename Scalar>
+    static DestinationBuffer make(const TensorBlockDescriptor& desc,
+                                  Scalar* data, const Dimensions& strides) {
+      return DestinationBuffer(data, strides, kind<Layout>(desc, strides));
+    }
+
+    template <int Layout>
+    static DestinationBufferKind kind(const TensorBlockDescriptor& desc,
+                                      const Dimensions& strides) {
+      const Dimensions& desc_dims = desc.dimensions();
+      const Dimensions& desc_strides = internal::strides<Layout>(desc_dims);
+      for (int i = 0; i < NumDims; ++i) {
+        if (desc_dims[i] == 1) continue;
+        if (desc_strides[i] != strides[i]) return kStrided;
+      }
+      return kContiguous;
+    }
+
+    // Storage pointer is type erased, to reduce template bloat, but we still
+    // keep the size of the underlying element type for error checking.
+    void* m_data;
+    size_t m_data_type_size;
+
+    // Destination buffer dimensions always match the dimensions of a tensor
+    // block descriptor it belongs to, however strides might be different.
+    Dimensions m_strides;
+
+    DestinationBufferKind m_kind;
+  };
+
+  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions,
+                        const DestinationBuffer& destination)
+      : m_offset(offset),
+        m_dimensions(dimensions),
+        m_destination(destination) {}
+
+  TensorBlockDescriptor(const IndexType offset, const Dimensions& dimensions)
+      : m_offset(offset),
+        m_dimensions(dimensions),
+        m_destination(DestinationBuffer()) {}
+
+  IndexType offset() const { return m_offset; }
+  const Dimensions& dimensions() const { return m_dimensions; }
+  IndexType dimension(int index) const { return m_dimensions[index]; }
+  IndexType size() const { return array_prod<IndexType>(m_dimensions); }
+
+  const DestinationBuffer& destination() const { return m_destination; }
+
+  template <int Layout, typename Scalar>
+  void AddDestinationBuffer(Scalar* dst_base, const Dimensions& dst_strides) {
+    eigen_assert(dst_base != NULL);
+    m_destination =
+        DestinationBuffer::template make<Layout>(*this, dst_base, dst_strides);
+  }
+
+  template <int Layout, typename Scalar, typename DstStridesIndexType>
+  void AddDestinationBuffer(
+      Scalar* dst_base,
+      const DSizes<DstStridesIndexType, NumDims>& dst_strides) {
+    // DSizes constructor will do index type promotion if it's safe.
+    AddDestinationBuffer<Layout>(dst_base, Dimensions(dst_strides));
+  }
+
+  TensorBlockDescriptor& DropDestinationBuffer() {
+    m_destination.m_data = NULL;
+    m_destination.m_kind = DestinationBuffer::kEmpty;
+    return *this;
+  }
+
+  bool HasDestinationBuffer() const {
+    return m_destination.kind() != DestinationBuffer::kEmpty;
+  }
+
+  // Returns a copy of `*this` with updated offset.
+  TensorBlockDescriptor WithOffset(IndexType offset) const {
+    return TensorBlockDescriptor(offset, m_dimensions, m_destination);
+  }
+
+ private:
+  // Offset and dimensions are immutable after construction. Block descriptor
+  // can only be mutated by adding or dropping destination.
+  const IndexType m_offset;
+  const Dimensions m_dimensions;
+  DestinationBuffer m_destination;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockMapper is responsible for iterating over the blocks of a tensor.
+
+template <int NumDims, int Layout, typename IndexType = Eigen::Index>
+class TensorBlockMapper {
+  typedef TensorBlockDescriptor<NumDims, IndexType> BlockDescriptor;
+
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  TensorBlockMapper() = default;
+  TensorBlockMapper(const DSizes<IndexType, NumDims>& dimensions,
+                    const TensorBlockResourceRequirements& requirements)
+      : m_tensor_dimensions(dimensions), m_requirements(requirements) {
+    // Compute block dimensions and the total number of blocks.
+    InitializeBlockDimensions();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockCount() const {
+    return m_total_block_count;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType blockTotalSize() const {
+    return m_block_dimensions.TotalSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DSizes<IndexType, NumDims>&
+  blockDimensions() const {
+    return m_block_dimensions;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor
+  blockDescriptor(IndexType block_index) const {
+    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
+
+    IndexType offset = 0;
+    DSizes<IndexType, NumDims> dimensions;
+
+    if (NumDims == 0) return BlockDescriptor(offset, dimensions);
+
+    // Iterate outer -> inner dimensions.
+    for (int i = NumDims - 1; i >= 0; --i) {
+      const int dim = isColMajor ? i : NumDims - i - 1;
+
+      const IndexType idx = block_index / m_block_strides[dim];
+      block_index -= idx * m_block_strides[dim];
+
+      const IndexType coord = idx * m_block_dimensions[dim];
+      dimensions[dim] = numext::mini(m_tensor_dimensions[dim] - coord,
+                                     m_block_dimensions[dim]);
+      offset += coord * m_tensor_strides[dim];
+    }
+
+    return {offset, dimensions};
+  }
+
+ private:
+  void InitializeBlockDimensions() {
+    // Requested block shape and size.
+    const TensorBlockShapeType shape_type = m_requirements.shape_type;
+    IndexType target_block_size =
+        numext::maxi<IndexType>(1, static_cast<IndexType>(m_requirements.size));
+
+    IndexType tensor_size = m_tensor_dimensions.TotalSize();
+
+    // Corner case: one of the dimensions is zero. Logic below is too complex
+    // to handle this case on a general basis, just use unit block size.
+    // Note: we must not yield blocks with zero dimensions (recipe for
+    // overflows/underflows, divisions by zero and NaNs later).
+    if (tensor_size == 0) {
+      for (int i = 0; i < NumDims; ++i) {
+        m_block_dimensions[i] = 1;
+      }
+      m_total_block_count = 0;
+      return;
+    }
+
+    // If tensor fits into a target block size, evaluate it as a single block.
+    if (tensor_size <= target_block_size) {
+      m_block_dimensions = m_tensor_dimensions;
+      m_total_block_count = 1;
+      // The only valid block index is `0`, and in this case we do not need
+      // to compute real strides for tensor or blocks (see blockDescriptor).
+      for (int i = 0; i < NumDims; ++i) {
+        m_tensor_strides[i] = 0;
+        m_block_strides[i] = 1;
+      }
+      return;
+    }
+
+    static const bool isColMajor = Layout == static_cast<int>(ColMajor);
+
+    // Block shape skewed towards inner dimension.
+    if (shape_type == TensorBlockShapeType::kSkewedInnerDims) {
+      IndexType coeff_to_allocate = target_block_size;
+
+      for (int i = 0; i < NumDims; ++i) {
+        const int dim = isColMajor ? i : NumDims - i - 1;
+        m_block_dimensions[dim] =
+            numext::mini(coeff_to_allocate, m_tensor_dimensions[dim]);
+        coeff_to_allocate = divup(
+            coeff_to_allocate,
+            numext::maxi(static_cast<IndexType>(1), m_block_dimensions[dim]));
+      }
+      eigen_assert(coeff_to_allocate == 1);
+
+    } else if (shape_type == TensorBlockShapeType::kUniformAllDims) {
+      // Tensor will not fit within 'target_block_size' budget: calculate tensor
+      // block dimension sizes based on "square" dimension size target.
+      const IndexType dim_size_target = convert_index<IndexType>(
+          std::pow(static_cast<float>(target_block_size),
+                   1.0f / static_cast<float>(m_block_dimensions.rank())));
+
+      for (int i = 0; i < NumDims; ++i) {
+        // TODO(andydavis) Adjust the inner most 'block_dim_size' to make it
+        // a multiple of the packet size. Note that reducing
+        // 'block_dim_size' in this manner can increase the number of
+        // blocks, and so will amplify any per-block overhead.
+        m_block_dimensions[i] =
+            numext::mini(dim_size_target, m_tensor_dimensions[i]);
+      }
+
+      // Add any un-allocated coefficients to inner dimension(s).
+      IndexType total_size = m_block_dimensions.TotalSize();
+      for (int i = 0; i < NumDims; ++i) {
+        const int dim = isColMajor ? i : NumDims - i - 1;
+
+        if (m_block_dimensions[dim] < m_tensor_dimensions[dim]) {
+          const IndexType total_size_other_dims =
+              total_size / m_block_dimensions[dim];
+          const IndexType alloc_avail =
+              divup<IndexType>(target_block_size, total_size_other_dims);
+          if (alloc_avail == m_block_dimensions[dim]) {
+            // Insufficient excess coefficients to allocate.
+            break;
+          }
+          m_block_dimensions[dim] =
+              numext::mini(m_tensor_dimensions[dim], alloc_avail);
+          total_size = total_size_other_dims * m_block_dimensions[dim];
+        }
+      }
+
+    } else {
+      eigen_assert(false);  // unknown block shape
+    }
+
+    eigen_assert(m_block_dimensions.TotalSize() >=
+                 numext::mini<IndexType>(target_block_size,
+                                         m_tensor_dimensions.TotalSize()));
+
+    // Calculate block counts by dimension and total block count.
+    DSizes<IndexType, NumDims> block_count;
+    for (int i = 0; i < NumDims; ++i) {
+      block_count[i] = divup(m_tensor_dimensions[i], m_block_dimensions[i]);
+    }
+    m_total_block_count = array_prod(block_count);
+
+    // Calculate block strides (used for enumerating blocks).
+    m_tensor_strides = strides<Layout>(m_tensor_dimensions);
+    m_block_strides = strides<Layout>(block_count);
+  }
+
+  DSizes<IndexType, NumDims> m_tensor_dimensions;
+  TensorBlockResourceRequirements m_requirements;
+
+  DSizes<IndexType, NumDims> m_block_dimensions;
+  IndexType m_total_block_count;
+
+  DSizes<IndexType, NumDims> m_tensor_strides;
+  DSizes<IndexType, NumDims> m_block_strides;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockScratchAllocator is responsible for allocating temporary buffers
+// for block evaluation (output or input block materialization). Given that
+// Eigen expression traversal order is deterministic, all temporary allocations
+// are happening in the same order, and usually have exactly the same size.
+// Scratch allocator keeps a trace of all dynamic allocations, and after the
+// first block evaluation is completed, we should be able to reuse all the
+// temporary buffers for the next block evaluation.
+
+template <typename Device>
+class TensorBlockScratchAllocator {
+ public:
+  explicit TensorBlockScratchAllocator(const Device& device)
+      : m_device(device), m_allocation_index(0) {}
+
+  ~TensorBlockScratchAllocator() {
+    for (size_t i = 0; i < m_allocations.size(); ++i) {
+      m_device.deallocate(m_allocations[i].ptr);
+    }
+  }
+
+  void* allocate(size_t size) {
+    // TODO(ezhulenev): Remove when replaced with inlined vector.
+    if (m_allocations.capacity() == 0) m_allocations.reserve(8);
+
+    // Check if we already have an existing allocation att current index.
+    const int num_allocations = static_cast<int>(m_allocations.size());
+    const bool has_allocation = m_allocation_index < num_allocations;
+
+    // Allocation index can't be larger than the number of allocations.
+    eigen_assert(m_allocation_index <= num_allocations);
+
+    // If we have existing allocation, and its size is larger or equal to
+    // requested size, we do nothing.
+
+    // If current allocation can't fit requested size, we deallocate it, and
+    // replace with a larger allocation.
+    if (has_allocation && m_allocations[m_allocation_index].size < size) {
+      m_device.deallocate(m_allocations[m_allocation_index].ptr);
+      m_allocations[m_allocation_index].ptr = m_device.allocate(size);
+      m_allocations[m_allocation_index].size = size;
+    }
+
+    // Make a new allocation if we don't have and existing one.
+    if (!has_allocation) {
+      Allocation allocation;
+      allocation.ptr = m_device.allocate(size);
+      allocation.size = size;
+      m_allocations.push_back(allocation);
+    }
+
+    eigen_assert(m_allocations[m_allocation_index].ptr != NULL);
+    eigen_assert(m_allocations[m_allocation_index].size >= size);
+
+    return m_allocations[m_allocation_index++].ptr;
+  }
+
+  void reset() { m_allocation_index = 0; }
+
+ private:
+  struct Allocation {
+    void* ptr;
+    size_t size;
+  };
+
+  const Device& m_device;
+  int m_allocation_index;
+  // TODO(ezhulenev): This should be an inlined vector.
+  std::vector<Allocation> m_allocations;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockKind represents all possible block kinds, that can be produced by
+// TensorEvaluator::evalBlock function.
+enum TensorBlockKind {
+  // Tensor block that is a lazy expression that must be assigned to a
+  // destination using TensorBlockAssign.
+  kExpr,
+
+  // Tensor block that is a view into a memory buffer owned by an underlying
+  // Tensor expression (e.g. it can be a view into a Tensor buffer).
+  kView,
+
+  // Tensor block that was materialized in a scratch memory buffer, allocated
+  // with TensorBlockScratchAllocator. This block must be copied to a
+  // destination, similar to a block of `kExpr` type.
+  kMaterializedInScratch,
+
+  // Tensor block that was materialized directly into the final output memory
+  // buffer. For example if the left side of an assignment is a Tensor, we can
+  // directly materialize the block in the destination memory.
+  //
+  // If strides in the output buffer do not match tensor block strides, the
+  // Tensor expression will be invalid, and should not be used by
+  // TensorBlockAssign or for constructing another block expression.
+  kMaterializedInOutput
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockNotImplemented should be used to defined TensorBlock typedef in
+// TensorEvaluators that do not support block evaluation.
+
+class TensorBlockNotImplemented {
+ public:
+  typedef void XprType;
+};
+
+// -------------------------------------------------------------------------- //
+// XprScalar extracts Scalar type from the Eigen expressions (if expression type
+// is not void). It's required to be able to define lazy block expression for
+// argument types, that do not support block evaluation.
+
+template <typename XprType>
+struct XprScalar {
+  typedef typename XprType::Scalar type;
+};
+template <>
+struct XprScalar<void> {
+  typedef void type;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorMaterializedBlock is a fully evaluated block of the original tensor,
+// and XprType is just a TensorMap over the data. This block type is typically
+// used to materialize blocks of tensor expressions, that can't be efficiently
+// represented as lazy Tensor expressions with fast coeff/packet operations,
+// e.g. we materialize all broadcasts into evaluated blocks.
+//
+// TensorMaterializedBlock does not own its memory buffer, it's either a memory
+// buffer that backs the original expression (e.g. block is just a view into a
+// Tensor), or a memory buffer allocated with scratch allocator, and in this
+// case the scratch allocator will deallocate it at the end of block based
+// expression execution.
+//
+// If the block was evaluated directly into the output buffer, and strides in
+// the output buffer do not match block strides, the TensorMap expression will
+// be invalid, and should never be used in block assignment or any other tensor
+// expression.
+
+template <typename Scalar, int NumDims, int Layout,
+          typename IndexType = Eigen::Index>
+class TensorMaterializedBlock {
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+  typedef TensorMap<const Tensor<Scalar, NumDims, Layout> > XprType;
+
+  TensorMaterializedBlock(TensorBlockKind kind, const Scalar* data,
+                          const Dimensions& dimensions, bool valid_expr = true)
+      : m_kind(kind),
+        m_data(data),
+        m_dimensions(dimensions),
+        m_expr(m_data, m_dimensions),
+        m_valid_expr(valid_expr) {
+    eigen_assert(m_kind == internal::TensorBlockKind::kView ||
+                 m_kind == internal::TensorBlockKind::kMaterializedInScratch ||
+                 m_kind == internal::TensorBlockKind::kMaterializedInOutput);
+  }
+
+  TensorBlockKind kind() const { return m_kind; }
+  // NOTE(ezhulenev): Returning XprType by value like in other block types
+  // causes asan failures. The theory is that XprType::Nested doesn't work
+  // properly for TensorMap.
+  const XprType& expr() const {
+    eigen_assert(m_valid_expr);
+    return m_expr;
+  }
+  const Scalar* data() const { return m_data; }
+  void cleanup() {}
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
+
+  // TensorMaterializedBlock can be backed by different types of storage:
+  //
+  //   (1) Contiguous block of memory allocated with scratch allocator.
+  //   (2) Contiguous block of memory reused from tensor block descriptor
+  //       destination buffer.
+  //   (3) Strided block of memory reused from tensor block descriptor
+  //       destination buffer.
+  //
+  class Storage {
+   public:
+    Scalar* data() const { return m_data; }
+    const Dimensions& dimensions() const { return m_dimensions; }
+    const Dimensions& strides() const { return m_strides; }
+
+    TensorMaterializedBlock AsTensorMaterializedBlock() const {
+      return TensorMaterializedBlock(
+          m_materialized_in_output
+              ? internal::TensorBlockKind::kMaterializedInOutput
+              : internal::TensorBlockKind::kMaterializedInScratch,
+          m_data, m_dimensions, !m_strided_storage);
+    }
+
+   private:
+    friend class TensorMaterializedBlock;
+
+    Storage(Scalar* data, const Dimensions& dimensions,
+            const Dimensions& strides, bool materialized_in_output,
+            bool strided_storage)
+        : m_data(data),
+          m_dimensions(dimensions),
+          m_strides(strides),
+          m_materialized_in_output(materialized_in_output),
+          m_strided_storage(strided_storage) {}
+
+    Scalar* m_data;
+    Dimensions m_dimensions;
+    Dimensions m_strides;
+    bool m_materialized_in_output;
+    bool m_strided_storage;
+  };
+
+  // Creates a storage for materialized block either from the block descriptor
+  // destination buffer, or allocates a new buffer with scratch allocator.
+  template <typename TensorBlockScratch>
+  EIGEN_STRONG_INLINE static Storage prepareStorage(
+      TensorBlockDesc& desc, TensorBlockScratch& scratch,
+      bool allow_strided_storage = false) {
+    // Try to reuse destination as an output block buffer.
+    typedef typename TensorBlockDesc::DestinationBuffer DestinationBuffer;
+
+    if (desc.destination().kind() == DestinationBuffer::kContiguous) {
+      Scalar* buffer = desc.destination().template data<Scalar>();
+      desc.DropDestinationBuffer();
+      return Storage(buffer, desc.dimensions(),
+                     internal::strides<Layout>(desc.dimensions()),
+                     /*materialized_in_output=*/true,
+                     /*strided_storage=*/false);
+
+    } else if (desc.destination().kind() == DestinationBuffer::kStrided &&
+               allow_strided_storage) {
+      Scalar* buffer = desc.destination().template data<Scalar>();
+      desc.DropDestinationBuffer();
+      return Storage(buffer, desc.dimensions(), desc.destination().strides(),
+                     /*materialized_in_output=*/true, /*strided_storage=*/true);
+
+    } else {
+      void* mem = scratch.allocate(desc.size() * sizeof(Scalar));
+      return Storage(static_cast<Scalar*>(mem), desc.dimensions(),
+                     internal::strides<Layout>(desc.dimensions()),
+                     /*materialized_in_output=*/false,
+                     /*strided_storage=*/false);
+    }
+  }
+
+  // Creates a materialized block for the given descriptor from a memory buffer.
+  template <typename DataDimensions, typename TensorBlockScratch>
+  EIGEN_STRONG_INLINE static TensorMaterializedBlock materialize(
+      const Scalar* data, const DataDimensions& data_dims,
+      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    eigen_assert(array_size<DataDimensions>::value == desc.dimensions().size());
+
+    // If a tensor block dimensions covers a contiguous block of the underlying
+    // memory, we can skip block buffer memory allocation, and construct a block
+    // from existing `data` memory buffer.
+    //
+    // Example: (RowMajor layout)
+    //   data_dims:          [11, 12, 13, 14]
+    //   desc.dimensions():  [1,   1,  3, 14]
+    //
+    // In this case we can construct a TensorBlock starting at
+    // `data + desc.offset()`, with a `desc.dimensions()` block sizes.
+    static const bool is_col_major = Layout == ColMajor;
+
+    // Find out how many inner dimensions have a matching size.
+    int num_matching_inner_dims = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      int dim = is_col_major ? i : NumDims - i - 1;
+      if (data_dims[dim] != desc.dimensions()[dim]) break;
+      ++num_matching_inner_dims;
+    }
+
+    // All the outer dimensions must be of size `1`, except a single dimension
+    // before the matching inner dimension (`3` in the example above).
+    bool can_use_direct_access = true;
+    for (int i = num_matching_inner_dims + 1; i < NumDims; ++i) {
+      int dim = is_col_major ? i : NumDims - i - 1;
+      if (desc.dimension(dim) != 1) {
+        can_use_direct_access = false;
+        break;
+      }
+    }
+
+    if (can_use_direct_access) {
+      const Scalar* block_start = data + desc.offset();
+      return TensorMaterializedBlock(internal::TensorBlockKind::kView,
+                                     block_start, desc.dimensions());
+
+    } else {
+      // Reuse destination buffer or allocate new buffer with scratch allocator.
+      const Storage storage = prepareStorage(desc, scratch);
+
+      typedef internal::TensorBlockIO<Scalar, IndexType, NumDims, Layout>
+          TensorBlockIO;
+      typedef typename TensorBlockIO::Dst TensorBlockIODst;
+      typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+      TensorBlockIOSrc src(internal::strides<Layout>(Dimensions(data_dims)),
+                           data, desc.offset());
+      TensorBlockIODst dst(storage.dimensions(), storage.strides(),
+                           storage.data());
+
+      TensorBlockIO::Copy(dst, src);
+      return storage.AsTensorMaterializedBlock();
+    }
+  }
+
+ private:
+  TensorBlockKind m_kind;
+  const Scalar* m_data;
+  Dimensions m_dimensions;
+  XprType m_expr;
+  bool m_valid_expr;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorCwiseUnaryBlock is a lazy tensor expression block that applies UnaryOp
+// functor to the blocks produced by the underlying Tensor expression.
+
+template <typename UnaryOp, typename ArgTensorBlock>
+class TensorCwiseUnaryBlock {
+  static const bool NoArgBlockAccess =
+      internal::is_void<typename ArgTensorBlock::XprType>::value;
+
+ public:
+  typedef typename conditional<
+      NoArgBlockAccess, void,
+      TensorCwiseUnaryOp<UnaryOp, const typename ArgTensorBlock::XprType> >::
+      type XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorCwiseUnaryBlock(const ArgTensorBlock& arg_block, const UnaryOp& functor)
+      : m_arg_block(arg_block), m_functor(functor) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+
+  XprType expr() const { return XprType(m_arg_block.expr(), m_functor); }
+  const Scalar* data() const { return NULL; }
+  void cleanup() { m_arg_block.cleanup(); }
+
+ private:
+  ArgTensorBlock m_arg_block;
+  UnaryOp m_functor;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorCwiseUnaryBlock is a lazy tensor expression block that applies BinaryOp
+// functor to the blocks produced by the underlying Tensor expression.
+
+template <typename BinaryOp, typename LhsTensorBlock, typename RhsTensorBlock>
+class TensorCwiseBinaryBlock {
+  static const bool NoArgBlockAccess =
+      internal::is_void<typename LhsTensorBlock::XprType>::value ||
+      internal::is_void<typename RhsTensorBlock::XprType>::value;
+
+ public:
+  typedef typename conditional<
+      NoArgBlockAccess, void,
+      TensorCwiseBinaryOp<BinaryOp, const typename LhsTensorBlock::XprType,
+                          const typename RhsTensorBlock::XprType> >::type
+      XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorCwiseBinaryBlock(const LhsTensorBlock& left_block,
+                         const RhsTensorBlock& right_block,
+                         const BinaryOp& functor)
+      : m_left_block(left_block),
+        m_right_block(right_block),
+        m_functor(functor) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+
+  XprType expr() const {
+    return XprType(m_left_block.expr(), m_right_block.expr(), m_functor);
+  }
+
+  const Scalar* data() const { return NULL; }
+
+  void cleanup() {
+    m_left_block.cleanup();
+    m_right_block.cleanup();
+  }
+
+ private:
+  LhsTensorBlock m_left_block;
+  RhsTensorBlock m_right_block;
+  BinaryOp m_functor;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorUnaryExprBlock is a lazy tensor expression block that can construct
+// an arbitrary tensor expression from a block of the underlying type (this is a
+// generalization of the TensorCwiseUnaryBlock for arbitrary expressions).
+
+template <typename BlockFactory, typename ArgTensorBlock>
+class TensorUnaryExprBlock {
+  typedef typename ArgTensorBlock::XprType ArgXprType;
+  static const bool NoArgBlockAccess = internal::is_void<ArgXprType>::value;
+
+ public:
+  typedef typename conditional<
+      NoArgBlockAccess, void,
+      typename BlockFactory::template XprType<ArgXprType>::type>::type XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorUnaryExprBlock(const ArgTensorBlock& arg_block,
+                       const BlockFactory& factory)
+      : m_arg_block(arg_block), m_factory(factory) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+  XprType expr() const { return m_factory.expr(m_arg_block.expr()); }
+  const Scalar* data() const { return NULL; }
+  void cleanup() { m_arg_block.cleanup(); }
+
+ private:
+  ArgTensorBlock m_arg_block;
+  BlockFactory m_factory;
+};
+
+// -------------------------------------------------------------------------- //
+// TensorTernaryExprBlock is a lazy tensor expression block that can construct
+// an arbitrary tensor expression from three blocks of the underlying type.
+
+template <typename BlockFactory, typename Arg1TensorBlock,
+          typename Arg2TensorBlock, typename Arg3TensorBlock>
+class TensorTernaryExprBlock {
+  typedef typename Arg1TensorBlock::XprType Arg1XprType;
+  typedef typename Arg2TensorBlock::XprType Arg2XprType;
+  typedef typename Arg3TensorBlock::XprType Arg3XprType;
+
+  static const bool NoArgBlockAccess = internal::is_void<Arg1XprType>::value ||
+                                       internal::is_void<Arg2XprType>::value ||
+                                       internal::is_void<Arg3XprType>::value;
+
+ public:
+  typedef typename conditional<
+      NoArgBlockAccess, void,
+      typename BlockFactory::template XprType<Arg1XprType, Arg2XprType,
+                                              Arg3XprType>::type>::type XprType;
+
+  typedef typename XprScalar<XprType>::type Scalar;
+
+  TensorTernaryExprBlock(const Arg1TensorBlock& arg1_block,
+                         const Arg2TensorBlock& arg2_block,
+                         const Arg3TensorBlock& arg3_block,
+                         const BlockFactory& factory)
+      : m_arg1_block(arg1_block),
+        m_arg2_block(arg2_block),
+        m_arg3_block(arg3_block),
+        m_factory(factory) {}
+
+  TensorBlockKind kind() const { return internal::TensorBlockKind::kExpr; }
+  XprType expr() const {
+    return m_factory.expr(m_arg1_block.expr(), m_arg2_block.expr(),
+                          m_arg3_block.expr());
+  }
+  const Scalar* data() const { return NULL; }
+  void cleanup() {
+    m_arg1_block.cleanup();
+    m_arg2_block.cleanup();
+    m_arg3_block.cleanup();
+  }
+
+ private:
+  Arg1TensorBlock m_arg1_block;
+  Arg2TensorBlock m_arg2_block;
+  Arg3TensorBlock m_arg3_block;
+  BlockFactory m_factory;
+};
+
+// -------------------------------------------------------------------------- //
+// StridedLinearBufferCopy provides a method to copy data between two linear
+// buffers with different strides, with optimized paths for scatter/gather.
+
+template <typename Scalar, typename IndexType>
+class StridedLinearBufferCopy {
+  typedef typename packet_traits<Scalar>::type Packet;
+  enum {
+    Vectorizable = packet_traits<Scalar>::Vectorizable,
+    PacketSize = packet_traits<Scalar>::size
+  };
+
+ public:
+  // Specifying linear copy kind statically gives ~30% speedup for small sizes.
+  enum class Kind {
+    Linear = 0,       // src_stride == 1 && dst_stride == 1
+    Scatter = 1,      // src_stride == 1 && dst_stride != 1
+    FillLinear = 2,   // src_stride == 0 && dst_stride == 1
+    FillScatter = 3,  // src_stride == 0 && dst_stride != 1
+    Gather = 4,       // dst_stride == 1
+    Random = 5        // everything else
+  };
+
+  struct Dst {
+    Dst(IndexType o, IndexType s, Scalar* d) : offset(o), stride(s), data(d) {}
+
+    IndexType offset;
+    IndexType stride;
+    Scalar* data;
+  };
+
+  struct Src {
+    Src(IndexType o, IndexType s, const Scalar* d)
+        : offset(o), stride(s), data(d) {}
+
+    IndexType offset;
+    IndexType stride;
+    const Scalar* data;
+  };
+
+  template <typename StridedLinearBufferCopy::Kind kind>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Dst& dst,
+                                                        const Src& src,
+                                                        const size_t count) {
+    Run<kind>(count, dst.offset, dst.stride, dst.data, src.offset, src.stride,
+              src.data);
+  }
+
+ private:
+  template <typename StridedLinearBufferCopy::Kind kind>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
+      const IndexType count, const IndexType dst_offset,
+      const IndexType dst_stride, Scalar* EIGEN_RESTRICT dst_data,
+      const IndexType src_offset, const IndexType src_stride,
+      const Scalar* EIGEN_RESTRICT src_data) {
+    const Scalar* src = &src_data[src_offset];
+    Scalar* dst = &dst_data[dst_offset];
+
+    if (!Vectorizable) {
+      for (Index i = 0; i < count; ++i) {
+        dst[i * dst_stride] = src[i * src_stride];
+      }
+      return;
+    }
+
+    const IndexType vectorized_size = count - PacketSize;
+    IndexType i = 0;
+
+    if (kind == StridedLinearBufferCopy::Kind::Linear) {
+      // ******************************************************************** //
+      // Linear copy from `src` to `dst`.
+      const IndexType unrolled_size = count - 4 * PacketSize;
+      eigen_assert(src_stride == 1 && dst_stride == 1);
+      for (; i <= unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          Packet p = ploadu<Packet>(src + i + j * PacketSize);
+          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
+        }
+      }
+      for (; i <= vectorized_size; i += PacketSize) {
+        Packet p = ploadu<Packet>(src + i);
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      for (; i < count; ++i) {
+        dst[i] = src[i];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Scatter) {
+      // Scatter from `src` to `dst`.
+      eigen_assert(src_stride == 1 && dst_stride != 1);
+      for (; i <= vectorized_size; i += PacketSize) {
+        Packet p = ploadu<Packet>(src + i);
+        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
+      }
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = src[i];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::FillLinear) {
+      // Fill `dst` with value at `*src`.
+      eigen_assert(src_stride == 0 && dst_stride == 1);
+      const IndexType unrolled_size = count - 4 * PacketSize;
+      Packet p = pload1<Packet>(src);
+      for (; i <= unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          pstoreu<Scalar, Packet>(dst + i + j * PacketSize, p);
+        }
+      }
+      for (; i <= vectorized_size; i += PacketSize) {
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      for (; i < count; ++i) {
+        dst[i] = *src;
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::FillScatter) {
+      // Scatter `*src` into `dst`.
+      eigen_assert(src_stride == 0 && dst_stride != 1);
+      Packet p = pload1<Packet>(src);
+      for (; i <= vectorized_size; i += PacketSize) {
+        pscatter<Scalar, Packet>(dst + i * dst_stride, p, dst_stride);
+      }
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = *src;
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Gather) {
+      // Gather from `src` into `dst`.
+      eigen_assert(dst_stride == 1);
+      for (; i <= vectorized_size; i += PacketSize) {
+        Packet p = pgather<Scalar, Packet>(src + i * src_stride, src_stride);
+        pstoreu<Scalar, Packet>(dst + i, p);
+      }
+      for (; i < count; ++i) {
+        dst[i] = src[i * src_stride];
+      }
+      // ******************************************************************** //
+    } else if (kind == StridedLinearBufferCopy::Kind::Random) {
+      // Random.
+      for (; i < count; ++i) {
+        dst[i * dst_stride] = src[i * src_stride];
+      }
+    } else {
+      eigen_assert(false);
+    }
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockIO copies data from `src` tensor block, to the `dst` tensor block.
+// It's possible to specify src->dst dimension mapping for the copy operation.
+// Dimensions of `dst` specify how many elements have to be copied, for the
+// `src` we need to know only stride to navigate through source memory buffer.
+
+template <typename Scalar, typename IndexType, int NumDims, int Layout>
+class TensorBlockIO {
+  static const bool IsColMajor = (Layout == ColMajor);
+
+  typedef StridedLinearBufferCopy<Scalar, IndexType> LinCopy;
+
+ public:
+  typedef DSizes<IndexType, NumDims> Dimensions;
+  typedef DSizes<int, NumDims> DimensionsMap;
+
+  struct Dst {
+    Dst(const Dimensions& dst_dims, const Dimensions& dst_strides, Scalar* dst,
+        IndexType dst_offset = 0)
+        : dims(dst_dims), strides(dst_strides), data(dst), offset(dst_offset) {}
+
+    Dimensions dims;
+    Dimensions strides;
+    Scalar* data;
+    IndexType offset;
+  };
+
+  struct Src {
+    Src(const Dimensions& src_strides, const Scalar* src,
+        IndexType src_offset = 0)
+        : strides(src_strides), data(src), offset(src_offset) {}
+
+    Dimensions strides;
+    const Scalar* data;
+    IndexType offset;
+  };
+
+  // Copies data to `dst` from `src`, using provided dimensions mapping:
+  //
+  //   src_dimension_index = dst_to_src_dim_map[dst_dimension_index]
+  //
+  // Returns the number of copied elements.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType Copy(
+      const Dst& dst, const Src& src, const DimensionsMap& dst_to_src_dim_map) {
+    // Copy single scalar value from `src` to `dst`.
+    if (NumDims == 0) {
+      *(dst.data + dst.offset) = *(src.data + src.offset);
+      return 1;
+    }
+
+    // Both `dst` and `src` must have contiguous innermost dimension. We also
+    // accept the special case with stride '0', because it's used as a trick to
+    // implement broadcasting.
+    {
+      int inner_dim = IsColMajor ? 0 : NumDims - 1;
+      EIGEN_UNUSED_VARIABLE(inner_dim);
+      eigen_assert(dst.strides[inner_dim] == 1 || dst.strides[inner_dim] == 0);
+      eigen_assert(src.strides[inner_dim] == 1 || src.strides[inner_dim] == 0);
+    }
+
+    // Give a shorter name to `dst_to_src_dim_map`.
+    const DimensionsMap& dim_map = dst_to_src_dim_map;
+
+    // Do not squeeze reordered inner dimensions.
+    int num_squeezable_dims = NumSqueezableInnerDims(dim_map);
+
+    // NOTE: We find the innermost dimension (contiguous in memory) in the dst
+    // block, and we write data linearly into that dimension, reading it from
+    // the src. If dimensions are reordered, we might end up reading data from
+    // the src with `stride != 1`.
+    //
+    // NOTE: Random-Read/Linear-Write can be up to ~2X faster than
+    // Linear-Read/Random-Write: https://stackoverflow.com/a/54935680
+
+    // Find the innermost dimension in the dst whose size is not 1. This is the
+    // effective inner dim.
+    int num_size_one_inner_dims = 0;
+    for (int i = 0; i < num_squeezable_dims; ++i) {
+      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
+      if (dst.dims[dst_dim] != 1) break;
+      num_size_one_inner_dims++;
+    }
+
+    // If all dimensions are of size 1, just copy a scalar from `src` to `dst`.
+    if (num_size_one_inner_dims == NumDims) {
+      *(dst.data + dst.offset) = *(src.data + src.offset);
+      return 1;
+    }
+
+    // Outermost dimension in the dst with `stride == 1` (contiguous in memory).
+    const int dst_stride1_dim = IsColMajor
+                                    ? num_size_one_inner_dims
+                                    : NumDims - num_size_one_inner_dims - 1;
+
+    // Dimension in the src that corresponds to the dst innermost dimension.
+    const int src_dim_for_dst_stride1_dim =
+        NumDims == 0 ? 1 : dim_map[dst_stride1_dim];
+
+    // Size of the innermost dimension (length of contiguous blocks of memory).
+    IndexType dst_inner_dim_size = NumDims == 0 ? 1 : dst.dims[dst_stride1_dim];
+
+    // Squeeze multiple inner dims into one if they are contiguous in `dst` and
+    // `src` memory, so we can do less linear copy calls.
+    for (int i = num_size_one_inner_dims + 1; i < num_squeezable_dims; ++i) {
+      const int dst_dim = IsColMajor ? i : NumDims - i - 1;
+      const IndexType dst_stride = dst.strides[dst_dim];
+      const IndexType src_stride = src.strides[dim_map[dst_dim]];
+      if (dst_inner_dim_size == dst_stride && dst_stride == src_stride) {
+        dst_inner_dim_size *= dst.dims[dst_dim];
+        ++num_size_one_inner_dims;
+      } else {
+        break;
+      }
+    }
+
+    // Setup strides to read data from `src` and write to `dst`.
+    IndexType input_offset = src.offset;
+    IndexType output_offset = dst.offset;
+    IndexType input_stride =
+        NumDims == 0 ? 1 : src.strides[src_dim_for_dst_stride1_dim];
+    IndexType output_stride = NumDims == 0 ? 1 : dst.strides[dst_stride1_dim];
+
+    const int at_least_1_dim = NumDims <= 1 ? 1 : NumDims - 1;
+    array<BlockIteratorState, at_least_1_dim> it;
+
+    // Initialize block iterator state. Squeeze away any dimension of size 1.
+    int idx = 0;  // currently initialized iterator state index
+    for (int i = num_size_one_inner_dims; i < NumDims - 1; ++i) {
+      const int dst_dim = IsColMajor ? i + 1 : NumDims - i - 2;
+      if (dst.dims[dst_dim] == 1) continue;
+
+      it[idx].size = dst.dims[dst_dim];
+      it[idx].input_stride = src.strides[dim_map[dst_dim]];
+      it[idx].output_stride = dst.strides[dst_dim];
+
+      it[idx].input_span = it[idx].input_stride * (it[idx].size - 1);
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+
+      idx++;
+    }
+
+    // Iterate copying data from src to dst.
+    const IndexType block_total_size = NumDims == 0 ? 1 : dst.dims.TotalSize();
+
+#define COPY_INNER_DIM(KIND)                                           \
+  IndexType num_copied = 0;                                            \
+  for (num_copied = 0; num_copied < block_total_size;                  \
+       num_copied += dst_inner_dim_size) {                             \
+    LinCopy::template Run<KIND>(                                       \
+        typename LinCopy::Dst(output_offset, output_stride, dst.data), \
+        typename LinCopy::Src(input_offset, input_stride, src.data),   \
+        dst_inner_dim_size);                                           \
+                                                                       \
+    for (int j = 0; j < idx; ++j) {                                    \
+      if (++it[j].count < it[j].size) {                                \
+        input_offset += it[j].input_stride;                            \
+        output_offset += it[j].output_stride;                          \
+        break;                                                         \
+      }                                                                \
+      it[j].count = 0;                                                 \
+      input_offset -= it[j].input_span;                                \
+      output_offset -= it[j].output_span;                              \
+    }                                                                  \
+  }                                                                    \
+  return num_copied;
+
+    if (input_stride == 1 && output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Linear);
+    } else if (input_stride == 1 && output_stride != 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Scatter);
+    } else if (input_stride == 0 && output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::FillLinear);
+    } else if (input_stride == 0 && output_stride != 1) {
+      COPY_INNER_DIM(LinCopy::Kind::FillScatter);
+    } else if (output_stride == 1) {
+      COPY_INNER_DIM(LinCopy::Kind::Gather);
+    } else {
+      COPY_INNER_DIM(LinCopy::Kind::Random);
+    }
+
+#undef COPY_INNER_DIM
+  }
+
+  // Copy from `src` to `dst` with an identity src->dst dimension map. Returns
+  // the number of copied elements.
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexType Copy(const Dst& dst,
+                                                              const Src& src) {
+    DimensionsMap dst_to_src_map;
+    for (int i = 0; i < NumDims; ++i) dst_to_src_map[i] = i;
+    return Copy(dst, src, dst_to_src_map);
+  }
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState()
+        : size(0),
+          count(0),
+          input_stride(0),
+          output_stride(0),
+          input_span(0),
+          output_span(0) {}
+
+    IndexType size;
+    IndexType count;
+    IndexType input_stride;
+    IndexType output_stride;
+    IndexType input_span;
+    IndexType output_span;
+  };
+
+  // Compute how many inner dimensions it's allowed to squeeze when doing IO
+  // between two tensor blocks. It's safe to squeeze inner dimensions, only
+  // if they are not reordered.
+  static int NumSqueezableInnerDims(const DimensionsMap& dim_map) {
+    int num_squeezable_dims = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      if (dim_map[dim] != dim) break;
+      num_squeezable_dims++;
+    }
+    return num_squeezable_dims;
+  }
+};
+
+// -------------------------------------------------------------------------- //
+// TensorBlockAssignment assigns a block expression of type `TensorBlockExpr` to
+// a Tensor block defined by `desc`, backed by a memory buffer at `target`.
+//
+// Currently there is no way to write from a Tensor expression to a block of
+// memory, if dimensions are reordered. If you need to do that, you should
+// materialize a Tensor block expression into a memory buffer, and then use
+// TensorBlockIO to copy data between two memory buffers with a custom
+// `target->src` dimension map (see definition above).
+//
+// Also currently the innermost dimension of `target` must have a stride '1'
+// (contiguous in memory). This restriction could be lifted with a `pscatter`,
+// but in practice it's never needed, and there is a similar TensorBlockIO
+// workaround for that.
+//
+// TODO(ezhulenev): TensorBlockAssignment is a special case of TensorBlockIO
+// where `src` is a tensor expression. Explore if it is possible to rewrite IO
+// to use expressions instead of pointers, and after that TensorBlockAssignment
+// will become an alias to IO.
+template <typename Scalar, int NumDims, typename TensorBlockExpr,
+          typename IndexType = Eigen::Index>
+class TensorBlockAssignment {
+  // We will use coeff/packet path to evaluate block expressions.
+  typedef TensorEvaluator<const TensorBlockExpr, DefaultDevice>
+      TensorBlockEvaluator;
+
+  typedef DSizes<IndexType, NumDims> Dimensions;
+
+  enum {
+    Vectorizable = packet_traits<Scalar>::Vectorizable,
+    PacketSize = packet_traits<Scalar>::size
+  };
+
+  template <bool Vectorizable, typename Evaluator>
+  struct InnerDimAssign {
+    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
+                                        const Evaluator& eval,
+                                        IndexType eval_offset) {
+      for (IndexType i = 0; i < count; ++i) {
+        target[i] = eval.coeff(eval_offset + i);
+      }
+    }
+  };
+
+  template <typename Evaluator>
+  struct InnerDimAssign<true, Evaluator> {
+    EIGEN_ALWAYS_INLINE static void Run(Scalar* target, IndexType count,
+                                        const Evaluator& eval,
+                                        IndexType eval_offset) {
+      typedef typename packet_traits<Scalar>::type Packet;
+
+      const IndexType unrolled_size = count - 4 * PacketSize;
+      const IndexType vectorized_size = count - PacketSize;
+      IndexType i = 0;
+
+      for (; i <= unrolled_size; i += 4 * PacketSize) {
+        for (int j = 0; j < 4; ++j) {
+          const IndexType idx = eval_offset + i + j * PacketSize;
+          Packet p = eval.template packet<Unaligned>(idx);
+          pstoreu<Scalar>(target + i + j * PacketSize, p);
+        }
+      }
+
+      for (; i <= vectorized_size; i += PacketSize) {
+        Packet p = eval.template packet<Unaligned>(eval_offset + i);
+        pstoreu<Scalar>(target + i, p);
+      }
+
+      for (; i < count; ++i) {
+        target[i] = eval.coeff(eval_offset + i);
+      }
+    }
+  };
+
+ public:
+  struct Target {
+    Target(const Dimensions& target_dims, const Dimensions& target_strides,
+           Scalar* target_data, IndexType target_offset = 0)
+        : dims(target_dims),
+          strides(target_strides),
+          data(target_data),
+          offset(target_offset) {}
+
+    Dimensions dims;
+    Dimensions strides;
+    Scalar* data;
+    IndexType offset;
+  };
+
+  static Target target(const Dimensions& target_dims,
+                       const Dimensions& target_strides, Scalar* target_data,
+                       IndexType target_offset = 0) {
+    return Target(target_dims, target_strides, target_data, target_offset);
+  }
+
+  template <typename TargetDimsIndexType, typename TargetStridesIndexType>
+  static Target target(
+      const DSizes<TargetDimsIndexType, NumDims>& target_dims,
+      const DSizes<TargetStridesIndexType, NumDims>& target_strides,
+      Scalar* target_data, IndexType target_offset = 0) {
+    // DSizes constructor will do index type promotion if it's safe.
+    return Target(Dimensions(target_dims), Dimensions(target_strides),
+                  target_data, target_offset);
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
+      const Target& target, const TensorBlockExpr& expr) {
+    // Prepare evaluator for block expression.
+    DefaultDevice default_device;
+    TensorBlockEvaluator eval(expr, default_device);
+
+    // Tensor block expression dimension should match destination dimensions.
+    eigen_assert(dimensions_match(target.dims, eval.dimensions()));
+
+    static const int Layout = TensorBlockEvaluator::Layout;
+    static const bool is_col_major = Layout == ColMajor;
+
+    // Initialize output inner dimension size based on a layout.
+    const IndexType output_size = NumDims == 0 ? 1 : target.dims.TotalSize();
+    const int inner_dim_idx = is_col_major ? 0 : NumDims - 1;
+    IndexType output_inner_dim_size = target.dims[inner_dim_idx];
+
+    // Target inner dimension stride must be '1'.
+    eigen_assert(target.strides[inner_dim_idx] == 1);
+
+    // Squeeze multiple inner dims into one if they are contiguous in `target`.
+    IndexType num_squeezed_dims = 0;
+    for (Index i = 1; i < NumDims; ++i) {
+      const Index dim = is_col_major ? i : NumDims - i - 1;
+      const IndexType target_stride = target.strides[dim];
+
+      if (output_inner_dim_size == target_stride) {
+        output_inner_dim_size *= target.dims[dim];
+        num_squeezed_dims++;
+      } else {
+        break;
+      }
+    }
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+
+    int idx = 0;  // currently initialized iterator state index
+    for (Index i = num_squeezed_dims; i < NumDims - 1; ++i) {
+      const Index dim = is_col_major ? i + 1 : NumDims - i - 2;
+
+      it[idx].count = 0;
+      it[idx].size = target.dims[dim];
+      it[idx].output_stride = target.strides[dim];
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+      idx++;
+    }
+
+    // We read block expression from the beginning, and start writing data to
+    // `target` at given offset.
+    IndexType input_offset = 0;
+    IndexType output_offset = target.offset;
+
+    // Iterate copying data from `eval` to `target`.
+    for (IndexType i = 0; i < output_size; i += output_inner_dim_size) {
+      // Assign to `target` at current offset.
+      InnerDimAssign<Vectorizable && TensorBlockEvaluator::PacketAccess,
+                     TensorBlockEvaluator>::Run(target.data + output_offset,
+                                                output_inner_dim_size, eval,
+                                                input_offset);
+
+      // Move input offset forward by the number of assigned coefficients.
+      input_offset += output_inner_dim_size;
+
+      // Update index.
+      for (int j = 0; j < idx; ++j) {
+        if (++it[j].count < it[j].size) {
+          output_offset += it[j].output_stride;
+          break;
+        }
+        it[j].count = 0;
+        output_offset -= it[j].output_span;
+      }
+    }
+  }
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState()
+        : count(0), size(0), output_stride(0), output_span(0) {}
+
+    IndexType count;
+    IndexType size;
+    IndexType output_stride;
+    IndexType output_span;
+  };
+};
+
+// -------------------------------------------------------------------------- //
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
new file mode 100644
index 0000000..a354132
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
@@ -0,0 +1,1093 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
+
+namespace Eigen {
+
+/** \class TensorBroadcasting
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor broadcasting class.
+  *
+  *
+  */
+namespace internal {
+template<typename Broadcast, typename XprType>
+struct traits<TensorBroadcastingOp<Broadcast, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Broadcast, typename XprType>
+struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
+{
+  typedef const TensorBroadcastingOp<Broadcast, XprType> EIGEN_DEVICE_REF type;
+};
+
+template<typename Broadcast, typename XprType>
+struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
+{
+  typedef TensorBroadcastingOp<Broadcast, XprType> type;
+};
+
+template <typename Dims>
+struct is_input_scalar {
+  static const bool value = false;
+};
+template <>
+struct is_input_scalar<Sizes<> > {
+  static const bool value = true;
+};
+#ifndef EIGEN_EMULATE_CXX11_META_H
+template <typename std::ptrdiff_t... Indices>
+struct is_input_scalar<Sizes<Indices...> > {
+  static const bool value = (Sizes<Indices...>::total_size == 1);
+};
+#endif
+
+}  // end namespace internal
+
+
+
+template<typename Broadcast, typename XprType>
+class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
+      : m_xpr(expr), m_broadcast(broadcast) {}
+
+    EIGEN_DEVICE_FUNC
+    const Broadcast& broadcast() const { return m_broadcast; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Broadcast m_broadcast;
+};
+
+
+// Eval as rvalue
+template<typename Broadcast, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
+{
+  typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  protected: //  all the non-static fields must have the same access control, otherwise the TensorEvaluator wont be standard layout;
+  bool isCopy, nByOne, oneByN;
+  public:
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  // We do block based broadcasting using a trick with 2x tensor rank and 0
+  // strides. See block method implementation for details.
+  typedef DSizes<Index, 2 * NumDims> BroadcastDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+ typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : isCopy(false), nByOne(false), oneByN(false),
+        m_device(device), m_broadcast(op.broadcast()), m_impl(op.expression(), device)
+  {
+
+    // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
+    // and store the result in a scalar. Instead one should reshape the scalar into a a N-D
+    // tensor with N >= 1 of 1 element first and then broadcast.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    const InputDimensions& input_dims = m_impl.dimensions();
+    isCopy = true;
+    for (int i = 0; i < NumDims; ++i) {
+      eigen_assert(input_dims[i] > 0);
+      m_dimensions[i] = input_dims[i] * m_broadcast[i];
+      if (m_broadcast[i] != 1) {
+        isCopy = false;
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
+      }
+    } else {
+      m_inputStrides[NumDims-1] = 1;
+      m_outputStrides[NumDims-1] = 1;
+      for (int i = NumDims-2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
+      }
+    }
+
+    if (input_dims[0] == 1) {
+      oneByN = true;
+      for (int i = 1; i < NumDims; ++i) {
+        if (m_broadcast[i] != 1) {
+          oneByN = false;
+          break;
+        }
+      }
+    } else if (input_dims[NumDims-1] == 1) {
+      nByOne = true;
+      for (int i = 0; i < NumDims-1; ++i) {
+        if (m_broadcast[i] != 1) {
+          nByOne = false;
+          break;
+        }
+      }
+    }
+
+    // Handle special format like NCHW, its input shape is '[1, N..., 1]' and
+    // broadcast shape is '[N, 1..., N]'
+    if (!oneByN && !nByOne) {
+      if (input_dims[0] == 1 && input_dims[NumDims-1] == 1 && NumDims > 2) {
+        nByOne = true;
+        oneByN = true;
+        for (int i = 1; i < NumDims-1; ++i) {
+          if (m_broadcast[i] != 1) {
+            nByOne = false;
+            oneByN = false;
+            break;
+          }
+        }
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
+  {
+    if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
+      return m_impl.coeff(0);
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      if (isCopy) {
+        return m_impl.coeff(index);
+      } else {
+        return coeffColMajor(index);
+      }
+    } else {
+      if (isCopy) {
+        return m_impl.coeff(index);
+      } else {
+        return coeffRowMajor(index);
+      }
+    }
+  }
+
+  // TODO: attempt to speed this up. The integer divisions and modulo are slow
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexColMajor(Index index) const {
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    if (internal::index_statically_eq<Broadcast>(0, 1)) {
+      eigen_assert(index < m_impl.dimensions()[0]);
+      inputIndex += index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
+        eigen_assert(index % m_impl.dimensions()[0] == 0);
+      } else {
+        inputIndex += (index % m_impl.dimensions()[0]);
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
+  {
+    return m_impl.coeff(indexColMajor(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index indexRowMajor(Index index) const {
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    if (internal::index_statically_eq<Broadcast>(NumDims - 1, 1)) {
+      eigen_assert(index < m_impl.dimensions()[NumDims - 1]);
+      inputIndex += index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(NumDims - 1, 1)) {
+        eigen_assert(index % m_impl.dimensions()[NumDims - 1] == 0);
+      } else {
+        inputIndex += (index % m_impl.dimensions()[NumDims - 1]);
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
+  {
+    return m_impl.coeff(indexRowMajor(index));
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
+  {
+    if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
+      return internal::pset1<PacketReturnType>(m_impl.coeff(0));
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      if (isCopy) {
+        #ifdef EIGEN_GPU_COMPILE_PHASE
+        // See PR 437: on NVIDIA P100 and K20m we observed a x3-4 speed up by enforcing
+        // unaligned loads here. The reason is unclear though.
+        return m_impl.template packet<Unaligned>(index);
+        #else
+        return m_impl.template packet<LoadMode>(index);
+        #endif
+      } else if (oneByN && !nByOne) {
+        return packetNByOne<LoadMode>(index);
+      } else if (!oneByN && nByOne) {
+        return packetOneByN<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
+      } else {
+        return packetColMajor<LoadMode>(index);
+      }
+    } else {
+      if (isCopy) {
+        #ifdef EIGEN_GPU_COMPILE_PHASE
+        // See above.
+        return m_impl.template packet<Unaligned>(index);
+        #else
+        return m_impl.template packet<LoadMode>(index);
+        #endif
+      } else if (oneByN && !nByOne) {
+        return packetOneByN<LoadMode>(index);
+      } else if (!oneByN && nByOne) {
+        return packetNByOne<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
+      } else {
+        return packetRowMajor<LoadMode>(index);
+      }
+    }
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByNByOne
+  (Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    Index startDim, endDim;
+    Index inputIndex, outputOffset, batchedIndex;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      startDim = NumDims - 1;
+      endDim = 1;
+    } else {
+      startDim = 0;
+      endDim = NumDims - 2;
+    }
+
+    batchedIndex = index % m_outputStrides[startDim];
+    inputIndex   = batchedIndex / m_outputStrides[endDim];
+    outputOffset = batchedIndex % m_outputStrides[endDim];
+
+    if (outputOffset + PacketSize <= m_outputStrides[endDim]) {
+      values[0] = m_impl.coeff(inputIndex);
+      return internal::pload1<PacketReturnType>(values);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
+        if (outputOffset + cur < m_outputStrides[endDim]) {
+          values[i] = m_impl.coeff(inputIndex);
+        } else {
+          ++inputIndex;
+          inputIndex = (inputIndex == m_inputStrides[startDim] ? 0 : inputIndex);
+          values[i] = m_impl.coeff(inputIndex);
+          outputOffset = 0;
+          cur = 0;
+        }
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    Index dim, inputIndex;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      dim = NumDims - 1;
+    } else {
+      dim = 0;
+    }
+
+    inputIndex = index % m_inputStrides[dim];
+    if (inputIndex + PacketSize <= m_inputStrides[dim]) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        if (inputIndex > m_inputStrides[dim]-1) {
+          inputIndex = 0;
+        }
+        values[i] = m_impl.coeff(inputIndex++);
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetNByOne(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    Index dim, inputIndex, outputOffset;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      dim = 1;
+    } else {
+      dim = NumDims - 2;
+    }
+
+    inputIndex   = index / m_outputStrides[dim];
+    outputOffset = index % m_outputStrides[dim];
+    if (outputOffset + PacketSize <= m_outputStrides[dim]) {
+      values[0] = m_impl.coeff(inputIndex);
+      return internal::pload1<PacketReturnType>(values);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
+        if (outputOffset + cur < m_outputStrides[dim]) {
+          values[i] = m_impl.coeff(inputIndex);
+        } else {
+          values[i] = m_impl.coeff(++inputIndex);
+          outputOffset = 0;
+          cur = 0;
+        }
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
+  // Ignore the LoadMode and always use unaligned loads since we can't guarantee
+  // the alignment at compile time.
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    const Index originalIndex = index;
+
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    Index innermostLoc;
+    if (internal::index_statically_eq<Broadcast>(0, 1)) {
+      eigen_assert(index < m_impl.dimensions()[0]);
+      innermostLoc = index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(0, 1)) {
+        eigen_assert(index % m_impl.dimensions()[0] == 0);
+        innermostLoc = 0;
+      } else {
+        innermostLoc = index % m_impl.dimensions()[0];
+      }
+    }
+    inputIndex += innermostLoc;
+
+    // Todo: this could be extended to the second dimension if we're not
+    // broadcasting alongside the first dimension, and so on.
+    if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      values[0] = m_impl.coeff(inputIndex);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize; ++i) {
+        if (innermostLoc + i < m_impl.dimensions()[0]) {
+          values[i] = m_impl.coeff(inputIndex+i);
+        } else {
+          values[i] = coeffColMajor(originalIndex+i);
+        }
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    const Index originalIndex = index;
+
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index idx = index / m_outputStrides[i];
+      if (internal::index_statically_eq<Broadcast>(i, 1)) {
+        eigen_assert(idx < m_impl.dimensions()[i]);
+        inputIndex += idx * m_inputStrides[i];
+      } else {
+        if (internal::index_statically_eq<InputDimensions>(i, 1)) {
+          eigen_assert(idx % m_impl.dimensions()[i] == 0);
+        } else {
+          inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
+        }
+      }
+      index -= idx * m_outputStrides[i];
+    }
+    Index innermostLoc;
+    if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
+      eigen_assert(index < m_impl.dimensions()[NumDims-1]);
+      innermostLoc = index;
+    } else {
+      if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
+        eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
+        innermostLoc = 0;
+      } else {
+        innermostLoc = index % m_impl.dimensions()[NumDims-1];
+      }
+    }
+    inputIndex += innermostLoc;
+
+    // Todo: this could be extended to the second dimension if we're not
+    // broadcasting alongside the first dimension, and so on.
+    if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) {
+      return m_impl.template packet<Unaligned>(inputIndex);
+    } else {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      values[0] = m_impl.coeff(inputIndex);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize; ++i) {
+        if (innermostLoc + i < m_impl.dimensions()[NumDims-1]) {
+          values[i] = m_impl.coeff(inputIndex+i);
+        } else {
+          values[i] = coeffRowMajor(originalIndex+i);
+        }
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    double compute_cost = TensorOpCost::AddCost<Index>();
+    if (!isCopy && NumDims > 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        compute_cost += TensorOpCost::DivCost<Index>();
+        if (internal::index_statically_eq<Broadcast>(i, 1)) {
+          compute_cost +=
+              TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+        } else {
+          if (!internal::index_statically_eq<InputDimensions>(i, 1)) {
+            compute_cost += TensorOpCost::MulCost<Index>() +
+                            TensorOpCost::ModCost<Index>() +
+                            TensorOpCost::AddCost<Index>();
+          }
+        }
+        compute_cost +=
+            TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+      }
+    }
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    // TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large
+    // tensors. But this might need further tuning.
+    const size_t target_size = m_device.firstLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        m_impl.getResourceRequirements(),
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    BlockBroadcastingParams params = blockBroadcastingParams(desc);
+
+    if (params.inner_dim_size == 0 || params.bcast_dim_size == 0) {
+      return emptyBlock();
+    }
+
+    // Prepare storage for the materialized broadcasting result.
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(desc, scratch);
+    ScalarNoConst* materialized_output = block_storage.data();
+
+    // We potentially will need to materialize input blocks.
+    size_t materialized_input_size = 0;
+    ScalarNoConst* materialized_input = NULL;
+
+    // Initialize block broadcating iterator state for outer dimensions (outer
+    // with regard to bcast dimension). Dimension in this array are always in
+    // inner_most -> outer_most order (col major layout).
+    array<BlockBroadcastingIteratorState, NumDims> it;
+    int idx = 0;
+
+    for (int i = params.inner_dim_count + 1; i < NumDims; ++i) {
+      const Index dim = IsColMajor ? i : NumDims - 1 - i;
+      it[idx].size = params.output_dims[dim];
+      it[idx].count = 0;
+      it[idx].output_stride = m_outputStrides[dim];
+      it[idx].output_span = it[idx].output_stride * (it[idx].size - 1);
+      idx++;
+    }
+
+    // Write output into the beginning of `materialized_output`.
+    Index output_offset = 0;
+
+    // We will fill output block by broadcasting along the bcast dim, and
+    // iterating over outer dimension.
+    const Index output_size = NumDims == 0 ? 1 : params.output_dims.TotalSize();
+
+    for (Index num_output_coeffs = 0; num_output_coeffs < output_size;) {
+      ScalarNoConst* bcast_output = materialized_output + num_output_coeffs;
+      Index bcast_offset = desc.offset() + output_offset;
+
+      // Broadcast along the bcast dimension.
+      num_output_coeffs += BroadcastBlockAlongBcastDim(
+          params, bcast_offset, scratch, bcast_output, &materialized_input,
+          &materialized_input_size);
+
+      // Switch to the next outer dimension.
+      for (int j = 0; j < idx; ++j) {
+        if (++it[j].count < it[j].size) {
+          output_offset += it[j].output_stride;
+          break;
+        }
+        it[j].count = 0;
+        output_offset -= it[j].output_span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+  Broadcast functor() const { return m_broadcast; }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(
+      cl::sycl::handler& cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+ private:
+  static const bool IsColMajor =
+      static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+  // We will build a general case block broadcasting on top of broadcasting
+  // primitive that will do broadcasting only for the inner dimension(s) along
+  // the first dimension smaller than the input size (it's called `bcast_dim`).
+  //
+  // Example:
+  //           dim:  0  1  2   (ColMajor)
+  //    input size: [9, 3, 6]
+  //    block size: [9, 2, 6]
+  //
+  // We will compute broadcasted block by iterating over the outer dimensions
+  // before `bcast_dim` (only dimension `2` in this example) and computing
+  // broadcasts along the `bcast_dim` (dimension `1` in this example).
+
+  // BlockBroadcastingParams holds precomputed parameters for broadcasting a
+  // single block along the broadcasting dimension. Sizes and strides along the
+  // `bcast_dim` might be invalid, they will be adjusted later in
+  // `BroadcastBlockAlongBcastDim`.
+  struct BlockBroadcastingParams {
+    Dimensions input_dims;      // input expression dimensions
+    Dimensions output_dims;     // output block sizes
+    Dimensions output_strides;  // output block strides
+
+    int inner_dim_count;   // count inner dimensions matching in size
+    int bcast_dim;         // broadcasting dimension index
+    Index bcast_dim_size;  // broadcasting dimension size
+    Index inner_dim_size;  // inner dimensions size
+
+    // Block sizes and strides for the input block where all dimensions before
+    // `bcast_dim` are equal to `1`.
+    Dimensions input_block_sizes;
+    Dimensions input_block_strides;
+
+    // Block sizes and strides for blocks with extra dimensions and strides `0`.
+    BroadcastDimensions bcast_block_sizes;
+    BroadcastDimensions bcast_block_strides;
+    BroadcastDimensions bcast_input_strides;
+  };
+
+  struct BlockBroadcastingIteratorState {
+    Index size;
+    Index count;
+    Index output_stride;
+    Index output_span;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlockBroadcastingParams
+  blockBroadcastingParams(TensorBlockDesc& desc) const {
+    BlockBroadcastingParams params;
+
+    params.input_dims = Dimensions(m_impl.dimensions());
+
+    // Output block sizes and strides.
+    params.output_dims = desc.dimensions();
+    params.output_strides = internal::strides<Layout>(params.output_dims);
+
+    // Find the broadcasting dimension (first dimension with output size smaller
+    // that the input size).
+    params.bcast_dim = 0;
+    params.bcast_dim_size = 1;
+    params.inner_dim_size = 1;
+
+    // Count the number of inner dimensions that have the same size in the block
+    // and in the broadcast expression.
+    params.inner_dim_count = 0;
+
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+
+      if (params.output_dims[dim] == m_dimensions[dim]) {
+        params.inner_dim_size *= params.output_dims[dim];
+        ++params.inner_dim_count;
+        continue;
+      }
+
+      // First non-matching dimension is the broadcasting dimension.
+      eigen_assert(params.output_dims[dim] < m_dimensions[dim]);
+      params.bcast_dim = dim;
+      params.bcast_dim_size = params.output_dims[dim];
+      break;
+    }
+
+    // Calculate the input block size for looking into the input.
+    for (int i = 0; i < params.inner_dim_count; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      params.input_block_sizes[dim] = params.input_dims[dim];
+    }
+    for (int i = params.inner_dim_count; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      params.input_block_sizes[dim] = 1;
+    }
+    params.input_block_strides =
+        internal::strides<Layout>(params.input_block_sizes);
+
+    // Broadcast with the 0-stride trick: Create 1 extra dim for each
+    // broadcast, set the input stride to 0.
+    //
+    // When ColMajor:
+    //
+    // - bcast_block_sizes:
+    //   [d_0, b_0, d_1, b_1, ...]
+    //
+    // - bcast_block_strides:
+    //   [output_block_strides[0], output_block_strides[0] * d_0,
+    //    output_block_strides[1], output_block_strides[1] * d_1,
+    //   ...]
+    //
+    // - bcast_input_strides:
+    //   [input_block_strides[0], 0,
+    //    input_block_strides[1], 0,
+    //   ...].
+    //
+    for (int i = 0; i < params.inner_dim_count; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+
+      const int copy_dim = IsColMajor ? 2 * i : 2 * NumDims - 2 * i - 1;
+      const int broadcast_dim = IsColMajor ? copy_dim + 1 : copy_dim - 1;
+
+      params.bcast_block_sizes[copy_dim] = params.input_dims[dim];
+      params.bcast_block_sizes[broadcast_dim] = m_broadcast[dim];
+      params.bcast_block_strides[copy_dim] = params.output_strides[dim];
+      params.bcast_block_strides[broadcast_dim] =
+          params.output_strides[dim] * params.input_dims[dim];
+      params.bcast_input_strides[copy_dim] = params.input_block_strides[dim];
+      params.bcast_input_strides[broadcast_dim] = 0;
+    }
+
+    for (int i = 2 * params.inner_dim_count; i < 2 * NumDims; ++i) {
+      const int dim = IsColMajor ? i : 2 * NumDims - i - 1;
+      params.bcast_block_sizes[dim] = 1;
+      params.bcast_block_strides[dim] = 0;
+      params.bcast_input_strides[dim] = 0;
+    }
+
+    return params;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock emptyBlock() const {
+    DSizes<Index, NumDims> dimensions;
+    for (int i = 0; i < NumDims; ++i) dimensions[i] = 0;
+    return TensorBlock(internal::TensorBlockKind::kView, NULL, dimensions);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlockAlongBcastDim(
+      BlockBroadcastingParams params, Index bcast_offset,
+      TensorBlockScratch& scratch, ScalarNoConst* materialized_output,
+      ScalarNoConst** materialized_input,
+      size_t* materialized_input_size) const {
+    if (params.bcast_dim_size == 1) {
+      // We just need one block read using the ready-set values above.
+      return BroadcastBlock(
+          params.input_block_sizes, params.input_block_strides,
+          params.bcast_block_sizes, params.bcast_block_strides,
+          params.bcast_input_strides, bcast_offset, 0, scratch,
+          materialized_output, materialized_input, materialized_input_size);
+
+    } else if (params.input_dims[params.bcast_dim] == 1) {
+      // Broadcast bcast dimension (< NumDims) by bcast_dim_size.
+      const int broadcast_bcast_dim =
+          IsColMajor ? 2 * params.inner_dim_count + 1
+                     : 2 * NumDims - 2 * params.inner_dim_count - 2;
+
+      params.bcast_block_sizes[broadcast_bcast_dim] = params.bcast_dim_size;
+      params.bcast_input_strides[broadcast_bcast_dim] = 0;
+      params.bcast_block_strides[broadcast_bcast_dim] =
+          params.output_strides[params.bcast_dim];
+
+      return BroadcastBlock(
+          params.input_block_sizes, params.input_block_strides,
+          params.bcast_block_sizes, params.bcast_block_strides,
+          params.bcast_input_strides, bcast_offset, 0, scratch,
+          materialized_output, materialized_input, materialized_input_size);
+
+    } else {
+      // Keep track of the total number of the coefficients written to the
+      // output block.
+      Index num_output_coeffs = 0;
+
+      // The general case. Let's denote the output block as
+      //
+      //   x[..., a:a+bcast_dim_size, :, ..., :]
+      //
+      // where a:a+bcast_dim_size is a slice on the bcast_dim dimension
+      // (< NumDims). We need to split the a:a+bcast_dim_size into possibly 3
+      // sub-blocks:
+      //
+      // (1) a:b, where b is the smallest multiple of
+      //     input_dims[bcast_dim_start] in [a, a+bcast_dim_size].
+      //
+      // (2) b:c, where c is the largest multiple of input_dims[bcast_dim_start]
+      //     in [a, a+bcast_dim_size].
+      //
+      // (3) c:a+bcast_dim_size .
+      //
+      // Or, when b and c do not exist, we just need to process the whole block
+      // together.
+
+      // Find a.
+      const Index bcast_dim_left_index =
+          bcast_offset / m_outputStrides[params.bcast_dim];
+
+      // Find b and c.
+      const Index input_bcast_dim_size = params.input_dims[params.bcast_dim];
+
+      // First multiple after a. This is b when <= bcast_dim_left_index +
+      // bcast_dim_size.
+      const Index first_multiple =
+          divup<Index>(bcast_dim_left_index, input_bcast_dim_size) *
+          input_bcast_dim_size;
+
+      if (first_multiple <= bcast_dim_left_index + params.bcast_dim_size) {
+        // b exists, so does c. Find it.
+        const Index last_multiple =
+            (bcast_dim_left_index + params.bcast_dim_size) /
+            input_bcast_dim_size * input_bcast_dim_size;
+        const int copy_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count
+                       : 2 * NumDims - 2 * params.inner_dim_count - 1;
+        const int broadcast_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count + 1
+                       : 2 * NumDims - 2 * params.inner_dim_count - 2;
+
+        if (first_multiple > bcast_dim_left_index) {
+          const Index head_size = first_multiple - bcast_dim_left_index;
+          params.input_block_sizes[params.bcast_dim] = head_size;
+          params.bcast_block_sizes[copy_bcast_dim] = head_size;
+          params.bcast_input_strides[copy_bcast_dim] =
+              params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] =
+              params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] *
+              params.input_dims[params.bcast_dim];
+
+          num_output_coeffs += BroadcastBlock(
+              params.input_block_sizes, params.input_block_strides,
+              params.bcast_block_sizes, params.bcast_block_strides,
+              params.bcast_input_strides, bcast_offset, 0, scratch,
+              materialized_output, materialized_input, materialized_input_size);
+        }
+        if (first_multiple < last_multiple) {
+          params.input_block_sizes[params.bcast_dim] = input_bcast_dim_size;
+          params.bcast_block_sizes[copy_bcast_dim] = input_bcast_dim_size;
+          params.bcast_input_strides[copy_bcast_dim] =
+              params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] =
+              params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] =
+              (last_multiple - first_multiple) / input_bcast_dim_size;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] *
+              params.input_dims[params.bcast_dim];
+          const Index offset = (first_multiple - bcast_dim_left_index) *
+                               m_outputStrides[params.bcast_dim];
+
+          num_output_coeffs += BroadcastBlock(
+              params.input_block_sizes, params.input_block_strides,
+              params.bcast_block_sizes, params.bcast_block_strides,
+              params.bcast_input_strides, bcast_offset, offset, scratch,
+              materialized_output, materialized_input, materialized_input_size);
+        }
+        if (last_multiple < bcast_dim_left_index + params.bcast_dim_size) {
+          const Index tail_size =
+              bcast_dim_left_index + params.bcast_dim_size - last_multiple;
+          params.input_block_sizes[params.bcast_dim] = tail_size;
+          params.bcast_block_sizes[copy_bcast_dim] = tail_size;
+          params.bcast_input_strides[copy_bcast_dim] =
+              params.input_block_strides[params.bcast_dim];
+          params.bcast_block_strides[copy_bcast_dim] =
+              params.output_strides[params.bcast_dim];
+          params.bcast_block_sizes[broadcast_bcast_dim] = 1;
+          params.bcast_input_strides[broadcast_bcast_dim] = 0;
+          params.bcast_block_strides[broadcast_bcast_dim] =
+              params.output_strides[params.bcast_dim] *
+              params.input_dims[params.bcast_dim];
+          const Index offset = (last_multiple - bcast_dim_left_index) *
+                               m_outputStrides[params.bcast_dim];
+
+          num_output_coeffs += BroadcastBlock(
+              params.input_block_sizes, params.input_block_strides,
+              params.bcast_block_sizes, params.bcast_block_strides,
+              params.bcast_input_strides, bcast_offset, offset, scratch,
+              materialized_output, materialized_input, materialized_input_size);
+        }
+      } else {
+        // b and c do not exist.
+        const int copy_bcast_dim =
+            IsColMajor ? 2 * params.inner_dim_count
+                       : 2 * NumDims - 2 * params.inner_dim_count - 1;
+        params.input_block_sizes[params.bcast_dim] = params.bcast_dim_size;
+        params.bcast_block_sizes[copy_bcast_dim] = params.bcast_dim_size;
+        params.bcast_input_strides[copy_bcast_dim] =
+            params.input_block_strides[params.bcast_dim];
+        params.bcast_block_strides[copy_bcast_dim] =
+            params.output_strides[params.bcast_dim];
+
+        num_output_coeffs += BroadcastBlock(
+            params.input_block_sizes, params.input_block_strides,
+            params.bcast_block_sizes, params.bcast_block_strides,
+            params.bcast_input_strides, bcast_offset, 0, scratch,
+            materialized_output, materialized_input, materialized_input_size);
+      }
+
+      return num_output_coeffs;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index BroadcastBlock(
+      const Dimensions& input_block_sizes,
+      const Dimensions& input_block_strides,
+      const BroadcastDimensions& bcast_block_sizes,
+      const BroadcastDimensions& bcast_block_strides,
+      const BroadcastDimensions& bcast_input_strides, Index bcast_offset,
+      Index offset, TensorBlockScratch& scratch,
+      ScalarNoConst* materialized_output, ScalarNoConst** materialized_input,
+      size_t* materialized_input_size) const {
+    // ---------------------------------------------------------------------- //
+    // Tensor block descriptor for reading block from the input.
+    const Index input_offset = bcast_offset + offset;
+    TensorBlockDesc input_desc(
+        IsColMajor ? indexColMajor(input_offset) : indexRowMajor(input_offset),
+        input_block_sizes);
+
+    ArgTensorBlock input_block = m_impl.block(input_desc, scratch);
+
+    // ---------------------------------------------------------------------- //
+    // Materialize input block into a temporary memory buffer only if it's not
+    // already available in the arg block.
+    const ScalarNoConst* input_buffer = NULL;
+
+    if (input_block.data() != NULL) {
+      // Input block already has raw data, there is no need to materialize it.
+      input_buffer = input_block.data();
+
+    } else {
+      // Otherwise we have to do block assignment into a temporary buffer.
+
+      // Maybe reuse previously allocated buffer, or allocate a new one with a
+      // scratch allocator.
+      const size_t input_total_size = input_block_sizes.TotalSize();
+      if (*materialized_input == NULL ||
+          *materialized_input_size < input_total_size) {
+        *materialized_input_size = input_total_size;
+        void* mem = scratch.allocate(*materialized_input_size * sizeof(Scalar));
+        *materialized_input = static_cast<ScalarNoConst*>(mem);
+      }
+
+      typedef internal::TensorBlockAssignment<
+          ScalarNoConst, NumDims, typename ArgTensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(input_block_sizes, input_block_strides,
+                                        *materialized_input),
+          input_block.expr());
+
+      input_buffer = *materialized_input;
+    }
+
+    // ---------------------------------------------------------------------- //
+    // Copy data from materialized input block to the materialized output, using
+    // given broadcast strides (strides with zeroes).
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, 2 * NumDims, Layout>
+        TensorBlockIO;
+
+    typename TensorBlockIO::Src src(bcast_input_strides, input_buffer);
+    typename TensorBlockIO::Dst dst(bcast_block_sizes, bcast_block_strides,
+                                      materialized_output + offset);
+
+    return TensorBlockIO::Copy(dst, src);
+  }
+
+protected:
+  const Device EIGEN_DEVICE_REF m_device;
+  const typename internal::remove_reference<Broadcast>::type m_broadcast;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
new file mode 100644
index 0000000..3764573
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
@@ -0,0 +1,518 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
+
+namespace Eigen {
+
+/** \class TensorKChippingReshaping
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
+  *
+  *
+  */
+
+namespace internal {
+template<DenseIndex DimId, typename XprType>
+struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions - 1;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<DenseIndex DimId, typename XprType>
+struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
+{
+  typedef const TensorChippingOp<DimId, XprType> EIGEN_DEVICE_REF type;
+};
+
+template<DenseIndex DimId, typename XprType>
+struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
+{
+  typedef TensorChippingOp<DimId, XprType> type;
+};
+
+template <DenseIndex DimId>
+struct DimensionId
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) {
+    EIGEN_UNUSED_VARIABLE(dim);
+    eigen_assert(dim == DimId);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
+    return DimId;
+  }
+};
+template <>
+struct DimensionId<Dynamic>
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) : actual_dim(dim) {
+    eigen_assert(dim >= 0);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
+    return actual_dim;
+  }
+ private:
+  const DenseIndex actual_dim;
+};
+
+
+}  // end namespace internal
+
+
+
+template<DenseIndex DimId, typename XprType>
+class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
+{
+  public:
+    typedef TensorBase<TensorChippingOp<DimId, XprType> > Base;
+    typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
+        : m_xpr(expr), m_offset(offset), m_dim(dim) {
+    }
+
+    EIGEN_DEVICE_FUNC
+    const Index offset() const { return m_offset; }
+    EIGEN_DEVICE_FUNC
+    const Index dim() const { return m_dim.actualDim(); }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorChippingOp)
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Index m_offset;
+    const internal::DimensionId<DimId> m_dim;
+};
+
+
+// Eval as rvalue
+template<DenseIndex DimId, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
+{
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims-1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets.
+    IsAligned         = false,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
+    // Chipping of outer-most dimension is a trivial operation, because we can
+    // read and write directly from the underlying tensor using single offset.
+    IsOuterChipping   = (static_cast<int>(Layout) == ColMajor && DimId == NumInputDims - 1) ||
+                        (static_cast<int>(Layout) == RowMajor && DimId == 0),
+    // Chipping inner-most dimension.
+    IsInnerChipping   = (static_cast<int>(Layout) == ColMajor && DimId == 0) ||
+                        (static_cast<int>(Layout) == RowMajor && DimId == NumInputDims - 1),
+    // Prefer block access if the underlying expression prefers it, otherwise
+    // only if chipping is not trivial.
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess ||
+                        !IsOuterChipping,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef internal::TensorBlockDescriptor<NumInputDims, Index>
+      ArgTensorBlockDesc;
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_dim(op.dim()), m_device(device)
+  {
+    EIGEN_STATIC_ASSERT((NumInputDims >= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(NumInputDims > m_dim.actualDim());
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    eigen_assert(op.offset() < input_dims[m_dim.actualDim()]);
+
+    int j = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (i != m_dim.actualDim()) {
+        m_dimensions[j] = input_dims[i];
+        ++j;
+      }
+    }
+
+    m_stride = 1;
+    m_inputStride = 1;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < m_dim.actualDim(); ++i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    } else {
+      for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
+        m_stride *= input_dims[i];
+        m_inputStride *= input_dims[i];
+      }
+    }
+    m_inputStride *= input_dims[m_dim.actualDim()];
+    m_inputOffset = m_stride * op.offset();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(srcCoeff(index));
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    if (isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      Index inputIndex = index * m_inputStride + m_inputOffset;
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = m_impl.coeff(inputIndex);
+        inputIndex += m_inputStride;
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    } else if (isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer division.
+      eigen_assert(m_stride > index);
+      return m_impl.template packet<LoadMode>(index + m_inputOffset);
+    } else {
+      const Index idx = index / m_stride;
+      const Index rem = index - idx * m_stride;
+      if (rem + PacketSize <= m_stride) {
+        Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
+        return m_impl.template packet<LoadMode>(inputIndex);
+      } else {
+        // Cross the stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+       EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = coeff(index);
+          ++index;
+        }
+        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+        return rslt;
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    double cost = 0;
+    if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
+         m_dim.actualDim() == 0) ||
+        (static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
+         m_dim.actualDim() == NumInputDims - 1)) {
+      cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
+    } else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
+                m_dim.actualDim() == NumInputDims - 1) ||
+               (static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
+                m_dim.actualDim() == 0)) {
+      cost += TensorOpCost::AddCost<Index>();
+    } else {
+      cost += 3 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() +
+              3 * TensorOpCost::AddCost<Index>();
+    }
+
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
+        m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool root_of_expr_ast = false) const {
+    const Index chip_dim = m_dim.actualDim();
+
+    DSizes<Index, NumInputDims> input_block_dims;
+    for (int i = 0; i < NumInputDims; ++i) {
+      input_block_dims[i]
+            = i < chip_dim ? desc.dimension(i)
+            : i > chip_dim ? desc.dimension(i - 1)
+            : 1;
+    }
+
+    ArgTensorBlockDesc arg_desc(srcCoeff(desc.offset()), input_block_dims);
+
+    // Try to reuse destination buffer for materializing argument block.
+    if (desc.HasDestinationBuffer()) {
+      DSizes<Index, NumInputDims> arg_destination_strides;
+      for (int i = 0; i < NumInputDims; ++i) {
+      arg_destination_strides[i]
+            = i < chip_dim ? desc.destination().strides()[i]
+            : i > chip_dim ? desc.destination().strides()[i - 1]
+            : 0; // for dimensions of size `1` stride should never be used.
+      }
+
+      arg_desc.template AddDestinationBuffer<Layout>(
+          desc.destination().template data<ScalarNoConst>(),
+          arg_destination_strides);
+    }
+
+    ArgTensorBlock arg_block = m_impl.block(arg_desc, scratch, root_of_expr_ast);
+    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
+
+    if (arg_block.data() != NULL) {
+      // Forward argument block buffer if possible.
+      return TensorBlock(arg_block.kind(), arg_block.data(),
+                           desc.dimensions());
+
+    } else {
+      // Assign argument block expression to a buffer.
+
+      // Prepare storage for the materialized chipping result.
+      const typename TensorBlock::Storage block_storage =
+          TensorBlock::prepareStorage(desc, scratch);
+
+      typedef internal::TensorBlockAssignment<
+          ScalarNoConst, NumInputDims, typename ArgTensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(
+              arg_desc.dimensions(),
+              internal::strides<Layout>(arg_desc.dimensions()),
+              block_storage.data()),
+          arg_block.expr());
+
+      return block_storage.AsTensorMaterializedBlock();
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
+    typename Storage::Type result = constCast(m_impl.data());
+    if (isOuterChipping() && result) {
+      return result + m_inputOffset;
+    } else {
+      return NULL;
+    }
+  }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
+  {
+    Index inputIndex;
+    if (isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(m_stride == 1);
+      inputIndex = index * m_inputStride + m_inputOffset;
+    } else if (isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer
+      // division.
+      eigen_assert(m_stride > index);
+      inputIndex = index + m_inputOffset;
+    } else {
+      const Index idx = index / m_stride;
+      inputIndex = idx * m_inputStride + m_inputOffset;
+      index -= idx * m_stride;
+      inputIndex += index;
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isInnerChipping() const {
+    return IsInnerChipping ||
+           (static_cast<int>(Layout) == ColMajor && m_dim.actualDim() == 0) ||
+           (static_cast<int>(Layout) == RowMajor && m_dim.actualDim() == NumInputDims - 1);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool isOuterChipping() const {
+    return IsOuterChipping ||
+           (static_cast<int>(Layout) == ColMajor && m_dim.actualDim() == NumInputDims-1) ||
+           (static_cast<int>(Layout) == RowMajor && m_dim.actualDim() == 0);
+  }
+
+  Dimensions m_dimensions;
+  Index m_stride;
+  Index m_inputOffset;
+  Index m_inputStride;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const internal::DimensionId<DimId> m_dim;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+
+// Eval as lvalue
+template<DenseIndex DimId, typename ArgType, typename Device>
+struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
+  : public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
+  typedef TensorChippingOp<DimId, ArgType> XprType;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims-1;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  enum {
+    IsAligned     = false,
+    PacketAccess  = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess   = TensorEvaluator<ArgType, Device>::RawAccess,
+    Layout        = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess     = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+    { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+
+    if (this->isInnerChipping()) {
+      // m_stride is equal to 1, so let's avoid the integer division.
+      eigen_assert(this->m_stride == 1);
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+      Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        this->m_impl.coeffRef(inputIndex) = values[i];
+        inputIndex += this->m_inputStride;
+      }
+    } else if (this->isOuterChipping()) {
+      // m_stride is always greater than index, so let's avoid the integer division.
+      eigen_assert(this->m_stride > index);
+      this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
+    } else {
+      const Index idx = index / this->m_stride;
+      const Index rem = index - idx * this->m_stride;
+      if (rem + PacketSize <= this->m_stride) {
+        const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
+        this->m_impl.template writePacket<StoreMode>(inputIndex, x);
+      } else {
+        // Cross stride boundary. Fallback to slow path.
+        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+        internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+        EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          this->coeffRef(index) = values[i];
+          ++index;
+        }
+      }
+    }
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlockDesc& desc, const TensorBlock& block) {
+    assert(this->m_impl.data() != NULL);
+
+    const Index chip_dim = this->m_dim.actualDim();
+
+    DSizes<Index, NumInputDims> input_block_dims;
+    for (int i = 0; i < NumInputDims; ++i) {
+      input_block_dims[i] = i < chip_dim ? desc.dimension(i)
+                          : i > chip_dim ? desc.dimension(i - 1)
+                          : 1;
+    }
+
+    typedef TensorReshapingOp<const DSizes<Index, NumInputDims>,
+                              const typename TensorBlock::XprType>
+        TensorBlockExpr;
+
+    typedef internal::TensorBlockAssignment<Scalar, NumInputDims,
+                                            TensorBlockExpr, Index>
+        TensorBlockAssign;
+
+    TensorBlockAssign::Run(
+        TensorBlockAssign::target(
+            input_block_dims,
+            internal::strides<Layout>(this->m_impl.dimensions()),
+            this->m_impl.data(), this->srcCoeff(desc.offset())),
+        block.expr().reshape(input_block_dims));
+  }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
new file mode 100644
index 0000000..5235a8e
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h
@@ -0,0 +1,377 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
+
+namespace Eigen {
+
+/** \class TensorConcatenationOp
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor concatenation class.
+  *
+  *
+  */
+namespace internal {
+template<typename Axis, typename LhsXprType, typename RhsXprType>
+struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename promote_storage_type<typename LhsXprType::Scalar,
+                                        typename RhsXprType::Scalar>::ret Scalar;
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
+                                      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const int NumDimensions = traits<LhsXprType>::NumDimensions;
+  static const int Layout = traits<LhsXprType>::Layout;
+  enum { Flags = 0 };
+  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                               typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>::type PointerType;
+};
+
+template<typename Axis, typename LhsXprType, typename RhsXprType>
+struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
+{
+  typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
+};
+
+template<typename Axis, typename LhsXprType, typename RhsXprType>
+struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
+{
+  typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+
+template<typename Axis, typename LhsXprType, typename RhsXprType>
+class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
+{
+  public:
+    typedef TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> Base;
+    typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
+    typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
+    typedef typename internal::traits<TensorConcatenationOp>::Index Index;
+    typedef typename internal::nested<TensorConcatenationOp>::type Nested;
+    typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
+                                                    typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
+        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename LhsXprType::Nested>::type&
+    lhsExpression() const { return m_lhs_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename RhsXprType::Nested>::type&
+    rhsExpression() const { return m_rhs_xpr; }
+
+    EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorConcatenationOp)
+  protected:
+    typename LhsXprType::Nested m_lhs_xpr;
+    typename RhsXprType::Nested m_rhs_xpr;
+    const Axis m_axis;
+};
+
+
+// Eval as rvalue
+template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
+{
+  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
+  static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  enum {
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<LeftArgType, Device>::PacketAccess &&
+                        TensorEvaluator<RightArgType, Device>::PacketAccess,
+    BlockAccess       = false,
+    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
+    RawAccess         = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    eigen_assert(0 <= m_axis && m_axis < NumDims);
+    const Dimensions& lhs_dims = m_leftImpl.dimensions();
+    const Dimensions& rhs_dims = m_rightImpl.dimensions();
+    {
+      int i = 0;
+      for (; i < m_axis; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
+      eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
+      eigen_assert(rhs_dims[i] > 0);
+      m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
+      for (++i; i < NumDims; ++i) {
+        eigen_assert(lhs_dims[i] > 0);
+        eigen_assert(lhs_dims[i] == rhs_dims[i]);
+        m_dimensions[i] = lhs_dims[i];
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_leftStrides[0] = 1;
+      m_rightStrides[0] = 1;
+      m_outputStrides[0] = 1;
+
+      for (int j = 1; j < NumDims; ++j) {
+        m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
+        m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
+        m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
+      }
+    } else {
+      m_leftStrides[NumDims - 1] = 1;
+      m_rightStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+
+      for (int j = NumDims - 2; j >= 0; --j) {
+        m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
+        m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
+        m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
+  {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup()
+  {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
+  // See CL/76180724 comments for more ideas.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    // Collect dimension-wise indices (subs).
+    array<Index, NumDims> subs;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        subs[i] = index / m_outputStrides[i];
+        index -= subs[i] * m_outputStrides[i];
+      }
+      subs[0] = index;
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        subs[i] = index / m_outputStrides[i];
+        index -= subs[i] * m_outputStrides[i];
+      }
+      subs[NumDims - 1] = index;
+    }
+
+    const Dimensions& left_dims = m_leftImpl.dimensions();
+    if (subs[m_axis] < left_dims[m_axis]) {
+      Index left_index;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        left_index = subs[0];
+        EIGEN_UNROLL_LOOP
+        for (int i = 1; i < NumDims; ++i) {
+          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
+        }
+      } else {
+        left_index = subs[NumDims - 1];
+        EIGEN_UNROLL_LOOP
+        for (int i = NumDims - 2; i >= 0; --i) {
+          left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
+        }
+      }
+      return m_leftImpl.coeff(left_index);
+    } else {
+      subs[m_axis] -= left_dims[m_axis];
+      const Dimensions& right_dims = m_rightImpl.dimensions();
+      Index right_index;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        right_index = subs[0];
+        EIGEN_UNROLL_LOOP
+        for (int i = 1; i < NumDims; ++i) {
+          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
+        }
+      } else {
+        right_index = subs[NumDims - 1];
+        EIGEN_UNROLL_LOOP
+        for (int i = NumDims - 2; i >= 0; --i) {
+          right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
+        }
+      }
+      return m_rightImpl.coeff(right_index);
+    }
+  }
+
+  // TODO(phli): Add a real vectorization.
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
+                                           2 * TensorOpCost::MulCost<Index>() +
+                                           TensorOpCost::DivCost<Index>() +
+                                           TensorOpCost::ModCost<Index>());
+    const double lhs_size = m_leftImpl.dimensions().TotalSize();
+    const double rhs_size = m_rightImpl.dimensions().TotalSize();
+    return (lhs_size / (lhs_size + rhs_size)) *
+               m_leftImpl.costPerCoeff(vectorized) +
+           (rhs_size / (lhs_size + rhs_size)) *
+               m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  #ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_leftImpl.bind(cgh);
+    m_rightImpl.bind(cgh);
+  }
+  #endif
+
+  protected:
+    Dimensions m_dimensions;
+    array<Index, NumDims> m_outputStrides;
+    array<Index, NumDims> m_leftStrides;
+    array<Index, NumDims> m_rightStrides;
+    TensorEvaluator<LeftArgType, Device> m_leftImpl;
+    TensorEvaluator<RightArgType, Device> m_rightImpl;
+    const Axis m_axis;
+};
+
+// Eval as lvalue
+template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
+  struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
+  : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
+  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
+  typedef typename Base::Dimensions Dimensions;
+  enum {
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<LeftArgType, Device>::PacketAccess &&
+                        TensorEvaluator<RightArgType, Device>::PacketAccess,
+    BlockAccess       = false,
+    PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
+    RawAccess         = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device)
+    : Base(op, device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    // Collect dimension-wise indices (subs).
+    array<Index, Base::NumDims> subs;
+    for (int i = Base::NumDims - 1; i > 0; --i) {
+      subs[i] = index / this->m_outputStrides[i];
+      index -= subs[i] * this->m_outputStrides[i];
+    }
+    subs[0] = index;
+
+    const Dimensions& left_dims = this->m_leftImpl.dimensions();
+    if (subs[this->m_axis] < left_dims[this->m_axis]) {
+      Index left_index = subs[0];
+      for (int i = 1; i < Base::NumDims; ++i) {
+        left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
+      }
+      return this->m_leftImpl.coeffRef(left_index);
+    } else {
+      subs[this->m_axis] -= left_dims[this->m_axis];
+      const Dimensions& right_dims = this->m_rightImpl.dimensions();
+      Index right_index = subs[0];
+      for (int i = 1; i < Base::NumDims; ++i) {
+        right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
+      }
+      return this->m_rightImpl.coeffRef(right_index);
+    }
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    for (int i = 0; i < packetSize; ++i) {
+      coeffRef(index+i) = values[i];
+    }
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
new file mode 100644
index 0000000..8b35f79
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContraction.h
@@ -0,0 +1,1023 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
+
+namespace Eigen {
+
+/** \class TensorContraction
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor contraction class.
+  *
+  *
+  */
+namespace internal {
+
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename gebp_traits<typename remove_const<typename LhsXprType::Scalar>::type,
+                               typename remove_const<typename RhsXprType::Scalar>::type>::ResScalar Scalar;
+
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
+                                      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+
+  // From NumDims below.
+  static const int NumDimensions = traits<LhsXprType>::NumDimensions + traits<RhsXprType>::NumDimensions - 2 * array_size<Dimensions>::value;
+  static const int Layout = traits<LhsXprType>::Layout;
+  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                               typename traits<LhsXprType>::PointerType,
+                               typename traits<RhsXprType>::PointerType>::type
+      PointerType;
+
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, Eigen::Dense>
+{
+  typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>& type;
+};
+
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >::type>
+{
+  typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> type;
+};
+
+template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename OutputKernelType_, typename Device_>
+struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_, OutputKernelType_>, Device_> > {
+  typedef Indices_ Indices;
+  typedef LeftArgType_ LeftArgType;
+  typedef RightArgType_ RightArgType;
+  typedef OutputKernelType_ OutputKernelType;
+  typedef Device_ Device;
+
+  // From NumDims below.
+  static const int NumDimensions = traits<LeftArgType_>::NumDimensions + traits<RightArgType_>::NumDimensions - 2 * array_size<Indices_>::value;
+};
+
+// Helper class to allocate and deallocate temporary memory for packed buffers.
+template <typename LhsScalar, typename RhsScalar>
+struct TensorContractionBlockMemAllocator {
+  typedef void* BlockMemHandle;
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static BlockMemHandle allocate(Device& d, const Index bm,
+                                                   const Index bk,
+                                                   const Index bn,
+                                                   LhsScalar** lhs_block,
+                                                   RhsScalar** rhs_block) {
+    eigen_assert(lhs_block);
+    eigen_assert(rhs_block);
+    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
+    char* block_mem = static_cast<char*>(d.allocate(sz.lhs_size + sz.rhs_size));
+    eigen_assert(block_mem);
+    *lhs_block = reinterpret_cast<LhsScalar*>(block_mem);
+    *rhs_block = reinterpret_cast<RhsScalar*>(block_mem + sz.lhs_size);
+    return block_mem;
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static BlockMemHandle allocateSlices(
+      Device& d, const Index bm, const Index bk, const Index bn,
+      const Index num_lhs, const Index num_rhs, const Index num_slices,
+      std::vector<LhsScalar*>* lhs_blocks,
+      std::vector<RhsScalar*>* rhs_blocks) {
+    eigen_assert(num_slices > 0);
+    eigen_assert(num_lhs >= 0 && num_rhs >= 0);
+    eigen_assert(num_lhs == 0 || lhs_blocks);
+    eigen_assert(num_rhs == 0 || rhs_blocks);
+    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);
+    void* block_mem = d.allocate(
+        (num_lhs * sz.lhs_size + num_rhs * sz.rhs_size) * num_slices);
+    eigen_assert(block_mem);
+    char* mem = static_cast<char*>(block_mem);
+
+    for (Index x = 0; x < num_slices; x++) {
+      if (num_lhs > 0) lhs_blocks[x].resize(num_lhs);
+      for (Index m = 0; m < num_lhs; m++) {
+        lhs_blocks[x][m] = reinterpret_cast<LhsScalar*>(mem);
+        mem += sz.lhs_size;
+      }
+      if (num_rhs > 0) rhs_blocks[x].resize(num_rhs);
+      for (Index n = 0; n < num_rhs; n++) {
+        rhs_blocks[x][n] = reinterpret_cast<RhsScalar*>(mem);
+        mem += sz.rhs_size;
+      }
+    }
+
+    return block_mem;
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
+    d.deallocate(handle);
+  }
+
+ private:
+  struct BlockSizes {
+    Index lhs_size;
+    Index rhs_size;
+  };
+  EIGEN_DEVICE_FUNC static BlockSizes ComputeLhsRhsBlockSizes(const Index bm,
+                                                              const Index bk,
+                                                              const Index bn) {
+    Index align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
+    BlockSizes sz;
+    sz.lhs_size = divup<Index>(bm * bk * sizeof(LhsScalar), align) * align;
+    sz.rhs_size = divup<Index>(bn * bk * sizeof(RhsScalar), align) * align;
+    return sz;
+  }
+};
+
+// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in
+// ColMajor storage order. This property is guaranteed by the
+// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack
+// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix
+// multiplication for these blocks. Default tensor contraction uses
+// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see
+// GeneralBlocPanelKernel.h for details).
+//
+// By specializing contraction kernels we can use other low level libraries to
+// perform matrix multiplication, and still rely on Eigen contraction evaluator.
+// This also includes full support in TensorContractionThreadPool, assuming that
+// underlying gemm do not use it's own threading.
+//
+// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of
+//   multiplication, lhs tensor and rhs tensor respectively.
+//
+// - StorageIndex - index type for the tensor expressions. In practice almost
+//   always is Eigen::Index.
+//
+// - OutputMapper provides access to the memory of the output matrix. In
+//   practice it's always column major blas_data_mapper (it must be of ResScalar
+//   type).
+//
+// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional
+//   view into the Lhs/Rhs tensor expressions. In practice it's
+//   TensorContractionInputMapper, or some specialization of it based on the
+//   type of tensor expression (e.g. TensorImagePatchOp has optimized input
+//   mapper).
+template <typename ResScalar, typename LhsScalar, typename RhsScalar,
+    typename StorageIndex, typename OutputMapper, typename LhsMapper,
+    typename RhsMapper>
+struct TensorContractionKernel {
+  // True if `invoke()` supports `beta` in `C <- alpha * A * B + beta * C`
+  // (otherwise beta should be always equal to 1).
+  enum { HasBeta = false };
+
+  EIGEN_DEVICE_FUNC
+  TensorContractionKernel(StorageIndex m_, StorageIndex k_, StorageIndex n_,
+                          StorageIndex bm_, StorageIndex bk_, StorageIndex bn_)
+      : m(m_), k(k_), n(n_), bm(bm_), bk(bk_), bn(bn_) {}
+
+  // Pack blocks of Lhs and Rhs into contiguous blocks in memory.
+  typedef LhsScalar* LhsBlock;
+  typedef RhsScalar* RhsBlock;
+
+  // Packed Lhs/Rhs block memory allocator.
+  typedef TensorContractionBlockMemAllocator<LhsScalar, RhsScalar>
+      BlockMemAllocator;
+  typedef typename BlockMemAllocator::BlockMemHandle BlockMemHandle;
+
+  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef internal::gemm_pack_lhs<
+      LhsScalar, StorageIndex, typename LhsMapper::SubMapper, Traits::mr,
+      Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor>
+      LhsPacker;
+
+  typedef internal::gemm_pack_rhs<RhsScalar, StorageIndex,
+                                  typename RhsMapper::SubMapper, Traits::nr,
+                                  ColMajor>
+      RhsPacker;
+
+  typedef internal::gebp_kernel<LhsScalar, RhsScalar, StorageIndex,
+                                OutputMapper, Traits::mr, Traits::nr,
+      /*ConjugateLhs*/ false, /*ConjugateRhs*/ false>
+      GebpKernel;
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC BlockMemHandle allocate(Device& d, LhsBlock* lhs_block,
+                                            RhsBlock* rhs_block) {
+    return BlockMemAllocator::allocate(d, bm, bk, bn, lhs_block, rhs_block);
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC BlockMemHandle allocateSlices(
+      Device& d, const StorageIndex num_lhs, const StorageIndex num_rhs,
+      const StorageIndex num_slices, std::vector<LhsBlock>* lhs_blocks,
+      std::vector<RhsBlock>* rhs_blocks) {
+    return BlockMemAllocator::allocateSlices(
+        d, bm, bk, bn, num_lhs, num_rhs, num_slices, lhs_blocks, rhs_blocks);
+  }
+
+  template <typename Device>
+  EIGEN_DEVICE_FUNC static void deallocate(Device& d, BlockMemHandle handle) {
+    BlockMemAllocator::deallocate(d, handle);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packLhs(
+      LhsBlock* lhsBlock, const typename LhsMapper::SubMapper& data_mapper,
+      const StorageIndex depth, const StorageIndex rows) {
+    LhsPacker()(*lhsBlock, data_mapper, depth, rows, /*stride*/ 0,
+        /*offset*/ 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void packRhs(
+      RhsBlock* rhsBlock, const typename RhsMapper::SubMapper& data_mapper,
+      const StorageIndex depth, const StorageIndex cols) {
+    RhsPacker()(*rhsBlock, data_mapper, depth, cols);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void invoke(
+      const OutputMapper& output_mapper, const LhsBlock& lhsBlock,
+      const RhsBlock& rhsBlock, const StorageIndex rows,
+      const StorageIndex depth, const StorageIndex cols,
+      const ResScalar alpha, const ResScalar beta) {
+    // Default GEBP kernel does not support beta.
+    eigen_assert(beta == ResScalar(1));
+    static const int kComputeStrideFromBlockDimensions = -1;
+    GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
+        /*strideA*/ kComputeStrideFromBlockDimensions,
+        /*strideB*/ kComputeStrideFromBlockDimensions,
+        /*offsetA*/ 0, /*offsetB*/ 0);
+  }
+
+ private:
+  // These are dimensions of the original Tensors, and selected block sizes. The
+  // actual block sizes passed to all function above might be smaller because of
+  // the partial blocks at the end.
+  const StorageIndex m;
+  const StorageIndex k;
+  const StorageIndex n;
+  const StorageIndex bm;
+  const StorageIndex bk;
+  const StorageIndex bn;
+};
+
+}  // end namespace internal
+
+// Tensor contraction params that should enable to get from output matrix
+// 2-dimensional coordinates to the output tensor dimensions.
+struct TensorContractionParams {
+  // TensorContraction evaluator assumes that both tensors are in ColMajor
+  // layout, if tensors are in RowMajor evaluator swap lhs with rhs.
+  bool swapped_arguments;
+};
+
+// Output kernel allows to fuse operations into the tensor contraction.
+//
+// Examples:
+//   1. Elementwise Relu transformation following Conv2D.
+//   2. AddBias to the Conv2D output channels dimension.
+//
+// The NoOpOutputKernel implements an output kernel that does absolutely nothing.
+struct NoOpOutputKernel {
+  /**
+   * Tensor contraction evaluator calls this kernel after finishing each block
+   * of output matrix. Output blocks belong to the 2-dimensional output tensor.
+   *
+   * TensorContractionParams contains contraction dimensions information
+   * required to map output 2-d space into the expected output tensor space
+   * (potentially higher dimensional).
+   *
+   * \param[in] output_mapper Access to output tensor memory
+   * \param[in] params   Tensor contraction parameters
+   * \param[in] i        Index of a first row available through output_mapper
+   * \param[in] j        Index of a first column available through output_mapper
+   * \param[in] num_rows Number of available rows
+   * \param[in] num_cols Number of available columns
+   */
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(
+      const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,
+      const TensorContractionParams& params, Index i,
+      Index j, Index num_rows, Index num_cols) const {
+    EIGEN_UNUSED_VARIABLE(output_mapper);
+    EIGEN_UNUSED_VARIABLE(params);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(num_rows);
+    EIGEN_UNUSED_VARIABLE(num_cols);
+  }
+};
+
+template<typename Indices, typename LhsXprType, typename RhsXprType, typename OutputKernelType = const NoOpOutputKernel>
+class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType, OutputKernelType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
+  typedef typename internal::gebp_traits<typename LhsXprType::CoeffReturnType,
+                                         typename RhsXprType::CoeffReturnType>::ResScalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorContractionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorContractionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(
+      const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims,
+      const OutputKernelType& output_kernel = OutputKernelType())
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims),
+        m_output_kernel(output_kernel) {}
+
+  EIGEN_DEVICE_FUNC
+  const Indices& indices() const { return m_indices; }
+
+  /** \returns the nested expressions */
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename LhsXprType::Nested>::type&
+  lhsExpression() const { return m_lhs_xpr; }
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename RhsXprType::Nested>::type&
+  rhsExpression() const { return m_rhs_xpr; }
+
+  EIGEN_DEVICE_FUNC
+  const OutputKernelType& outputKernel() const { return m_output_kernel; }
+
+  protected:
+    typename LhsXprType::Nested m_lhs_xpr;
+    typename RhsXprType::Nested m_rhs_xpr;
+    const Indices m_indices;
+    const OutputKernelType m_output_kernel;
+};
+
+
+template<typename Derived>
+struct TensorContractionEvaluatorBase : internal::no_assignment_operator
+{
+  typedef typename internal::traits<Derived>::Indices Indices;
+  typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
+  typedef typename internal::traits<Derived>::RightArgType RightArgType;
+  typedef typename internal::traits<Derived>::OutputKernelType OutputKernelType;
+  typedef typename internal::traits<Derived>::Device Device;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = true,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = false,
+    PreferBlockAccess = false,
+    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluatorType;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluatorType;
+
+  static const int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static const int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static const int ContractDims = internal::array_size<Indices>::value;
+  static const int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  EIGEN_STRONG_INLINE
+  TensorContractionEvaluatorBase(const XprType& op, const Device& device)
+      : m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
+                          op.lhsExpression(), op.rhsExpression()), device),
+        m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
+                           op.rhsExpression(), op.lhsExpression()), device),
+        m_device(device),
+        m_output_kernel(op.outputKernel()),
+        m_result(NULL) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
+         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+
+    DSizes<Index, LDims> eval_left_dims;
+    DSizes<Index, RDims> eval_right_dims;
+    array<IndexPair<Index>, ContractDims> eval_op_indices;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // For ColMajor, we keep using the existing dimensions
+      for (int i = 0; i < LDims; i++) {
+        eval_left_dims[i] = m_leftImpl.dimensions()[i];
+      }
+      for (int i = 0; i < RDims; i++) {
+        eval_right_dims[i] = m_rightImpl.dimensions()[i];
+      }
+      // We keep the pairs of contracting indices.
+      for (int i = 0; i < ContractDims; i++) {
+        eval_op_indices[i].first = op.indices()[i].first;
+        eval_op_indices[i].second = op.indices()[i].second;
+      }
+    } else {
+      // For RowMajor, we need to reverse the existing dimensions
+      for (int i = 0; i < LDims; i++) {
+        eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];
+      }
+      for (int i = 0; i < RDims; i++) {
+        eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];
+      }
+      // We need to flip all the pairs of contracting indices as well as
+      // reversing the dimensions.
+      for (int i = 0; i < ContractDims; i++) {
+        eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second;
+        eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first;
+      }
+    }
+
+    // Check for duplicate axes and make sure the first index in eval_op_indices
+    // is increasing. Using O(n^2) sorting is OK since ContractDims is small
+    for (int i = 0; i < ContractDims; i++) {
+      for (int j = i + 1; j < ContractDims; j++) {
+        eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first &&
+                     eval_op_indices[j].second != eval_op_indices[i].second &&
+                     "contraction axes should be unique");
+        if (eval_op_indices[j].first < eval_op_indices[i].first) {
+          numext::swap(eval_op_indices[j], eval_op_indices[i]);
+        }
+      }
+    }
+
+    array<Index, LDims> lhs_strides;
+    lhs_strides[0] = 1;
+    for (int i = 0; i < LDims-1; ++i) {
+      lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i];
+    }
+
+    array<Index, RDims> rhs_strides;
+    rhs_strides[0] = 1;
+    for (int i = 0; i < RDims-1; ++i) {
+      rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i];
+    }
+
+    if (m_i_strides.size() > 0) m_i_strides[0] = 1;
+    if (m_j_strides.size() > 0) m_j_strides[0] = 1;
+    if (m_k_strides.size() > 0) m_k_strides[0] = 1;
+
+    m_i_size = 1;
+    m_j_size = 1;
+    m_k_size = 1;
+
+    // To compute the dimension, we simply concatenate the non-contracting
+    // dimensions of the left and then the right tensor. Additionally, we also
+    // compute the strides corresponding to the left non-contracting
+    // dimensions and right non-contracting dimensions.
+    m_lhs_inner_dim_contiguous = true;
+    int dim_idx = 0;
+    Index nocontract_idx = 0;
+
+    for (int i = 0; i < LDims; i++) {
+      // find if we are contracting on index i of left tensor
+      bool contracting = false;
+      for (int j = 0; j < ContractDims; j++) {
+        if (eval_op_indices[j].first == i) {
+          contracting = true;
+          break;
+        }
+      }
+      if (!contracting) {
+        // add dimension size to output dimensions
+        m_dimensions[dim_idx] = eval_left_dims[i];
+        m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];
+        if (dim_idx != i) {
+          m_lhs_inner_dim_contiguous = false;
+        }
+        if (nocontract_idx+1 < internal::array_size<left_nocontract_t>::value) {
+          m_i_strides[nocontract_idx+1] =
+              m_i_strides[nocontract_idx] * eval_left_dims[i];
+        } else {
+          m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];
+        }
+        dim_idx++;
+        nocontract_idx++;
+      }
+    }
+
+    nocontract_idx = 0;
+    for (int i = 0; i < RDims; i++) {
+      bool contracting = false;
+      // find if we are contracting on index i of right tensor
+      for (int j = 0; j < ContractDims; j++) {
+        if (eval_op_indices[j].second == i) {
+          contracting = true;
+          break;
+        }
+      }
+      if (!contracting) {
+        m_dimensions[dim_idx] = eval_right_dims[i];
+        if (nocontract_idx+1 < internal::array_size<right_nocontract_t>::value) {
+          m_j_strides[nocontract_idx+1] =
+              m_j_strides[nocontract_idx] * eval_right_dims[i];
+        } else {
+          m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];
+        }
+        m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];
+        dim_idx++;
+        nocontract_idx++;
+      }
+    }
+
+    // Now compute the strides corresponding to the contracting dimensions. We
+    // assumed above that non-contracting axes are represented in the same order
+    // in the matrix as they are in the tensor. This is not the case for
+    // contracting axes. As the contracting axes must be of the same size in
+    // each tensor, we'll only look at the first tensor here.
+    m_rhs_inner_dim_contiguous = true;
+    m_rhs_inner_dim_reordered = false;
+    for (int i = 0; i < ContractDims; i++) {
+      Index left = eval_op_indices[i].first;
+      Index right = eval_op_indices[i].second;
+
+      Index size = eval_left_dims[left];
+      eigen_assert(size == eval_right_dims[right] &&
+                   "Contraction axes must be same size");
+
+      if (i+1 < static_cast<int>(internal::array_size<contract_t>::value)) {
+        m_k_strides[i+1] = m_k_strides[i] * size;
+      } else {
+        m_k_size = m_k_strides[i] * size;
+      }
+      m_left_contracting_strides[i] = lhs_strides[left];
+      m_right_contracting_strides[i] = rhs_strides[right];
+
+      if (i > 0 && right < eval_op_indices[i-1].second) {
+        m_rhs_inner_dim_reordered = true;
+      }
+      if (right != i) {
+        m_rhs_inner_dim_contiguous = false;
+      }
+    }
+
+    // If the layout is RowMajor, we need to reverse the m_dimensions
+    if (static_cast<int>(Layout) == static_cast<int>(RowMajor)) {
+      for (int i = 0, j = NumDims - 1; i < j; i++, j--) {
+        numext::swap(m_dimensions[i], m_dimensions[j]);
+      }
+    }
+
+    // A set of parameters that will allow output kernel to get from output
+    // tensor dimensions (i, j) into the original tensor dimensions.
+    // TODO(ezhulenev): Add parameters required to infer output tensor index for
+    // more complex contractions than 2x2 on internal dimension.
+    m_tensor_contraction_params.swapped_arguments = static_cast<int>(Layout) == RowMajor;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
+      m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [this, done, dest](bool) {
+        if (dest) {
+          evalToAsync(dest, [done]() { done(false); });
+        } else {
+          m_result = static_cast<EvaluatorPointerType>(
+              m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
+          evalToAsync(m_result, [done]() { done(true); });
+        }
+      });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+#ifndef TENSOR_CONTRACTION_DISPATCH
+#define TENSOR_CONTRACTION_DISPATCH(METHOD, ALIGNMENT, ARGS) \
+  if (this->m_lhs_inner_dim_contiguous) {                    \
+    if (this->m_rhs_inner_dim_contiguous) {                  \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<true, true, true, ALIGNMENT> ARGS;            \
+      } else {                                               \
+        METHOD<true, true, false, ALIGNMENT> ARGS;           \
+      }                                                      \
+    } else {                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<true, false, true, ALIGNMENT> ARGS;           \
+      } else {                                               \
+        METHOD<true, false, false, ALIGNMENT> ARGS;          \
+      }                                                      \
+    }                                                        \
+  } else {                                                   \
+    if (this->m_rhs_inner_dim_contiguous) {                  \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<false, true, true, ALIGNMENT> ARGS;           \
+      } else {                                               \
+        METHOD<false, true, false, ALIGNMENT> ARGS;          \
+      }                                                      \
+    } else {                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                 \
+        METHOD<false, false, true, ALIGNMENT> ARGS;          \
+      } else {                                               \
+        METHOD<false, false, false, ALIGNMENT> ARGS;         \
+      }                                                      \
+    }                                                        \
+  }
+#endif
+
+#ifndef TENSOR_CONTRACTION_ASYNC_DISPATCH
+#define TENSOR_CONTRACTION_ASYNC_DISPATCH(METHOD, DONE, ALIGNMENT, ARGS, FN) \
+  if (this->m_lhs_inner_dim_contiguous) {                                    \
+    if (this->m_rhs_inner_dim_contiguous) {                                  \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, true, true, true, ALIGNMENT> ARGS)->FN;            \
+      } else {                                                               \
+        (new METHOD<DONE, true, true, false, ALIGNMENT> ARGS)->FN;           \
+      }                                                                      \
+    } else {                                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, true, false, true, ALIGNMENT> ARGS)->FN;           \
+      } else {                                                               \
+        (new METHOD<DONE, true, false, false, ALIGNMENT> ARGS)->FN;          \
+      }                                                                      \
+    }                                                                        \
+  } else {                                                                   \
+    if (this->m_rhs_inner_dim_contiguous) {                                  \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, false, true, true, ALIGNMENT> ARGS)->FN;           \
+      } else {                                                               \
+        (new METHOD<DONE, false, true, false, ALIGNMENT> ARGS)->FN;          \
+      }                                                                      \
+    } else {                                                                 \
+      if (this->m_rhs_inner_dim_reordered) {                                 \
+        (new METHOD<DONE, false, false, true, ALIGNMENT> ARGS)->FN;          \
+      } else {                                                               \
+        (new METHOD<DONE, false, false, false, ALIGNMENT> ARGS)->FN;         \
+      }                                                                      \
+    }                                                                        \
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC void evalTo(Scalar* buffer) const {
+   static_cast<const Derived*>(this)->template evalProduct<Unaligned>(buffer);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalToCallback>
+  void evalToAsync(Scalar* buffer, EvalToCallback done) const {
+    static_cast<const Derived*>(this)
+        ->template evalProductAsync<EvalToCallback, Unaligned>(buffer,
+                                                               std::move(done));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
+            bool rhs_inner_dim_reordered, int Alignment>
+  void evalProductSequential(Scalar* buffer) const {
+    if (this->m_j_size == 1) {
+      this->template evalGemv<lhs_inner_dim_contiguous,
+                              rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
+                              Alignment>(buffer);
+    } else {
+      this->template evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
+                              rhs_inner_dim_reordered, Alignment>(buffer);
+    }
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  #if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+  #endif
+  void evalGemv(Scalar* buffer) const {
+    const Index rows = m_i_size;
+    const Index cols = m_k_size;
+
+    typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
+    typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
+    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+    const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
+    const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
+    const int lhs_alignment = LeftEvaluator::IsAligned ? Aligned : Unaligned;
+    const int rhs_alignment = RightEvaluator::IsAligned ? Aligned : Unaligned;
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
+                                                   LeftEvaluator, left_nocontract_t,
+                                                   contract_t, lhs_packet_size,
+                                                   lhs_inner_dim_contiguous,
+                                                   false, lhs_alignment> LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
+                                                   RightEvaluator, right_nocontract_t,
+                                                   contract_t, rhs_packet_size,
+                                                   rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, rhs_alignment> RhsMapper;
+
+    LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides,
+                  m_left_contracting_strides, m_k_strides);
+    RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides,
+                  m_right_contracting_strides, m_k_strides);
+
+    const Scalar alpha(1);
+    const Index resIncr(1);
+
+    // zero out the result buffer (which must be of size at least rows * sizeof(Scalar)
+    m_device.memset(buffer, 0, rows * sizeof(Scalar));
+
+    internal::general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false>::run(
+        rows, cols, lhs, rhs,
+        buffer, resIncr, alpha);
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+    m_output_kernel(OutputMapper(buffer, rows), m_tensor_contraction_params,
+                    static_cast<Index>(0), static_cast<Index>(0), rows,
+                    static_cast<Index>(1));
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  #if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+  #endif
+  void evalGemm(Scalar* buffer) const {
+    // columns in left side, rows in right side
+    const Index k = this->m_k_size;
+    this->template evalGemmPartial<lhs_inner_dim_contiguous,
+                                   rhs_inner_dim_contiguous,
+                                   rhs_inner_dim_reordered,
+                                   Alignment, true>(buffer, 0, k, 1);
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
+      bool rhs_inner_dim_reordered, int Alignment>
+  EIGEN_DEVICE_FUNC void evalGemmPartialWithoutOutputKernel(
+      Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
+    evalGemmPartial<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,
+                    rhs_inner_dim_reordered, Alignment,
+        /*use_output_kernel*/ false>(buffer, k_start, k_end,
+                                     num_threads);
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment, bool use_output_kernel>
+  EIGEN_DEVICE_FUNC void evalGemmPartial(Scalar* buffer, Index k_start, Index k_end, int num_threads) const {
+    eigen_assert(k_end >= k_start && k_start >= 0 && k_end <= this->m_k_size);
+    // columns in slice on left side, rows on right side
+    const Index k_slice = k_end - k_start;
+
+    // rows in left side
+    const Index m = this->m_i_size;
+
+    // columns in right side
+    const Index n = this->m_j_size;
+
+    // define data mappers for Lhs and Rhs
+    typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
+    typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
+
+    typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+    typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+    const Index lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
+    const Index rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
+
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
+                                                   LeftEvaluator, left_nocontract_t,
+                                                   contract_t, lhs_packet_size,
+                                                   lhs_inner_dim_contiguous,
+                                                   false, Unaligned> LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
+                                                   RightEvaluator, right_nocontract_t,
+                                                   contract_t, rhs_packet_size,
+                                                   rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, Unaligned> RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+    typedef internal::TensorContractionKernel<
+        Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
+        TensorContractionKernel;
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+    OutputMapper output(buffer, m);
+
+    // Sizes of the blocks to load in cache. See the Goto paper for details.
+    internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar,
+                                        Index, internal::ShardByCol>
+        blocking(k_slice, m, n, num_threads);
+    const Index kc = blocking.kc();
+    const Index mc = numext::mini(m, blocking.mc());
+    const Index nc = numext::mini(n, blocking.nc());
+
+    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
+    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
+
+    LhsBlock blockA;
+    RhsBlock blockB;
+
+    TensorContractionKernel kernel(m, k_slice, n, mc, kc, nc);
+
+    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
+    const BlockMemHandle packed_mem =
+        kernel.allocate(this->m_device, &blockA, &blockB);
+
+    // If a contraction kernel does not support beta, explicitly initialize
+    // output buffer with zeroes.
+    if (!TensorContractionKernel::HasBeta) {
+      this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
+    }
+
+    for(Index i2=0; i2<m; i2+=mc)
+    {
+      const Index actual_mc = numext::mini(i2+mc,m)-i2;
+      for (Index k2 = k_start; k2 < k_end; k2 += kc) {
+        // make sure we don't overshoot right edge of left matrix, then pack vertical panel
+        const Index actual_kc = numext::mini(k2 + kc, k_end) - k2;
+        kernel.packLhs(&blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        // If kernel supports beta, there is no need to initialize output
+        // buffer with zeroes.
+        const Scalar alpha = Scalar(1);
+        const Scalar beta = (TensorContractionKernel::HasBeta && k2 == k_start)
+                                ? Scalar(0)
+                                : Scalar(1);
+
+        // series of horizontal blocks
+        for (Index j2 = 0; j2 < n; j2 += nc) {
+          // make sure we don't overshoot right edge of right matrix, then pack block
+          const Index actual_nc = numext::mini(j2 + nc, n) - j2;
+          kernel.packRhs(&blockB, rhs.getSubMapper(k2, j2), actual_kc,
+                         actual_nc);
+
+          // call gebp (matrix kernel)
+          // The parameters here are copied from Eigen's GEMM implementation
+          const OutputMapper output_mapper = output.getSubMapper(i2, j2);
+          kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,
+                        actual_nc, alpha, beta);
+
+          // We are done with this [i2, j2] output block.
+          if (use_output_kernel && k2 + kc >= k_end) {
+            m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2,
+                            actual_mc, actual_nc);
+          }
+        }
+      }
+    }
+
+    kernel.deallocate(this->m_device, packed_mem);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+
+    if (m_result != NULL) {
+      m_device.deallocate(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_result[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_result; }
+
+protected:
+  Dimensions m_dimensions;
+
+  contract_t m_k_strides;
+  contract_t m_left_contracting_strides;
+  contract_t m_right_contracting_strides;
+
+  bool m_lhs_inner_dim_contiguous;
+  bool m_rhs_inner_dim_contiguous;
+  bool m_rhs_inner_dim_reordered;
+
+  left_nocontract_t m_i_strides;
+  right_nocontract_t m_j_strides;
+  left_nocontract_t m_left_nocontract_strides;
+  right_nocontract_t m_right_nocontract_strides;
+
+  Index m_i_size;
+  Index m_j_size;
+  Index m_k_size;
+
+  TensorContractionParams m_tensor_contraction_params;
+
+  TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
+  TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
+  const Device EIGEN_DEVICE_REF m_device;
+  OutputKernelType m_output_kernel;
+  EvaluatorPointerType m_result;
+};
+
+
+// evaluator for default device
+template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType, typename Device>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> :
+    public TensorContractionEvaluatorBase<
+      TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> > {
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  enum {
+    Layout = TensorEvaluator<LeftArgType, Device>::Layout
+  };
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
+
+  static const int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static const int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static const int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static const int NumDims = LDims + RDims - 2 * ContractDims;
+
+  // Could we use NumDimensions here?
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  TensorEvaluator(const XprType& op, const Device& device) :
+      Base(op, device) { }
+
+  template <int Alignment>
+  void evalProduct(Scalar* buffer) const {
+    TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential, Alignment, (buffer));
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
new file mode 100644
index 0000000..974feb0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionBlocking.h
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
+
+
+namespace Eigen {
+namespace internal {
+
+enum {
+  ShardByRow = 0,
+  ShardByCol = 1
+};
+
+
+// Default Blocking Strategy
+template<typename ResScalar, typename LhsScalar, typename RhsScalar, typename StorageIndex, int ShardingType = ShardByCol>
+class TensorContractionBlocking {
+ public:
+
+ /*
+   adding EIGEN_DEVICE_FUNC unconditionally to 'TensorContractionBlocking' constructor in `TensorContractionBlocking.h`
+     requires adding EIGEN_DEVICE_FUNC to `computeProductBlockingSizes` in `GeneralBlockPanelKernel.h`
+     which in turn, requires adding EIGEN_DEVICE_FUNC to `evaluateProductBlockingSizesHeuristic` in `GeneralBlockPanelKernel.h`
+     which in turn, requires adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in `GeneralBlockPanelKernel.h`
+     (else HIPCC will error out)
+
+   However adding EIGEN_DEVICE_FUNC to `manage_caching_sizes` in `GeneralBlockPanelKernel.h`
+   results in NVCC erroring out with the following error
+
+   ../Eigen/src/Core/products/GeneralBlockPanelKernel.h(57): error #2901:
+      dynamic initialization is not supported for function-scope static variables within a __device__/__global__ function
+ */
+
+  #if !defined(EIGEN_HIPCC)
+  EIGEN_DEVICE_FUNC
+  #endif
+ TensorContractionBlocking(StorageIndex k, StorageIndex m, StorageIndex n, StorageIndex num_threads = 1) :
+      kc_(k), mc_(m), nc_(n)
+  {
+    if (ShardingType == ShardByCol) {
+      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, mc_, nc_, num_threads);
+    }
+    else {
+      computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, nc_, mc_, num_threads);
+    }
+
+    const int rhs_packet_size = internal::packet_traits<RhsScalar>::size;
+    kc_ = (rhs_packet_size <= 8 || kc_ <= rhs_packet_size) ?
+      kc_ : (kc_ / rhs_packet_size) * rhs_packet_size;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex kc() const { return kc_; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex mc() const { return mc_; }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE StorageIndex nc() const { return nc_; }
+
+ private:
+  StorageIndex kc_;
+  StorageIndex mc_;
+  StorageIndex nc_;
+};
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
new file mode 100644
index 0000000..3f315fe
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionCuda.h
@@ -0,0 +1,6 @@
+
+#if defined(__clang__) || defined(__GNUC__)
+#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorContractionGpu.h file"
+#endif
+
+#include "TensorContractionGpu.h"
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
new file mode 100644
index 0000000..c818038
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionGpu.h
@@ -0,0 +1,1413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2015 Navdeep Jaitly <ndjaitly@google.com>
+// Copyright (C) 2014 Eric Martin <eric@ericmart.in>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
+
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+namespace Eigen {
+
+template<typename Scalar, typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper, bool needs_edge_check>
+__device__ EIGEN_STRONG_INLINE void
+EigenContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
+                               const OutputMapper output, Scalar* lhs_shmem, Scalar* rhs_shmem,
+                       const Index m_size, const Index n_size, const Index k_size) {
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  // declare and initialize 64 registers for output 8x8 block
+
+  // prefetch registers
+  Scalar lhs_pf0;
+  Scalar lhs_pf1;
+  Scalar lhs_pf2;
+  Scalar lhs_pf3;
+  Scalar lhs_pf4;
+  Scalar lhs_pf5;
+  Scalar lhs_pf6;
+  Scalar lhs_pf7;
+
+  Scalar rhs_pf0;
+  Scalar rhs_pf1;
+  Scalar rhs_pf2;
+  Scalar rhs_pf3;
+  Scalar rhs_pf4;
+  Scalar rhs_pf5;
+  Scalar rhs_pf6;
+  Scalar rhs_pf7;
+
+  // shared memory is formatted
+  // (contract idx in block, nocontract idx in block, block idx)
+  // where block idx is column major. This transposition limits the number of
+  // bank conflicts when reading the LHS. The core idea is that since the contracting
+  // index is shared by both sides, then the contracting index should be in threadIdx.x.
+
+  // On the LHS, we pad each row inside of each block with an extra element. This makes
+  // each block 8 rows of 9 elements, which is 72 elements. This gives no bank conflicts
+  // on writes and very few 2-way conflicts on reads. There is an 8x8 grid of these blocks.
+
+  // On the RHS we just add 8 padding elements to the end of each block. This gives no bank
+  // conflicts on writes and also none on reads.
+
+  // storage indices
+  const Index lhs_store_idx_base = threadIdx.y * 72 + threadIdx.x * 9 + threadIdx.z;
+  const Index rhs_store_idx_base = threadIdx.y * 72 + threadIdx.z * 8 + threadIdx.x;
+
+  const Index lhs_store_idx_0 = lhs_store_idx_base + 576 * 0;
+  const Index lhs_store_idx_1 = lhs_store_idx_base + 576 * 1;
+  const Index lhs_store_idx_2 = lhs_store_idx_base + 576 * 2;
+  const Index lhs_store_idx_3 = lhs_store_idx_base + 576 * 3;
+  const Index lhs_store_idx_4 = lhs_store_idx_base + 576 * 4;
+  const Index lhs_store_idx_5 = lhs_store_idx_base + 576 * 5;
+  const Index lhs_store_idx_6 = lhs_store_idx_base + 576 * 6;
+  const Index lhs_store_idx_7 = lhs_store_idx_base + 576 * 7;
+
+  const Index rhs_store_idx_0 = rhs_store_idx_base + 576 * 0;
+  const Index rhs_store_idx_1 = rhs_store_idx_base + 576 * 1;
+  const Index rhs_store_idx_2 = rhs_store_idx_base + 576 * 2;
+  const Index rhs_store_idx_3 = rhs_store_idx_base + 576 * 3;
+  const Index rhs_store_idx_4 = rhs_store_idx_base + 576 * 4;
+  const Index rhs_store_idx_5 = rhs_store_idx_base + 576 * 5;
+  const Index rhs_store_idx_6 = rhs_store_idx_base + 576 * 6;
+  const Index rhs_store_idx_7 = rhs_store_idx_base + 576 * 7;
+
+  // in the loading code, the following variables are important:
+  // threadIdx.x: the vertical position in an 8x8 block
+  // threadIdx.y: the vertical index of the 8x8 block in the grid
+  // threadIdx.z: the horizontal position in an 8x8 block
+  // k: the horizontal index of the 8x8 block in the grid
+  //
+  // The k parameter is implicit (it was the loop counter for a loop that went
+  // from 0 to <8, but now that loop is unrolled in the below code.
+
+  const Index load_idx_vert = threadIdx.x + 8 * threadIdx.y;
+  const Index lhs_vert = base_m + load_idx_vert;
+
+#define prefetchIntoRegisters(base_k)                           \
+  {                                                             \
+    lhs_pf0 = conv(0);                                          \
+    lhs_pf1 = conv(0);                                          \
+    lhs_pf2 = conv(0);                                          \
+    lhs_pf3 = conv(0);                                          \
+    lhs_pf4 = conv(0);                                          \
+    lhs_pf5 = conv(0);                                          \
+    lhs_pf6 = conv(0);                                          \
+    lhs_pf7 = conv(0);                                          \
+                                                                \
+    rhs_pf0 = conv(0);                                          \
+    rhs_pf1 = conv(0);                                          \
+    rhs_pf2 = conv(0);                                          \
+    rhs_pf3 = conv(0);                                          \
+    rhs_pf4 = conv(0);                                          \
+    rhs_pf5 = conv(0);                                          \
+    rhs_pf6 = conv(0);                                          \
+    rhs_pf7 = conv(0);                                          \
+                                                                \
+    if (!needs_edge_check || lhs_vert < m_size) {               \
+      const Index lhs_horiz_0 = base_k + threadIdx.z + 0 * 8;   \
+      const Index lhs_horiz_1 = base_k + threadIdx.z + 1 * 8;   \
+      const Index lhs_horiz_2 = base_k + threadIdx.z + 2 * 8;   \
+      const Index lhs_horiz_3 = base_k + threadIdx.z + 3 * 8;   \
+      const Index lhs_horiz_4 = base_k + threadIdx.z + 4 * 8;   \
+      const Index lhs_horiz_5 = base_k + threadIdx.z + 5 * 8;   \
+      const Index lhs_horiz_6 = base_k + threadIdx.z + 6 * 8;   \
+      const Index lhs_horiz_7 = base_k + threadIdx.z + 7 * 8;   \
+                                                                \
+      if (!needs_edge_check || lhs_horiz_7 < k_size) {          \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
+        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                   \
+        lhs_pf7 = lhs(lhs_vert, lhs_horiz_7);                   \
+      } else if (lhs_horiz_6 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
+        lhs_pf6 = lhs(lhs_vert, lhs_horiz_6);                   \
+      } else if (lhs_horiz_5 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
+        lhs_pf5 = lhs(lhs_vert, lhs_horiz_5);                   \
+      } else if (lhs_horiz_4 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
+        lhs_pf4 = lhs(lhs_vert, lhs_horiz_4);                   \
+      } else if (lhs_horiz_3 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+        lhs_pf3 = lhs(lhs_vert, lhs_horiz_3);                   \
+      } else if (lhs_horiz_2 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+        lhs_pf2 = lhs(lhs_vert, lhs_horiz_2);                   \
+      } else if (lhs_horiz_1 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+        lhs_pf1 = lhs(lhs_vert, lhs_horiz_1);                   \
+      } else if (lhs_horiz_0 < k_size) {                        \
+        lhs_pf0 = lhs(lhs_vert, lhs_horiz_0);                   \
+      }                                                         \
+    }                                                           \
+                                                                \
+    const Index rhs_vert = base_k + load_idx_vert;              \
+    if (!needs_edge_check || rhs_vert < k_size) {               \
+      const Index rhs_horiz_0 = base_n + threadIdx.z + 0 * 8;   \
+      const Index rhs_horiz_1 = base_n + threadIdx.z + 1 * 8;   \
+      const Index rhs_horiz_2 = base_n + threadIdx.z + 2 * 8;   \
+      const Index rhs_horiz_3 = base_n + threadIdx.z + 3 * 8;   \
+      const Index rhs_horiz_4 = base_n + threadIdx.z + 4 * 8;   \
+      const Index rhs_horiz_5 = base_n + threadIdx.z + 5 * 8;   \
+      const Index rhs_horiz_6 = base_n + threadIdx.z + 6 * 8;   \
+      const Index rhs_horiz_7 = base_n + threadIdx.z + 7 * 8;   \
+                                                                \
+      if (rhs_horiz_7 < n_size) {                               \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
+        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                   \
+        rhs_pf7 = rhs(rhs_vert, rhs_horiz_7);                   \
+      } else if (rhs_horiz_6 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
+        rhs_pf6 = rhs(rhs_vert, rhs_horiz_6);                   \
+      } else if (rhs_horiz_5 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
+        rhs_pf5 = rhs(rhs_vert, rhs_horiz_5);                   \
+      } else if (rhs_horiz_4 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
+        rhs_pf4 = rhs(rhs_vert, rhs_horiz_4);                   \
+      } else if (rhs_horiz_3 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+        rhs_pf3 = rhs(rhs_vert, rhs_horiz_3);                   \
+      } else if (rhs_horiz_2 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+        rhs_pf2 = rhs(rhs_vert, rhs_horiz_2);                   \
+      } else if (rhs_horiz_1 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+        rhs_pf1 = rhs(rhs_vert, rhs_horiz_1);                   \
+      } else if (rhs_horiz_0 < n_size) {                        \
+        rhs_pf0 = rhs(rhs_vert, rhs_horiz_0);                   \
+      }                                                         \
+    }                                                           \
+  }                                                             \
+
+#define writeRegToShmem(_)                      \
+  lhs_shmem[lhs_store_idx_0] = lhs_pf0;         \
+  rhs_shmem[rhs_store_idx_0] = rhs_pf0;         \
+                                                \
+  lhs_shmem[lhs_store_idx_1] = lhs_pf1;         \
+  rhs_shmem[rhs_store_idx_1] = rhs_pf1;         \
+                                                \
+  lhs_shmem[lhs_store_idx_2] = lhs_pf2;         \
+  rhs_shmem[rhs_store_idx_2] = rhs_pf2;         \
+                                                \
+  lhs_shmem[lhs_store_idx_3] = lhs_pf3;         \
+  rhs_shmem[rhs_store_idx_3] = rhs_pf3;         \
+                                                \
+  lhs_shmem[lhs_store_idx_4] = lhs_pf4;         \
+  rhs_shmem[rhs_store_idx_4] = rhs_pf4;         \
+                                                \
+  lhs_shmem[lhs_store_idx_5] = lhs_pf5;         \
+  rhs_shmem[rhs_store_idx_5] = rhs_pf5;         \
+                                                \
+  lhs_shmem[lhs_store_idx_6] = lhs_pf6;         \
+  rhs_shmem[rhs_store_idx_6] = rhs_pf6;         \
+                                                \
+  lhs_shmem[lhs_store_idx_7] = lhs_pf7;         \
+  rhs_shmem[rhs_store_idx_7] = rhs_pf7;         \
+
+  // declare and initialize result array
+#define res(i, j) _res_##i##j
+#define initResultRow(i)                        \
+  Scalar res(i, 0) = conv(0);                   \
+  Scalar res(i, 1) = conv(0);                   \
+  Scalar res(i, 2) = conv(0);                   \
+  Scalar res(i, 3) = conv(0);                   \
+  Scalar res(i, 4) = conv(0);                   \
+  Scalar res(i, 5) = conv(0);                   \
+  Scalar res(i, 6) = conv(0);                   \
+  Scalar res(i, 7) = conv(0);                   \
+
+  internal::scalar_cast_op<int, Scalar> conv;
+  initResultRow(0);
+  initResultRow(1);
+  initResultRow(2);
+  initResultRow(3);
+  initResultRow(4);
+  initResultRow(5);
+  initResultRow(6);
+  initResultRow(7);
+#undef initResultRow
+
+  for (Index base_k = 0; base_k < k_size; base_k += 64) {
+    // wait for previous iteration to finish with shmem. Despite common sense,
+    // the code is a bit faster with this here then at bottom of loop
+    __syncthreads();
+
+    prefetchIntoRegisters(base_k);
+    writeRegToShmem();
+
+    #undef prefetchIntoRegisters
+    #undef writeRegToShmem
+
+    // wait for shared mem packing to be done before starting computation
+    __syncthreads();
+
+    // compute 8x8 matrix product by outer product. This involves packing one column
+    // of LHS and one row of RHS into registers (takes 16 registers).
+
+#define lcol(i) _lcol##i
+    Scalar lcol(0);
+    Scalar lcol(1);
+    Scalar lcol(2);
+    Scalar lcol(3);
+    Scalar lcol(4);
+    Scalar lcol(5);
+    Scalar lcol(6);
+    Scalar lcol(7);
+
+#define rrow(j) _rrow##j
+    Scalar rrow(0);
+    Scalar rrow(1);
+    Scalar rrow(2);
+    Scalar rrow(3);
+    Scalar rrow(4);
+    Scalar rrow(5);
+    Scalar rrow(6);
+    Scalar rrow(7);
+
+    // Now x corresponds to k, y to m, and z to n
+    const Scalar* lhs_block = &lhs_shmem[threadIdx.x + 9 * threadIdx.y];
+    const Scalar* rhs_block = &rhs_shmem[threadIdx.x + 8 * threadIdx.z];
+
+#define lhs_element(i, j) lhs_block[72 * ((i) + 8 * (j))]
+#define rhs_element(i, j) rhs_block[72 * ((i) + 8 * (j))]
+
+#define loadData(i, j)                          \
+    lcol(0) = lhs_element(0, j);               \
+    rrow(0) = rhs_element(i, 0);               \
+    lcol(1) = lhs_element(1, j);               \
+    rrow(1) = rhs_element(i, 1);               \
+    lcol(2) = lhs_element(2, j);               \
+    rrow(2) = rhs_element(i, 2);               \
+    lcol(3) = lhs_element(3, j);               \
+    rrow(3) = rhs_element(i, 3);               \
+    lcol(4) = lhs_element(4, j);               \
+    rrow(4) = rhs_element(i, 4);               \
+    lcol(5) = lhs_element(5, j);               \
+    rrow(5) = rhs_element(i, 5);               \
+    lcol(6) = lhs_element(6, j);               \
+    rrow(6) = rhs_element(i, 6);               \
+    lcol(7) = lhs_element(7, j);               \
+    rrow(7) = rhs_element(i, 7);               \
+
+#define computeCol(j)                           \
+    res(0, j) += lcol(0) * rrow(j);             \
+    res(1, j) += lcol(1) * rrow(j);             \
+    res(2, j) += lcol(2) * rrow(j);             \
+    res(3, j) += lcol(3) * rrow(j);             \
+    res(4, j) += lcol(4) * rrow(j);             \
+    res(5, j) += lcol(5) * rrow(j);             \
+    res(6, j) += lcol(6) * rrow(j);             \
+    res(7, j) += lcol(7) * rrow(j);             \
+
+#define computePass(i)                          \
+    loadData(i, i);                             \
+                                                \
+    computeCol(0);                              \
+    computeCol(1);                              \
+    computeCol(2);                              \
+    computeCol(3);                              \
+    computeCol(4);                              \
+    computeCol(5);                              \
+    computeCol(6);                              \
+    computeCol(7);                              \
+
+    computePass(0);
+    computePass(1);
+    computePass(2);
+    computePass(3);
+    computePass(4);
+    computePass(5);
+    computePass(6);
+    computePass(7);
+
+#undef lcol
+#undef rrow
+#undef lhs_element
+#undef rhs_element
+#undef loadData
+#undef computeCol
+#undef computePass
+  } // end loop over k
+
+  // we've now iterated over all of the large (ie width 64) k blocks and
+  // accumulated results in registers. At this point thread (x, y, z) contains
+  // the sum across all big k blocks of the product of little k block of index (x, y)
+  // with block of index (y, z). To compute the final output, we need to reduce
+  // the 8 threads over y by summation.
+#if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
+#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor(res(i, j), mask)
+#else
+#define shuffleInc(i, j, mask) res(i, j) += __shfl_xor_sync(0xFFFFFFFF, res(i, j), mask)
+#endif
+
+#define reduceRow(i, mask)                      \
+  shuffleInc(i, 0, mask);                       \
+  shuffleInc(i, 1, mask);                       \
+  shuffleInc(i, 2, mask);                       \
+  shuffleInc(i, 3, mask);                       \
+  shuffleInc(i, 4, mask);                       \
+  shuffleInc(i, 5, mask);                       \
+  shuffleInc(i, 6, mask);                       \
+  shuffleInc(i, 7, mask);                       \
+
+#define reduceMatrix(mask)                      \
+  reduceRow(0, mask);                           \
+  reduceRow(1, mask);                           \
+  reduceRow(2, mask);                           \
+  reduceRow(3, mask);                           \
+  reduceRow(4, mask);                           \
+  reduceRow(5, mask);                           \
+  reduceRow(6, mask);                           \
+  reduceRow(7, mask);                           \
+
+  // actually perform the reduction, now each thread of index (_, y, z)
+  // contains the correct values in its registers that belong in the output
+  // block
+  reduceMatrix(1);
+  reduceMatrix(2);
+  reduceMatrix(4);
+
+#undef shuffleInc
+#undef reduceRow
+#undef reduceMatrix
+
+  // now we need to copy the 64 values into main memory. We can't split work
+  // among threads because all variables are in registers. There's 2 ways
+  // to do this:
+  // (1) have 1 thread do 64 writes from registers into global memory
+  // (2) have 1 thread do 64 writes into shared memory, and then 8 threads
+  //     each do 8 writes into global memory. We can just overwrite the shared
+  //     memory from the problem we just solved.
+  // (2) is slightly faster than (1) due to less branching and more ILP
+
+  // TODO: won't yield much gain, but could just use currently unused shared mem
+  //       and then we won't have to sync
+  // wait for shared mem to be out of use
+  __syncthreads();
+
+#define writeResultShmem(i, j)                                          \
+  lhs_shmem[i + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j] = res(i, j); \
+
+#define writeRow(i)                             \
+  writeResultShmem(i, 0);                       \
+  writeResultShmem(i, 1);                       \
+  writeResultShmem(i, 2);                       \
+  writeResultShmem(i, 3);                       \
+  writeResultShmem(i, 4);                       \
+  writeResultShmem(i, 5);                       \
+  writeResultShmem(i, 6);                       \
+  writeResultShmem(i, 7);                       \
+
+  if (threadIdx.x == 0) {
+    writeRow(0);
+    writeRow(1);
+    writeRow(2);
+    writeRow(3);
+    writeRow(4);
+    writeRow(5);
+    writeRow(6);
+    writeRow(7);
+  }
+#undef writeResultShmem
+#undef writeRow
+
+  const int max_i_write = numext::mini((int)((m_size - base_m - threadIdx.y + 7) / 8), 8);
+  const int max_j_write = numext::mini((int)((n_size - base_n - threadIdx.z + 7) / 8), 8);
+
+  if (threadIdx.x < max_i_write) {
+    if (max_j_write == 8) {
+      // TODO: can i trade bank conflicts for coalesced writes?
+      Scalar val0 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 0];
+      Scalar val1 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 1];
+      Scalar val2 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 2];
+      Scalar val3 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 3];
+      Scalar val4 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 4];
+      Scalar val5 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 5];
+      Scalar val6 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 6];
+      Scalar val7 = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * 7];
+
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 0) = val0;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 1) = val1;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 2) = val2;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 3) = val3;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 4) = val4;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 5) = val5;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 6) = val6;
+      output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * 7) = val7;
+    } else {
+#pragma unroll 7
+      for (int j = 0; j < max_j_write; j++) {
+        Scalar val = lhs_shmem[threadIdx.x + 8 * threadIdx.y + 64 * threadIdx.z + 512 * j];
+        output(base_m + threadIdx.y + 8 * threadIdx.x, base_n + threadIdx.z + 8 * j) = val;
+      }
+    }
+  }
+#undef res
+}
+
+
+template<typename Scalar, typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(512, 1)
+#else
+__launch_bounds__(512)
+#endif
+EigenContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
+                       const OutputMapper output,
+                       const Index m_size, const Index n_size, const Index k_size) {
+  __shared__ Scalar lhs_shmem[72 * 64];
+  __shared__ Scalar rhs_shmem[72 * 64];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  if (base_m + 63 < m_size && base_n + 63 < n_size) {
+    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
+  } else {
+    EigenContractionKernelInternal<Scalar, Index, LhsMapper, RhsMapper, OutputMapper, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size);
+  }
+}
+
+
+template<typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
+         bool CHECK_RHS_BOUNDARY>
+__device__ __forceinline__ void
+EigenFloatContractionKernelInternal16x16(const LhsMapper lhs, const RhsMapper rhs,
+                       const OutputMapper output, float2 lhs_shmem2[][16],
+                       float2 rhs_shmem2[][8], const Index m_size,
+                       const Index n_size, const Index k_size,
+                       const Index base_m, const Index base_n) {
+
+  // prefetch registers
+  float4 lhs_pf0, rhs_pf0;
+
+  float4 results[4];
+  for (int i=0; i < 4; i++) {
+    results[i].x = results[i].y = results[i].z = results[i].w = 0;
+  }
+
+#define prefetch_lhs(reg, row, col)                            \
+    if (!CHECK_LHS_BOUNDARY) {                                 \
+      if (col < k_size) {                                      \
+        reg =lhs.template loadPacket<float4,Unaligned>(row, col);     \
+      }                                                        \
+    } else {                                                   \
+      if (col < k_size) {                                      \
+        if (row + 3 < m_size) {                                \
+          reg =lhs.template loadPacket<float4,Unaligned>(row, col);   \
+        } else if (row + 2 < m_size) {                         \
+          reg.x =lhs(row + 0, col);                            \
+          reg.y =lhs(row + 1, col);                            \
+          reg.z =lhs(row + 2, col);                            \
+        } else if (row + 1 < m_size) {                         \
+          reg.x =lhs(row + 0, col);                            \
+          reg.y =lhs(row + 1, col);                            \
+        } else if (row  < m_size) {                            \
+          reg.x =lhs(row + 0, col);                            \
+        }                                                      \
+      }                                                        \
+    }							       \
+
+  Index lhs_vert = base_m+threadIdx.x*4;
+
+  for (Index k = 0; k < k_size; k += 16) {
+
+    lhs_pf0 = internal::pset1<float4>(0);
+    rhs_pf0 = internal::pset1<float4>(0);
+
+    Index lhs_horiz = threadIdx.y+k;
+    prefetch_lhs(lhs_pf0, lhs_vert, lhs_horiz)
+
+    Index rhs_vert = k+(threadIdx.x%4)*4;
+    Index rhs_horiz0 = (threadIdx.x>>2)+threadIdx.y*4+base_n;
+
+    if (!CHECK_RHS_BOUNDARY) {
+      if ((rhs_vert + 3) < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
+      } else if (rhs_vert + 2 < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+      } else if (rhs_vert + 1 < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+      } else if (rhs_vert  < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+      }
+    } else {
+      if (rhs_horiz0 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
+        } else if ((rhs_vert + 2) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        } else if ((rhs_vert + 1) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        } else if (rhs_vert  < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        }
+      }
+    }
+    float x1, x2 ;
+    // the following can be a bitwise operation..... some day.
+    if((threadIdx.x%8) < 4) {
+      x1 = rhs_pf0.y;
+      x2 = rhs_pf0.w;
+    } else {
+      x1 = rhs_pf0.x;
+      x2 = rhs_pf0.z;
+    }
+    #if defined(EIGEN_HIPCC) || (defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000)
+    x1 = __shfl_xor(x1, 4);
+    x2 = __shfl_xor(x2, 4);
+    #else
+    x1 = __shfl_xor_sync(0xFFFFFFFF, x1, 4);
+    x2 = __shfl_xor_sync(0xFFFFFFFF, x2, 4);
+    #endif
+    if((threadIdx.x%8) < 4) {
+      rhs_pf0.y = x1;
+      rhs_pf0.w = x2;
+    } else {
+      rhs_pf0.x = x1;
+      rhs_pf0.z = x2;
+    }
+
+    // We have 64 features.
+    // Row 0 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 0, 1.
+    // Row 1 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 2, 3.
+    // ...
+    // Row 31 -> times (0, 4, 8, 12, 1, 5, 9, 13) for features 62, 63
+    // Row 32 -> times (2, 6, 10, 14, 3, 7, 11, 15) for features 0, 1
+    // ...
+    rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2][threadIdx.x%8] = make_float2(rhs_pf0.x, rhs_pf0.y);
+    rhs_shmem2[(threadIdx.x>>3)+ threadIdx.y*2+32][threadIdx.x%8] = make_float2(rhs_pf0.z, rhs_pf0.w);
+
+    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // ...
+    // Row 15 (time 15) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61)
+    // Row 16 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63)
+    // ...
+
+    lhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(lhs_pf0.x, lhs_pf0.y);
+    lhs_shmem2[threadIdx.y+16][threadIdx.x] = make_float2(lhs_pf0.z, lhs_pf0.w);
+
+
+#define add_vals(fl1, fl2, fr1, fr2)\
+    results[0].x += fl1.x * fr1.x;\
+    results[0].y += fl1.y * fr1.x;\
+    results[0].z += fl2.x * fr1.x;\
+    results[0].w += fl2.y * fr1.x;\
+\
+    results[1].x += fl1.x * fr1.y;\
+    results[1].y += fl1.y * fr1.y;\
+    results[1].z += fl2.x * fr1.y;\
+    results[1].w += fl2.y * fr1.y;\
+\
+    results[2].x += fl1.x * fr2.x;\
+    results[2].y += fl1.y * fr2.x;\
+    results[2].z += fl2.x * fr2.x;\
+    results[2].w += fl2.y * fr2.x;\
+\
+    results[3].x += fl1.x * fr2.y;\
+    results[3].y += fl1.y * fr2.y;\
+    results[3].z += fl2.x * fr2.y;\
+    results[3].w += fl2.y * fr2.y;\
+
+    __syncthreads();
+
+    // Do the multiplies.
+    #pragma unroll
+    for (int koff = 0; koff < 16; koff ++) {
+      // 32 x threads.
+      float2 fl1 = lhs_shmem2[koff][threadIdx.x];
+      float2 fl2 = lhs_shmem2[koff + 16][threadIdx.x];
+
+      int start_feature = threadIdx.y * 4;
+      float2 fr1 = rhs_shmem2[(start_feature>>1) + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
+      float2 fr2 = rhs_shmem2[(start_feature>>1) + 1 + 32*((koff%4)/2)][koff/4 + (koff%2)*4];
+
+      add_vals(fl1, fl2, fr1, fr2)
+    }
+    __syncthreads();
+  }
+
+#undef prefetch_lhs
+#undef add_vals
+
+  Index horiz_base = threadIdx.y*4+base_n;
+  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
+    for (int i = 0; i < 4; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }
+  } else if (!CHECK_RHS_BOUNDARY) {
+    // CHECK LHS
+    if (lhs_vert + 3 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    } else if (lhs_vert + 2 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      }
+    } else if (lhs_vert + 1 < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      }
+    } else if (lhs_vert  < m_size) {
+      for (int i = 0; i < 4; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+      }
+    }
+  } else if (!CHECK_LHS_BOUNDARY) {
+    // CHECK RHS
+    /*
+    int ncols_rem = fminf(n_size- horiz_base, 4);
+    for (int i = 0; i < ncols_rem; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }*/
+    for (int i = 0; i < 4; i++) {
+      if (horiz_base+i < n_size) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+       }
+    }
+  } else {
+    // CHECK both boundaries.
+    for (int i = 0; i < 4; i++) {
+      if (horiz_base+i < n_size) {
+        if (lhs_vert < m_size)
+          output(lhs_vert, horiz_base + i) = results[i].x;
+        if (lhs_vert + 1 < m_size)
+          output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        if (lhs_vert + 2 < m_size)
+          output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        if (lhs_vert + 3 < m_size)
+          output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  }
+}
+
+
+template<typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper, bool CHECK_LHS_BOUNDARY,
+         bool CHECK_RHS_BOUNDARY>
+__device__ __forceinline__ void
+EigenFloatContractionKernelInternal(const LhsMapper lhs, const RhsMapper rhs,
+                       const OutputMapper output, float2 lhs_shmem2[][32],
+                       float2 rhs_shmem2[][8], const Index m_size,
+                       const Index n_size, const Index k_size,
+                       const Index base_m, const Index base_n) {
+
+  // prefetch registers
+  float4 lhs_pf0, lhs_pf1, lhs_pf2, lhs_pf3;
+  float4 rhs_pf0, rhs_pf1;
+
+  float4 results[8];
+  for (int i=0; i < 8; i++) {
+    results[i].x = results[i].y = results[i].z = results[i].w = 0;
+  }
+
+  Index lhs_vert = base_m+threadIdx.x*4+(threadIdx.y%4)*32;
+  for (Index k = 0; k < k_size; k += 32) {
+    lhs_pf0 = internal::pset1<float4>(0);
+    lhs_pf1 = internal::pset1<float4>(0);
+    lhs_pf2 = internal::pset1<float4>(0);
+    lhs_pf3 = internal::pset1<float4>(0);
+
+    rhs_pf0 = internal::pset1<float4>(0);
+    rhs_pf1 = internal::pset1<float4>(0);
+
+     if (!CHECK_LHS_BOUNDARY) {
+      if ((threadIdx.y/4+k+24) < k_size) {
+        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+        lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
+        lhs_pf3 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
+      } else if ((threadIdx.y/4+k+16) < k_size) {
+        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+        lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
+      } else if ((threadIdx.y/4+k+8) < k_size) {
+        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+        lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+      } else if ((threadIdx.y/4+k) < k_size) {
+        lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+      }
+    } else {
+      // just CHECK_LHS_BOUNDARY
+      if (lhs_vert + 3 < m_size) {
+        if ((threadIdx.y/4+k+24) < k_size) {
+          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+          lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
+          lhs_pf3 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+24));
+        } else if ((threadIdx.y/4+k+16) < k_size) {
+          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+          lhs_pf2 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+16));
+        } else if ((threadIdx.y/4+k+8) < k_size) {
+          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+          lhs_pf1 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k+8));
+        } else if ((threadIdx.y/4+k) < k_size) {
+          lhs_pf0 =lhs.template loadPacket<float4,Unaligned>(lhs_vert, (threadIdx.y/4+k));
+        }
+      } else if (lhs_vert + 2 < m_size) {
+        if ((threadIdx.y/4+k+24) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
+          lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
+          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
+          lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
+          lhs_pf3.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+24));
+        } else if ((threadIdx.y/4+k+16) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
+          lhs_pf2.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+16));
+        } else if ((threadIdx.y/4+k+8) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+          lhs_pf1.z =lhs(lhs_vert + 2, (threadIdx.y/4+k+8));
+        } else if ((threadIdx.y/4+k) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf0.z =lhs(lhs_vert + 2, (threadIdx.y/4+k));
+        }
+      } else if (lhs_vert + 1 < m_size) {
+        if ((threadIdx.y/4+k+24) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
+          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
+          lhs_pf3.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+24));
+        } else if ((threadIdx.y/4+k+16) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+          lhs_pf2.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+16));
+        } else if ((threadIdx.y/4+k+8) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf1.y =lhs(lhs_vert + 1, (threadIdx.y/4+k+8));
+        } else if ((threadIdx.y/4+k) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf0.y =lhs(lhs_vert + 1, (threadIdx.y/4+k));
+        }
+      } else if (lhs_vert < m_size) {
+        if ((threadIdx.y/4+k+24) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+          lhs_pf3.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+24));
+        } else if ((threadIdx.y/4+k+16) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+          lhs_pf2.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+16));
+        } else if ((threadIdx.y/4+k+8) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+          lhs_pf1.x =lhs(lhs_vert + 0, (threadIdx.y/4+k+8));
+        } else if ((threadIdx.y/4+k) < k_size) {
+          lhs_pf0.x =lhs(lhs_vert + 0, (threadIdx.y/4+k));
+        }
+      }
+    }
+    __syncthreads();
+    Index rhs_vert = k+threadIdx.x*4;
+    Index rhs_horiz0 = threadIdx.y*2+base_n;
+    Index rhs_horiz1 = threadIdx.y*2+1+base_n;
+    if (!CHECK_RHS_BOUNDARY) {
+      if ((rhs_vert + 3) < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
+        rhs_pf1 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz1);
+      } else if (rhs_vert + 2 < k_size) {
+        // just CHECK_RHS_BOUNDARY
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+        rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
+      } else if (rhs_vert + 1 < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+      } else if (rhs_vert  < k_size) {
+        rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+      }
+    } else {
+      if (rhs_horiz1 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
+          rhs_pf1 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz1);
+        } else if (rhs_vert + 2 < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+          rhs_pf1.z = rhs(rhs_vert + 2, rhs_horiz1);
+        } else if (k+threadIdx.x*4 + 1 < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+          rhs_pf1.y = rhs(rhs_vert + 1, rhs_horiz1);
+        } else if (k+threadIdx.x*4  < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf1.x = rhs(rhs_vert, rhs_horiz1);
+        }
+      } else if (rhs_horiz0 < n_size) {
+        if ((rhs_vert + 3) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0 = rhs.template loadPacket<float4,Unaligned>(rhs_vert, rhs_horiz0);
+        } else if ((rhs_vert + 2) < k_size) {
+          // just CHECK_RHS_BOUNDARY
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+          rhs_pf0.z = rhs(rhs_vert + 2, rhs_horiz0);
+        } else if ((rhs_vert + 1) < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+          rhs_pf0.y = rhs(rhs_vert + 1, rhs_horiz0);
+        } else if (rhs_vert  < k_size) {
+          rhs_pf0.x = rhs(rhs_vert, rhs_horiz0);
+        }
+      }
+    }
+    __syncthreads();
+    // Loaded. Do computation
+    // Row 0 -> times (0, 4, 8, .. 28) for features 0, 1.
+    // Row 1 -> times (0, 4, 8, .. 28) for features 2, 3.
+    // ..
+    // Row 31 -> times (0, 4, 8, .. 28) for features 62, 63
+    rhs_shmem2[threadIdx.y][threadIdx.x] = make_float2(rhs_pf0.x, rhs_pf1.x);
+    // Row 32 -> times (1, 5, 9, .. 29) for features 0, 1.
+    // Row 33 -> times (1, 5, 9, .. 29) for features 2, 3.
+    // ..
+    rhs_shmem2[threadIdx.y+32][threadIdx.x] = make_float2(rhs_pf0.y, rhs_pf1.y);
+    // Row 64 -> times (2, 6, 10, .. 30) for features 0, 1.
+    // Row 65 -> times (2, 6, 10, .. 30) for features 2, 3.
+    rhs_shmem2[threadIdx.y+64][threadIdx.x] = make_float2(rhs_pf0.z, rhs_pf1.z);
+    // Row 96 -> times (3, 7, 11, .. 31) for features 0, 1.
+    // Row 97 -> times (3, 7, 11, .. 31) for features 2, 3.
+    rhs_shmem2[threadIdx.y+96][threadIdx.x] = make_float2(rhs_pf0.w, rhs_pf1.w);
+
+    // LHS.
+    // Row 0 (time 0) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
+    // Row 1 (time 1) -> features (0, 1), (4, 5), .. (28, 29), (32, 33), ..  (60, 61) .. (124, 125)
+    // ...
+    // Row 8 (time 0) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
+    // Row 15 (time 7) -> features (2, 3), (6, 7), .. (30, 31), (34, 35), ..  (62, 63) .. (126, 127)
+
+
+#define add_vals(a_feat1, a_feat2, f1, f2, f3, f4)\
+      results[0].x += a_feat1.x * f1.x;\
+      results[1].x += a_feat1.x * f1.y;\
+      results[2].x += a_feat1.x * f2.x;\
+      results[3].x += a_feat1.x * f2.y;\
+      results[4].x += a_feat1.x * f3.x;\
+      results[5].x += a_feat1.x * f3.y;\
+      results[6].x += a_feat1.x * f4.x;\
+      results[7].x += a_feat1.x * f4.y;\
+\
+      results[0].y += a_feat1.y * f1.x;\
+      results[1].y += a_feat1.y * f1.y;\
+      results[2].y += a_feat1.y * f2.x;\
+      results[3].y += a_feat1.y * f2.y;\
+      results[4].y += a_feat1.y * f3.x;\
+      results[5].y += a_feat1.y * f3.y;\
+      results[6].y += a_feat1.y * f4.x;\
+      results[7].y += a_feat1.y * f4.y;\
+\
+      results[0].z += a_feat2.x * f1.x;\
+      results[1].z += a_feat2.x * f1.y;\
+      results[2].z += a_feat2.x * f2.x;\
+      results[3].z += a_feat2.x * f2.y;\
+      results[4].z += a_feat2.x * f3.x;\
+      results[5].z += a_feat2.x * f3.y;\
+      results[6].z += a_feat2.x * f4.x;\
+      results[7].z += a_feat2.x * f4.y;\
+\
+      results[0].w += a_feat2.y * f1.x;\
+      results[1].w += a_feat2.y * f1.y;\
+      results[2].w += a_feat2.y * f2.x;\
+      results[3].w += a_feat2.y * f2.y;\
+      results[4].w += a_feat2.y * f3.x;\
+      results[5].w += a_feat2.y * f3.y;\
+      results[6].w += a_feat2.y * f4.x;\
+      results[7].w += a_feat2.y * f4.y;\
+
+    lhs_shmem2[threadIdx.y/4][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.x, lhs_pf0.y);
+    lhs_shmem2[threadIdx.y/4+8][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.x, lhs_pf1.y);
+    lhs_shmem2[threadIdx.y/4+16][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.x, lhs_pf2.y);
+    lhs_shmem2[threadIdx.y/4+24][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.x, lhs_pf3.y);
+
+    lhs_shmem2[threadIdx.y/4 + 32][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf0.z, lhs_pf0.w);
+    lhs_shmem2[threadIdx.y/4 + 40][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf1.z, lhs_pf1.w);
+    lhs_shmem2[threadIdx.y/4 + 48][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf2.z, lhs_pf2.w);
+    lhs_shmem2[threadIdx.y/4 + 56][threadIdx.x+(threadIdx.y%4)*8] = make_float2(lhs_pf3.z, lhs_pf3.w);
+
+    __syncthreads();
+
+    // Do the multiplies.
+    #pragma unroll
+    for (int koff = 0; koff < 32; koff ++) {
+      float2 a3 = lhs_shmem2[koff][threadIdx.x + (threadIdx.y % 4) * 8];
+      float2 a4 = lhs_shmem2[koff + 32][threadIdx.x + (threadIdx.y % 4) * 8];
+
+      // first feature is at (threadIdx.y/4) * 8 last is at start + 8.
+      int start_feature = (threadIdx.y / 4) * 8;
+
+      float2 br1 = rhs_shmem2[start_feature/2 +     (koff % 4) * 32][koff/4];
+      float2 br2 = rhs_shmem2[start_feature/2 + 1 + (koff % 4) * 32][koff/4];
+      float2 br3 = rhs_shmem2[start_feature/2 + 2 + (koff % 4) * 32][koff/4];
+      float2 br4 = rhs_shmem2[start_feature/2 + 3 + (koff % 4) * 32][koff/4];
+
+      add_vals(a3, a4, br1, br2, br3, br4)
+    }
+    __syncthreads();
+  } // end loop over k
+
+  __syncthreads();
+  Index horiz_base = (threadIdx.y/4)*8+base_n;
+  if (!CHECK_LHS_BOUNDARY && !CHECK_RHS_BOUNDARY) {
+    for (int i = 0; i < 8; i++) {
+      output(lhs_vert, horiz_base + i) = results[i].x;
+      output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      output(lhs_vert + 3, horiz_base + i) = results[i].w;
+    }
+  } else if (!CHECK_RHS_BOUNDARY) {
+    if (lhs_vert + 3 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    } else if (lhs_vert + 2 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+      }
+    } else if (lhs_vert + 1 < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+      }
+    } else if (lhs_vert  < m_size) {
+      for (int i = 0; i < 8; i++) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+      }
+    }
+  } else if (!CHECK_LHS_BOUNDARY) {
+    // CHECK BOUNDARY_B
+    for (int i = 0; i < 8; i++) {
+      if (horiz_base + i < n_size) {
+        output(lhs_vert, horiz_base + i) = results[i].x;
+        output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  } else {
+    // CHECK both boundaries.
+    for (int i = 0; i < 8; i++) {
+      if (horiz_base + i < n_size) {
+        if (lhs_vert < m_size)
+          output(lhs_vert, horiz_base + i) = results[i].x;
+        if (lhs_vert + 1 < m_size)
+          output(lhs_vert + 1, horiz_base + i) = results[i].y;
+        if (lhs_vert + 2 < m_size)
+          output(lhs_vert + 2, horiz_base + i) = results[i].z;
+        if (lhs_vert + 3 < m_size)
+          output(lhs_vert + 3, horiz_base + i) = results[i].w;
+      }
+    }
+  }
+}
+
+
+template<typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(256, 1)
+#else
+__launch_bounds__(256)
+#endif
+EigenFloatContractionKernel(const LhsMapper lhs, const RhsMapper rhs,
+                       const OutputMapper output,
+                       const Index m_size, const Index n_size, const Index k_size) {
+  __shared__ float2 lhs_shmem[64*32];
+  __shared__ float2 rhs_shmem[128*8];
+
+  typedef float2 LHS_MEM[64][32];
+  typedef float2 RHS_MEM[128][8];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 128 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  bool check_rhs = (base_n + 63) >= n_size;
+  bool check_lhs128 = (base_m + 127) >= m_size;
+
+  if (!check_rhs) {
+    if (!check_lhs128) {
+      // >= 128 rows left
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(
+                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(
+                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    }
+  } else {
+    if (!check_lhs128) {
+      // >= 128 rows left
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(
+                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(
+                     lhs, rhs, output, *((LHS_MEM *) lhs_shmem), *((RHS_MEM *) rhs_shmem), m_size, n_size, k_size, base_m, base_n);
+    }
+  }
+}
+
+template<typename Index, typename LhsMapper,
+         typename RhsMapper, typename OutputMapper>
+__global__ void
+#if defined(EIGEN_HIPCC)
+__launch_bounds__(256, 1)
+#else
+__launch_bounds__(256)
+#endif
+EigenFloatContractionKernel16x16(const LhsMapper lhs, const RhsMapper rhs,
+                       const OutputMapper output,
+                       const Index m_size, const Index n_size, const Index k_size) {
+  __shared__ float2 lhs_shmem[32][16];
+  __shared__ float2 rhs_shmem[64][8];
+
+  const Index m_block_idx = blockIdx.x;
+  const Index n_block_idx = blockIdx.y;
+
+  const Index base_m = 64 * m_block_idx;
+  const Index base_n = 64 * n_block_idx;
+
+  if (base_m + 63 < m_size) {
+    if (base_n + 63 < n_size) {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, false, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    }
+  } else {
+    if (base_n + 63 < n_size) {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, false>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    } else {
+      EigenFloatContractionKernelInternal16x16<Index, LhsMapper, RhsMapper, OutputMapper, true, true>(lhs, rhs, output, lhs_shmem, rhs_shmem, m_size, n_size, k_size, base_m, base_n);
+    }
+  }
+}
+
+
+template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice> :
+    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, GpuDevice> > {
+
+  typedef GpuDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
+
+  enum {
+    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
+  };
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
+
+  static const int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static const int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static const int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, LDims> left_dim_mapper_t;
+  typedef array<Index, RDims> right_dim_mapper_t;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static const int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  // typedefs needed in evalTo
+  typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
+  typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+  typedef typename LeftEvaluator::Dimensions LeftDimensions;
+  typedef typename RightEvaluator::Dimensions RightDimensions;
+
+  TensorEvaluator(const XprType& op, const Device& device) :
+      Base(op, device)
+  {
+    EIGEN_STATIC_ASSERT( (internal::is_same<OutputKernelType, const NoOpOutputKernel>::value),
+                          GPU_TENSOR_CONTRACTION_DOES_NOT_SUPPORT_OUTPUT_KERNELS);
+  }
+
+  // We need to redefine this method to make nvcc happy
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
+    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
+    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      this->m_result = static_cast<Scalar *>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar)));
+      evalTo(this->m_result);
+      return true;
+    }
+  }
+
+  void evalTo(Scalar* buffer) const {
+    if (this->m_lhs_inner_dim_contiguous) {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, true, true, Unaligned>(buffer);
+        }
+        else {
+          evalTyped<true, true, false, Unaligned>(buffer);
+        }
+      }
+      else {
+       if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, false, true, Unaligned>(buffer);
+        }
+        else {
+          evalTyped<true, false, false, Unaligned>(buffer);
+        }
+      }
+    }
+    else {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, true, true, Unaligned>(buffer);
+        }
+        else {
+          evalTyped<false, true, false, Unaligned>(buffer);
+        }
+      }
+      else {
+       if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, false, true, Unaligned>(buffer);
+        }
+        else {
+          evalTyped<false, false, false, Unaligned>(buffer);
+        }
+      }
+    }
+  }
+
+  template <typename LhsScalar, typename RhsScalar, typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels {
+    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
+    const Index m_blocks = (m + 63) / 64;
+    const Index n_blocks = (n + 63) / 64;
+    const dim3 num_blocks(m_blocks, n_blocks, 1);
+    const dim3 block_size(8, 8, 8);
+    LAUNCH_GPU_KERNEL((EigenContractionKernel<Scalar, Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
+    }
+  };
+
+  template <typename Index, typename LhsMapper, typename RhsMapper, typename OutputMapper> struct LaunchKernels<float, float, Index, LhsMapper, RhsMapper, OutputMapper> {
+    static void Run(const LhsMapper& lhs, const RhsMapper& rhs, const OutputMapper& output, Index m, Index n, Index k, const GpuDevice& device) {
+      if (m < 768 || n < 768) {
+        const Index m_blocks = (m + 63) / 64;
+        const Index n_blocks = (n + 63) / 64;
+        const dim3 num_blocks(m_blocks, n_blocks, 1);
+        const dim3 block_size(16, 16, 1);
+        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel16x16<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
+      } else {
+        const Index m_blocks = (m + 127) / 128;
+        const Index n_blocks = (n + 63) / 64;
+        const dim3 num_blocks(m_blocks, n_blocks, 1);
+        const dim3 block_size(8, 32, 1);
+        LAUNCH_GPU_KERNEL((EigenFloatContractionKernel<Index, LhsMapper, RhsMapper, OutputMapper>), num_blocks, block_size, 0, device, lhs, rhs, output, m, n, k);
+      }
+    }
+  };
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  void evalTyped(Scalar* buffer) const {
+    // columns in left side, rows in right side
+    const Index k = this->m_k_size;
+    EIGEN_UNUSED_VARIABLE(k)
+
+    // rows in left side
+    const Index m = this->m_i_size;
+
+    // columns in right side
+    const Index n = this->m_j_size;
+
+    // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
+    this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
+
+    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
+                                                   LeftEvaluator, left_nocontract_t,
+                                                   contract_t, 4,
+                                                   lhs_inner_dim_contiguous,
+                                                   false, Unaligned> LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
+                                                   RightEvaluator, right_nocontract_t,
+                                                   contract_t, 4,
+                                                   rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, Unaligned> RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+    OutputMapper output(buffer, m);
+
+#if defined(EIGEN_USE_HIP)
+    setGpuSharedMemConfig(hipSharedMemBankSizeEightByte);
+#else
+    setGpuSharedMemConfig(cudaSharedMemBankSizeEightByte);
+#endif
+
+    LaunchKernels<LhsScalar, RhsScalar, Index, LhsMapper, RhsMapper, OutputMapper>::Run(lhs, rhs, output,  m, n, k, this->m_device);
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_USE_GPU and EIGEN_GPUCC
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_GPU_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
new file mode 100644
index 0000000..9ab900b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionMapper.h
@@ -0,0 +1,575 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
+
+namespace Eigen {
+
+namespace internal {
+
+enum {
+  Rhs = 0,
+  Lhs = 1
+};
+
+/*
+ * Implementation of the Eigen blas_data_mapper class for tensors.
+ */
+/// The make pointer class is used by sycl in order to build the mapper class on the device. For other platform the default make pointer is used which
+/// is scalar * for CoeffLoader.
+template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_ = MakePointer>
+struct CoeffLoader;
+
+template <typename Scalar, typename Index, int side, typename Tensor,
+          typename nocontract_t, typename contract_t, int packet_size,
+          bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,
+          template <class> class MakePointer_ = MakePointer>
+class BaseTensorContractionMapper;
+
+template <typename Tensor, bool HasRawAccess, template <class> class MakePointer_>
+struct CoeffLoader {
+  enum {
+    DirectOffsets = false
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_tensor(tensor) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index) {
+    eigen_assert(false && "unsupported");
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type
+  data() const {
+    eigen_assert(false && "unsupported");
+    return NULL;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return m_tensor.coeff(index); }
+
+ template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ typename Tensor::PacketReturnType packet(typename Tensor::Index index) const
+  {
+    return m_tensor.template packet<LoadMode>(index);
+  }
+
+  #ifdef EIGEN_USE_SYCL
+  // The placeholder accessors require to be bound to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_tensor.bind(cgh);
+  }
+  #endif
+
+ private:
+  const Tensor m_tensor;
+};
+
+template <typename Tensor, template <class> class MakePointer_>
+struct CoeffLoader<Tensor, true, MakePointer_> {
+  enum {
+    DirectOffsets = true
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffLoader(const Tensor& tensor) : m_data(tensor.data()) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) {
+    m_data += offset;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename MakePointer_<const typename Tensor::Scalar>::Type
+  data() const {
+    return m_data;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename Tensor::Scalar coeff(typename Tensor::Index index) const { return loadConstant(m_data+index); }
+
+ template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ typename Tensor::PacketReturnType packet(typename Tensor::Index index) const
+  {
+    return internal::ploadt_ro<typename Tensor::PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  #ifdef EIGEN_USE_SYCL
+  // The placeholder accessors require to be bound to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_data.bind(cgh);
+  }
+  #endif
+ private:
+  typedef typename Tensor::Scalar Scalar;
+
+  typename MakePointer_<const Scalar>::Type m_data;
+};
+
+template<typename Scalar, typename Index, int side,
+         typename Tensor,
+         typename nocontract_t, typename contract_t,
+         int packet_size, bool inner_dim_contiguous, int Alignment, template <class> class MakePointer_ = MakePointer>
+class SimpleTensorContractionMapper {
+  public:
+  EIGEN_DEVICE_FUNC
+  SimpleTensorContractionMapper(const Tensor& tensor,
+                                const nocontract_t& nocontract_strides,
+                                const nocontract_t& ij_strides,
+                                const contract_t& contract_strides,
+                                const contract_t& k_strides) :
+      m_tensor(tensor),
+      m_nocontract_strides(nocontract_strides),
+      m_ij_strides(ij_strides),
+      m_contract_strides(contract_strides),
+      m_k_strides(k_strides) { }
+
+  enum {
+    DirectOffsets = CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>::DirectOffsets
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void offsetBuffer(typename Tensor::Index offset) {
+    m_tensor.offsetBuffer(offset);
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE void prefetch(Index /*i*/) { }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE Scalar operator()(Index row) const {
+    // column major assumption
+    return operator()(row, 0);
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE Scalar operator()(Index row, Index col) const {
+    return m_tensor.coeff(computeIndex(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE Index computeIndex(Index row, Index col) const {
+    const bool left = (side == Lhs);
+    EIGEN_UNUSED_VARIABLE(left); // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
+    Index nocontract_val = left ? row : col;
+    Index linidx = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
+      const Index idx = nocontract_val / m_ij_strides[i];
+      linidx += idx * m_nocontract_strides[i];
+      nocontract_val -= idx * m_ij_strides[i];
+    }
+    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
+      if (side == Lhs && inner_dim_contiguous) {
+        eigen_assert(m_nocontract_strides[0] == 1);
+        linidx += nocontract_val;
+      } else {
+        linidx += nocontract_val * m_nocontract_strides[0];
+      }
+    }
+
+    Index contract_val = left ? col : row;
+    if(array_size<contract_t>::value > 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
+        const Index idx = contract_val / m_k_strides[i];
+        linidx += idx * m_contract_strides[i];
+        contract_val -= idx * m_k_strides[i];
+      }
+
+      if (side == Rhs && inner_dim_contiguous) {
+        eigen_assert(m_contract_strides[0] == 1);
+        linidx += contract_val;
+      } else {
+        linidx += contract_val * m_contract_strides[0];
+      }
+    }
+
+    return linidx;
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE IndexPair<Index> computeIndexPair(Index row, Index col, const Index distance) const {
+    const bool left = (side == Lhs);
+    EIGEN_UNUSED_VARIABLE(left); // annoying bug in g++8.1: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=85963
+    Index nocontract_val[2] = {left ? row : col, left ? row + distance : col};
+    Index linidx[2] = {0, 0};
+    if (array_size<typename Tensor::Dimensions>::value > array_size<contract_t>::value) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<nocontract_t>::value) - 1; i > 0; i--) {
+        const Index idx0 = nocontract_val[0] / m_ij_strides[i];
+        const Index idx1 = nocontract_val[1] / m_ij_strides[i];
+        linidx[0] += idx0 * m_nocontract_strides[i];
+        linidx[1] += idx1 * m_nocontract_strides[i];
+        nocontract_val[0] -= idx0 * m_ij_strides[i];
+        nocontract_val[1] -= idx1 * m_ij_strides[i];
+      }
+      if (side == Lhs && inner_dim_contiguous) {
+        eigen_assert(m_nocontract_strides[0] == 1);
+        linidx[0] += nocontract_val[0];
+        linidx[1] += nocontract_val[1];
+      } else {
+        linidx[0] += nocontract_val[0] * m_nocontract_strides[0];
+        linidx[1] += nocontract_val[1] * m_nocontract_strides[0];
+      }
+    }
+
+    Index contract_val[2] = {left ? col : row, left ? col : row + distance};
+    if (array_size<contract_t>::value> 0) {
+      EIGEN_UNROLL_LOOP
+      for (int i = static_cast<int>(array_size<contract_t>::value) - 1; i > 0; i--) {
+        const Index idx0 = contract_val[0] / m_k_strides[i];
+        const Index idx1 = contract_val[1] / m_k_strides[i];
+        linidx[0] += idx0 * m_contract_strides[i];
+        linidx[1] += idx1 * m_contract_strides[i];
+        contract_val[0] -= idx0 * m_k_strides[i];
+        contract_val[1] -= idx1 * m_k_strides[i];
+      }
+
+      if (side == Rhs && inner_dim_contiguous) {
+        eigen_assert(m_contract_strides[0] == 1);
+        linidx[0] += contract_val[0];
+        linidx[1] += contract_val[1];
+      } else {
+        linidx[0] += contract_val[0] * m_contract_strides[0];
+        linidx[1] += contract_val[1] * m_contract_strides[0];
+      }
+    }
+    return IndexPair<Index>(linidx[0], linidx[1]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index firstAligned(Index size) const {
+    // Only claim alignment when we can compute the actual stride (ie when we're
+    // dealing with the lhs with inner_dim_contiguous. This is because the
+    // matrix-vector product relies on the stride when dealing with aligned inputs.
+    return (Alignment == Aligned) && (side == Lhs) && inner_dim_contiguous ? 0 : size;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index stride() const {
+    return ((side == Lhs) && inner_dim_contiguous && array_size<contract_t>::value > 0) ? m_contract_strides[0] : 1;
+  }
+
+  #ifdef EIGEN_USE_SYCL
+  // The placeholder accessors require to be bound to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_tensor.bind(cgh);
+  }
+  #endif
+
+  const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& tensor() const {
+    return m_tensor;
+  }
+
+  const nocontract_t& nocontract_strides() const {
+    return m_nocontract_strides;
+  }
+  const nocontract_t& ij_strides() const { return m_ij_strides; }
+  const contract_t& contract_strides() const { return m_contract_strides; }
+  const contract_t& k_strides() const { return m_k_strides; }
+
+ protected:
+  CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_> m_tensor;
+  const nocontract_t m_nocontract_strides;
+  const nocontract_t m_ij_strides;
+  const contract_t m_contract_strides;
+  const contract_t m_k_strides;
+};
+
+template<typename Scalar, typename Index, int side,
+         typename Tensor,
+         typename nocontract_t, typename contract_t,
+         int packet_size, bool inner_dim_contiguous,
+         bool inner_dim_reordered, int Alignment, template <class> class MakePointer_>
+class BaseTensorContractionMapper : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment, MakePointer_>
+{
+ public:
+  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, Alignment, MakePointer_> ParentMapper;
+
+  EIGEN_DEVICE_FUNC
+  BaseTensorContractionMapper(const Tensor& tensor,
+                              const nocontract_t& nocontract_strides,
+                              const nocontract_t& ij_strides,
+                              const contract_t& contract_strides,
+                              const contract_t& k_strides) :
+  ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
+
+  template <typename PacketT,int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  typename internal::enable_if<internal::unpacket_traits<PacketT>::size==packet_size,PacketT>::type
+  load(Index i, Index j) const
+  {
+    // whole method makes column major assumption
+
+    // don't need to add offsets for now (because operator handles that)
+    // current code assumes packet size must be a multiple of 2
+    EIGEN_STATIC_ASSERT(packet_size % 2 == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    if (Tensor::PacketAccess && inner_dim_contiguous && !inner_dim_reordered) {
+      const Index index = this->computeIndex(i, j);
+      eigen_assert(this->computeIndex(i+packet_size-1, j) == index + packet_size-1);
+      return this->m_tensor.template packet<AlignmentType>(index);
+    }
+
+    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, packet_size - 1);
+    const Index first = indexPair.first;
+    const Index lastIdx = indexPair.second;
+
+    // We can always do optimized packet reads from left hand side right now, because
+    // the vertical matrix dimension on the left hand side is never contracting.
+    // On the right hand side we need to check if the contracting dimensions may have
+    // been shuffled first.
+    if (Tensor::PacketAccess &&
+        (side == Lhs || internal::array_size<contract_t>::value <= 1 || !inner_dim_reordered) &&
+        (lastIdx - first) == (packet_size - 1)) {
+
+      return this->m_tensor.template packet<AlignmentType>(first);
+    }
+
+    EIGEN_ALIGN_MAX Scalar data[packet_size];
+
+    data[0] = this->m_tensor.coeff(first);
+    EIGEN_UNROLL_LOOP
+    for (Index k = 1; k < packet_size - 1; k += 2) {
+      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
+      data[k] = this->m_tensor.coeff(internal_pair.first);
+      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
+    }
+    data[packet_size - 1] = this->m_tensor.coeff(lastIdx);
+
+    return pload<PacketT>(data);
+  }
+
+  template <typename PacketT,int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  typename internal::enable_if<internal::unpacket_traits<PacketT>::size!=packet_size,PacketT>::type
+  load(Index i, Index j) const
+  {
+    const Index requested_packet_size = internal::unpacket_traits<PacketT>::size;
+    EIGEN_ALIGN_MAX Scalar data[requested_packet_size];
+
+    const IndexPair<Index> indexPair = this->computeIndexPair(i, j, requested_packet_size - 1);
+    const Index first = indexPair.first;
+    const Index lastIdx = indexPair.second;
+
+    data[0] = this->m_tensor.coeff(first);
+    for (Index k = 1; k < requested_packet_size - 1; k += 2) {
+      const IndexPair<Index> internal_pair = this->computeIndexPair(i + k, j, 1);
+      data[k] = this->m_tensor.coeff(internal_pair.first);
+      data[k + 1] = this->m_tensor.coeff(internal_pair.second);
+    }
+    data[requested_packet_size - 1] = this->m_tensor.coeff(lastIdx);
+
+    return pload<PacketT>(data);
+  }
+
+  template <typename PacketT,int AlignmentType>
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
+    return this->load<PacketT,AlignmentType>(i,j);
+  }
+};
+
+
+template<typename Scalar, typename Index, int side,
+         typename Tensor,
+         typename nocontract_t, typename contract_t,
+         bool inner_dim_contiguous,
+         bool inner_dim_reordered, int Alignment, template <class> class MakePointer_>
+class BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_>
+  : public SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment, MakePointer_>
+{
+ public:
+  typedef SimpleTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, 1, inner_dim_contiguous, Alignment, MakePointer_> ParentMapper;
+
+  EIGEN_DEVICE_FUNC
+  BaseTensorContractionMapper(const Tensor& tensor,
+                              const nocontract_t& nocontract_strides,
+                              const nocontract_t& ij_strides,
+                              const contract_t& contract_strides,
+                              const contract_t& k_strides) :
+  ParentMapper(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
+
+  template <typename PacketT,int> EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE PacketT loadPacket(Index i, Index j) const {
+    EIGEN_ALIGN_MAX Scalar data[1];
+    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
+    return pload<PacketT>(data);
+  }
+  template <typename PacketT,int> EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE PacketT load(Index i, Index j) const {
+    EIGEN_ALIGN_MAX Scalar data[1];
+    data[0] = this->m_tensor.coeff(this->computeIndex(i, j));
+    return pload<PacketT>(data);
+  }
+};
+
+
+template<typename Scalar, typename Index, int side,
+         typename Tensor,
+         typename nocontract_t, typename contract_t,
+         int packet_size,
+         bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment, template <class> class MakePointer_=MakePointer>
+class TensorContractionSubMapper {
+ public:
+
+  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> ParentMapper;
+  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> Self;
+  typedef Self LinearMapper;
+
+  enum {
+    // We can use direct offsets iff the parent mapper supports then and we can compute the strides.
+    // TODO: we should also enable direct offsets for the Rhs case.
+    UseDirectOffsets = ParentMapper::DirectOffsets && (side == Lhs) && inner_dim_contiguous && (array_size<contract_t>::value > 0)
+  };
+
+  EIGEN_DEVICE_FUNC TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset)
+      : m_base_mapper(base_mapper), m_vert_offset(vert_offset), m_horiz_offset(horiz_offset) {
+    // Bake the offsets into the buffer used by the base mapper whenever possible. This avoids the need to recompute
+    // this offset every time we attempt to access a coefficient.
+    if (UseDirectOffsets) {
+      Index stride = m_base_mapper.stride();
+      m_base_mapper.offsetBuffer(vert_offset + horiz_offset * stride);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper(i, 0);
+    }
+    return m_base_mapper(i + m_vert_offset, m_horiz_offset);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper(i, j);
+    }
+    return m_base_mapper(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT,Alignment>(i, 0);
+    }
+    return m_base_mapper.template loadPacket<PacketT,Alignment>(i + m_vert_offset, m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template loadPacket<PacketT,Alignment>(i, j);
+    }
+    return m_base_mapper.template loadPacket<PacketT,Alignment>(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT loadPacket(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return m_base_mapper.template load<PacketT,AlignmentType>(i, j);
+    }
+    return m_base_mapper.template loadPacket<PacketT,AlignmentType>(i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketT& p) const {
+    if (UseDirectOffsets) {
+      m_base_mapper.storePacket(i, 0, p);
+    }
+    m_base_mapper.storePacket(i + m_vert_offset, m_horiz_offset, p);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    if (UseDirectOffsets) {
+      return LinearMapper(m_base_mapper, i, j);
+    }
+    return LinearMapper(m_base_mapper, i + m_vert_offset, j + m_horiz_offset);
+  }
+
+  template <typename PacketT, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i) const {
+    EIGEN_STATIC_ASSERT((internal::is_same<PacketT, PacketT>::value), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    const int ActualAlignment = (AlignmentType == Aligned) && (Alignment == Aligned) ? Aligned : Unaligned;
+    if (UseDirectOffsets) {
+     return m_base_mapper.template loadPacket<PacketT,ActualAlignment>(i, 0);
+    }
+    return m_base_mapper.template loadPacket<PacketT,ActualAlignment>(i + m_vert_offset, m_horiz_offset);
+  }
+
+  template <typename PacketT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool aligned(Index) const {
+    return false;
+  }
+
+  #ifdef EIGEN_USE_SYCL
+  // The placeholder accessors require to be bound to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_base_mapper.bind(cgh);
+  }
+  #endif
+
+  const ParentMapper& base_mapper() const { return m_base_mapper; }
+  Index vert_offset() const { return m_vert_offset; }
+  Index horiz_offset() const { return m_horiz_offset; }
+
+ private:
+  ParentMapper m_base_mapper;
+  const Index m_vert_offset;
+  const Index m_horiz_offset;
+};
+
+
+template<typename Scalar_, typename Index, int side,
+         typename Tensor,
+         typename nocontract_t, typename contract_t,
+         int packet_size,
+         bool inner_dim_contiguous, bool inner_dim_reordered, int Alignment,  template <class> class MakePointer_=MakePointer>
+class TensorContractionInputMapper
+  : public BaseTensorContractionMapper<Scalar_, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> {
+
+ public:
+  typedef Scalar_ Scalar;
+  typedef BaseTensorContractionMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> Base;
+  typedef TensorContractionSubMapper<Scalar, Index, side, Tensor, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment, MakePointer_> SubMapper;
+  typedef SubMapper VectorMapper;
+
+  EIGEN_DEVICE_FUNC TensorContractionInputMapper(const Tensor& tensor,
+                               const nocontract_t& nocontract_strides,
+                               const nocontract_t& ij_strides,
+                               const contract_t& contract_strides,
+                               const contract_t& k_strides)
+      : Base(tensor, nocontract_strides, ij_strides, contract_strides, k_strides) { }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(*this, i, j);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
+    return VectorMapper(*this, i, j);
+  }
+  
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const CoeffLoader<Tensor, Tensor::RawAccess, MakePointer_>& get_tensor() const {
+    return Base::m_tensor;
+  }
+};
+
+
+template <typename T> struct TensorContractionInputMapperTrait;
+
+template<typename Scalar_, typename Index_, int side_,
+         typename Tensor_,
+         typename nocontract_t_, typename contract_t_,
+         int packet_size_,
+         bool inner_dim_contiguous_, bool inner_dim_reordered_, int Alignment_,  template <class> class MakePointer_>
+struct TensorContractionInputMapperTrait<TensorContractionInputMapper<Scalar_, Index_, side_, Tensor_, 
+                                                    nocontract_t_, contract_t_, packet_size_, inner_dim_contiguous_, 
+                                                    inner_dim_reordered_, Alignment_, MakePointer_> > {
+
+      typedef Tensor_ XprType;
+      static const bool  inner_dim_contiguous = inner_dim_contiguous_;
+      static const bool  inner_dim_reordered = inner_dim_reordered_;
+  };  
+
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MAPPER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
new file mode 100644
index 0000000..473c228
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
@@ -0,0 +1,1650 @@
+// This file is part of Eigen, a lightweight C++ template library for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla Public License v. 2.0. If a copy of the MPL was not
+// distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorContractionSycl.h
+ *
+ * \brief:
+ *  TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
+
+namespace Eigen {
+
+namespace TensorSycl {
+namespace internal {
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+/*!
+ * \brief TVPanelSize, a template class used for setting the panel size required for launching General TensorVector
+ * contraction kernel on various hardware devices.
+ *
+ * \tparam Scalar: determines the element type of the tensor/vector
+ *
+ * \tparam StorageIndex  determines the Index type.
+ *
+ * \tparam NCWindow: determines the number of non-contracting element to be process by each work-group
+ *
+ * \tparam CFactor: determines the number of contracting element to be process by each thread
+ *
+ * \tparam NCFactor: determines the number of non-contracting element to be process by each thread
+ */
+template <typename Scalar, typename StorageIndex, StorageIndex NCWindow, StorageIndex CFactor, StorageIndex NCFactor>
+struct TVPanelSize {
+  // LocalThreadSizeC: determines total number of thread per workgroup for the contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeC = EIGEN_SYCL_LOCAL_THREAD_DIM0;
+  // LocalThreadSizeNC: determines total number of thread per workgroup for the non-contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeNC = EIGEN_SYCL_LOCAL_THREAD_DIM1;
+  // TileSizeDimNC: determines the tile size for the non-contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex TileSizeDimNC = NCWindow / NCFactor;
+  // TileSizeDimC: determines the tile size for the contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex TileSizeDimC = CFactor * LocalThreadSizeNC * LocalThreadSizeC;
+  // WorkLoadPerThreadNC : determines workload per thread for loading the non-contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadNC = TileSizeDimNC / LocalThreadSizeNC;
+  // WorkLoadPerThreadC: determines workload per thread for loading the non-contracting dimension
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadC = TileSizeDimC / LocalThreadSizeC;
+  // BC : determines if supporting bank conflict is required
+  static EIGEN_CONSTEXPR bool BC = false;
+};
+#endif
+
+/*!
+ * \brief TTPanelSize, a template class used for setting the panel size required for launching General Tensor Tensor
+ contraction kernel on various hardware devices.
+ *
+ * \tparam Scalar: determines the element type of the tensor
+ *
+ * \tparam StorageIndex: determines the Index type.
+ *
+ * \tparam REG_SIZE_M: determines workload per thread for loading the M dimension This can be varied based on the
+ available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro).
+ *
+ * \tparam REG_SIZE_N: determines workload per thread for loading the N dimension This can be varied based on the
+ available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro).
+ *
+ * \tparam TSDK: determines Tile size for dimension K. The packet size is assumed to be considered
+ */
+
+template <typename Scalar, typename StorageIndex, StorageIndex REG_SIZE_M, StorageIndex REG_SIZE_N, StorageIndex TSDK>
+struct TTPanelSize {
+  // TileSizeDimK: determines Tile size for dimension K. The packet size is assumed to be considered
+  static EIGEN_CONSTEXPR StorageIndex TileSizeDimK = TSDK;
+  // WorkLoadPerThreadM : determines workload per thread for loading the M dimension This can be varied based on the
+  // available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro//
+#ifndef EIGEN_SYCL_REG_M
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadM = REG_SIZE_M;
+#else
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadM = EIGEN_SYCL_REG_M;
+#endif
+// WorkLoadPerThreadN : determines workload per thread for loading the N dimension This can be varied based on the
+// available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro
+#ifndef EIGEN_SYCL_REG_N
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadN = REG_SIZE_N;
+#else
+  static EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadN = EIGEN_SYCL_REG_N;
+#endif
+  // LocalThreadSizeM: determines total number of thread per workgroup for the m dimension
+  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeM = EIGEN_SYCL_LOCAL_THREAD_DIM0;
+  // LocalThreadSizeN: determines total number of thread per workgroup for the n dimension
+  static EIGEN_CONSTEXPR StorageIndex LocalThreadSizeN = EIGEN_SYCL_LOCAL_THREAD_DIM1;
+  // TileSizeDimM: determines the tile size for the m dimension
+  static EIGEN_CONSTEXPR StorageIndex TileSizeDimM = LocalThreadSizeM * WorkLoadPerThreadM;
+  // TileSizeDimN: determines the tile size for the n dimension
+  static EIGEN_CONSTEXPR StorageIndex TileSizeDimN = LocalThreadSizeN * WorkLoadPerThreadN;
+  // LoadPerThreadLhs: determines workload per thread for loading Lhs Tensor. This must be divisable by packetsize
+  static EIGEN_CONSTEXPR StorageIndex LoadPerThreadLhs =
+      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimN));
+  // LoadPerThreadRhs: determines workload per thread for loading Rhs Tensor. This must be divisable by packetsize
+  static EIGEN_CONSTEXPR StorageIndex LoadPerThreadRhs =
+      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimM));
+  // BC : determines if supporting bank conflict is required
+  static EIGEN_CONSTEXPR bool BC = true;
+  // DoubleBuffer: determines if double buffering technique should be used (This can be disabled by
+  // EIGEN_SYCL_DISABLE_DOUBLE_BUFFER macro when the device doesnot have sufficient  local memory)
+  static EIGEN_CONSTEXPR bool DoubleBuffer =
+#ifdef EIGEN_SYCL_DISABLE_DOUBLE_BUFFER
+      false;
+#else
+      true;
+#endif
+};
+
+/* !
+ * \brief contraction_type: an enum class representing the Tensor Contraction implementation algorithm. This is used to
+ * specialize the contraction algorithm based on device support for dedicated local memory.
+ */
+enum class contraction_type { local, no_local };
+/* !
+ * \brief data_source an enum class determining the location of the data in a memory hierarchy (global, local, private).
+ */
+enum class data_source { global_mem, local_mem, private_mem };
+
+/*!
+ * \brief read, a template function used for loading the data from global
+ memory. This function is used to guarantee coalesced and vectorized load whenever possible
+ *
+ * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
+ vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+ contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+ when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam TensorMapper: determines the input tensor mapper type
+ *
+ * \tparam StorageIndex: determines the Index type
+
+ * \param tensorMapper: is the input tensor
+ *
+ * \param NCIndex: is the non-contracting dim index
+ *
+ * \param CIndex is the contracting dim index
+ *
+ * \param ld: is the leading dimension of the flattened tensor
+ */
+template <bool PacketLoad, bool is_coalesced_layout, bool, typename PacketType, typename TensorMapper,
+          typename StorageIndex>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<PacketLoad, PacketType>::type read(
+    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &ld) {
+  const StorageIndex row = (is_coalesced_layout) ? NCIndex : CIndex;
+  const StorageIndex col = (is_coalesced_layout) ? CIndex : NCIndex;
+  return tensorMapper.get_tensor().template packet<Unaligned>(row + (col * ld));
+}
+
+/*!
+ * \brief read, special overload of read function, when the read access is not vectorized
+ *
+ * \tparam PacketLoad: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \param is_coalesced_layout: determines whether or not the Tensor data in a memory can be access coalesced and
+  vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+  contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+  when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \tparam PacketType: determines the type of packet
+ *
+ * \tparam TensorMapper: determines the input tensor mapper type
+ *
+ * \tparam StorageIndex: determines the Index type
+
+ * \param tensorMapper: is the input tensor
+ *
+ * \param NCIndex: is the non-contracting dim index
+ *
+ * \param CIndex: is the contracting dim index
+ */
+template <bool PacketLoad, bool, bool IsRhs, typename PacketType, typename TensorMapper, typename StorageIndex>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!PacketLoad, PacketType>::type read(
+    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &) {
+  const StorageIndex row = (IsRhs) ? CIndex : NCIndex;
+  const StorageIndex col = (IsRhs) ? NCIndex : CIndex;
+  return tensorMapper(row, col);
+}
+
+/*!
+ * \brief write, a template function used for storing the data to local memory. This function is used to guarantee
+ * coalesced and vectorized store whenever possible.
+ *
+ * \tparam StorageIndex: determines the Index type
+ *
+ * \param ld is the leading dimension of the local memory. ld is a compile time value for the local memory
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ *
+ * \param CIndex is the contracting dim index
+ */
+
+template <typename StorageIndex, StorageIndex ld, data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    typename ::Eigen::internal::enable_if<dt != data_source::global_mem, void>::type
+    write(PacketType &packet_data, DataScalar ptr) {
+  EIGEN_CONSTEXPR int PacketSize = Eigen::internal::unpacket_traits<PacketType>::size;
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < PacketSize; i++) {
+    *ptr = PacketWrapper<PacketType, PacketSize>::scalarize(i, packet_data);
+    ptr += ld;
+  }
+}
+
+/*!
+ * \brief Overloading the write function for storing the data to global memory, when vectorization enabled This function
+ * is used to guarantee coalesced and vectorized store whenever possible.
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ */
+
+template <data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<
+    Eigen::internal::unpacket_traits<PacketType>::size != 1 && dt == data_source::global_mem, void>::type
+write(PacketType &packet_data, DataScalar *ptr) {
+  ::Eigen::internal::pstoreu<DataScalar, PacketType>(ptr, packet_data);
+}
+
+/*!
+ * \brief Overloading the write function for storing the data to global memory, when vectorization is disabled.
+ *
+ * \tparam data_source: an enum value representing if the location of the data in a memory hierarchy.
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam DataScalar: determines the output data type
+ *
+ * \param packet_data: the data to be written in the local memory
+ *
+ * \param ptr: a pointer to the local memory
+ */
+template <data_source dt, typename PacketType, typename DataScalar>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<
+    Eigen::internal::unpacket_traits<PacketType>::size == 1 && dt == data_source::global_mem, void>::type
+write(PacketType &packet_data, DataScalar *ptr) {
+  *ptr = packet_data;
+}
+
+/*!
+ * \brief check_boundary: is used to check the edge condition for non-internal blocks.
+ *
+ * \tparam is_internal: determines if the block is internal
+ */
+template <bool is_internal>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary(bool) {
+  return true;
+}
+
+/*!
+ * \brief check_boundary: specialization of the check_boundary for non-internal blocks.
+ *
+ * \param cond: true when the data is in range. Otherwise false
+ */
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary<false>(bool cond) {
+  return cond;
+}
+
+/*!
+ * \brief BlockProperties is a template class that provides different characteristic of a block of each Tensor processed
+ * by each workgroup.
+ *
+ * \tparam is_transposed: iff true, determines whether or not the block of the Tensor is transposed
+ *
+ * \tparam packet_load_: determines if the each element of this tensor block should be loaded in a packet mode
+ *
+ * \tparam PacketType:  determines the type of packet
+ *
+ * \tparam OutType: determines the type of each element for this block of tensor. If packet load is true, it will be
+ * packetType; Otherwise it will be scalar Type
+ *
+ * \param elements_per_access determines the size of each element based on OutType
+ *
+ * \param is_coalesced_layout  determines whether or not the Tensor data in a memory can be access coalesced and
+ * vectorized when possible. Coalesced memory access is a key factor in Kernel performance. When a tensor is 2d and the
+ * contracting dimension is 1, it is always possible to accessed tensor data coalesced and vectorized. This is the case
+ * when RHS(right hand side) Tensor is transposed or when LHS(left hand side) Tensor is not transposed.
+ *
+ * \param nc_stride determines the stride of non-contracting dimension to access the next adjustment element within the
+ * Tensor Block for each workgroup
+ *
+ * \param c_stride  determines the stride of contracting dimension to access the next adjustment element within the
+ * Tensor Block for each workgroup
+ */
+template <bool is_transposed, bool is_rhs_, bool packet_load_, typename PacketType>
+struct BlockProperties {
+  static EIGEN_CONSTEXPR bool packet_load = packet_load_;
+  typedef typename Eigen::internal::unpacket_traits<PacketType>::type OutScalar;
+  static EIGEN_CONSTEXPR bool is_rhs = is_rhs_;
+  typedef typename Eigen::internal::conditional<packet_load, PacketType, OutScalar>::type OutType;
+  static EIGEN_CONSTEXPR int elements_per_access = Eigen::internal::unpacket_traits<OutType>::size;
+  static EIGEN_CONSTEXPR bool is_coalesced_layout = !(is_transposed ^ is_rhs);
+  static EIGEN_CONSTEXPR int nc_stride = (is_coalesced_layout ? elements_per_access : 1);
+  static EIGEN_CONSTEXPR int c_stride = (is_coalesced_layout ? 1 : elements_per_access);
+};
+
+/*!
+ * \brief ThreadProperties is a template class that provides each thread's properties within a workgroup.  Please see
+ * the sycl-1.2.1 specification (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for the workgroup,
+ * work-items
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \param linearLocalThreadId: determines the linearized location of a thread within a work-group
+ *
+ * \param kGroupId: determines the logical group id in a k dimension of the flattened tensor. It will be > 1 when
+ * tall/skinny algorithm is used
+ *
+ * \param mGroupOffset: determines the logical start position of all thread within a workgroup for the m dimension of
+ * the flattened tensor.
+ *
+ * \param kGroupOffset determines the logical start position of all thread within a workgroup for the k dimension of the
+ * flattened tensor. It will be > 1 when tall/skinny algorithm is used.
+ *
+ * \param mLocalOffset: determines the logical start position of each thread within a workgroup for the m dimension of a
+ * flattened tensor. The position determines the distance of each thread within the workgroup from each other
+ * independent from their global position.
+ *
+ * \param nLocalOffset: determines the logical start position of each thread within a workgroup for the n dimension of a
+ * flattened tensor. The position determines the distance of each thread within the workgroup from each other
+ * independent from their global position.
+ *
+ * \param mGlobalOffset: determines the logical start position of each thread a thread for the m dimension on a
+ * flattened tensor
+ *
+ * \param nGlobalOffset: determines the logical start position of each thread a thread for the n dimension on a
+ * flattened tensor
+ *
+ * \param kSize : determine the number of the k elements of the flattened Tensor to be processed by each thread for the
+ * given tensor block. This is !=K dimension of Flattened Tensor when Tall/Skinny matrix is used.
+ *
+ * \param is_internal : this will determined if the thread within the work-group computes an internal block of tensor or
+ * the edge blocks. When it is internal, there is no need to check the boundaries and all the if stantement can be
+ * resolve by compiler.
+ */
+template <typename StorageIndex>
+struct ThreadProperties {
+  const StorageIndex linearLocalThreadId;
+  const StorageIndex kGroupId;
+  const StorageIndex mGroupOffset;
+  const StorageIndex nGroupOffset;
+  const StorageIndex kGroupOffset;
+  const StorageIndex mLocalOffset;
+  const StorageIndex nLocalOffset;
+  const StorageIndex mGlobalOffset;
+  const StorageIndex nGlobalOffset;
+  StorageIndex kSize;
+  const bool is_internal;
+  // this is used to adjust the last block
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ThreadProperties(
+      const StorageIndex linearLocalThreadId_, const StorageIndex kGroupId_, const StorageIndex mGroupOffset_,
+      const StorageIndex nGroupOffset_, const StorageIndex kGroupOffset_, const StorageIndex mLocalOffset_,
+      const StorageIndex nLocalOffset_, const StorageIndex mGlobalOffset_, const StorageIndex nGlobalOffset_,
+      StorageIndex kSize_, const bool is_internal_)
+      : linearLocalThreadId(linearLocalThreadId_),
+        kGroupId(kGroupId_),
+        mGroupOffset(mGroupOffset_),
+        nGroupOffset(nGroupOffset_),
+        kGroupOffset(kGroupOffset_),
+        mLocalOffset(mLocalOffset_),
+        nLocalOffset(nLocalOffset_),
+        mGlobalOffset(mGlobalOffset_),
+        nGlobalOffset(nGlobalOffset_),
+        kSize(kSize_),
+        is_internal(is_internal_) {}
+};
+
+/*!
+ * \brief TensorContractionKernel is a template class that provides Tensor -Tensor contraction operation.
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam LhsScalar: determines the left-hand-side scalar type
+ *
+ * \tparam RhsScalar: determines the right-hand-side scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam LhsMapper determines the tensor contraction mapper type for left-hand-side matrix
+ *
+ * \tparam RhsMapper determines the tensor contraction mapper type for right-hand-side matrix
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Properties: determines the Contraction Panel properties
+ *
+ * \tparam TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \tparam input_mapper_properties : determine if the input tensors are matrix. If they are matrix, special memory
+ access is used to guarantee that always the memory access are coalesced.
+ *
+ * \tptaram IsFinal : determine if this is the final kernel. If so, the result will be written in a final output.
+ Otherwise, the result of contraction will be written iin a temporary buffer. This is the case when Tall/Skinny
+ contraction is used. So in this case, a final reduction step is required to compute final output.
+
+ * \tparam contraction_tp: it is an enum value representing whether the local memroy/no local memory implementation of
+ the algorithm to be used
+ *
+ * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
+ *
+ * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
+ *
+ * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
+ *
+ * \param out_res: determines the output tensor containing the contraction result
+ *
+ * \param groupSizeM: a logical number determining the number of work-group for m dimension
+ *
+ * \param groupSizeN: a logical number determining the number of work-group for n dimension
+ *
+ * \param numTiles: determines total number of tiles on the k dimension
+ *
+ * \param TripleDim: determines the M, K, N dimensions for the flatten tensors in order to treat them as a matrix
+ */
+template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
+          typename RhsMapper, typename StorageIndex, typename Properties, typename TripleDim, bool Vectorizable,
+          typename input_mapper_properties, bool IsFinal, contraction_type contraction_tp>
+class TensorContractionKernel {
+ public:
+  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
+      PacketReturnType;
+  static EIGEN_CONSTEXPR int PacketSize =
+      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
+  static EIGEN_CONSTEXPR bool is_lhs_transposed =
+      !::Eigen::internal::TensorContractionInputMapperTrait<LhsMapper>::inner_dim_contiguous;
+  static EIGEN_CONSTEXPR bool is_rhs_transposed =
+      !::Eigen::internal::TensorContractionInputMapperTrait<RhsMapper>::inner_dim_contiguous;
+
+  typedef BlockProperties<is_lhs_transposed, false, input_mapper_properties::is_lhs_matrix && Vectorizable,
+                          PacketReturnType>
+      LHSBlockProperties;
+
+  typedef BlockProperties<is_rhs_transposed, true, input_mapper_properties::is_rhs_matrix && Vectorizable,
+                          PacketReturnType>
+      RHSBlockProperties;
+
+  static EIGEN_CONSTEXPR StorageIndex NStride =
+      contraction_tp == contraction_type::local ? Properties::WorkLoadPerThreadN : RHSBlockProperties::nc_stride;
+
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+  typedef cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::local_space> local_ptr;
+  typedef OutScalar * /*cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::private_space>*/ private_ptr;
+  typedef
+      typename ::Eigen::internal::conditional<contraction_tp == contraction_type::local, local_ptr, private_ptr>::type
+          tile_ptr;
+  static EIGEN_CONSTEXPR StorageIndex LSDL = contraction_tp == contraction_type::local
+                                                 ? Properties::TileSizeDimM + Properties::BC
+                                                 : Properties::WorkLoadPerThreadM;
+  static EIGEN_CONSTEXPR StorageIndex LSDR = contraction_tp == contraction_type::local
+                                                 ? Properties::TileSizeDimN + Properties::BC
+                                                 : Properties::WorkLoadPerThreadN;
+  static EIGEN_CONSTEXPR StorageIndex LocalOffset = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
+
+  /**
+   * \brief MemHolder this is a place holder struct for creating memory hierarchy in SYCL. Inside SYCL kernel it is not
+   * allowed to have dynamic memory allocation. While the local memory is created outside of the kernel and passed to
+   * the kernel as an accessor, the private memory can only allowed to be allocated statically. Since we are abstracting
+   * the TiledMemory for both local and private memory, the MemHolder structs is used as a helper to abstract out
+   * different type of memory needed when local/no_local memory computation is called.
+   *
+   * \tparam contraction_type: it is an enum value representing whether the local memroy/no local memory implementation
+   of the algorithm to be used
+   * \tparam the private memory size
+   * \param ptr the tile memory pointer type
+   */
+  template <contraction_type, StorageIndex>
+  struct MemHolder {
+    tile_ptr ptr;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MemHolder(local_ptr block_start_ptr) : ptr(block_start_ptr) {}
+  };
+  /**
+   * \brief specialization of memHolder class when no local memory kernel is used.
+   */
+  template <StorageIndex MemSize>
+  struct MemHolder<contraction_type::no_local, MemSize> {
+    OutScalar ptr[MemSize] = {OutScalar{0}};
+  };
+  /**
+   * \brief TiledMemory: contains required memory pointer for loading  each tile of the TensorContraction panel from
+   * global memory to local/private memory when local/no_local algorithm used.
+   *
+   * \param lhs_scratch_extract : determines the LHS tile memory. It is either private or local memory based on the
+   * selected contraction_type.
+   *
+   * \param rhs_scratch_extract : determines the RHS tile memory. It is either private or local memory based on the
+   * selected contraction_type.
+   *
+   * \param lhs_extract_index: determins the position of each thread on a local memory for lhs input. When private
+   * memory is used this is set to zero as this is not applicable in case of private memory.
+   *
+   * \param rhs_extract_index: determins the position of each thread on a local memory for rhs input. When private
+   * memory is used this is set to zero as this is not applicable in case of private memory.
+   *
+   * \param lhs_scratch_compute : determines the  location to load for computation for lhs_local memory. This is the
+   * same as lhs_scratch_extract for private memory.
+   *
+   * \param rhs_scratch_compute : determines the  location to load for computation for rhs_local memory. This is the
+   * same as rhs_scratch_extract for private memory.
+   */
+  struct TiledMemory {
+    MemHolder<contraction_tp, Properties::WorkLoadPerThreadM * Properties::TileSizeDimK> lhs_scratch_extract;
+    MemHolder<contraction_tp, Properties::WorkLoadPerThreadN * Properties::TileSizeDimK> rhs_scratch_extract;
+    tile_ptr lhs_scratch_ptr_compute;
+    tile_ptr rhs_scratch_ptr_compute;
+    const std::pair<StorageIndex, StorageIndex> lhs_extract_index;
+    const std::pair<StorageIndex, StorageIndex> rhs_extract_index;
+    template <contraction_type tp = contraction_tp>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TiledMemory(const ThreadProperties<StorageIndex> &, local_ptr,
+                typename ::Eigen::internal::enable_if<tp == contraction_type::no_local>::type * = 0)
+        : lhs_scratch_extract{},
+          rhs_scratch_extract{},
+          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr),
+          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr),
+          lhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})),
+          rhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})) {}
+
+    template <contraction_type tp = contraction_tp>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TiledMemory(const ThreadProperties<StorageIndex> &thread_properties, local_ptr block_start_ptr,
+                typename ::Eigen::internal::enable_if<tp == contraction_type::local>::type * = 0)
+        : lhs_scratch_extract{block_start_ptr},
+          rhs_scratch_extract{lhs_scratch_extract.ptr +
+                              ((Properties::DoubleBuffer + 1) * LSDL * Properties::TileSizeDimK)},
+          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr + thread_properties.mLocalOffset),
+          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr + thread_properties.nLocalOffset),
+          lhs_extract_index(
+              local_id_extract<LHSBlockProperties, Properties::TileSizeDimM>(thread_properties.linearLocalThreadId)),
+          rhs_extract_index(
+              local_id_extract<RHSBlockProperties, Properties::TileSizeDimN>(thread_properties.linearLocalThreadId)) {}
+  };
+
+  Scratch scratch;
+  const LhsMapper lhs;
+  const RhsMapper rhs;
+  OutAccessor out_res;
+  const StorageIndex groupSizeM;
+  const StorageIndex groupSizeN;
+  const StorageIndex numTiles;
+  const TripleDim triple_dim;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
+                                                                const RhsMapper rhs_, OutAccessor out_res_,
+                                                                const StorageIndex groupSizeM_,
+                                                                const StorageIndex groupSizeN_,
+                                                                const StorageIndex numTiles_,
+                                                                const TripleDim triple_dim_)
+      : scratch(scratch_),
+        lhs(lhs_),
+        rhs(rhs_),
+        out_res(out_res_),
+        groupSizeM(groupSizeM_),
+        groupSizeN(groupSizeN_),
+        numTiles(numTiles_),
+        triple_dim(triple_dim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
+                                                                const RhsMapper rhs_, OutAccessor out_res_,
+                                                                const StorageIndex groupSizeM_,
+                                                                const StorageIndex numTiles_,
+                                                                const TripleDim triple_dim_)
+      : TensorContractionKernel(scratch_, lhs_, rhs_, out_res_, groupSizeM_, 1, numTiles_, triple_dim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
+    const StorageIndex nLocalThreadId = linearLocalThreadId / Properties::LocalThreadSizeM;
+    const StorageIndex mLocalThreadId = linearLocalThreadId % Properties::LocalThreadSizeM;
+    const StorageIndex mGroupId = itemID.get_group(0) % groupSizeM;
+    const StorageIndex tmp = itemID.get_group(0) / groupSizeM;
+    const StorageIndex nGroupId = IsFinal ? tmp : tmp % groupSizeN;
+    const StorageIndex kGroupId = IsFinal ? 0 : tmp / groupSizeN;
+    const StorageIndex mGroupOffset = mGroupId * Properties::TileSizeDimM;
+    const StorageIndex nGroupOffset = nGroupId * Properties::TileSizeDimN;
+    const StorageIndex mLocalOffset = PacketSize * mLocalThreadId;
+    const StorageIndex nLocalOffset = NStride * nLocalThreadId;
+    const StorageIndex mGlobalOffset = mGroupOffset + mLocalOffset;
+    const StorageIndex nGlobalOffset = nGroupOffset + nLocalOffset;
+
+    const StorageIndex kSizePerWG = IsFinal ? triple_dim.K : numTiles * Properties::TileSizeDimK;
+    StorageIndex kGroupOffset = kGroupId * kSizePerWG;
+    const bool is_internal = triple_dim.M - mGroupOffset >= Properties::TileSizeDimM &&
+                             triple_dim.N - nGroupOffset >= Properties::TileSizeDimN &&
+                             triple_dim.K - kGroupOffset >= kSizePerWG;
+    // this is used to adjust the last block
+    StorageIndex kSize = IsFinal ? triple_dim.K : std::min(kSizePerWG, triple_dim.K - kGroupOffset);
+    // This is used to find out the lats K offset so that kGroupOffset -kSize can compute the coffset for loading to
+    // tile
+    kGroupOffset += kSize;
+
+    auto thread_properties =
+        ThreadProperties<StorageIndex>(linearLocalThreadId, kGroupId, mGroupOffset, nGroupOffset, kGroupOffset,
+                                       mLocalOffset, nLocalOffset, mGlobalOffset, nGlobalOffset, kSize, is_internal);
+
+    auto out_ptr = out_res.get_pointer() + (IsFinal ? 0 : thread_properties.kGroupId * triple_dim.M * triple_dim.N);
+
+    (thread_properties.is_internal) ? compute_panel<true>(itemID, thread_properties, out_ptr)
+                                    : compute_panel<false>(itemID, thread_properties, out_ptr);
+  }
+  // The compute block computes the contraction operation private block for each thread and store the resutl in the
+  // privateRes memory of Each computation the compute block function is independent of local and no local concepts as
+  // it only compute the block on each thread's private memory space
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_block_per_tile(OutScalar *lhs_block_ptr, OutScalar *rhs_block_ptr,
+                                                                    PacketReturnType *privateRes) {
+    StorageIndex idx = 0;
+    EIGEN_CONSTEXPR StorageIndex lhs_stride =
+        contraction_tp == contraction_type::local ? (PacketSize * Properties::LocalThreadSizeM) : 1;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN; wLPTN++) {
+      auto rhsPacket = PacketReturnType{*(rhs_block_ptr + wLPTN)};
+      StorageIndex lhs_index = 0;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
+        PacketReturnType lhsPack{};
+        Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::set_packet(lhsPack,
+                                                                                             lhs_block_ptr + lhs_index);
+        privateRes[idx] = ::Eigen::internal::pmadd(lhsPack, rhsPacket, privateRes[idx]);
+
+        lhs_index += lhs_stride;
+        idx++;
+      }
+    }
+  }
+  // The store function write the computed contraction operation in the private memory of each thread to the global
+  // memory. The store function is independent of local and no local concepts s that it can be abstract out in the base
+  // class.
+  template <bool is_internal_block, StorageIndex PrivateNStride, typename OutPtr>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void store(OutPtr *out_ptr, PacketReturnType *privateRes,
+                                                   StorageIndex mGlobalOffset, StorageIndex nGlobalOffset) {
+    auto chk_bound = [&](const StorageIndex &mIndex, const StorageIndex &nIndex) EIGEN_DEVICE_FUNC {
+      return (mIndex + PacketSize - 1 < triple_dim.M && nGlobalOffset + nIndex < triple_dim.N);
+    };
+    // when local memory is not used M and N are both accessed in a coalesced way. However, when local memory is
+    // available the k*N is transposed in the local to N*K therefore, each blocks operates on blockId*
+    // WorkLoadPerThreadN slice of N
+    EIGEN_CONSTEXPR StorageIndex GlobalNStride =
+        contraction_tp == contraction_type::local ? 1 : Properties::LocalThreadSizeN;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN / PrivateNStride; wLPTN++) {
+      // output leading dimension
+      StorageIndex outputLD = 0;
+      // When local memory is used the PrivateNstride is always 1 because the coalesed access on N is loaded into Local
+      // memory and extracting from local to global is the same as no transposed version. However, when local memory is
+      // not used and RHS is transposed we packetize the load for RHS.
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex nId = 0; nId < PrivateNStride; nId++) {
+        StorageIndex globalRow = mGlobalOffset;
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
+          PacketReturnType privetOut = privateRes[wLPTM];
+          if (check_boundary<is_internal_block>(chk_bound(globalRow, nId))) {
+            // Store the final results in C. The C matrix has always M as a first StorageIndex and N as a second
+            // StorageIndex Therefore it is always coalesced layout
+            write<data_source::global_mem>(privetOut, out_ptr + outputLD + globalRow);
+          } else {
+            EIGEN_UNROLL_LOOP
+            for (StorageIndex mId = 0; mId < PacketSize; mId++) {
+              StorageIndex mOffset = globalRow + mId;
+              if (mOffset < triple_dim.M && (nGlobalOffset + nId < triple_dim.N)) {
+                out_ptr[mOffset + outputLD] =
+                    Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::scalarize(mId, privetOut);
+              }
+            }
+          }
+          globalRow += (PacketSize * Properties::LocalThreadSizeM);
+        }
+        outputLD += triple_dim.M;
+        privateRes += Properties::WorkLoadPerThreadM / PacketSize;
+      }
+      out_ptr += (GlobalNStride * outputLD);
+
+      nGlobalOffset += (PrivateNStride * GlobalNStride);
+    }
+  }
+  // when no local memory is used the following extract_block will be enabled
+  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename PrivateReg,
+            contraction_type contract_tp = contraction_tp>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<contract_tp == contraction_type::no_local>::type
+      extract_block(const Input &inpt, PrivateReg private_ptr, const std::pair<StorageIndex, StorageIndex> &,
+                    const StorageIndex &ncOffset, const StorageIndex cOffset) {
+    EIGEN_CONSTEXPR StorageIndex LocalThreadSizeNC =
+        InputBlockProperties::is_rhs ? Properties::LocalThreadSizeN : Properties::LocalThreadSizeM;
+    EIGEN_CONSTEXPR StorageIndex WorkLoadPerThreadNC =
+        InputBlockProperties::is_rhs ? Properties::WorkLoadPerThreadN : Properties::WorkLoadPerThreadM;
+    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
+
+    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
+      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
+              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
+    };
+    const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
+    StorageIndex cIndex = cOffset;
+
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex cId = 0; cId < Properties::TileSizeDimK / InputBlockProperties::c_stride; cId++) {
+      StorageIndex ncIndex = ncOffset;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex ncId = 0; ncId < WorkLoadPerThreadNC / InputBlockProperties::nc_stride; ncId++) {
+        if (check_boundary<is_internal_block>(chk_bound(cIndex, ncIndex))) {
+          auto val =
+              read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                   InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, ncIndex, cIndex, ld);
+
+          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
+                data_source::private_mem>(val, private_ptr);
+        } else {
+          EIGEN_UNROLL_LOOP
+          for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+            const StorageIndex ncInd = ncIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
+            const StorageIndex cInd = cIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
+            OutScalar val =
+                (ncInd < NC && cInd < triple_dim.K)
+                    ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                          inpt, ncInd, cInd, ld)
+                    : OutScalar(0);
+            write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
+                  data_source::private_mem>(
+                val, private_ptr + (InputBlockProperties::is_coalesced_layout ? i : 0) +
+                         ((InputBlockProperties::is_coalesced_layout ? 0 : i) * WorkLoadPerThreadNC));
+          }
+        }
+
+        // if it is lhs we have to load it packetised when the packet size is > 1, because the output is coalesced. So
+        // even if M is not accessed in a coalesced mode, we have to load packet_size number of m per thread.
+        ncIndex = (!InputBlockProperties::is_rhs && InputBlockProperties::nc_stride == 1 && PacketSize != 1)
+                      ? ncOffset + (ncId + 1) % PacketSize + ((ncId + 1) / PacketSize) * LocalThreadSizeNC
+                      : (ncIndex + InputBlockProperties::nc_stride * LocalThreadSizeNC);
+        private_ptr += InputBlockProperties::nc_stride;
+      }
+      // the previous for loop ( private_ptr += (ncId * nc_stride)) has already moved ptr with one WorkLoadPerThreadNC
+      private_ptr += (InputBlockProperties::c_stride - 1) * WorkLoadPerThreadNC;
+      cIndex += InputBlockProperties::c_stride;
+    }
+  }
+  template <typename InputBlockProperties, StorageIndex TileSizeDimNC>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::pair<StorageIndex, StorageIndex> local_id_extract(
+      const StorageIndex &linearLocalThreadId) {
+    const StorageIndex localThreadNC =
+        (InputBlockProperties::is_coalesced_layout)
+            ? linearLocalThreadId % (TileSizeDimNC / InputBlockProperties::nc_stride)
+            : linearLocalThreadId / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    const StorageIndex localThreadC =
+        (InputBlockProperties::is_coalesced_layout)
+            ? linearLocalThreadId / (TileSizeDimNC / InputBlockProperties::nc_stride)
+            : linearLocalThreadId % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    return std::pair<StorageIndex, StorageIndex>(localThreadNC, localThreadC);
+  }
+
+  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<db && ctp == contraction_type::local>::type
+      sync_mem(const cl::sycl::nd_item<1> &, bool &db_offset) noexcept {
+    db_offset = !db_offset;
+  }
+
+  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<!db && ctp == contraction_type::local>::type
+      sync_mem(const cl::sycl::nd_item<1> &itemID, bool &) noexcept {
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+  }
+
+  template <contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<ctp == contraction_type::no_local>::type
+      sync_mem(const cl::sycl::nd_item<1> &, bool &) noexcept {
+    return;
+  }
+
+  template <bool need_sync, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<need_sync && ctp == contraction_type::no_local>::type
+      sync_thread(const cl::sycl::nd_item<1> &
+#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
+                      itemID
+#endif
+                  ) noexcept {
+#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
+    itemID.barrier(cl::sycl::access::fence_spacce::local_space);
+#else
+    return;
+#endif
+  }
+  template <bool need_sync, contraction_type ctp = contraction_tp>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<need_sync && ctp == contraction_type::local>::type
+      sync_thread(const cl::sycl::nd_item<1> &itemID) {
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+  }
+  template <bool need_sync>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!need_sync>::type sync_thread(
+      const cl::sycl::nd_item<1> &) {
+    return;
+  }
+
+  template <bool is_internal_block>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_tile_per_panel(const cl::sycl::nd_item<1> &itemID,
+                                                                    ThreadProperties<StorageIndex> &thread_properties,
+                                                                    TiledMemory &tiled_input_block,
+                                                                    PacketReturnType *privateRes, bool &db_offset) {
+    // Tiling the Rhs block from global to local memory
+    extract_block<RHSBlockProperties, is_internal_block>(
+        rhs, tiled_input_block.rhs_scratch_extract.ptr + (db_offset * Properties::TileSizeDimK * LSDR),
+        tiled_input_block.rhs_extract_index,
+        contraction_tp == contraction_type::local ? thread_properties.nGroupOffset : thread_properties.nGlobalOffset,
+        thread_properties.kGroupOffset - thread_properties.kSize);
+
+    sync_thread<contraction_tp == contraction_type::no_local>(itemID);
+
+    // Tiling the Lhs block from global to local memory
+    extract_block<LHSBlockProperties, is_internal_block>(
+        lhs, tiled_input_block.lhs_scratch_extract.ptr + (db_offset * LSDL * Properties::TileSizeDimK),
+        tiled_input_block.lhs_extract_index,
+        contraction_tp == contraction_type::local ? thread_properties.mGroupOffset : thread_properties.mGlobalOffset,
+        thread_properties.kGroupOffset - thread_properties.kSize);
+
+    // itemID.barrier(cl::sycl::access::fence_space::local_space);
+    sync_thread<contraction_tp == contraction_type::local>(itemID);
+    // switch to compute mede
+    StorageIndex lhs_offset = (db_offset * LSDL * Properties::TileSizeDimK);
+    StorageIndex rhs_offset = (db_offset * Properties::TileSizeDimK * LSDR);
+    // Loop over the values of a single tile
+    for (StorageIndex k = 0; k < Properties::TileSizeDimK; k++) {
+      compute_block_per_tile(tiled_input_block.lhs_scratch_ptr_compute + lhs_offset,
+                             tiled_input_block.rhs_scratch_ptr_compute + rhs_offset, privateRes);
+      lhs_offset += LSDL;
+      rhs_offset += LSDR;
+    }
+    // computing the K index for the next tile
+    thread_properties.kSize -= Properties::TileSizeDimK;
+    sync_mem(itemID, db_offset);
+  }
+
+  // when local memory is available the following compute_panel will be enabled
+  template <bool is_internal_block, typename OutPtr>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(const cl::sycl::nd_item<1> &itemID,
+                                                           ThreadProperties<StorageIndex> &thread_properties,
+                                                           OutPtr out_ptr) {
+    auto tiled_input_block = TiledMemory{thread_properties, scratch.get_pointer()};
+    // Allocate register space
+    PacketReturnType privateRes[Properties::WorkLoadPerThreadM * Properties::WorkLoadPerThreadN / PacketSize] = {
+        PacketReturnType{0}};
+    bool db_offset = 0;
+
+    while (thread_properties.kSize >= Properties::TileSizeDimK) {
+      compute_tile_per_panel<is_internal_block>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
+    }
+    if (thread_properties.kSize > 0) {
+      compute_tile_per_panel<false>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
+    }
+
+    // Storing the final results in the output
+    store<is_internal_block,
+          contraction_tp == contraction_type::local ? static_cast<StorageIndex>(1) : RHSBlockProperties::nc_stride>(
+        out_ptr + thread_properties.nGlobalOffset * triple_dim.M, privateRes, thread_properties.mGlobalOffset,
+        thread_properties.nGlobalOffset);
+  }
+  // When local memory is available the following extract_block will be enabled
+  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename Local,
+            contraction_type contract_tp = contraction_tp>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      typename ::Eigen::internal::enable_if<contract_tp == contraction_type::local>::type
+      extract_block(const Input &inpt, Local local_ptr, const std::pair<StorageIndex, StorageIndex>& local_index,
+                    const StorageIndex &ncOffset, const StorageIndex cOffset) {
+    EIGEN_CONSTEXPR StorageIndex TileSizeDimNC =
+        InputBlockProperties::is_rhs ? Properties::TileSizeDimN : Properties::TileSizeDimM;
+    EIGEN_CONSTEXPR StorageIndex LoadPerThread =
+        InputBlockProperties::is_rhs ? Properties::LoadPerThreadRhs : Properties::LoadPerThreadLhs;
+    EIGEN_CONSTEXPR StorageIndex LSD = InputBlockProperties::is_rhs ? LSDR : LSDL;
+    static_assert(((LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride) == 0) &&
+                   (LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride) == 0)),
+                  " LocalOffset must be divisable by stride");
+    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
+    StorageIndex localThreadNC = local_index.first;
+    StorageIndex localThreadC = local_index.second;
+    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
+      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
+              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
+    };
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex lPT = 0; lPT < LoadPerThread / InputBlockProperties::elements_per_access; lPT++) {
+      const StorageIndex CIndex = cOffset + (InputBlockProperties::c_stride * localThreadC);
+      const StorageIndex NCIndex = ncOffset + (InputBlockProperties::nc_stride * localThreadNC);
+      const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
+      if (check_boundary<is_internal_block>(chk_bound(CIndex, NCIndex))) {
+        auto val =
+            read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                 InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, NCIndex, CIndex, ld);
+        write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
+            val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
+                     (InputBlockProperties::c_stride * localThreadC * LSD));
+      } else {
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+          const StorageIndex nCInd = NCIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
+          const StorageIndex cInd = CIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
+          OutScalar val =
+              (nCInd < NC && cInd < triple_dim.K)
+                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                        inpt, nCInd, cInd, ld)
+                  : OutScalar(0);
+
+          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
+              val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
+                       (InputBlockProperties::is_coalesced_layout ? i : 0) +
+                       ((InputBlockProperties::c_stride * localThreadC +
+                         (InputBlockProperties::is_coalesced_layout ? 0 : i)) *
+                        LSD));
+        }
+      }
+      localThreadNC += (InputBlockProperties::is_coalesced_layout)
+                           ? LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride)
+                           : LocalOffset / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+      localThreadC += (InputBlockProperties::is_coalesced_layout)
+                          ? LocalOffset / (TileSizeDimNC / InputBlockProperties::nc_stride)
+                          : LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
+    }
+  }
+};
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+
+/*!
+ * \brief GeneralVectorTensor is a template class that provides Tensor -vector contraction operation, which is a special
+ * case of Tensor Tensor contraction.
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam VectorMapper: determines the tensor contraction mapper for the vector input (can be lhs or rhs)
+ *
+ * \tparam TensorMapper: determines the tensor contraction mapper for the tensor input (can be lhs or rhs)
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Properties: determines the Contraction Panel properties
+ *
+ * \tparam KFactor: determines the number of elements in K dimension in a Tile
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \tparam is_lhs_vec: determines whether lhs is a vector or rhs is a vector
+ *
+ * \tparam IsFinal: determine if this is the final kernel. If so, the result will be written in a final output.
+ * Otherwise, the result of contraction will be written iin a temporary buffer.
+ *
+ * \param scratch: determines the local memory containing the vector block for each work-group
+ *
+ * \param vec: determines the vector input (tensor mapper)
+ *
+ * \param mat: determines the tensor input (tensor mapper)
+ *
+ * \param out_res: determines the output vector containing the contraction result
+ *
+ * \param nonContractGroupSize: a logical number determining the number of work-group for non-contracting dimension
+ *
+ * \param nonContractDim: determines the size of non contracting dimension for the flattened tensor
+ *
+ * \param contractDim: determines the size of non contracting dimension for the flattened tensor
+ *
+ */
+template <typename OutScalar, typename OutAccessor, typename VectorMapper, typename TensorMapper, typename StorageIndex,
+          typename Properties, StorageIndex KFactor, bool Vectorizable, bool is_lhs_vec, bool IsFinal>
+struct GeneralVectorTensor {
+  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
+      PacketReturnType;
+  static EIGEN_CONSTEXPR int PacketSize =
+      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+
+  static EIGEN_CONSTEXPR StorageIndex OutScratchOffset =
+      KFactor * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
+
+  // Since the access layout for a vector can always be coalesced, when LHS is a vector, we pass false and false to make
+  // sure that the !^ is true When RHS is a vector, we pass true and true to make sure that the !^ is true.
+  typedef BlockProperties<is_lhs_vec ? false : true, is_lhs_vec ? false : true, Vectorizable, PacketReturnType>
+      VecBlockProperties;
+
+  Scratch scratch;
+  const VectorMapper vec;
+  const TensorMapper mat;
+  OutAccessor out_res;
+  const StorageIndex nonContractGroupSize;
+  const StorageIndex nonContractDim;
+  const StorageIndex contractDim;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE GeneralVectorTensor(Scratch scratch_, const VectorMapper vec_,
+                                                            const TensorMapper mat_, OutAccessor out_res_,
+                                                            const StorageIndex nonContractGroupSize_,
+                                                            const StorageIndex nonContractDim_,
+                                                            const StorageIndex contractDim_)
+      : scratch(scratch_),
+        vec(vec_),
+        mat(mat_),
+        out_res(out_res_),
+        nonContractGroupSize(nonContractGroupSize_),
+        nonContractDim(nonContractDim_),
+        contractDim(contractDim_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    auto scratch_ptr = scratch.get_pointer();
+    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
+    StorageIndex nonContractId = is_lhs_vec ? linearLocalThreadId / Properties::LocalThreadSizeC
+                                            : linearLocalThreadId % Properties::LocalThreadSizeNC;
+    StorageIndex contractId = is_lhs_vec ? linearLocalThreadId % Properties::LocalThreadSizeC
+                                         : linearLocalThreadId / Properties::LocalThreadSizeNC;
+    const StorageIndex cGroupSize = itemID.get_group_range(0) / nonContractGroupSize;
+    const StorageIndex nonContractGroupId =
+        is_lhs_vec ? itemID.get_group(0) / cGroupSize : itemID.get_group(0) % nonContractGroupSize;
+    const StorageIndex contractGroupId =
+        is_lhs_vec ? itemID.get_group(0) % cGroupSize : itemID.get_group(0) / nonContractGroupSize;
+    auto out_ptr = out_res.get_pointer() + (IsFinal ? 0 : contractGroupId * nonContractDim);
+
+    const StorageIndex nonContractGroupOffset = nonContractGroupId * Properties::TileSizeDimNC;
+    const StorageIndex contractGroupOffset = contractGroupId * Properties::TileSizeDimC;
+    auto outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
+    const StorageIndex globalNonContractDimOffset = nonContractGroupOffset + nonContractId;
+    const StorageIndex globalContractDimOffset = contractGroupOffset + contractId;
+    auto local_output = scratch_ptr + OutScratchOffset;
+    const bool is_internal = nonContractDim - nonContractGroupOffset >= Properties::TileSizeDimNC &&
+                             contractDim - contractGroupOffset >= Properties::TileSizeDimC;
+    is_internal
+        ? compute_panel<true>(itemID, vec, mat, local_output, out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+                              scratch_ptr, contractGroupOffset,
+#endif
+                              nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
+                              nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex)
+        : compute_panel<false>(itemID, vec, mat, local_output, out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+                               scratch_ptr, contractGroupOffset,
+#endif
+                               nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
+                               nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex);
+  }
+  template <bool is_internal_block, typename OutPtr>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(
+      const cl::sycl::nd_item<1> &itemID, const VectorMapper &vec, const TensorMapper &mat, OutScalar *local_output,
+      OutPtr out_ptr,
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      OutScalar *scratch_ptr, const StorageIndex contractGroupOffset,
+#endif
+      const StorageIndex nonContractGroupOffset, const StorageIndex linearLocalThreadId, StorageIndex contractDim,
+      StorageIndex nonContractDim, StorageIndex contractId, StorageIndex nonContractId,
+      StorageIndex globalContractDimOffset, StorageIndex globalNonContractDimOffset, StorageIndex outScratchIndex) {
+    OutScalar outScalar[Properties::WorkLoadPerThreadNC] = {OutScalar(0)};
+    // Reading the vector
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+    const StorageIndex vectorOffset = contractGroupOffset + linearLocalThreadId;
+    extract_block<VecBlockProperties, is_internal_block, KFactor,
+                  Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC>(vec, scratch_ptr, linearLocalThreadId,
+                                                                                vectorOffset, contractDim);
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+    auto in_scratch_ptr = scratch_ptr + contractId;
+#endif
+
+    StorageIndex privateOffsetC = 0;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex i = 0; i < Properties::WorkLoadPerThreadC; i++) {
+      StorageIndex privateOffsetNC = 0;
+      bool contract_conds = ((globalContractDimOffset + privateOffsetC) < contractDim);
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      auto vecScalar = *in_scratch_ptr;
+#else
+      auto vecScalar = (check_boundary<is_internal_block>(contract_conds))
+                           ? vec(is_lhs_vec ? StorageIndex(0) : globalContractDimOffset + privateOffsetC,
+                                 is_lhs_vec ? globalContractDimOffset + privateOffsetC : StorageIndex(0))
+                           : OutScalar(0);
+#endif
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+        auto matScalar = (check_boundary<is_internal_block>(
+                             contract_conds && ((globalNonContractDimOffset + privateOffsetNC) < nonContractDim)))
+                             ? mat(is_lhs_vec ? globalContractDimOffset + privateOffsetC
+                                              : globalNonContractDimOffset + privateOffsetNC,
+                                   is_lhs_vec ? globalNonContractDimOffset + privateOffsetNC
+                                              : globalContractDimOffset + privateOffsetC)
+                             : OutScalar(0);
+
+        outScalar[j] = cl::sycl::mad(matScalar, vecScalar, outScalar[j]);
+        privateOffsetNC += Properties::LocalThreadSizeNC;
+      }
+      privateOffsetC += Properties::LocalThreadSizeC;
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+      in_scratch_ptr += Properties::LocalThreadSizeC;
+#endif
+    }
+
+    auto out_scratch_ptr = local_output + outScratchIndex;
+    // Each block of 16*16 element in shared memory should reduce to 16*1
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+      *out_scratch_ptr = outScalar[j];
+
+      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+    }
+    if (is_lhs_vec) {
+      nonContractId = linearLocalThreadId % Properties::LocalThreadSizeNC;
+      contractId = linearLocalThreadId / Properties::LocalThreadSizeNC;
+      outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
+    }
+
+    out_scratch_ptr = local_output + outScratchIndex;
+    EIGEN_UNROLL_LOOP
+    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex offset = Properties::LocalThreadSizeC >> 1; offset > 0; offset >>= 1) {
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (contractId < offset) {
+          StorageIndex myNeigbourId = (Properties::LocalThreadSizeNC * offset);
+          *out_scratch_ptr += out_scratch_ptr[myNeigbourId];
+        }
+      }
+      // moving to next 16 by 16 block
+      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+    }
+
+    if (contractId == 0) {
+      out_scratch_ptr = local_output + nonContractId;
+      StorageIndex global_final_offset = nonContractGroupOffset + nonContractId;
+      out_ptr += global_final_offset;
+      EIGEN_UNROLL_LOOP
+      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
+        if (check_boundary<is_internal_block>(global_final_offset < nonContractDim)) {
+          auto res = *out_scratch_ptr;
+
+          *out_ptr = res;
+          out_ptr += Properties::LocalThreadSizeNC;
+        }
+        // moving to next 16 by 16 block to ge the next 16 reduced elements
+        out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
+        if (!(is_internal_block)) global_final_offset += Properties::LocalThreadSizeNC;
+      }
+    }
+  }
+
+  template <typename InputBlockProperties, bool is_internal_block, int CFactor, int GroupSize, typename Input,
+            typename Local>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_block(const Input &inpt, Local *local_ptr,
+                                                                  const StorageIndex &linearLocalThreadId,
+                                                                  const StorageIndex &cOffset, const StorageIndex &C) {
+    local_ptr += InputBlockProperties::c_stride * linearLocalThreadId;
+    StorageIndex cIndex = cOffset;
+    for (StorageIndex cId = 0; cId < CFactor / InputBlockProperties::c_stride; cId++) {
+      if (check_boundary<is_internal_block>(cIndex + InputBlockProperties::c_stride - 1 < C)) {
+        auto val = read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
+                        InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, StorageIndex(0),
+                                                                                              cIndex, StorageIndex(1));
+        write<StorageIndex, 1, data_source::local_mem>(val, local_ptr);
+      } else {
+        EIGEN_UNROLL_LOOP
+        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
+          OutScalar val =
+              (cIndex + i < C)
+                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
+                        inpt, StorageIndex(0), cIndex + i, StorageIndex(1))
+                  : OutScalar(0);
+          write<StorageIndex, 1, data_source::local_mem>(val, local_ptr + i);
+        }
+      }
+      local_ptr += InputBlockProperties::c_stride * GroupSize;
+      cIndex += InputBlockProperties::c_stride * GroupSize;
+    }
+  }
+};
+#endif
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+
+/*!
+ * \brief GeneralScalarContraction is a template class that provides the scalar value of Tensor -Tensor contraction
+ * operation, when all the dimensions are contracting dimensions. This Kernel reduces two tensors to an scalar
+ *
+ * \tparam OutScalar: determines the output scalar type
+ *
+ * \tparam LhsScalar: determines the left-hand-side scalar type
+ *
+ * \tparam RhsScalar: determines the right-hand-side scalar type
+ *
+ * \tparam OutAccessor: determines the sycl accessor type for out put (please see the sycl-1.2.1 specification
+ * (https://www.khronos.org/registry/SYCL/specs/sycl-1.2.1.pdf) for accessor definition)
+ *
+ * \tparam LhsMapper: determines the tensor contraction mapper type for left-hand-side matrix
+ *
+ * \tparam RhsMapper: determines the tensor contraction mapper type for right-hand-side matrix
+ *
+ * \tparam StorageIndex: determines the StorageIndex Type
+ *
+ * \tparam Vectorizable: determines whether or not the vectorization is enabled for the Eigen expression.
+ *
+ * \param scratch: local memory containing tiles of LHS and RHS tensors for each work-group
+ *
+ * \param lhs: determines the left-hand-side flattened tensor (tensor mapper)
+ *
+ * \param rhs: determines the right-hand-side flattened tensor (tensor mapper)
+ *
+ * \param out_res: determines the output tensor containing the contraction result
+ *
+ * \param rng: determins the total input data size
+ */
+template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
+          typename RhsMapper, typename StorageIndex, bool Vectorizable>
+struct GeneralScalarContraction {
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
+  Scratch scratch;
+  const LhsMapper lhs;
+  const RhsMapper rhs;
+  OutAccessor out_res;
+  const StorageIndex rng;
+
+  EIGEN_DEVICE_FUNC
+  GeneralScalarContraction(Scratch scratch_, const LhsMapper lhs_, const RhsMapper rhs_, OutAccessor out_res_,
+                           const StorageIndex rng_)
+      : scratch(scratch_), lhs(lhs_), rhs(rhs_), out_res(out_res_), rng(rng_) {}
+
+  EIGEN_DEVICE_FUNC void operator()(cl::sycl::nd_item<1> itemID) {
+    auto out_ptr = out_res.get_pointer();
+    auto scratch_ptr = scratch.get_pointer().get();
+
+    StorageIndex globalid = itemID.get_global_id(0);
+    StorageIndex localid = itemID.get_local_id(0);
+    OutScalar accumulator = OutScalar(0);
+    for (StorageIndex i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      accumulator = cl::sycl::mad(lhs(0, i), rhs(i, 0), accumulator);
+    }
+    auto out_scratch_ptr = scratch_ptr + localid;
+    *out_scratch_ptr = accumulator;
+    for (StorageIndex offset = itemID.get_local_range(0) >> 1; offset > 0; offset >>= 1) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        *out_scratch_ptr = (accumulator += out_scratch_ptr[offset]);
+      }
+    }
+    if (localid == 0) {
+      out_ptr[itemID.get_group(0)] = accumulator;
+    }
+  }
+};
+#endif
+
+}  // namespace internal
+}  // namespace TensorSycl
+
+template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>,
+                       Eigen::SyclDevice>
+    : public TensorContractionEvaluatorBase<TensorEvaluator<
+          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Eigen::SyclDevice>> {
+  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
+                "SYCL tensor contraction does not support output kernels.");
+
+  typedef Eigen::SyclDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::Index StorageIndex;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Base::Storage Storage;
+  typedef typename Base::EvaluatorPointerType EvaluatorPointerType;
+  struct TripleDim {
+    const StorageIndex M;
+    const StorageIndex N;
+    const StorageIndex K;
+    TripleDim(const StorageIndex M_, const StorageIndex N_, const StorageIndex K_) : M(M_), N(N_), K(K_) {}
+  };
+  enum {
+    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+  };
+
+  static EIGEN_CONSTEXPR int LDims = Base::LDims;
+  static EIGEN_CONSTEXPR int RDims = Base::RDims;
+  static EIGEN_CONSTEXPR int ContractDims = Base::ContractDims;
+
+  typedef array<StorageIndex, LDims> left_dim_mapper_t;
+  typedef array<StorageIndex, RDims> right_dim_mapper_t;
+
+  typedef array<StorageIndex, ContractDims> contract_t;
+  typedef array<StorageIndex, LDims - ContractDims> left_nocontract_t;
+  typedef array<StorageIndex, RDims - ContractDims> right_nocontract_t;
+
+  static const int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<StorageIndex, NumDims> Dimensions;
+
+  typedef TensorEvaluator<typename Base::EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<typename Base::EvalRightArgType, Device> RightEvaluator;
+  typedef typename Eigen::internal::remove_const<typename LeftEvaluator::CoeffReturnType>::type LhsScalar;
+  typedef typename Eigen::internal::remove_const<typename RightEvaluator::CoeffReturnType>::type RhsScalar;
+
+  typedef typename LeftEvaluator::Dimensions LeftDimensions;
+  typedef typename RightEvaluator::Dimensions RightDimensions;
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered>
+  struct input_mapper_propertis {
+    static EIGEN_CONSTEXPR bool is_lhs_matrix = (LDims == 2 && ContractDims == 1) || lhs_inner_dim_contiguous;
+    static EIGEN_CONSTEXPR bool is_rhs_matrix =
+        (RDims == 2 && ContractDims == 1) || (rhs_inner_dim_contiguous && !rhs_inner_dim_reordered);
+  };
+
+  TensorEvaluator(const XprType &op, const Device &device) : Base(op, device) {}
+
+  // We need to redefine this method to make nvcc happy
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(typename Base::EvaluatorPointerType data) {
+    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
+    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
+    if (!data) {
+      this->m_result = this->m_device.get(
+          static_cast<Scalar *>(this->m_device.allocate_temp(this->dimensions().TotalSize() * sizeof(Scalar))));
+      data = this->m_result;
+    }
+    evalToSycl(data);
+    return (this->m_result != NULL);
+  }
+  const Eigen::SyclDevice &device() const { return this->m_device; }
+  void evalToSycl(typename Base::EvaluatorPointerType buffer) const {
+    if (this->m_lhs_inner_dim_contiguous) {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<true, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<true, false, false, Unaligned>(buffer);
+        }
+      }
+    } else {
+      if (this->m_rhs_inner_dim_contiguous) {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, true, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, true, false, Unaligned>(buffer);
+        }
+      } else {
+        if (this->m_rhs_inner_dim_reordered) {
+          evalTyped<false, false, true, Unaligned>(buffer);
+        } else {
+          evalTyped<false, false, false, Unaligned>(buffer);
+        }
+      }
+    }
+  }
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
+  void evalTyped(typename Base::EvaluatorPointerType buffer) const {
+    const auto triple_dim = TripleDim{this->m_i_size, this->m_j_size, this->m_k_size};
+    typedef internal::TensorContractionInputMapper<
+        LhsScalar, StorageIndex, internal::Lhs, LeftEvaluator, left_nocontract_t, contract_t,
+        PacketType<CoeffReturnType, Device>::size, lhs_inner_dim_contiguous, false, Unaligned, MakeSYCLPointer>
+        LhsMapper;
+
+    typedef internal::TensorContractionInputMapper<RhsScalar, StorageIndex, internal::Rhs, RightEvaluator,
+                                                   right_nocontract_t, contract_t,
+                                                   PacketType<CoeffReturnType, Device>::size, rhs_inner_dim_contiguous,
+                                                   rhs_inner_dim_reordered, Unaligned, MakeSYCLPointer>
+        RhsMapper;
+
+    // initialize data mappers
+    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
+                  this->m_left_contracting_strides, this->m_k_strides);
+
+    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
+                  this->m_right_contracting_strides, this->m_k_strides);
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+    if (triple_dim.M == 1 && triple_dim.N == 1) {
+      launchSC(buffer, lhs, rhs, triple_dim.K);
+    } else
+#endif
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+        if (triple_dim.M != 1 && triple_dim.N == 1) {
+      LaunchVT<false>(buffer, rhs, lhs, triple_dim.M, triple_dim.K);
+    } else if (triple_dim.M == 1 && triple_dim.N != 1) {
+      LaunchVT<true>(buffer, lhs, rhs, triple_dim.N, triple_dim.K);
+    } else  // This is equivalent of if (m!=1 && n!=1)
+#endif
+    {
+      typedef input_mapper_propertis<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered>
+          inpt_mapper_properties;
+#ifndef EIGEN_SYCL_DISABLE_SKINNY
+      bool skinny = false;
+      auto platform_name = this->device().getPlatformName();
+      // This is based on empirical calculation for AMD r9-nano and Fiji
+      if (platform_name.find("AMD") == 0) {
+        skinny = (triple_dim.M < triple_dim.K || triple_dim.N < triple_dim.K) &&
+                 ((triple_dim.M < 1024 && triple_dim.N < 1024) ||
+                  (uint64_t(triple_dim.M * triple_dim.N) < uint64_t(triple_dim.K)));
+      } else {
+        skinny = (((std::max(triple_dim.K, triple_dim.N) / std::min(triple_dim.K, triple_dim.N)) > 100) ||
+                  ((std::max(triple_dim.K, triple_dim.M) / std::min(triple_dim.K, triple_dim.M)) > 100) ||
+                  ((std::max(triple_dim.N, triple_dim.M) / std::min(triple_dim.N, triple_dim.M)) > 100));
+      }
+      if (skinny)
+        adjustTT<true, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
+      else
+#endif  // EIGEN_SYCL_DISABLE_SKINNY
+        adjustTT<false, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
+    }
+  }
+
+  template <bool skinny, typename input_mapper_properties, typename LhsMapper, typename RhsMapper>
+  void EIGEN_ALWAYS_INLINE adjustTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                                    const TripleDim &triple_dim) const {
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
+    if (device().has_local_memory()) {
+      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 16> PanelParameters;
+      launchTT<TensorSycl::internal::contraction_type::local, skinny, input_mapper_properties, PanelParameters>(
+          buffer, lhs, rhs, triple_dim);
+    }
+#endif
+#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF
+    if (!(device().has_local_memory())) {
+      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 4> PanelParameters;
+      launchTT<TensorSycl::internal::contraction_type::no_local, skinny, input_mapper_properties, PanelParameters>(
+          buffer, lhs, rhs, triple_dim);
+    }
+#endif
+  }
+
+  template <TensorSycl::internal::contraction_type ct, bool skinny, typename input_mapper_properties,
+            typename Properties, typename LhsMapper, typename RhsMapper>
+  void launchTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                const TripleDim &triple_dim) const {
+    const StorageIndex roundUpM = Eigen::TensorSycl::internal::roundUp(triple_dim.M, Properties::TileSizeDimM);
+    const StorageIndex roundUpN = Eigen::TensorSycl::internal::roundUp(triple_dim.N, Properties::TileSizeDimN);
+    const StorageIndex groupSizeM = roundUpM / Properties::TileSizeDimM;
+    const StorageIndex groupSizeN = roundUpN / Properties::TileSizeDimN;
+
+    const StorageIndex roundUpK = Eigen::TensorSycl::internal::roundUp(triple_dim.K, Properties::TileSizeDimK);
+    StorageIndex totalTilesK = roundUpK / Properties::TileSizeDimK;
+    StorageIndex groupSizeK =
+        skinny
+            ? std::max(std::min(totalTilesK,
+                                (StorageIndex)(device().getPowerOfTwo(device().getNumSyclMultiProcessors(), true) * 4) /
+                                    (groupSizeM * groupSizeN)),
+                       StorageIndex(1))
+            : StorageIndex(1);
+
+    const StorageIndex numTilesPerGroup = Eigen::TensorSycl::internal::roundUp(totalTilesK, groupSizeK) / groupSizeK;
+
+    const StorageIndex totalGroupSize = groupSizeM * groupSizeN * groupSizeK;
+
+    const StorageIndex localRange = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
+    const StorageIndex globalRange = totalGroupSize * localRange;
+
+    const StorageIndex scratchSize = (ct == TensorSycl::internal::contraction_type::local)
+                                         ? ((Properties::DoubleBuffer + 1) *
+                                            (Properties::TileSizeDimM + Properties::BC) * (Properties::TileSizeDimK)) +
+                                               ((Properties::DoubleBuffer + 1) * (Properties::TileSizeDimK) *
+                                                (Properties::TileSizeDimN + Properties::BC))
+                                         : StorageIndex(1);
+
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (groupSizeK == 1) {
+      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
+                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
+                                                            PacketAccess, input_mapper_properties, true, ct>
+          ContractKernelName;
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+          lhs, rhs, buffer, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup, triple_dim);
+    } else {
+      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
+                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
+                                                            PacketAccess, input_mapper_properties, false, ct>
+          ContractKernelName;
+      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
+          device().allocate_temp(triple_dim.M * triple_dim.N * groupSizeK * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+          lhs, rhs, tmp_global_accessor, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup,
+          triple_dim);
+
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      auto op = Op();
+      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
+                                                               EvaluatorPointerType, Op>
+          ReductionKernel;
+
+      device().template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
+          tmp_global_accessor, buffer,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(StorageIndex(
+                                    Eigen::TensorSycl::internal::roundUp(triple_dim.M * triple_dim.N, localRange))),
+                                cl::sycl::range<1>(localRange)),
+          StorageIndex(1), op, StorageIndex(triple_dim.M * triple_dim.N), groupSizeK);
+
+      device().deallocate_temp(temp_pointer);
+    }
+  }
+
+#ifndef EIGEN_SYCL_DISABLE_GEMV
+  template <bool is_lhs_vec, typename VectorMapper, typename TensorMapper, typename StorageIndex>
+  void EIGEN_ALWAYS_INLINE LaunchVT(EvaluatorPointerType buffer, const VectorMapper &vec, const TensorMapper &mat,
+                                    StorageIndex NC, StorageIndex C) const {
+    const StorageIndex nonContractDim = NC;
+    EIGEN_CONSTEXPR StorageIndex NCFactor = 1;
+    EIGEN_CONSTEXPR StorageIndex CFactor = 1;
+    EIGEN_CONSTEXPR StorageIndex NCWindow = 16;
+    typedef Eigen::TensorSycl::internal::TVPanelSize<CoeffReturnType, StorageIndex, NCWindow, CFactor, NCFactor>
+        Properties;
+    const StorageIndex roundUpC = Eigen::TensorSycl::internal::roundUp(C, Properties::TileSizeDimC);
+    const StorageIndex cNumGroups = roundUpC / (Properties::LocalThreadSizeC * Properties::WorkLoadPerThreadC);
+    const StorageIndex roundUpNC = Eigen::TensorSycl::internal::roundUp(nonContractDim, Properties::TileSizeDimNC);
+    const StorageIndex nCNumGroups = roundUpNC / (Properties::LocalThreadSizeNC * Properties::WorkLoadPerThreadNC);
+    const StorageIndex globalRange =
+        (roundUpNC / (Properties::WorkLoadPerThreadNC)) * (roundUpC / (Properties::WorkLoadPerThreadC));
+    const StorageIndex localRange = Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC;
+    const StorageIndex scratchSize =
+        (Properties::WorkLoadPerThreadNC + CFactor) * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (cNumGroups > 1) {
+      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
+                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
+                                                               is_lhs_vec, false>
+          ContractKernelName;
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(device().allocate_temp(nonContractDim * cNumGroups * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+          vec, mat, tmp_global_accessor, thread_range, scratchSize, nCNumGroups, nonContractDim, C);
+
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
+                                                               EvaluatorPointerType, Op>
+          ReductionKernel;
+
+      device().template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
+          tmp_global_accessor, buffer,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(Eigen::TensorSycl::internal::roundUp(nonContractDim, localRange)),
+                                cl::sycl::range<1>(localRange)),
+          StorageIndex(1), Op(), nonContractDim, cNumGroups);
+
+      device().deallocate_temp(temp_pointer);
+    } else {
+      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
+                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
+                                                               is_lhs_vec, true>
+          ContractKernelName;
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
+          vec, mat, buffer, thread_range, scratchSize, nCNumGroups, nonContractDim, C);
+    }
+  }
+#endif
+
+#ifndef EIGEN_SYCL_DISABLE_SCALAR
+  template <typename LhsMapper, typename RhsMapper>
+  EIGEN_ALWAYS_INLINE void launchSC(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
+                                    StorageIndex K) const {
+    EIGEN_STATIC_ASSERT(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
+                          (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
+                        "The Local thread size must be a power of 2 for the reduction "
+                        "operation");
+    EIGEN_CONSTEXPR StorageIndex local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
+
+    // Here we force the code not to be more than 2-step reduction: Our empirical research shows that if each thread
+    // reduces at least 512 elementss individually, we get better performance.
+    const StorageIndex num_work_group = ((K + (512 * local_range - 1)) / (512 * local_range) > 1 ? local_range : 1);
+    const StorageIndex global_range = num_work_group * local_range;
+
+    typedef Eigen::TensorSycl::internal::GeneralScalarContraction<
+        CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType, LhsMapper, RhsMapper, StorageIndex, false>
+        ContractKernelName;
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+    if (num_work_group > 1) {
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(device().allocate_temp(num_work_group * sizeof(CoeffReturnType)));
+      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, tmp_global_accessor,
+                                                                                    thread_range, local_range, K);
+      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
+      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
+                                                          EvaluatorPointerType, StorageIndex, local_range>
+          GenericRKernel;
+      device().template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
+          tmp_global_accessor, buffer,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(local_range), cl::sycl::range<1>(local_range)), local_range, Op());
+
+      device().deallocate_temp(temp_pointer);
+    } else {
+      device().template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, buffer, thread_range,
+                                                                                    local_range, K);
+    }
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    this->m_leftImpl.cleanup();
+    this->m_rightImpl.cleanup();
+
+    if (this->m_result) {
+      this->m_device.deallocate_temp(this->m_result);
+      this->m_result = NULL;
+    }
+  }
+  // The placeholder accessors must bound to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    this->m_leftImpl.bind(cgh);
+    this->m_rightImpl.bind(cgh);
+    this->m_result.bind(cgh);
+  }
+};
+}  // namespace Eigen
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
new file mode 100644
index 0000000..21be6ea
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
@@ -0,0 +1,1679 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
+
+// evaluator for thread pool device
+#ifdef EIGEN_USE_THREADS
+
+namespace Eigen {
+
+template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> :
+    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> > {
+
+  typedef ThreadPoolDevice Device;
+
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
+  typedef TensorContractionEvaluatorBase<Self> Base;
+
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  enum {
+    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
+  };
+
+  // Most of the code is assuming that both input tensors are ColMajor. If the
+  // inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
+  // If we want to compute A * B = C, where A is LHS and B is RHS, the code
+  // will pretend B is LHS and A is RHS.
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
+  typedef typename internal::conditional<
+    static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
+
+  static const int LDims =
+      internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
+  static const int RDims =
+      internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
+  static const int ContractDims = internal::array_size<Indices>::value;
+
+  typedef array<Index, LDims> left_dim_mapper_t;
+  typedef array<Index, RDims> right_dim_mapper_t;
+
+  typedef array<Index, ContractDims> contract_t;
+  typedef array<Index, LDims - ContractDims> left_nocontract_t;
+  typedef array<Index, RDims - ContractDims> right_nocontract_t;
+
+  static const int NumDims = LDims + RDims - 2 * ContractDims;
+
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  // typedefs needed in evalTo
+  typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
+  typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
+  typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
+
+  typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
+  typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
+
+  TensorEvaluator(const XprType& op, const Device& device) :
+      Base(op, device) {}
+
+  template <int Alignment>
+  void evalProduct(Scalar* buffer) const {
+    evalProductImpl<NoCallback, Alignment>(buffer, NoCallback());
+  }
+
+  template <typename EvalToCallback, int Alignment>
+  void evalProductAsync(Scalar* buffer, EvalToCallback done) const {
+    evalProductImpl<EvalToCallback, Alignment>(buffer, std::move(done));
+  }
+
+  template <typename DoneCallback, int Alignment>
+  void evalProductImpl(Scalar* buffer, DoneCallback done) const {
+    // This function computes a lot of heuristics in multiple steps, and it
+    // also has multiple exit points. To keep it sane, readable and all in one
+    // place, sync/async execution decision is made at runtime at the very end.
+    //
+    // (1) In sync mode we allocate Context on the stack, submit computations
+    //     to the device thread pool, and block on a barrier until it is
+    //     completed.
+    //
+    // (2) In async mode we allocate Context on the heap, and after all tasks
+    //     are finished, we call provided the done callback, and delete a
+    //     context from the heap.
+    //
+    // (*) EvalParallelContext & EvalShardedByInnerDimContext owns all the state
+    // and temporary buffers, requried for executing the tensor contraction.
+    // They are responsible for cleaning it up after contraction is done.
+    static const bool IsEvalInSyncMode =
+        std::is_same<DoneCallback, NoCallback>::value;
+
+    const Index m = this->m_i_size;
+    const Index n = this->m_j_size;
+    const Index k = this->m_k_size;
+    if (m == 0 || n == 0 || k == 0) return;
+
+    // Compute a set of algorithm parameters:
+    // - kernel block sizes (bm, bn, bk)
+    // - task grain sizes (number of kernels executed per task: gm, gn)
+    // - number of threads
+    // - sharding by row/column
+    // - parallel packing or first lhs then rhs
+    // and some derived parameters:
+    // - number of tasks (nm, nn, nk)
+    // - number of kernels (nm0, nn0)
+    // Unfortunately, all these parameters are tightly interdependent.
+    // So in some cases we first compute approximate values, then compute other
+    // values based on these approximations and then refine the approximations.
+
+    // There are lots of heuristics here. There is some reasoning behind them,
+    // but ultimately they are just tuned on contraction benchmarks for
+    // different input configurations, thread counts and instruction sets.
+    // So feel free to question any of them.
+
+    // Compute whether we want to shard by row or by column.
+    // This is a first approximation, it will be refined later. Since we don't
+    // know number of threads yet we use 2, because what's we are most
+    // interested in at this point is whether it makes sense to use
+    // parallelization at all or not.
+    bool shard_by_col = shardByCol(m, n, 2);
+
+    // First approximation of kernel blocking sizes.
+    // Again, we don't know number of threads yet, so we use 2.
+    Index bm, bn, bk;
+    if (shard_by_col) {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
+                                          internal::ShardByCol>
+          blocking(k, m, n, 2);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    } else {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
+                                          internal::ShardByRow>
+          blocking(k, m, n, 2);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    }
+
+    // Compute optimal number of threads.
+    // Note: we use bk instead of k here because we are interested in amount of
+    // _parallelizable_ computations, and computations are not parallelizable
+    // across k dimension.
+    const TensorOpCost cost =
+        contractionCost(m, n, bm, bn, bk, shard_by_col, false);
+    int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
+        static_cast<double>(n) * m, cost, this->m_device.numThreads());
+    int num_threads_by_k = numThreadsInnerDim(m, n, k);
+    if (shardByInnerDim(m, n, k, num_threads, num_threads_by_k)) {
+      // We are in the scenario where it is more effective to shard by the
+      // inner dimension.
+      if (IsEvalInSyncMode) {
+        EvalShardedByInnerDimContext<DoneCallback> ctx(
+            this, num_threads_by_k, buffer, m, n, k, std::move(done));
+        ctx.template run<Alignment>();
+      } else {
+        auto* ctx = new EvalShardedByInnerDimContext<DoneCallback>(
+            this, num_threads_by_k, buffer, m, n, k, std::move(done));
+        ctx->template runAsync<Alignment>();
+      }
+
+      return;
+    }
+
+    // TODO(dvyukov): this is a stop-gap to prevent regressions while the cost
+    // model is not tuned. Remove this when the cost model is tuned.
+    if (n == 1) num_threads = 1;
+
+    if (num_threads == 1) {
+      TENSOR_CONTRACTION_DISPATCH(this->template evalProductSequential,
+                                  Unaligned, (buffer));
+      if (!IsEvalInSyncMode) done();
+      return;
+    }
+
+    // Now that we know number of threads, recalculate sharding and blocking.
+    shard_by_col = shardByCol(m, n, num_threads);
+    if (shard_by_col) {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
+                                          internal::ShardByCol>
+          blocking(k, m, n, num_threads);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    } else {
+      internal::TensorContractionBlocking<Scalar, LhsScalar, RhsScalar, Index,
+                                          internal::ShardByRow>
+          blocking(k, m, n, num_threads);
+      bm = blocking.mc();
+      bn = blocking.nc();
+      bk = blocking.kc();
+    }
+
+    // Number of kernels for each dimension.
+    Index nm0 = divup(m, bm);
+    Index nn0 = divup(n, bn);
+    Index nk = divup(k, bk);
+
+    // Calculate task grain size (number of kernels executed per task).
+    // This task size coarsening serves two purposes:
+    // 1. It reduces per-task overheads including synchronization overheads.
+    // 2. It allows to use caches better (reuse the same packed rhs in several
+    // consecutive kernels).
+    Index gm = 1;
+    Index gn = 1;
+    // If we are sharding by column, then we prefer to reduce rows first.
+    if (shard_by_col) {
+      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
+      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
+    } else {
+      gn = coarsenN(m, n, bm, bn, bk, gm, num_threads, shard_by_col);
+      gm = coarsenM(m, n, bm, bn, bk, gn, num_threads, shard_by_col);
+    }
+    // Number of tasks in each dimension.
+    Index nm = divup(nm0, gm);
+    Index nn = divup(nn0, gn);
+
+    // If there is enough concurrency in the sharding dimension, we choose not
+    // to paralellize by the other dimension, and execute all kernels in sync
+    // mode. This reduces parallelism from the nm x nn down to nn
+    // (shard_by_col==true) or nm (shard_by_col==false).
+    const Index sharding_dim_tasks = shard_by_col ? nn : nm;
+    const int num_worker_threads = this->m_device.numThreadsInPool();
+
+    // With small number of threads we want to make sure that we do not reduce
+    // parallelism too much. With large number of threads we trade maximum
+    // parallelism for better memory locality.
+    const float oversharding_factor =
+        num_worker_threads <= 4  ? 8.0 :
+        num_worker_threads <= 8  ? 4.0 :
+        num_worker_threads <= 16 ? 2.0 :
+        num_worker_threads <= 32 ? 1.0 :
+        num_worker_threads <= 64 ? 0.8 : /* num_worker_threads > 64 */ 0.6;
+
+    const bool parallelize_by_sharding_dim_only =
+        sharding_dim_tasks >= oversharding_factor * num_worker_threads;
+
+    // Last by not least, decide whether we want to issue both lhs and rhs
+    // packing in parallel; or issue lhs packing first, and then issue rhs
+    // packing when lhs packing completes (for !shard_by_col lhs and rhs are
+    // swapped). Parallel packing allows more parallelism (for both packing and
+    // kernels), while sequential packing provides better locality (once
+    // a thread finishes rhs packing it proceed to kernels with that rhs).
+    // First, we are interested in parallel packing if there are few tasks.
+    bool parallel_pack = num_threads >= nm * nn;
+    // Also do parallel packing if all data fits into L2$.
+    if (m * bk * Index(sizeof(LhsScalar)) + n * bk * Index(sizeof(RhsScalar)) <=
+        l2CacheSize() * num_threads)
+      parallel_pack = true;
+    // But don't do it if we will use each rhs only once. Locality seems to be
+    // more important in this case.
+    if ((shard_by_col ? nm : nn) == 1) parallel_pack = false;
+    // Also don't get in the way of parallelize_by_sharding_dim_only
+    // optimization.
+    if (parallelize_by_sharding_dim_only) parallel_pack = false;
+
+    // TODO(ezhulnev): With if contexpr we don't need SyncEvalParallelContext.
+    if (IsEvalInSyncMode) {
+#define CONTEXT_ARGS                                                        \
+  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, \
+   nn0, shard_by_col, parallel_pack, parallelize_by_sharding_dim_only,      \
+   NoCallback())                                                            \
+      .run()
+      TENSOR_CONTRACTION_DISPATCH(SyncEvalParallelContext, Alignment,
+                                  CONTEXT_ARGS);
+#undef CONTEXT_ARGS
+
+    } else {
+#define CONTEXT_ARGS                                                        \
+  (this, num_threads, buffer, m, n, k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, \
+   nn0, shard_by_col, parallel_pack, parallelize_by_sharding_dim_only,      \
+   std::move(done))
+      TENSOR_CONTRACTION_ASYNC_DISPATCH(EvalParallelContext, DoneCallback,
+                                        Alignment, CONTEXT_ARGS, run());
+#undef CONTEXT_ARGS
+    }
+  }
+
+  // ------------------------------------------------------------------------ //
+
+  // Dummy struct to represent an empty DoneCallback.
+
+  struct NoCallback {
+    void operator()() {
+      eigen_assert(false && "NoCallback should never be called");
+    }
+  };
+
+  // ------------------------------------------------------------------------ //
+
+  template <typename DoneCallback, typename Context>
+  class EvalParallelNotification;
+
+  // Synchronous evaluation notification that blocks caller thread in Wait().
+  template <typename Context>
+  class EvalParallelNotification<NoCallback, Context> {
+   public:
+    EvalParallelNotification(Context*, NoCallback) {}
+    void Notify() { done_.Notify(); }
+    void Wait() { done_.Wait(); }
+   private:
+    Eigen::Notification done_;
+  };
+
+  // Asynchronous evaluation notification that does not block in Wait().
+  template <typename DoneCallback, typename Context>
+  class EvalParallelNotification {
+   public:
+    EvalParallelNotification(Context* ctx, DoneCallback done)
+        : ctx_(ctx), done_(std::move(done)) {}
+
+    void Notify() {
+      // Make a copy of done callback, because it will be destructed when we
+      // will delete context in the next line (EvalParallelNotification is a
+      // data member of EvalParallelContext class).
+      DoneCallback done_copy = std::move(done_);
+
+      // Delete parallel evaluation context.
+      delete ctx_;
+
+      // Now safely call the done callback.
+      done_copy();
+    }
+
+    void Wait() {}
+
+   private:
+    Context* ctx_;
+    DoneCallback done_;
+  };
+
+  // Context orchestrates sync/async parallel contraction evaluation. When it is
+  // executed in asynchronous mode, it owns all the shared state that might be
+  // accessible by block packing and kernel tasks.
+
+  template <typename DoneCallback, bool lhs_inner_dim_contiguous,
+            bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered,
+            int Alignment>
+  class EvalParallelContext {
+   public:
+    typedef internal::TensorContractionInputMapper<
+        LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t,
+        contract_t, internal::packet_traits<LhsScalar>::size,
+        lhs_inner_dim_contiguous, false, Unaligned>
+        LhsMapper;
+    typedef internal::TensorContractionInputMapper<
+        RhsScalar, Index, internal::Rhs, RightEvaluator, right_nocontract_t,
+        contract_t, internal::packet_traits<RhsScalar>::size,
+        rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Unaligned>
+        RhsMapper;
+
+    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+
+    typedef internal::TensorContractionKernel<
+        Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
+        TensorContractionKernel;
+
+    typedef typename TensorContractionKernel::LhsBlock LhsBlock;
+    typedef typename TensorContractionKernel::RhsBlock RhsBlock;
+    typedef typename TensorContractionKernel::BlockMemHandle BlockMemHandle;
+
+    EvalParallelContext(const Self* self, int num_threads, Scalar* buffer,
+                        Index tm, Index tn, Index tk, Index bm, Index bn,
+                        Index bk, Index nm, Index nn, Index nk, Index gm,
+                        Index gn, Index nm0, Index nn0, bool shard_by_col,
+                        bool parallel_pack,
+                        bool parallelize_by_sharding_dim_only,
+                        DoneCallback done)
+        : created_by_thread_id_(std::this_thread::get_id()),
+          done_(this, std::move(done)),
+          device_(self->m_device),
+          lhs_(self->m_leftImpl, self->m_left_nocontract_strides,
+               self->m_i_strides, self->m_left_contracting_strides,
+               self->m_k_strides),
+          rhs_(self->m_rightImpl, self->m_right_nocontract_strides,
+               self->m_j_strides, self->m_right_contracting_strides,
+               self->m_k_strides),
+          buffer_(buffer),
+          output_(buffer, tm),
+          output_kernel_(self->m_output_kernel),
+          tensor_contraction_params_(self->m_tensor_contraction_params),
+          num_threads_(num_threads),
+          shard_by_col_(shard_by_col),
+          parallel_pack_(parallel_pack),
+          parallelize_by_sharding_dim_only_(parallelize_by_sharding_dim_only),
+          m_(tm),
+          n_(tn),
+          k_(tk),
+          bm_(bm),
+          bn_(bn),
+          bk_(bk),
+          nm_(nm),
+          nn_(nn),
+          nk_(nk),
+          gm_(gm),
+          gn_(gn),
+          nm0_(nm0),
+          nn0_(nn0),
+          kernel_(m_, k_, n_, bm_, bk_, bn_),
+          num_thread_local_allocations_(0),
+          // We reserve 2X more capacity for a thread local values, than the
+          // number of threads in the pool to efficiently handle task stealing
+          // by threads that are not managed by the pool.
+          thread_local_capacity(2 * (parallelize_by_sharding_dim_only_
+                                         ? device_.numThreadsInPool()
+                                         : 0)),
+          // We will use only one of the Lhs/Rhs thread local storage depending
+          // on the shard_by_col value and we parallelize by sharding dim ONLY.
+          lhs_thread_local_blocks_(shard_by_col_ ? 0 : thread_local_capacity,
+                                   {*this}, {*this}),
+          rhs_thread_local_blocks_(shard_by_col_ ? thread_local_capacity : 0,
+                                   {*this}, {*this}) {
+      // These two options are mutually exclusive.
+      eigen_assert(!(parallel_pack && parallelize_by_sharding_dim_only));
+
+      for (Index x = 0; x < P; x++) {
+        // Normal number of notifications for k slice switch is
+        // nm_ + nn_ + nm_ * nn_. However, first P - 1 slices will receive only
+        // nm_ + nn_ notifications, because they will not receive notifications
+        // from preceding kernels.
+        state_switch_[x] =
+            x == 0
+                ? 1
+                : (parallel_pack_ ? nn_ + nm_ : (shard_by_col_ ? nn_ : nm_)) +
+                      (x == P - 1 ? nm_ * nn_ : 0);
+        state_packing_ready_[x] =
+            parallel_pack_ ? 0 : (shard_by_col_ ? nm_ : nn_);
+        state_kernel_[x] = new std::atomic<uint8_t>*[nm_];
+        for (Index m = 0; m < nm_; m++) {
+          state_kernel_[x][m] = new std::atomic<uint8_t>[nn_];
+          // Kernels generally receive 3 notifications (previous kernel + 2
+          // packing), but the first slice won't get notifications from previous
+          // kernels.
+          for (Index n = 0; n < nn_; n++)
+            state_kernel_[x][m][n].store(
+                (x == 0 ? 0 : 1) + (parallel_pack_ ? 2 : 1),
+                std::memory_order_relaxed);
+        }
+      }
+
+      // Allocate memory for packed rhs/lhs matrices.
+      packed_mem_ = kernel_.allocateSlices(            //
+          device_,                                     //
+          /*num_lhs=*/nm0_,                            //
+          /*num_rhs=*/nn0_,                            //
+          /*num_slices=*/std::min<Index>(nk_, P - 1),  //
+          packed_lhs_, packed_rhs_);
+
+      if (parallelize_by_sharding_dim_only_) {
+        const int num_worker_threads = device_.numThreadsInPool();
+
+        if (shard_by_col) {
+          can_use_thread_local_packed_ = new std::atomic<bool>[nn_];
+          for (int i = 0; i < nn_; ++i)
+            can_use_thread_local_packed_[i].store(true,
+                                                  std::memory_order_relaxed);
+
+          Index num_blocks = num_worker_threads * gn_;
+          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
+              device_,                                              //
+              /*num_lhs=*/0,                                        //
+              /*num_rhs=*/num_blocks,                               //
+              /*num_slices=*/1,                                     //
+              /*lhs_blocks=*/nullptr, &rhs_thread_local_pre_allocated_);
+
+        } else {
+          can_use_thread_local_packed_ = new std::atomic<bool>[nm_];
+          for (int i = 0; i < nm_; ++i)
+            can_use_thread_local_packed_[i].store(true,
+                                                  std::memory_order_relaxed);
+
+          Index num_blocks = num_worker_threads * gm_;
+          thread_local_pre_alocated_mem_ = kernel_.allocateSlices(  //
+              device_,                                              //
+              /*num_lhs=*/num_blocks,                               //
+              /*num_rhs=*/0,                                        //
+              /*num_slices=*/1, &lhs_thread_local_pre_allocated_,   //
+              /*rhs_blocks=*/nullptr);
+        }
+      }
+    }
+
+    ~EvalParallelContext() {
+      for (Index x = 0; x < P; x++) {
+        for (Index m = 0; m < nm_; m++) delete[] state_kernel_[x][m];
+        delete[] state_kernel_[x];
+      }
+      kernel_.deallocate(device_, packed_mem_);
+      if (parallelize_by_sharding_dim_only_) {
+        kernel_.deallocate(device_, thread_local_pre_alocated_mem_);
+        delete[] can_use_thread_local_packed_;
+      }
+    }
+
+    void run() {
+      // Kick off packing of the first slice.
+      signal_switch(0, 1);
+
+      // Wait for overall completion.
+      //
+      // If parallel evaluation is executed in async mode, this is a no-op, and
+      // Wait() will return immediately. In synchronous mode it will block the
+      // caller thread until it will receive notification from last task.
+      //
+      // In async mode, last task when completed will call done callback from
+      // the same thread, and will delete this context.
+      //
+      // TODO(dvyukov): This wait can lead to deadlock if contraction is
+      // evaluated in synchronous mode. If nthreads contractions are
+      // concurrently submitted from worker threads, this wait will block all
+      // worker threads and the system will deadlock.
+      done_.Wait();
+    }
+
+   private:
+    std::thread::id created_by_thread_id_;
+
+    // This notification is specialized on the type of DoneCallback and can be
+    // blocking or non-blocking.
+    EvalParallelNotification<DoneCallback, EvalParallelContext> done_;
+
+    const Device& device_;
+    LhsMapper lhs_;
+    RhsMapper rhs_;
+    Scalar* const buffer_;
+    OutputMapper output_;
+    OutputKernelType output_kernel_;
+    TensorContractionParams tensor_contraction_params_;
+    const int num_threads_;
+    const bool shard_by_col_;
+    const bool parallel_pack_;
+    const bool parallelize_by_sharding_dim_only_;
+    // Matrix sizes.
+    const Index m_;
+    const Index n_;
+    const Index k_;
+    // Block sizes.
+    const Index bm_;
+    const Index bn_;
+    const Index bk_;
+    // Number of tasks.
+    const Index nm_;
+    const Index nn_;
+    const Index nk_;
+    // Task grain sizes (number of kernels executed per task).
+    const Index gm_;
+    const Index gn_;
+    // Number of blocks (this is different from ni_/nn_ because of task size
+    // coarsening).
+    const Index nm0_;
+    const Index nn0_;
+    // Tensor contraction kernel.
+    TensorContractionKernel kernel_;
+
+    // Parallelization strategy.
+    //
+    // Blocks related to the same k block can run in parallel because they write
+    // to different output blocks. So we parallelize within k slices, this
+    // gives us parallelism level of m x n. Before we can start any kernels
+    // related to k-th slice, we need to issue m lhs packing tasks and n rhs
+    // packing tasks.
+    //
+    // However, there is a bottleneck when we are finishing kernels for k-th
+    // slice (at the very end there is only 1 runnable kernel). To mitigate this
+    // bottleneck we allow kernels from k-th and k+1-th slices to run in
+    // parallel. Note that (m, n, k) and (m, n, k+1) kernels write to the same
+    // output block, so they must not run in parallel.
+    //
+    // This gives us the following dependency graph.
+    // On each k slice we have m x n kernel tasks, m lhs paking tasks and n rhs
+    // packing tasks.
+    // Kernel (m, n, k) can start when:
+    //  - kernel (m, n, k-1) has finished
+    //  - lhs packing (m, k) has finished
+    //  - rhs packing (n, k) has finished
+    // Lhs/rhs packing can start when:
+    //  - all k-1 packing has finished (artificially imposed to limit amount of
+    //  parallel packing)
+    //
+    // On top of that we limit runnable tasks to two consecutive k slices.
+    // This is done to limit amount of memory we need for packed lhs/rhs
+    // (for each k slice we need m*bk + n*bk memory in packed_lhs_/packed_rhs_).
+    //
+    // state_switch_ tracks when we are ready to switch to the next k slice.
+    // state_kernel_[m][n] tracks when we are ready to kick off kernel (m, n).
+    // These variable are rolling over 3 consecutive k slices: first two we are
+    // actively executing + one to track completion of kernels in the second
+    // slice.
+    static const Index P = 3;
+
+    // Handle to the allocated temporary storage for Lhs/Rhs blocks.
+    BlockMemHandle packed_mem_;
+    std::vector<LhsBlock> packed_lhs_[P - 1];
+    std::vector<RhsBlock> packed_rhs_[P - 1];
+
+    // If we choose to parallelize only by the sharding dimension, each thread
+    // will have it's own "thead local" (not a c++ thread local storage) memory
+    // for packed_lhs or packed_rhs (shard_by_col = false of true). This memory
+    // can't be passed to a kernel that might execute on a different thread.
+    //
+    // In practice when we are ready to pack memory for the sharding dimension
+    // (rhs if shard_by_col==true) of the K-th slice, all kernels for K-1 slice
+    // already computed (99% of the time), and we can pack data into the thread
+    // local storage, and guarantee that all the kernels will be executed
+    // immediately in the same thread. This significantly increases L1 cache hit
+    // ratio and reduces pressure on the memory bus.
+    //
+    // It's still possible that kernel for the K-th slice will be ready before
+    // completion of the K-1 kernel, so we have to allocate "global" packed_lhs_
+    // and packed_rhs_ to allow kernels to be executed later on a thread
+    // different from the thread that was used for packing.
+
+    // Handle for pre-allocated thread local memory buffers.
+    BlockMemHandle thread_local_pre_alocated_mem_;
+
+    // Only one of these will be initialized depending on shard_by_col value
+    // (the size will be `num_worker_threads * num_grains_in_the_sharding_dim`).
+    std::vector<LhsBlock> lhs_thread_local_pre_allocated_;
+    std::vector<RhsBlock> rhs_thread_local_pre_allocated_;
+
+    // How many thread local blocks were already allocated.
+    std::atomic<int> num_thread_local_allocations_;
+    const int thread_local_capacity;
+
+    // We will use pre-allocated Lhs/Rhs blocks defined above, if the number of
+    // unique threads in a system is below or equal to the number of threads in
+    // a thread pool. We will fallback on dynamic memory allocation after that.
+
+    // ThreadLocalBlocks is a container for Lhs or Rhs thread local buffers. Its
+    // size is equal to the grain size in Lhs/Rhs sharding dimension.
+    template <typename BlockType>
+    class ThreadLocalBlocks {
+     public:
+      ThreadLocalBlocks() = default;
+
+      ThreadLocalBlocks(BlockType* base, size_t grain_size)
+          : is_pre_allocated_(true),
+            thread_local_pre_allocated_base_(base),
+            grain_size_(grain_size) {}
+
+      ThreadLocalBlocks(BlockMemHandle mem_handle,
+                        std::vector<BlockType> blocks)
+          : is_pre_allocated_(false),
+            mem_handle_(std::move(mem_handle)),
+            blocks_(std::move(blocks)) {}
+
+      BlockType& block(int grain_index) {
+        eigen_assert(grain_index >= 0);
+        eigen_assert(static_cast<size_t>(grain_index) < size());
+        return is_pre_allocated_ ? thread_local_pre_allocated_base_[grain_index]
+                                 : blocks_[grain_index];
+      }
+
+      void Release(EvalParallelContext& ctx) const {
+        if (!is_pre_allocated_) {
+          ctx.kernel_.deallocate(ctx.device_, mem_handle_);
+        }
+      }
+
+      size_t size() const {
+        return is_pre_allocated_ ? grain_size_ : blocks_.size();
+      }
+
+     private:
+      bool is_pre_allocated_;
+
+      // Reuse pre-allocated thread local buffers.
+      BlockType* thread_local_pre_allocated_base_ = nullptr;
+      size_t grain_size_ = 0;
+
+      // These will be initialized only if `is_pre_allocated == false`.
+      BlockMemHandle mem_handle_{};
+      std::vector<BlockType> blocks_;
+    };
+
+    // ThreadLocalBlocksInitialize callable does custom thread local blocks
+    // initialization, and will reuse pre-allocated buffers if possible, or will
+    // dynamically allocate new memory.
+    //
+    // Lhs/Rhs blocks might be of the same type, so we have to pass explicitly
+    // for what side do we plan to do block allocation.
+    template <typename BlockType, bool is_rhs>
+    class ThreadLocalBlocksInitialize {
+      static constexpr bool kIsLhs =
+          !is_rhs && std::is_same<BlockType, LhsBlock>::value;
+      static const bool kIsRhs =
+          is_rhs && std::is_same<BlockType, RhsBlock>::value;
+      static_assert(kIsLhs || kIsRhs, "Unkown block type");
+
+      using Blocks = ThreadLocalBlocks<BlockType>;
+
+     public:
+      ThreadLocalBlocksInitialize(EvalParallelContext& ctx)
+          : ctx_(ctx),
+            num_worker_threads_(ctx_.device_.numThreadsInPool()) {}
+
+      void operator()(Blocks& blocks) {
+        const int n = ctx_.num_thread_local_allocations_.fetch_add(
+            1, std::memory_order_relaxed);
+
+        if (n >= num_worker_threads_) {
+          ThreadLocalBlocksAllocator<is_rhs>::allocate(ctx_, blocks);
+        } else {
+          ThreadLocalBlocksAllocator<is_rhs>::reuse(ctx_, n, blocks);
+        }
+      }
+
+     private:
+      // NOTE(ezhulenev): Without 'if constexpr' we have to put calls to
+      // TensorContractionKernel::allocateSlices into template specializations.
+      // Also explicit specializations are not allowed at class scope in C++03,
+      // EvalCtx type parameter is just a workaround for that limitation.
+      template <bool pack_rhs, typename EvalCtx = EvalParallelContext>
+      struct ThreadLocalBlocksAllocator;
+
+      template <typename EvalCtx>
+      struct ThreadLocalBlocksAllocator</*pack_rhs=*/true, EvalCtx> {
+        static void allocate(EvalCtx& ctx, Blocks& blocks) {
+          std::vector<RhsBlock> rhs_blocks;
+          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(
+              ctx.device_,
+              /*num_lhs=*/0,
+              /*num_rhs=*/ctx.gn_,
+              /*num_slices=*/1,
+              /*lhs_blocks=*/nullptr, /*rhs_blocks=*/&rhs_blocks);
+
+          blocks = ThreadLocalBlocks<RhsBlock>(std::move(mem_handle),
+                                               std::move(rhs_blocks));
+        }
+
+        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
+          RhsBlock* ptr = &ctx.rhs_thread_local_pre_allocated_[ctx.gn_ * index];
+          blocks = ThreadLocalBlocks<RhsBlock>(ptr, ctx.gn_);
+        }
+      };
+
+      template <typename EvalCtx>
+      struct ThreadLocalBlocksAllocator</*pack_rhs=*/false, EvalCtx> {
+        static void allocate(EvalCtx& ctx, Blocks& blocks) {
+          std::vector<LhsBlock> lhs_blocks;
+          BlockMemHandle mem_handle = ctx.kernel_.allocateSlices(
+              ctx.device_,
+              /*num_lhs=*/ctx.gm_,
+              /*num_rhs=*/0,
+              /*num_slices=*/1,
+              /*lhs_blocks=*/&lhs_blocks, /*rhs_blocks=*/nullptr);
+
+          blocks = ThreadLocalBlocks<LhsBlock>(std::move(mem_handle),
+                                               std::move(lhs_blocks));
+        }
+
+        static void reuse(EvalCtx& ctx, int index, Blocks& blocks) {
+          LhsBlock* ptr = &ctx.lhs_thread_local_pre_allocated_[ctx.gm_ * index];
+          blocks = ThreadLocalBlocks<LhsBlock>(ptr, ctx.gm_);
+        }
+      };
+
+      EvalParallelContext& ctx_;
+      const int num_worker_threads_;
+    };
+
+    template <typename BlockType>
+    class ThreadLocalBlocksRelease {
+     public:
+      using Blocks = ThreadLocalBlocks<BlockType>;
+      ThreadLocalBlocksRelease(EvalParallelContext& ctx) : ctx_(ctx) {}
+      void operator()(Blocks& blocks) { blocks.Release(ctx_); }
+
+     private:
+      EvalParallelContext& ctx_;
+    };
+
+    // ThreadLocalBlocks initialization callables.
+    using ThreadLocalLhsInit =
+        ThreadLocalBlocksInitialize<LhsBlock, /*is_rhs=*/false>;
+    using ThreadLocalRhsInit =
+        ThreadLocalBlocksInitialize<RhsBlock, /*is_rhs=*/true>;
+
+    // ThreadLocalBlocks release callables.
+    using ThreadLocalLhsRelease = ThreadLocalBlocksRelease<LhsBlock>;
+    using ThreadLocalRhsRelease = ThreadLocalBlocksRelease<RhsBlock>;
+
+    // Thread local containers for Lhs/Rhs block packs. In practice only one of
+    // them will be used, depending on the shard_by_col value.
+    Eigen::ThreadLocal<ThreadLocalBlocks<LhsBlock>, ThreadLocalLhsInit,
+                       ThreadLocalLhsRelease>
+        lhs_thread_local_blocks_;
+    Eigen::ThreadLocal<ThreadLocalBlocks<RhsBlock>, ThreadLocalRhsInit,
+                       ThreadLocalRhsRelease>
+        rhs_thread_local_blocks_;
+
+    // After a particular shard for Kth slice missed thread local execution
+    // opportunity (K-1 slice didn't complete kernels execution), we can no
+    // longer schedule K+1 and following slices in thread local mode, because
+    // there is no more guarantee that previous kernels were executed
+    // sequentially in the same thread (size is nn_ or nm_).
+    std::atomic<bool>* can_use_thread_local_packed_;
+
+    std::atomic<uint8_t>** state_kernel_[P];
+    // state_switch_ is frequently modified by worker threads, while other
+    // fields are read-only after constructor. Let's move it to a separate cache
+    // line to reduce cache-coherency traffic.
+    char pad_[128];
+    std::atomic<Index> state_packing_ready_[P];
+    std::atomic<Index> state_switch_[P];
+
+    LhsBlock& packed_lhs(Index m, Index k, Index m1, bool use_thread_local) {
+      if (use_thread_local) {
+        eigen_assert(!shard_by_col_);
+        ThreadLocalBlocks<LhsBlock>& blocks = lhs_thread_local_blocks_.local();
+
+        Index grain_index = m1 - m * gm_;
+        return blocks.block(internal::convert_index<int>(grain_index)); // FIXME better make ThreadLocalBlocks use Eigen::Index?
+      } else {
+        return packed_lhs_[k % (P - 1)][m1];
+      }
+    }
+
+    RhsBlock& packed_rhs(Index n, Index k, Index n1, bool use_thread_local) {
+      if (use_thread_local) {
+        eigen_assert(shard_by_col_);
+        ThreadLocalBlocks<RhsBlock>& blocks = rhs_thread_local_blocks_.local();
+
+        Index grain_index = n1 - n * gn_;
+        return blocks.block(internal::convert_index<int>(grain_index)); // FIXME better make ThreadLocalBlocks use Eigen::Index?
+      } else {
+        return packed_rhs_[k % (P - 1)][n1];
+      }
+    }
+
+    // In following two methods (pack_lhs and pack_rhs), if we know for sure
+    // that we'll be able to immediately call a kernel with packed data, and do
+    // not submit it to the thread pool, we can use thread local memory for
+    // packed data.
+    //
+    // We can only reliably check it if we are running all kernels in sync mode
+    // (parallelize only by sharding dim). If kernel for m==0 (n==0) is ready to
+    // run, it's guaranteed that all kernels with larger values of m (n) are
+    // also ready, because we execute them in the same order for all K slices.
+
+    void pack_lhs(Index m, Index k) {
+      bool use_thread_local = false;
+
+      if (parallelize_by_sharding_dim_only_ && !shard_by_col_ &&
+          can_use_thread_local_packed_[m].load(std::memory_order_relaxed)) {
+        if (state_kernel_[k % P][m][0].load(std::memory_order_relaxed) == 1) {
+          use_thread_local = true;
+        } else {
+          // If we can't guarantee that all kernels in `k` slice will be
+          // executed sequentially in current thread, it's no longer safe to use
+          // thread local memory in following slices along the k dimensions.
+          eigen_assert(k > 0);
+          can_use_thread_local_packed_[m].store(false,
+                                                std::memory_order_relaxed);
+        }
+      }
+
+      const Index mend = m * gm_ + gm(m);
+      for (Index m1 = m * gm_; m1 < mend; m1++)
+        kernel_.packLhs(&packed_lhs(m, k, m1, use_thread_local),
+                        lhs_.getSubMapper(m1 * bm_, k * bk_), bk(k), bm(m1));
+
+      if (!parallel_pack_ && shard_by_col_) {
+        assert(!use_thread_local);
+        signal_packing(k);
+      } else {
+        signal_switch(k + 1);
+        for (Index n = nn_ - 1; n >= 0; n--) {
+          bool sync = parallelize_by_sharding_dim_only_ || n == 0;
+          signal_kernel(m, n, k, sync, use_thread_local);
+        }
+      }
+    }
+
+    void pack_rhs(Index n, Index k) {
+      bool use_thread_local = false;
+
+      if (parallelize_by_sharding_dim_only_ && shard_by_col_ &&
+          can_use_thread_local_packed_[n].load(std::memory_order_relaxed)) {
+        if (state_kernel_[k % P][0][n].load(std::memory_order_relaxed) == 1) {
+          use_thread_local = true;
+        } else {
+          // If we can't guarantee that all kernels in `k` slice will be
+          // executed sequentially in current thread, it's no longer safe to use
+          // thread local memory in followig slices along the k dimensions.
+          eigen_assert(k > 0);
+          can_use_thread_local_packed_[n].store(false,
+                                                std::memory_order_relaxed);
+        }
+      }
+
+      const Index nend = n * gn_ + gn(n);
+      for (Index n1 = n * gn_; n1 < nend; n1++) {
+        if (!TensorContractionKernel::HasBeta && k == 0) {
+          // Zero the output memory in parallel, only if contraction kernel does
+          // not support `beta`. Otherwise we will pass beta 0.0 to the first
+          // call to the `TensorContractionKernel::invoke()`.
+          //
+          // On 10000x2x10000 mm zeroing can easily take half of time. Zero (bn
+          // x m) row. Safe to do here because all kernels that will write to
+          // this memory depend on completion of this task. Note: don't call
+          // device_.memset() here. device_.memset() blocks on thread pool
+          // worker thread, which can lead to underutilization and deadlocks.
+          memset(buffer_ + n1 * bn_ * m_, 0, bn(n1) * m_ * sizeof(Scalar));
+        }
+        kernel_.packRhs(&packed_rhs(n, k, n1, use_thread_local),
+                        rhs_.getSubMapper(k * bk_, n1 * bn_), bk(k), bn(n1));
+      }
+
+      if (parallel_pack_ || shard_by_col_) {
+        signal_switch(k + 1);
+        for (Index m = nm_ - 1; m >= 0; m--) {
+          bool sync = parallelize_by_sharding_dim_only_ || m == 0;
+          signal_kernel(m, n, k, sync, use_thread_local);
+        }
+      } else {
+        assert(!use_thread_local);
+        signal_packing(k);
+      }
+    }
+
+    void kernel(Index m, Index n, Index k, bool use_thread_local) {
+      // Note: order of iteration matters here. Iteration over m is innermost
+      // because we want to reuse the same packed rhs in consecutive tasks
+      // (rhs fits into L2$ while lhs only into L3$).
+      const Index nend = n * gn_ + gn(n);
+      const Index mend = m * gm_ + gm(m);
+
+      // NOTE: output = alpha * LHS * RHS + beta * output.
+      const Scalar alpha = Scalar(1);
+      const Scalar beta =
+          (TensorContractionKernel::HasBeta && k == 0) ? Scalar(0) : Scalar(1);
+
+      if (shard_by_col_) {
+        for (Index n1 = n * gn_; n1 < nend; n1++) {
+          for (Index m1 = m * gm_; m1 < mend; m1++) {
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
+            kernel_.invoke(
+                output_mapper,
+                packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
+                packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1),
+                bk(k), bn(n1), alpha, beta);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_,
+                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
+          }
+        }
+      } else {
+        for (Index m1 = m * gm_; m1 < mend; m1++)
+          for (Index n1 = n * gn_; n1 < nend; n1++) {
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
+            kernel_.invoke(
+                output_mapper,
+                packed_lhs(m, k, m1, !shard_by_col_ && use_thread_local),
+                packed_rhs(n, k, n1, shard_by_col_ && use_thread_local), bm(m1),
+                bk(k), bn(n1), alpha, beta);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_,
+                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
+          }
+      }
+      signal_kernel(m, n, k + 1, /*sync=*/false, /*use_thread_local=*/false);
+      signal_switch(k + 2);
+    }
+
+    void signal_packing(Index k) {
+      eigen_assert(!parallel_pack_);
+      Index s = state_packing_ready_[k % P].fetch_sub(1);
+      eigen_assert(s > 0);
+      if (s != 1) return;
+      state_packing_ready_[k % P] = shard_by_col_ ? nm_ : nn_;
+      enqueue_packing(k, shard_by_col_);
+    }
+
+    void signal_kernel(Index m, Index n, Index k, bool sync,
+                       bool use_thread_local) {
+      std::atomic<uint8_t>* state = &state_kernel_[k % P][m][n];
+      Index s = state->load();
+      eigen_assert(s > 0);
+      if (s != 1 && state->fetch_sub(1) != 1) {
+        eigen_assert(!use_thread_local);
+        return;
+      }
+      state->store(parallel_pack_ ? 3 : 2, std::memory_order_relaxed);
+      if (sync) {
+        kernel(m, n, k, use_thread_local);
+      } else {
+        eigen_assert(!use_thread_local);
+        device_.enqueueNoNotification(
+            [=]() { kernel(m, n, k, use_thread_local); });
+      }
+    }
+
+    void signal_switch(Index k, Index v = 1) {
+      Index s = state_switch_[k % P].fetch_sub(v);
+      eigen_assert(s >= v);
+      if (s != v) return;
+
+      // Ready to switch to the next k slice.
+      // Reset counter for the next iteration.
+      state_switch_[k % P] =
+          (parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)) +
+          nm_ * nn_;
+      if (k < nk_) {
+        // Issue lhs/rhs packing. Their completion will in turn kick off
+        // kernels.
+        if (parallel_pack_) {
+          enqueue_packing(k, !shard_by_col_);
+          enqueue_packing(k, shard_by_col_);
+        } else if (shard_by_col_) {
+          enqueue_packing(k, false);
+        } else {
+          enqueue_packing(k, true);
+        }
+
+        // Termination handling.
+        // Because kernel completion signals k + 2 switch, we need to finish nk
+        // + 2 slices without issuing any tasks on nk + 1 slice. So here we
+        // pretend that all nk + 1 packing tasks just finish instantly; so that
+        // nk + 2 switch only waits for completion of nk kernels.
+      } else if (k == nk_) {
+        signal_switch(k + 1,
+                      parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_));
+      } else {
+        done_.Notify();
+      }
+    }
+
+    // Enqueue all rhs/lhs packing for k-th slice.
+    void enqueue_packing(Index k, bool rhs) {
+      enqueue_packing_helper(0, rhs ? nn_ : nm_, k, rhs);
+    }
+
+    void enqueue_packing_helper(Index start, Index end, Index k, bool rhs) {
+      if (end - start == 1) {
+        if (rhs)
+          pack_rhs(start, k);
+        else
+          pack_lhs(start, k);
+      } else {
+        while (end - start > 1) {
+          Index mid = (start + end) / 2;
+          device_.enqueueNoNotification(
+              [=]() { enqueue_packing_helper(mid, end, k, rhs); });
+          end = mid;
+        }
+
+        // Decide if we want to run first packing task (start == 0) in
+        // async mode if we parallelize only by sharding dim:
+        // (1) pack_lhs and pack_rhs call signal_switch before completing
+        //     all calls to signal_kernel, which in sync mode might lead
+        //     to the execution of the first kernel of the k+1 slice, before
+        //     completing a call to the last kernel of the k slice.
+        // (2) all pack tasks for sharded dim must be executed in a thread
+        //     pool to get pre-allocated thead local buffers.
+        bool pack_async =
+          (start == 0) &&
+          (parallelize_by_sharding_dim_only_&& shard_by_col_ == rhs) &&
+          (k > 0 || std::this_thread::get_id() == created_by_thread_id_);
+
+        if (pack_async) {
+          device_.enqueueNoNotification(
+              [=]() { enqueue_packing_helper(start, end, k, rhs); });
+        } else {
+          enqueue_packing_helper(start, end, k, rhs);
+        }
+      }
+    }
+
+    // Block sizes with accounting for potentially incomplete last block.
+    Index bm(Index m) const { return m + 1 < nm0_ ? bm_ : m_ + bm_ - bm_ * nm0_; }
+    Index bn(Index n) const { return n + 1 < nn0_ ? bn_ : n_ + bn_ - bn_ * nn0_; }
+    Index bk(Index k) const { return k + 1 < nk_ ? bk_ : k_ + bk_ - bk_ * nk_; }
+    // Task grain sizes accounting for potentially incomplete last task.
+    Index gm(Index m) const { return m + 1 < nm_ ? gm_ : nm0_ + gm_ - gm_ * nm_; }
+    Index gn(Index n) const { return n + 1 < nn_ ? gn_ : nn0_ + gn_ - gn_ * nn_; }
+
+    EvalParallelContext(const EvalParallelContext&) = delete;
+    void operator=(const EvalParallelContext&) = delete;
+  };
+
+  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
+            bool rhs_inner_dim_reordered, int Alignment>
+  using SyncEvalParallelContext =
+      EvalParallelContext<NoCallback, lhs_inner_dim_contiguous,
+                          rhs_inner_dim_contiguous, rhs_inner_dim_reordered,
+                          Alignment>;
+
+  // ------------------------------------------------------------------------ //
+
+  // EvalShardedByInnerDimContext orchestrates sync/async contraction
+  // evaluation, when we shard by inner dimension. When it is executed in
+  // asynchronous mode, it owns all the shared state that might be accessible by
+  // block processing tasks.
+
+  template <typename DoneCallback>
+  struct EvalShardedByInnerDimContext {
+    EvalShardedByInnerDimContext(const Self* self, int num_threads,
+                                 Scalar* result_buffer,
+                                 Index m_size, Index n_size, Index k_size,
+                                 DoneCallback done_callback)
+        : evaluator(self),
+          m_lhs_inner_dim_contiguous(evaluator->m_lhs_inner_dim_contiguous),
+          m_rhs_inner_dim_contiguous(evaluator->m_rhs_inner_dim_contiguous),
+          m_rhs_inner_dim_reordered(evaluator->m_rhs_inner_dim_reordered),
+          result(result_buffer),
+          m(m_size),
+          n(n_size),
+          k(k_size),
+          done(std::move(done_callback)),
+          buffer_size_bytes(m * n * sizeof(Scalar)),
+          block_size(blockSize(k, num_threads)),
+          num_blocks(divup<Index>(k, block_size)),
+          num_pending_blocks(internal::convert_index<int>(num_blocks)),
+          l0_ranges(divup<Index>(num_blocks, l0_size)),
+          l0_state(l0_ranges),
+          block_buffers(num_blocks) {
+      // Keep count of pending gemm tasks for each l0 range.
+      for (int i = 0; i < l0_ranges; ++i) {
+        const Index num_pending_tasks = actualRangeSize(l0_ranges, l0_size, i);
+        l0_state.emplace_back(internal::convert_index<int>(num_pending_tasks));
+      }
+
+      // Allocate temporary buffers for each block.
+      for (Index block_idx = 0; block_idx < num_blocks; ++block_idx) {
+        Scalar* buf = block_idx == 0
+                          ? result
+                          : static_cast<Scalar*>(evaluator->m_device.allocate(
+                                buffer_size_bytes));
+        block_buffers.emplace_back(buf);
+      }
+    }
+
+    ~EvalShardedByInnerDimContext() {
+      for (Index i = 1; i < num_blocks; ++i) {
+        evaluator->m_device.deallocate(block_buffers[i]);
+      }
+    }
+
+    template <int Alignment>
+    void run() {
+      Barrier barrier(internal::convert_index<int>(num_blocks));
+      eval<Alignment>(barrier, 0, num_blocks);
+      barrier.Wait();
+
+      // Aggregate partial sums from l0 ranges.
+      aggregateL0Blocks<Alignment>();
+
+      // Apply output kernel.
+      applyOutputKernel();
+    }
+
+    template <int Alignment>
+    void runAsync() {
+      evalAsync<Alignment>(0, num_blocks);
+    }
+
+   private:
+    // The underlying GEMM kernel assumes that k is a multiple of
+    // the packet size and subtle breakage occurs if this is violated.
+    static const Index packet_size = internal::packet_traits<RhsScalar>::size;
+
+    const Self* evaluator;  // TensorContraction evaluator
+
+    // These fields required fromTENSOR_CONTRACTION_DISPATCH macro.
+    bool m_lhs_inner_dim_contiguous;
+    bool m_rhs_inner_dim_contiguous;
+    bool m_rhs_inner_dim_reordered;
+
+    Scalar* result;
+
+    Index m;
+    Index n;
+    Index k;
+
+    DoneCallback done;
+
+    // ----------------------------------------------------------------------//
+    // Algorithm parameters.
+
+    // We will compute partial results into the buffers of this size.
+    Index buffer_size_bytes;
+
+    Index block_size;
+    Index num_blocks;
+
+    // Keep track of pending tasks when evaluate in async mode.
+    std::atomic<int> num_pending_blocks;
+
+    // We compute partial gemm results in parallel, and to get the final result
+    // we need to add them all together. For the large number of threads (>= 48)
+    // this adds a very expensive sequential step at the end.
+    //
+    // We split the [0, num_blocks) into small ranges, and when a task for the
+    // block finishes its partial gemm computation, it checks if it was the last
+    // gemm in the range, and if so, it will add all blocks of the range.
+    //
+    // After all tasks done, we need to add only these pre-aggregated blocks.
+
+    // For now we use just a single level of ranges to compute pre-aggregated
+    // partial sums, but in general we can use more layers to compute tree
+    // aggregation in parallel and reduce the size of the sequential step.
+    //
+    // TODO(ezhulenev): Add multilevel tree aggregation? Probably will make
+    // sense only if number of threads >= ~128?
+    static const Index l0_size = 4;
+    Index l0_ranges;
+
+    // Keep count of pending gemm tasks for each l0 range.
+    MaxSizeVector<std::atomic<int>> l0_state;  // [0, l0_ranges)
+
+    // Buffers allocated for each temporary block computation.
+    MaxSizeVector<Scalar*> block_buffers;  // [0, num_blocks)
+
+    template <int Alignment>
+    void processBlock(Index block_idx, Index begin, Index end) {
+      Scalar* buf = block_buffers[block_idx];
+
+      TENSOR_CONTRACTION_DISPATCH(
+          evaluator->template evalGemmPartialWithoutOutputKernel, Alignment,
+          (buf, begin, end,
+           /*num_threads=*/internal::convert_index<int>(num_blocks)));
+
+      // Check if it was the last task in l0 range.
+      const Index l0_index = block_idx / l0_size;
+      const int v = l0_state[l0_index].fetch_sub(1);
+      eigen_assert(v >= 1);
+
+      // If we processed the last block of the range, we can aggregate all
+      // partial results into the first block of the range.
+      if (v == 1) {
+        const Index rng_size = actualRangeSize(l0_ranges, l0_size, l0_index);
+        const Index dst_block_idx = l0_index * l0_size;
+
+        if (rng_size == l0_size) {
+          addAllToBuffer<Alignment>(
+              m * n,
+              /*src_buf0=*/block_buffers[dst_block_idx + 1],
+              /*src_buf1=*/block_buffers[dst_block_idx + 2],
+              /*src_buf2=*/block_buffers[dst_block_idx + 3],
+              /*dst_buf= */ block_buffers[dst_block_idx]);
+        } else {
+          // Aggregate blocks of potentially incomplete last range.
+          for (int i = 1; i < rng_size; ++i) {
+            addToBuffer<Alignment>(m * n,
+                                   /*src_buf=*/block_buffers[dst_block_idx + i],
+                                   /*dst_buf=*/block_buffers[dst_block_idx]);
+          }
+        }
+      }
+    }
+
+    // Aggregate partial sums from l0 ranges.
+    template <int Alignment>
+    void aggregateL0Blocks() const {
+      Index l0_index = 1;
+
+      for (; l0_index + 2 < l0_ranges; l0_index += 3) {
+        addAllToBuffer<Alignment>(
+            m * n,
+            /*src_buf0=*/block_buffers[(l0_index + 0) * l0_size],
+            /*src_buf1=*/block_buffers[(l0_index + 1) * l0_size],
+            /*src_buf2=*/block_buffers[(l0_index + 2) * l0_size],
+            /*dst_buf= */ block_buffers[0]);
+      }
+
+      for (; l0_index < l0_ranges; ++l0_index) {
+        addToBuffer<Alignment>(m * n, block_buffers[l0_index * l0_size],
+                               block_buffers[0]);
+      }
+    }
+
+    void applyOutputKernel() const {
+      typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
+      evaluator->m_output_kernel(
+          OutputMapper(result, m), evaluator->m_tensor_contraction_params,
+          static_cast<Eigen::Index>(0), static_cast<Eigen::Index>(0), m, n);
+    }
+
+    // Compute block size with accounting for potentially incomplete last block.
+    Index actualBlockSize(Index block_idx) const {
+      return block_idx + 1 < num_blocks
+                 ? block_size
+                 : k + block_size - block_size * num_blocks;
+    };
+
+    // Compute range size with accounting for potentially incomplete last range.
+    Index actualRangeSize(Index num_ranges, Index range_size,
+                          Index range_idx) const {
+      eigen_assert(range_idx < num_ranges);
+      return range_idx + 1 < num_ranges
+                 ? range_size
+                 : num_blocks + range_size - range_size * num_ranges;
+    };
+
+    template <int Alignment>
+    EIGEN_STRONG_INLINE static void addToBuffer(size_t n, const Scalar* src_buf,
+                                                Scalar* tgt_buf) {
+      const int output_packet_size =
+          internal::unpacket_traits<PacketReturnType>::size;
+      size_t i = 0;
+      const size_t num_packets = n / output_packet_size;
+      for (; i < output_packet_size * num_packets; i += output_packet_size) {
+        const PacketReturnType src_val =
+            internal::pload<PacketReturnType>(src_buf + i);
+        const PacketReturnType tgt_val =
+            internal::ploadt<PacketReturnType, Alignment>(tgt_buf + i);
+        const PacketReturnType sum = internal::padd(src_val, tgt_val);
+        internal::pstoret<Scalar, PacketReturnType, Alignment>(tgt_buf + i,
+                                                               sum);
+      }
+      for (; i < n; ++i) {
+        tgt_buf[i] += src_buf[i];
+      }
+    }
+
+    template <int Alignment>
+    EIGEN_STRONG_INLINE static void addAllToBuffer(size_t n,
+                                                   const Scalar* src_buf0,
+                                                   const Scalar* src_buf1,
+                                                   const Scalar* src_buf2,
+                                                   Scalar* dst_buf) {
+      using ::Eigen::internal::padd;
+      using ::Eigen::internal::pload;
+      using ::Eigen::internal::ploadt;
+      using ::Eigen::internal::pstoret;
+
+      const int output_packet_size =
+          internal::unpacket_traits<PacketReturnType>::size;
+
+      size_t i = 0;
+      const size_t num_packets = n / output_packet_size;
+      for (; i < output_packet_size * num_packets; i += output_packet_size) {
+        const auto src_val0 = pload<PacketReturnType>(src_buf0 + i);
+        const auto src_val1 = pload<PacketReturnType>(src_buf1 + i);
+        const auto src_val2 = pload<PacketReturnType>(src_buf2 + i);
+
+        const auto dst_val = ploadt<PacketReturnType, Alignment>(dst_buf + i);
+        const auto sum =
+            padd(padd(dst_val, src_val0), padd(src_val1, src_val2));
+
+        pstoret<Scalar, PacketReturnType, Alignment>(dst_buf + i, sum);
+      }
+      for (; i < n; ++i) {
+        dst_buf[i] += src_buf0[i] + src_buf1[i] + src_buf2[i];
+      }
+    }
+
+    template <int Alignment>
+    void eval(Barrier& barrier, Index start_block_idx, Index end_block_idx) {
+      while (end_block_idx - start_block_idx > 1) {
+        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
+        evaluator->m_device.enqueueNoNotification(
+            [this, &barrier, mid_block_idx, end_block_idx]() {
+              eval<Alignment>(barrier, mid_block_idx, end_block_idx);
+            });
+        end_block_idx = mid_block_idx;
+      }
+
+      Index block_idx = start_block_idx;
+      Index block_start = block_idx * block_size;
+      Index block_end = block_start + actualBlockSize(block_idx);
+
+      processBlock<Alignment>(block_idx, block_start, block_end);
+      barrier.Notify();
+    }
+
+    template <int Alignment>
+    void evalAsync(Index start_block_idx, Index end_block_idx) {
+      while (end_block_idx - start_block_idx > 1) {
+        Index mid_block_idx = (start_block_idx + end_block_idx) / 2;
+        evaluator->m_device.enqueueNoNotification(
+            [this, mid_block_idx, end_block_idx]() {
+              evalAsync<Alignment>(mid_block_idx, end_block_idx);
+            });
+        end_block_idx = mid_block_idx;
+      }
+
+      Index block_idx = start_block_idx;
+
+      Index block_start = block_idx * block_size;
+      Index block_end = block_start + actualBlockSize(block_idx);
+
+      processBlock<Alignment>(block_idx, block_start, block_end);
+
+      int v = num_pending_blocks.fetch_sub(1);
+      eigen_assert(v >= 1);
+
+      if (v == 1) {
+        // Aggregate partial sums from l0 ranges.
+        aggregateL0Blocks<Alignment>();
+
+        // Apply output kernel.
+        applyOutputKernel();
+
+        // NOTE: If we call `done` callback before deleting this (context),
+        // it might deallocate Self* pointer captured by context, and we'll
+        // fail in destructor trying to deallocate temporary buffers.
+
+        // Move done call back from context before it will be destructed.
+        DoneCallback done_copy = std::move(done);
+
+        // We are confident that we are the last one who touches context.
+        delete this;
+
+        // Now safely call the done callback.
+        done_copy();
+      }
+    }
+
+    // Cost model doesn't capture well the cost associated with constructing
+    // tensor contraction mappers and computing loop bounds in gemm_pack_lhs
+    // and gemm_pack_rhs, so we specify minimum desired block size.
+    static Index blockSize(Index k, int num_threads) {
+      const auto round_up = [=](Index index) -> Index {
+        const Index kmultiple = packet_size <= 8 ? 8 : packet_size;
+        return divup<Index>(index, kmultiple) * kmultiple;
+      };
+
+      const Index target_block_size = round_up(divup<Index>(k, num_threads));
+      const Index desired_min_block_size = 12 * packet_size;
+
+      return numext::mini<Index>(
+          k, numext::maxi<Index>(desired_min_block_size, target_block_size));
+    }
+
+    EvalShardedByInnerDimContext(const EvalShardedByInnerDimContext&) = delete;
+    void operator=(const EvalShardedByInnerDimContext&) = delete;
+  };
+
+  // ------------------------------------------------------------------------ //
+
+  // Below are the function used by evalProductImpl heuristics, trying to select
+  // optimcal parameters for parallelization algorithm.
+
+  // Decide whether we want to shard m x n contraction by columns or by rows.
+  static bool shardByCol(Index m, Index n, Index num_threads) {
+    // Note: we are comparing both n and m against Traits::nr, it is not
+    // a mistake. We are trying to figure out how both n and m will fit into
+    // the main sharding dimension.
+
+    // Sharding by column is the default
+    // ... unless there is enough data for vectorization over rows
+    if (m / num_threads >= Traits::nr &&
+        // and not enough data for vectorization over columns
+        (n / num_threads < Traits::nr ||
+         // ... or barely enough data for vectorization over columns,
+         // but it is not evenly dividable across threads
+         (n / num_threads < 4 * Traits::nr &&
+          (n % (num_threads * Traits::nr)) != 0 &&
+          // ... and it is evenly dividable across threads for rows
+          ((m % (num_threads * Traits::nr)) == 0 ||
+           // .. or it is not evenly dividable for both dimensions but
+           // there is much more data over rows so that corner effects are
+           // mitigated.
+           (m / n >= 6)))))
+      return false;
+    // Wait, or if matrices are just substantially prolonged over the other
+    // dimension.
+    if (n / num_threads < 16 * Traits::nr && m > n * 32) return false;
+    return true;
+  }
+
+  Index coarsenM(Index m, Index n, Index bm, Index bn, Index bk, Index gn,
+                 int num_threads, bool shard_by_col) const {
+    Index gm = 1;
+    Index gm1 = 1;
+    Index nm0 = divup(m, bm);
+    Index nm1 = nm0;
+    for (;;) {
+      // Find the next candidate for m grain size. It needs to result in
+      // different number of blocks. E.g. if we have 10 kernels, we want to try
+      // 5 and 10, but not 6, 7, 8 and 9.
+      while (gm1 <= nm0 && nm1 == divup(nm0, gm1)) gm1++;
+      if (gm1 > nm0) break;
+      // Check the candidate.
+      int res = checkGrain(m, n, bm, bn, bk, gm1, gn, gm, gn, num_threads,
+                           shard_by_col);
+      if (res < 0) break;
+      nm1 = divup(nm0, gm1);
+      if (res == 0) continue;
+      // Commit new grain size.
+      gm = gm1;
+    }
+    return gm;
+  }
+
+  Index coarsenN(Index m, Index n, Index bm, Index bn, Index bk, Index gm,
+                 int num_threads, bool shard_by_col) const {
+    Index gn = 1;
+    Index gn1 = 1;
+    Index nn0 = divup(n, bn);
+    Index nn1 = nn0;
+    for (;;) {
+      while (gn1 <= nn0 && nn1 == divup(nn0, gn1)) gn1++;
+      if (gn1 > nn0) break;
+      int res = checkGrain(m, n, bm, bn, bk, gm, gn1, gm, gn, num_threads,
+                           shard_by_col);
+      if (res < 0) break;
+      nn1 = divup(nn0, gn1);
+      if (res == 0) continue;
+      gn = gn1;
+    }
+    return gn;
+  }
+
+  // checkGrain checks whether grain (gm, gn) is suitable and is better than
+  // (oldgm, oldgn).
+  int checkGrain(Index m, Index n, Index bm, Index bn, Index bk, Index gm,
+                 Index gn, Index oldgm, Index oldgn, int num_threads,
+                 bool shard_by_col) const {
+    const TensorOpCost cost =
+        contractionCost(bm * gm, bn * gn, bm, bn, bk, shard_by_col, true);
+    double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(
+        static_cast<double>(bm) * gm * bn * gn, cost);
+    // If the task is too small, then we agree on it regardless of anything
+    // else. Otherwise synchronization overheads will dominate.
+    if (taskSize < 1) return 1;
+    // If it is too large, then we reject it and all larger tasks.
+    if (taskSize > 2) return -1;
+    // Now we are in presumably good task size range.
+    // The main deciding factor here is parallelism. Consider that we have 12
+    // kernels and 4 threads. Grains of 2, 3 and 4 all yield good task sizes.
+    // But 2/4 yield 6/3 tasks, which gives us parallelism of 0.75 (at most 3/4
+    // of cores will be busy). While grain size 3 gives us 4 tasks, which gives
+    // us parallelism of 1 (we can load all cores).
+    Index nm0 = divup(m, bm);
+    Index nn0 = divup(n, bn);
+    Index new_tasks = divup(nm0, gm) * divup(nn0, gn);
+    double new_parallelism = static_cast<double>(new_tasks) /
+                             (divup<int>(new_tasks, num_threads) * num_threads);
+    Index old_tasks = divup(nm0, oldgm) * divup(nn0, oldgn);
+    double old_parallelism = static_cast<double>(old_tasks) /
+                             (divup<int>(old_tasks, num_threads) * num_threads);
+    if (new_parallelism > old_parallelism || new_parallelism == 1) return 1;
+    return 0;
+  }
+
+  TensorOpCost contractionCost(Index m, Index n, Index bm, Index bn, Index bk,
+                               bool shard_by_col, bool prepacked) const {
+    const int packed_size = std::min<int>(PacketType<LhsScalar, Device>::size,
+                                          PacketType<RhsScalar, Device>::size);
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    const double kd = static_cast<double>(bk);
+    double compute_bandwidth = computeBandwidth(false, bm, bn, bk);
+    // Computations.
+    TensorOpCost cost = TensorOpCost(0, 0, kd * compute_bandwidth, true, packed_size);
+    // Output stores.
+    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
+    if (prepacked) {
+      // Packing and kernels are executed in different tasks. When we calculate
+      // task grain size we look only at kernel cost assuming that kernel
+      // is more expensive than packing.
+      return cost;
+    }
+    // Lhs/rhs loads + computations.
+    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * (kd / n);
+    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * (kd / m);
+    // Lhs packing memory cost does not contribute considerably to overall
+    // execution time because lhs is prefetched early and accessed sequentially.
+    if (shard_by_col)
+      lhsCost.dropMemoryCost();
+    else
+      rhsCost.dropMemoryCost();
+    return cost + lhsCost + rhsCost;
+  }
+
+  // Decide whether we want to shard m x k x n contraction over the inner
+  // (contraction) dimension (k).
+  static bool shardByInnerDim(Index m, Index n, Index k, int num_threads,
+                              int num_threads_by_k) {
+    std::ptrdiff_t bufsize = m * n * sizeof(Scalar);
+    bool shard_by_k = false;
+    if (n == 1 ||                // If mat*vec or...
+        num_threads_by_k < 2 ||  // running single threaded or...
+        num_threads_by_k <
+            num_threads ||  // sharding by k gives less parallelism or...
+        bufsize > l3CacheSize() / num_threads_by_k ||  // need more buffer space
+        // than L3 cache or...
+        k / num_threads_by_k < 2 * Traits::nr) {  // k per thread is tiny.
+      shard_by_k = false;
+    } else if (numext::maxi(m, n) / num_threads <
+                   Traits::nr ||  // both other dimensions are tiny or...
+               // k per thread is not small and...
+               (k / num_threads_by_k > 8 * Traits::nr &&
+                // one of the outer dimensions is tiny or sharding by k offers
+                // more parallelism.
+                (numext::mini(m, n) < 2 * Traits::nr ||
+                 num_threads_by_k > num_threads))) {
+      shard_by_k = true;
+    }
+    return shard_by_k;
+  }
+
+  TensorOpCost contractionCostPerInnerDim(Index m, Index n, Index k) const {
+    // Compute cost.
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    TensorOpCost cost(0, 0, (computeBandwidth(true, m, n, k) * m) * n, true, output_packet_size);
+    // Output stores.
+    cost += TensorOpCost(0, sizeof(CoeffReturnType), 0, true, output_packet_size);
+    TensorOpCost lhsCost = this->m_leftImpl.costPerCoeff(true) * m;
+    TensorOpCost rhsCost = this->m_rightImpl.costPerCoeff(true) * n;
+    // Since the inner gemm kernel is always sharded by column, the lhs
+    // load cost is negligible.
+    lhsCost.dropMemoryCost();
+    return cost + lhsCost + rhsCost;
+  }
+
+  int numThreadsInnerDim(Index m, Index n, Index k) const {
+    const int output_packet_size = internal::unpacket_traits<PacketReturnType>::size;
+    TensorOpCost cost = contractionCostPerInnerDim(m, n, k);
+    double total_parallel_cost =
+        TensorCostModel<ThreadPoolDevice>::totalCost(k, cost);
+    // Cost of reduction step accumulating the m*n per-thread buffers into the
+    // result.
+    double reduction_cost = TensorCostModel<ThreadPoolDevice>::totalCost(
+        m * n, TensorOpCost(2, 1, 1, true, output_packet_size));
+    int num_threads = 1;
+    double min_cost = total_parallel_cost;
+    double kPerThreadOverHead = 3000;
+    double kFixedOverHead = 100000;
+    for (int nt = 2; nt <= this->m_device.numThreads(); nt += 2) {
+      double sequential_cost =
+          kFixedOverHead + nt * (reduction_cost + kPerThreadOverHead);
+      double parallel_cost = total_parallel_cost / nt + sequential_cost;
+      if (parallel_cost < min_cost) {
+        num_threads = nt;
+        min_cost = parallel_cost;
+      }
+    }
+    return num_threads;
+  }
+
+  double computeBandwidth(bool shard_by_col, Index bm, Index bn,
+                          Index bk) const {
+    // Peak VFMA bandwidth is 0.5. However if we have not enough data for
+    // vectorization bandwidth drops. The 4.0 and 2.0 bandwidth is determined
+    // experimentally.
+    double computeBandwidth =
+        bk == 1 ? 4.0
+                : (shard_by_col ? bn : bm) < Traits::nr ||
+                          (shard_by_col ? bm : bn) < Traits::mr
+                      ? 2.0
+                      : 0.5;
+#ifndef EIGEN_VECTORIZE_FMA
+    // Bandwidth of all of VFMA/MULPS/ADDPS is 0.5 on latest Intel processors.
+    // However for MULPS/ADDPS we have dependent sequence of 2 such
+    // instructions,
+    // so overall bandwidth is 1.0.
+    if (computeBandwidth == 0.5) computeBandwidth = 1.0;
+#endif
+    return computeBandwidth;
+  }
+
+};
+
+} // end namespace Eigen
+
+#endif  // EIGEN_USE_THREADS
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_THREAD_POOL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
new file mode 100644
index 0000000..09d2da9
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h
@@ -0,0 +1,456 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
+
+namespace Eigen {
+
+/** \class TensorConversionOp
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor conversion class. This class makes it possible to vectorize
+  * type casting operations when the number of scalars per packet in the source
+  * and the destination type differ
+  */
+namespace internal {
+template<typename TargetType, typename XprType>
+struct traits<TensorConversionOp<TargetType, XprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef TargetType Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = traits<XprType>::NumDimensions;
+  static const int Layout = traits<XprType>::Layout;
+  enum { Flags = 0 };
+  typedef typename TypeConversion<Scalar, typename traits<XprType>::PointerType>::type PointerType;
+};
+
+template<typename TargetType, typename XprType>
+struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense>
+{
+  typedef const TensorConversionOp<TargetType, XprType>& type;
+};
+
+template<typename TargetType, typename XprType>
+struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type>
+{
+  typedef TensorConversionOp<TargetType, XprType> type;
+};
+
+}  // end namespace internal
+
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio>
+struct PacketConverter;
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl) {}
+
+  template<int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index));
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl) {}
+
+  template<int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl) {}
+
+  template<int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
+    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
+    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 8, 1> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl) {}
+
+  template<int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+
+    SrcPacket src1 = m_impl.template packet<LoadMode>(index);
+    SrcPacket src2 = m_impl.template packet<LoadMode>(index + 1 * SrcPacketSize);
+    SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
+    SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
+    SrcPacket src5 = m_impl.template packet<LoadMode>(index + 4 * SrcPacketSize);
+    SrcPacket src6 = m_impl.template packet<LoadMode>(index + 5 * SrcPacketSize);
+    SrcPacket src7 = m_impl.template packet<LoadMode>(index + 6 * SrcPacketSize);
+    SrcPacket src8 = m_impl.template packet<LoadMode>(index + 7 * SrcPacketSize);
+    TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4, src5, src6, src7, src8);
+    return result;
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+};
+
+template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int TgtCoeffRatio>
+struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, TgtCoeffRatio> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketConverter(const TensorEvaluator& impl)
+      : m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {}
+
+  template<int LoadMode, typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
+    const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
+    // Only call m_impl.packet() when we have direct access to the underlying data. This
+    // ensures that we don't compute the subexpression twice. We may however load some
+    // coefficients twice, but in practice this doesn't negatively impact performance.
+    if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) {
+      // Force unaligned memory loads since we can't ensure alignment anymore
+      return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index));
+    } else {
+      const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size;
+      typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+      typedef typename internal::unpacket_traits<TgtPacket>::type TgtType;
+      internal::scalar_cast_op<SrcType, TgtType> converter;
+      EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < TgtPacketSize; ++i) {
+        values[i] = converter(m_impl.coeff(index+i));
+      }
+      TgtPacket rslt = internal::pload<TgtPacket>(values);
+      return rslt;
+    }
+  }
+
+ private:
+  const TensorEvaluator& m_impl;
+  const typename TensorEvaluator::Index m_maxIndex;
+};
+
+template<typename TargetType, typename XprType>
+class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors>
+{
+  public:
+    typedef typename internal::traits<TensorConversionOp>::Scalar Scalar;
+    typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
+    typedef typename internal::traits<TensorConversionOp>::Index Index;
+    typedef typename internal::nested<TensorConversionOp>::type Nested;
+    typedef Scalar CoeffReturnType;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr)
+        : m_xpr(xpr) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+};
+
+template <bool SameType, typename Eval, typename EvalPointerType> struct ConversionSubExprEval {
+  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType) {
+    impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+};
+
+template <typename Eval, typename EvalPointerType> struct ConversionSubExprEval<true, Eval, EvalPointerType> {
+  static EIGEN_STRONG_INLINE bool run(Eval& impl, EvalPointerType data) {
+    return impl.evalSubExprsIfNeeded(data);
+  }
+};
+
+#ifdef EIGEN_USE_THREADS
+template <bool SameType, typename Eval, typename EvalPointerType,
+          typename EvalSubExprsCallback>
+struct ConversionSubExprEvalAsync {
+  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType, EvalSubExprsCallback done) {
+    impl.evalSubExprsIfNeededAsync(nullptr, std::move(done));
+  }
+};
+
+template <typename Eval, typename EvalPointerType,
+          typename EvalSubExprsCallback>
+struct ConversionSubExprEvalAsync<true, Eval, EvalPointerType,
+                                  EvalSubExprsCallback> {
+  static EIGEN_STRONG_INLINE void run(Eval& impl, EvalPointerType data, EvalSubExprsCallback done) {
+    impl.evalSubExprsIfNeededAsync(data, std::move(done));
+  }
+};
+#endif
+
+namespace internal {
+
+template <typename SrcType, typename TargetType, bool IsSameT>
+struct CoeffConv {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    internal::scalar_cast_op<SrcType, TargetType> converter;
+    return converter(impl.coeff(index));
+  }
+};
+
+template <typename SrcType, typename TargetType>
+struct CoeffConv<SrcType, TargetType, true> {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    return impl.coeff(index);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode, bool ActuallyVectorize, bool IsSameT>
+struct PacketConv {
+  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+
+  static const int PacketSize = internal::unpacket_traits<TargetPacket>::size;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    internal::scalar_cast_op<SrcType, TargetType> converter;
+    EIGEN_ALIGN_MAX typename internal::remove_const<TargetType>::type values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = converter(impl.coeff(index+i));
+    }
+    TargetPacket rslt = internal::pload<TargetPacket>(values);
+    return rslt;
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode, bool IsSameT>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, true, IsSameT> {
+  typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
+    const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
+    PacketConverter<TensorEvaluator<ArgType, Device>, SrcPacket, TargetPacket,
+                    SrcCoeffRatio, TgtCoeffRatio> converter(impl);
+    return converter.template packet<LoadMode>(index);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/false, /*IsSameT=*/true> {
+  typedef typename internal::unpacket_traits<TargetPacket>::type TargetType;
+  static const int PacketSize = internal::unpacket_traits<TargetPacket>::size;
+
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    EIGEN_ALIGN_MAX typename internal::remove_const<TargetType>::type values[PacketSize];
+    for (int i = 0; i < PacketSize; ++i) values[i] = impl.coeff(index+i);
+    return internal::pload<TargetPacket>(values);
+  }
+};
+
+template <typename SrcPacket, typename TargetPacket, int LoadMode>
+struct PacketConv<SrcPacket, TargetPacket, LoadMode, /*ActuallyVectorize=*/true, /*IsSameT=*/true> {
+  template <typename ArgType, typename Device>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TargetPacket run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
+    return impl.template packet<LoadMode>(index);
+  }
+};
+
+}  // namespace internal
+
+// Eval as rvalue
+template<typename TargetType, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
+{
+  typedef TensorConversionOp<TargetType, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef TargetType Scalar;
+  typedef TargetType CoeffReturnType;
+  typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename PacketType<SrcType, Device>::type PacketSourceType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  static const bool IsSameType = internal::is_same<TargetType, SrcType>::value;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      =
+    #ifndef EIGEN_USE_SYCL
+                        true,
+    #else
+                        TensorEvaluator<ArgType, Device>::PacketAccess &
+                        internal::type_casting_traits<SrcType, TargetType>::VectorizedCast,
+    #endif
+    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess         = false
+  };
+
+  static const int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  struct TensorConversionOpBlockFactory {
+    template <typename ArgXprType>
+    struct XprType {
+      typedef TensorConversionOp<TargetType, const ArgXprType> type;
+    };
+
+    template <typename ArgXprType>
+    typename XprType<ArgXprType>::type expr(const ArgXprType& expr) const {
+      return typename XprType<ArgXprType>::type(expr);
+    }
+  };
+
+  typedef internal::TensorUnaryExprBlock<TensorConversionOpBlockFactory,
+                                         ArgTensorBlock>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : m_impl(op.expression(), device)
+  {
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
+  {
+    return ConversionSubExprEval<IsSameType, TensorEvaluator<ArgType, Device>, EvaluatorPointerType>::run(m_impl, data);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType data, EvalSubExprsCallback done) {
+    ConversionSubExprEvalAsync<IsSameType, TensorEvaluator<ArgType, Device>,
+                               EvaluatorPointerType,
+        EvalSubExprsCallback>::run(m_impl, data, std::move(done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup()
+  {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return internal::CoeffConv<SrcType, TargetType, IsSameType>::run(m_impl,index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType
+  packet(Index index) const {
+    // If we are not going to do the cast, we just need to check that base
+    // TensorEvaluator has packet access. Otherwise we also need to make sure,
+    // that we have an implementation of vectorized cast.
+    const bool Vectorizable =
+        IsSameType
+        ? TensorEvaluator<ArgType, Device>::PacketAccess
+        : int(TensorEvaluator<ArgType, Device>::PacketAccess) &
+          int(internal::type_casting_traits<SrcType, TargetType>::VectorizedCast);
+
+    return internal::PacketConv<PacketSourceType, PacketReturnType, LoadMode,
+                                Vectorizable, IsSameType>::run(m_impl, index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>();
+    if (vectorized) {
+      const double SrcCoeffRatio =
+          internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
+      const double TgtCoeffRatio =
+          internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
+      return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) +
+          TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize));
+    } else {
+      return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return m_impl.getResourceRequirements();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    return TensorBlock(m_impl.block(desc, scratch),
+                         TensorConversionOpBlockFactory());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  /// required by sycl in order to extract the sycl accessor
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
new file mode 100644
index 0000000..b20f80b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
@@ -0,0 +1,1132 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
+
+namespace Eigen {
+
+/** \class TensorConvolution
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor convolution class.
+  *
+  *
+  */
+namespace internal {
+
+template <typename Index, typename InputDims, int NumKernelDims, int Layout>
+class IndexMapper {
+ public:
+  IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
+              const array<Index, NumKernelDims>& indices) {
+
+    array<Index, NumDims> dimensions = input_dims;
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = indices[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      dimensions[index] = result_dim;
+    }
+
+    array<Index, NumDims> inputStrides;
+    array<Index, NumDims> outputStrides;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      inputStrides[0] = 1;
+      outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        inputStrides[i] = inputStrides[i-1] * input_dims[i-1];
+        outputStrides[i] = outputStrides[i-1] * dimensions[i-1];
+      }
+    } else {
+      inputStrides[NumDims - 1] = 1;
+      outputStrides[NumDims - 1] = 1;
+      for (int i = static_cast<int>(NumDims) - 2; i >= 0; --i) {
+        inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
+        outputStrides[i] = outputStrides[i + 1] * dimensions[i + 1];
+      }
+    }
+
+    array<Index, NumDims> gpuInputDimensions;
+    array<Index, NumDims> gpuOutputDimensions;
+    array<Index, NumDims> tmp = dimensions;
+    array<Index, NumDims> ordering;
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = i + offset;
+      ordering[index] = indices[i];
+      tmp[indices[i]] = -1;
+      gpuInputDimensions[index] = input_dims[indices[i]];
+      gpuOutputDimensions[index] = dimensions[indices[i]];
+    }
+
+    int written = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                      ? NumKernelDims
+                      : 0;
+    for (int i = 0; i < NumDims; ++i) {
+      if (tmp[i] >= 0) {
+        ordering[written] = i;
+        gpuInputDimensions[written] = input_dims[i];
+        gpuOutputDimensions[written] = dimensions[i];
+        ++written;
+      }
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      m_inputStrides[i] = inputStrides[ordering[i]];
+      m_outputStrides[i] = outputStrides[ordering[i]];
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumDims; ++i) {
+        if (i > NumKernelDims) {
+          m_gpuInputStrides[i] =
+              m_gpuInputStrides[i - 1] * gpuInputDimensions[i - 1];
+          m_gpuOutputStrides[i] =
+              m_gpuOutputStrides[i - 1] * gpuOutputDimensions[i - 1];
+        } else {
+          m_gpuInputStrides[i] = 1;
+          m_gpuOutputStrides[i] = 1;
+        }
+      }
+    } else {
+      for (int i = NumDims - 1; i >= 0; --i) {
+        if (static_cast<size_t>(i + 1) < offset) {
+          m_gpuInputStrides[i] =
+              m_gpuInputStrides[i + 1] * gpuInputDimensions[i + 1];
+          m_gpuOutputStrides[i] =
+              m_gpuOutputStrides[i + 1] * gpuOutputDimensions[i + 1];
+        } else {
+          m_gpuInputStrides[i] = 1;
+          m_gpuOutputStrides[i] = 1;
+        }
+      }
+    }
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputPlaneToTensorInputOffset(Index p) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int d = NumDims - 1; d > NumKernelDims; --d) {
+        const Index idx = p / m_gpuInputStrides[d];
+        inputIndex += idx * m_inputStrides[d];
+        p -= idx * m_gpuInputStrides[d];
+      }
+      inputIndex += p * m_inputStrides[NumKernelDims];
+    } else {
+      std::ptrdiff_t limit = 0;
+      if (NumKernelDims < NumDims) {
+        limit = NumDims - NumKernelDims - 1;
+      }
+      for (int d = 0; d < limit; ++d) {
+        const Index idx = p / m_gpuInputStrides[d];
+        inputIndex += idx * m_inputStrides[d];
+        p -= idx * m_gpuInputStrides[d];
+      }
+      inputIndex += p * m_inputStrides[limit];
+    }
+    return inputIndex;
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputPlaneToTensorOutputOffset(Index p) const {
+    Index outputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int d = NumDims - 1; d > NumKernelDims; --d) {
+        const Index idx = p / m_gpuOutputStrides[d];
+        outputIndex += idx * m_outputStrides[d];
+        p -= idx * m_gpuOutputStrides[d];
+      }
+      outputIndex += p * m_outputStrides[NumKernelDims];
+    } else {
+      std::ptrdiff_t limit = 0;
+      if (NumKernelDims < NumDims) {
+        limit = NumDims - NumKernelDims - 1;
+      }
+      for (int d = 0; d < limit; ++d) {
+        const Index idx = p / m_gpuOutputStrides[d];
+        outputIndex += idx * m_outputStrides[d];
+        p -= idx * m_gpuOutputStrides[d];
+      }
+      outputIndex += p * m_outputStrides[limit];
+    }
+    return outputIndex;
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuInputKernelToTensorInputOffset(Index i, Index j, Index k) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_inputStrides[offset] + j * m_inputStrides[offset + 1] +
+           k * m_inputStrides[offset + 2];
+  }
+
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Index mapGpuOutputKernelToTensorOutputOffset(Index i, Index j, Index k) const {
+    const size_t offset = static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                              ? 0
+                              : NumDims - NumKernelDims;
+    return i * m_outputStrides[offset] + j * m_outputStrides[offset + 1] +
+           k * m_outputStrides[offset + 2];
+  }
+
+ private:
+  static const int NumDims = internal::array_size<InputDims>::value;
+  array<Index, NumDims> m_inputStrides;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_gpuInputStrides;
+  array<Index, NumDims> m_gpuOutputStrides;
+};
+
+
+
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename promote_storage_type<typename InputXprType::Scalar,
+                                        typename KernelXprType::Scalar>::ret Scalar;
+  typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
+                                        typename traits<KernelXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<InputXprType>::Index,
+                                      typename traits<KernelXprType>::Index>::type Index;
+  typedef typename InputXprType::Nested LhsNested;
+  typedef typename KernelXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const int NumDimensions = traits<InputXprType>::NumDimensions;
+  static const int Layout = traits<InputXprType>::Layout;
+  typedef typename conditional<Pointer_type_promotion<typename InputXprType::Scalar, Scalar>::val,
+  typename traits<InputXprType>::PointerType, typename traits<KernelXprType>::PointerType>::type PointerType;
+
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
+{
+  typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
+};
+
+template<typename Dimensions, typename InputXprType, typename KernelXprType>
+struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type>
+{
+  typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Indices, typename InputXprType, typename KernelXprType>
+class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
+                                                  typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorConvolutionOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
+      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Indices& indices() const { return m_indices; }
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const typename internal::remove_all<typename InputXprType::Nested>::type&
+    inputExpression() const { return m_input_xpr; }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const typename internal::remove_all<typename KernelXprType::Nested>::type&
+    kernelExpression() const { return m_kernel_xpr; }
+
+  protected:
+    typename InputXprType::Nested m_input_xpr;
+    typename KernelXprType::Nested m_kernel_xpr;
+    const Indices m_indices;
+};
+
+
+template<typename Indices, typename InputArgType, typename KernelArgType, typename Device>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device>
+{
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
+  static const int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = int(TensorEvaluator<InputArgType, Device>::IsAligned) & int(TensorEvaluator<KernelArgType, Device>::IsAligned),
+    PacketAccess = int(TensorEvaluator<InputArgType, Device>::PacketAccess) & int(TensorEvaluator<KernelArgType, Device>::PacketAccess),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<InputArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStride[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
+      }
+    } else {
+      m_inputStride[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
+      }
+    }
+
+    m_dimensions = m_inputImpl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumKernelDims; ++i) {
+        const Index index = op.indices()[i];
+        const Index input_dim = input_dims[index];
+        const Index kernel_dim = kernel_dims[i];
+        const Index result_dim = input_dim - kernel_dim + 1;
+        m_dimensions[index] = result_dim;
+        if (i > 0) {
+          m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
+        } else {
+          m_kernelStride[0] = 1;
+        }
+        m_indexStride[i] = m_inputStride[index];
+      }
+
+      m_outputStride[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      for (int i = NumKernelDims - 1; i >= 0; --i) {
+        const Index index = op.indices()[i];
+        const Index input_dim = input_dims[index];
+        const Index kernel_dim = kernel_dims[i];
+        const Index result_dim = input_dim - kernel_dim + 1;
+        m_dimensions[index] = result_dim;
+        if (i < NumKernelDims - 1) {
+          m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
+        } else {
+          m_kernelStride[NumKernelDims - 1] = 1;
+        }
+        m_indexStride[i] = m_inputStride[index];
+      }
+
+      m_outputStride[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    preloadKernel();
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_local_kernel) {
+      m_device.deallocate((void*)m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+
+  void evalTo(typename XprType::Scalar* buffer) {
+    evalSubExprsIfNeeded(NULL);
+    for (int i = 0; i < dimensions().TotalSize(); ++i) {
+      buffer[i] += coeff(i);
+    }
+    cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    CoeffReturnType result = CoeffReturnType(0);
+    convolve(firstInput(index), 0, NumKernelDims-1, result);
+    return result;
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(const Index index) const
+  {
+    Index indices[2] = {index, index+PacketSize-1};
+    Index startInputs[2] = {0, 0};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_outputStride[i];
+        const Index idx1 = indices[1] / m_outputStride[i];
+        startInputs[0] += idx0 * m_inputStride[i];
+        startInputs[1] += idx1 * m_inputStride[i];
+        indices[0] -= idx0 * m_outputStride[i];
+        indices[1] -= idx1 * m_outputStride[i];
+      }
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_outputStride[i];
+        const Index idx1 = indices[1] / m_outputStride[i];
+        startInputs[0] += idx0 * m_inputStride[i];
+        startInputs[1] += idx1 * m_inputStride[i];
+        indices[0] -= idx0 * m_outputStride[i];
+        indices[1] -= idx1 * m_outputStride[i];
+      }
+    }
+    startInputs[0] += indices[0];
+    startInputs[1] += indices[1];
+
+    if (startInputs[1]-startInputs[0] == PacketSize-1) {
+      PacketReturnType result = internal::pset1<PacketReturnType>(0);
+      convolvePacket(startInputs[0], 0, NumKernelDims-1, result);
+      return result;
+    } else {
+      EIGEN_ALIGN_MAX Scalar data[PacketSize];
+      data[0] = Scalar(0);
+      convolve(startInputs[0], 0, NumKernelDims-1, data[0]);
+      for (int i = 1; i < PacketSize-1; ++i) {
+        data[i] = Scalar(0);
+        convolve(firstInput(index+i), 0, NumKernelDims-1, data[i]);
+      }
+      data[PacketSize-1] = Scalar(0);
+      convolve(startInputs[1], 0, NumKernelDims-1, data[PacketSize-1]);
+      return internal::pload<PacketReturnType>(data);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost =
+        TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+         TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) +
+                          m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized,
+                                       PacketSize));
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+ private:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStride[i];
+        startInput += idx * m_inputStride[i];
+        index -= idx * m_outputStride[i];
+      }
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStride[i];
+        startInput += idx * m_inputStride[i];
+        index -= idx * m_outputStride[i];
+      }
+    }
+    startInput += index;
+    return startInput;
+  }
+
+  EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
+    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
+      const Index input = firstIndex + j * m_indexStride[DimIndex];
+      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
+      if (DimIndex > 0) {
+        convolve(input, kernel, DimIndex-1, accum);
+      } else {
+        accum += m_inputImpl.coeff(input) * m_kernel[kernel];
+      }
+    }
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC void convolvePacket(Index firstIndex, Index firstKernel, int DimIndex, Packet& accum) const {
+    for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
+      const Index input = firstIndex + j * m_indexStride[DimIndex];
+      const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
+      if (DimIndex > 0) {
+        convolvePacket(input, kernel, DimIndex-1, accum);
+      } else {
+        accum = internal::pmadd<Packet>(m_inputImpl.template packet<Unaligned>(input), internal::pset1<Packet>(m_kernel[kernel]), accum);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    const Scalar* in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      Scalar* local = (Scalar*)m_device.allocate_temp(kernel_sz);
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(local, m_kernelArg);
+      const bool Vectorize = internal::IsVectorizable<Device, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, Device, Vectorize>::run(evalToTmp, m_device);
+
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  array<Index, NumDims> m_inputStride;
+  array<Index, NumDims> m_outputStride;
+
+  array<Index, NumKernelDims> m_indexStride;
+  array<Index, NumKernelDims> m_kernelStride;
+  TensorEvaluator<InputArgType, Device> m_inputImpl;
+  TensorEvaluator<KernelArgType, Device> m_kernelImpl;
+  Dimensions m_dimensions;
+
+  KernelArgType m_kernelArg;
+  const Scalar* m_kernel;
+  bool m_local_kernel;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+
+
+
+// Use an optimized implementation of the evaluation code for GPUs whenever possible.
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+template <int StaticKernelSize>
+struct GetKernelSize {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int /*kernelSize*/) const {
+    return StaticKernelSize;
+  }
+};
+template <>
+struct GetKernelSize<Dynamic> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int operator() (const int kernelSize) const {
+    return kernelSize;
+  }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims,
+          int StaticKernelSize>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel1D(
+    InputEvaluator eval,
+    const internal::IndexMapper<Index, InputDims, 1, InputEvaluator::Layout>
+        indexMapper,
+    const float* __restrict kernel, const int numPlanes, const int numX,
+    const int maxX, const int kernelSize, float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSize>()(kernelSize);
+  const int num_x_output = last_x - first_x + 1;
+
+  const int first_plane = blockIdx.y * blockDim.y;
+  const int plane_stride = blockDim.y * gridDim.y;
+
+  for (int p = first_plane + threadIdx.y; p < numPlanes; p += plane_stride) {
+    // Load inputs to shared memory
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = threadIdx.y * num_x_input;
+    #pragma unroll
+    for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+      const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x);
+      s[i + plane_kernel_offset] = eval.coeff(tensor_index);
+    }
+
+    __syncthreads();
+
+    // Compute the convolution
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+    #pragma unroll
+    for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+      const int kernel_offset = plane_kernel_offset + i;
+      float result = 0.0f;
+      #pragma unroll
+      for (int k = 0; k < GetKernelSize<StaticKernelSize>()(kernelSize); ++k) {
+        result += s[k + kernel_offset] * kernel[k];
+      }
+      const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x);
+      buffer[tensor_index] = result;
+    }
+    __syncthreads();
+  }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims,
+          int StaticKernelSizeX, int StaticKernelSizeY>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel2D(
+    InputEvaluator eval,
+    const internal::IndexMapper<Index, InputDims, 2, InputEvaluator::Layout>
+        indexMapper,
+    const float* __restrict kernel, const int numPlanes, const int numX,
+    const int maxX, const int numY, const int maxY, const int kernelSizeX,
+    const int kernelSizeY, float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + GetKernelSize<StaticKernelSizeX>()(kernelSizeX);
+  const int num_x_output = last_x - first_x + 1;
+
+  const int first_y = blockIdx.y * maxY;
+  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
+  const int num_y_input = last_y - first_y + GetKernelSize<StaticKernelSizeY>()(kernelSizeY);
+  const int num_y_output = last_y - first_y + 1;
+
+  const int first_plane = blockIdx.z * blockDim.z;
+  const int plane_stride = blockDim.z * gridDim.z;
+
+  for (int p = first_plane + threadIdx.z; p < numPlanes; p += plane_stride) {
+
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = threadIdx.z * num_y_input;
+
+    // Load inputs to shared memory
+    #pragma unroll
+    for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
+      const int input_offset = num_x_input * (j + plane_kernel_offset);
+      #pragma unroll
+      for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+        const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x, j+first_y);
+        s[i + input_offset] = eval.coeff(tensor_index);
+      }
+    }
+
+    __syncthreads();
+
+    // Convolution
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+    #pragma unroll
+    for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
+      #pragma unroll
+      for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+        float result = 0.0f;
+        #pragma unroll
+        for (int l = 0; l < GetKernelSize<StaticKernelSizeY>()(kernelSizeY); ++l) {
+          const int kernel_offset = kernelSizeX * l;
+          const int input_offset = i + num_x_input * (j + l + plane_kernel_offset);
+          #pragma unroll
+          for (int k = 0; k < GetKernelSize<StaticKernelSizeX>()(kernelSizeX); ++k) {
+            result += s[k + input_offset] * kernel[k + kernel_offset];
+          }
+        }
+        const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x, j+first_y);
+        buffer[tensor_index] = result;
+      }
+    }
+
+    __syncthreads();
+  }
+};
+
+template <typename InputEvaluator, typename Index, typename InputDims>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void EigenConvolutionKernel3D(
+    InputEvaluator eval,
+    const internal::IndexMapper<Index, InputDims, 3, InputEvaluator::Layout>
+        indexMapper,
+    const float* __restrict kernel, const size_t numPlanes, const size_t numX,
+    const size_t maxX, const size_t numY, const size_t maxY, const size_t numZ,
+    const size_t maxZ, const size_t kernelSizeX, const size_t kernelSizeY,
+    const size_t kernelSizeZ, float* buffer) {
+#if defined(EIGEN_HIPCC)
+  HIP_DYNAMIC_SHARED(float, s)
+#else
+  extern __shared__ float s[];
+#endif
+
+  // Load inputs to shared memory
+  const int first_x = blockIdx.x * maxX;
+  const int last_x = (first_x + maxX < numX ? first_x + maxX : numX) - 1;
+  const int num_x_input = last_x - first_x + kernelSizeX;
+
+  const int first_y = blockIdx.y * maxY;
+  const int last_y = (first_y + maxY < numY ? first_y + maxY : numY) - 1;
+  const int num_y_input = last_y - first_y + kernelSizeY;
+
+  const int first_z = blockIdx.z * maxZ;
+  const int last_z = (first_z + maxZ < numZ ? first_z + maxZ : numZ) - 1;
+  const int num_z_input = last_z - first_z + kernelSizeZ;
+
+  for (int p = 0; p < numPlanes; ++p) {
+
+    const int plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+    const int plane_kernel_offset = 0;
+
+    for (int k = threadIdx.z; k < num_z_input; k += blockDim.z) {
+      for (int j = threadIdx.y; j < num_y_input; j += blockDim.y) {
+        for (int i = threadIdx.x; i < num_x_input; i += blockDim.x) {
+          const int tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i+first_x, j+first_y, k+first_z);
+          s[i + num_x_input * (j + num_y_input * (k + plane_kernel_offset))] = eval.coeff(tensor_index);
+        }
+      }
+    }
+
+    __syncthreads();
+
+    // Convolution
+    const int num_z_output = last_z - first_z + 1;
+    const int num_y_output = last_y - first_y + 1;
+    const int num_x_output = last_x - first_x + 1;
+    const int plane_output_offset = indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p);
+
+    for (int k = threadIdx.z; k < num_z_output; k += blockDim.z) {
+      for (int j = threadIdx.y; j < num_y_output; j += blockDim.y) {
+        for (int i = threadIdx.x; i < num_x_output; i += blockDim.x) {
+          float result = 0.0f;
+          for (int n = 0; n < kernelSizeZ; ++n) {
+            for (int m = 0; m < kernelSizeY; ++m) {
+              for (int l = 0; l < kernelSizeX; ++l) {
+                result += s[i + l + num_x_input * (j + m + num_y_input * (k + n + plane_kernel_offset))] * kernel[l + kernelSizeX * (m + kernelSizeY * n)];
+              }
+            }
+          }
+          const int tensor_index = plane_output_offset + indexMapper.mapGpuOutputKernelToTensorOutputOffset(i+first_x, j+first_y, k+first_z);
+          buffer[tensor_index] = result;
+        }
+      }
+    }
+    __syncthreads();
+  }
+};
+
+
+
+template<typename Indices, typename InputArgType, typename KernelArgType>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, GpuDevice>
+{
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static const int NumDims =  internal::array_size<typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions>::value;
+  static const int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions KernelDimensions;
+
+  enum {
+    IsAligned = TensorEvaluator<InputArgType, GpuDevice>::IsAligned & TensorEvaluator<KernelArgType, GpuDevice>::IsAligned,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<InputArgType, GpuDevice>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const GpuDevice& device)
+      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, GpuDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, GpuDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, GpuDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
+
+    m_dimensions = m_inputImpl.dimensions();
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = op.indices()[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      m_dimensions[index] = result_dim;
+    }
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, GpuDevice>::type PacketReturnType;
+  typedef typename InputArgType::Scalar Scalar;
+  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
+    preloadKernel();
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      executeEval(data);
+      return false;
+    } else {
+      m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar));
+      executeEval(m_buf);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_buf) {
+      m_device.deallocate(m_buf);
+      m_buf = NULL;
+    }
+    if (m_local_kernel) {
+      m_device.deallocate((void*)m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+
+  EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    const Scalar* in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(local, m_kernelArg);
+      const bool PacketAccess = internal::IsVectorizable<GpuDevice, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, GpuDevice, PacketAccess>::run(evalToTmp, m_device);
+
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  static unsigned int ceil(unsigned int num, unsigned int denom) {
+    const unsigned int rounded_toward_zero = num / denom;
+    if (num > rounded_toward_zero * denom) {
+      return rounded_toward_zero + 1;
+    }
+    return rounded_toward_zero;
+  }
+
+  void executeEval(Scalar* data) const {
+    typedef typename TensorEvaluator<InputArgType, GpuDevice>::Dimensions InputDims;
+
+    const int maxSharedMem = m_device.sharedMemPerBlock();
+    const int maxThreadsPerBlock = m_device.maxGpuThreadsPerBlock();
+    const int maxBlocksPerProcessor = m_device.maxGpuThreadsPerMultiProcessor() / maxThreadsPerBlock;
+    const int numMultiProcessors = m_device.getNumGpuMultiProcessors();
+    const int warpSize = 32;
+
+    switch (NumKernelDims) {
+      case 1: {
+        const int kernel_size = m_kernelImpl.dimensions().TotalSize();
+
+        const int numX = dimensions()[m_indices[0]];
+        const int numP = dimensions().TotalSize() / numX;
+        int maxX;
+        dim3 block_size;
+
+        const int single_stride_dim =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor)
+                ? 0
+                : m_inputImpl.dimensions().rank() - 1;
+        if (m_indices[0] == single_stride_dim) {
+          // Maximum the reuse
+          const int inner_dim = ((maxSharedMem / (sizeof(Scalar)) - kernel_size + 1 + 31) / 32) * 32;
+          maxX = numext::mini<int>(inner_dim, numX);
+          const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size - 1 + maxX) * sizeof(Scalar)), numP);
+          block_size.x = numext::mini(maxThreadsPerBlock, maxX);
+          block_size.y = numext::mini<int>(maxThreadsPerBlock / block_size.x, maxP);
+        }
+        else {
+          // Read as much as possible alongside the inner most dimension, that is the plane
+          const int inner_dim = maxSharedMem / ((warpSize + kernel_size) * sizeof(Scalar));
+          const int maxP = numext::mini<int>(inner_dim, numP);
+          maxX = numext::mini<int>(maxSharedMem / (inner_dim * sizeof(Scalar)) - kernel_size + 1, numX);
+
+          block_size.x = numext::mini(warpSize, maxX);
+          block_size.y = numext::mini<int>(maxThreadsPerBlock/block_size.x, maxP);
+        }
+
+        const int shared_mem = block_size.y * (maxX + kernel_size - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        const int num_x_blocks = ceil(numX, maxX);
+        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
+        const int num_y_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks);
+
+        dim3 num_blocks(num_x_blocks, numext::mini<int>(num_y_blocks, ceil(numP, block_size.y)));
+
+
+        //cout << "launching 1D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " maxX: " << maxX << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
+
+        const array<Index, 1> indices(m_indices[0]);
+        const array<Index, 1> kernel_dims(m_kernelImpl.dimensions()[0]);
+        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(
+            m_inputImpl.dimensions(), kernel_dims, indices);
+        switch(kernel_size) {
+          case 4: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 4, data);
+            break;
+          }
+          case 7: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, 7, data);
+            break;
+          }
+          default: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel1D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, kernel_size, data);
+          }
+        }
+        break;
+      }
+
+      case 2: {
+        const int idxX =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1;
+        const int idxY =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0;
+        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
+        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
+
+        const int numX = dimensions()[m_indices[idxX]];
+        const int numY = dimensions()[m_indices[idxY]];
+        const int numP = dimensions().TotalSize() / (numX*numY);
+
+        const float scaling_factor = sqrtf(static_cast<float>(maxSharedMem) / (sizeof(Scalar) * kernel_size_y * kernel_size_x));
+
+        // Snap maxX to warp size
+        int inner_dim = ((static_cast<int>(scaling_factor * kernel_size_x) - kernel_size_x + 1 + 32) / 32) * 32;
+        const int maxX = numext::mini<int>(inner_dim, numX);
+        const int maxY = numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1)) - kernel_size_y + 1, numY);
+        const int maxP = numext::mini<int>(maxSharedMem / ((kernel_size_x - 1 + maxX) * (kernel_size_y - 1 + maxY) * sizeof(Scalar)), numP);
+
+        dim3 block_size;
+        block_size.x = numext::mini(1024, maxX);
+        block_size.y = numext::mini<int>(1024/block_size.x, maxY);
+        block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxP);
+
+        const int shared_mem = block_size.z * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        const int num_x_blocks = ceil(numX, maxX);
+        const int num_y_blocks = ceil(numY, maxY);
+        const int blocksPerProcessor = numext::mini(maxBlocksPerProcessor, maxSharedMem / shared_mem);
+        const int num_z_blocks = ceil(numMultiProcessors * blocksPerProcessor, num_x_blocks * num_y_blocks);
+
+        dim3 num_blocks(num_x_blocks, num_y_blocks, numext::mini<int>(num_z_blocks, ceil(numP, block_size.z)));
+
+
+        //cout << "launching 2D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y  << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z << " maxX: " << maxX << " maxY: " << maxY << " maxP: " << maxP << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
+
+        const array<Index, 2> indices(m_indices[idxX], m_indices[idxY]);
+        const array<Index, 2> kernel_dims(m_kernelImpl.dimensions()[idxX],
+                                          m_kernelImpl.dimensions()[idxY]);
+        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(
+            m_inputImpl.dimensions(), kernel_dims, indices);
+        switch (kernel_size_x) {
+          case 4: {
+            switch (kernel_size_y) {
+              case 7: {
+                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, 7>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, 7, data);
+                break;
+              }
+              default: {
+                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 4, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 4, kernel_size_y, data);
+                break;
+              }
+            }
+            break;
+          }
+          case 7: {
+            switch (kernel_size_y) {
+              case 4: {
+                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, 4>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, 4, data);
+                break;
+              }
+              default: {
+                LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, 7, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, 7, kernel_size_y, data);
+                break;
+              }
+            }
+            break;
+          }
+          default: {
+            LAUNCH_GPU_KERNEL((EigenConvolutionKernel2D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims, Dynamic, Dynamic>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, kernel_size_x, kernel_size_y, data);
+            break;
+          }
+        }
+        break;
+      }
+
+      case 3: {
+        const int idxX =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2;
+        const int idxY =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1;
+        const int idxZ =
+            static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0;
+
+        const int kernel_size_x = m_kernelImpl.dimensions()[idxX];
+        const int kernel_size_y = m_kernelImpl.dimensions()[idxY];
+        const int kernel_size_z = m_kernelImpl.dimensions()[idxZ];
+
+        const int numX = dimensions()[m_indices[idxX]];
+        const int numY = dimensions()[m_indices[idxY]];
+        const int numZ = dimensions()[m_indices[idxZ]];
+        const int numP = dimensions().TotalSize() / (numX*numY*numZ);
+
+        const int maxX = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * kernel_size_y * kernel_size_z) - kernel_size_x + 1, numX));
+        const int maxY = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * kernel_size_z) - kernel_size_y + 1, numY));
+        const int maxZ = numext::mini<int>(128, numext::mini<int>(maxSharedMem / (sizeof(Scalar) * (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1)) - kernel_size_z + 1, numZ));
+
+        dim3 block_size;
+        block_size.x = numext::mini(32, maxX);
+        block_size.y = numext::mini(32, maxY);
+        block_size.z = numext::mini<int>(1024/(block_size.x*block_size.y), maxZ);
+        dim3 num_blocks(ceil(numX, maxX), ceil(numY, maxY), ceil(numZ, maxZ));
+
+        const int shared_mem = (maxX + kernel_size_x - 1) * (maxY + kernel_size_y - 1) * (maxZ + kernel_size_z - 1) * sizeof(Scalar);
+        gpu_assert(shared_mem <= maxSharedMem);
+
+        //cout << "launching 3D kernel with block_size.x: " << block_size.x << " block_size.y: " << block_size.y  << " block_size.z: " << block_size.z << " num_blocks.x: " << num_blocks.x << " num_blocks.y: " << num_blocks.y << " num_blocks.z: " << num_blocks.z  << " shared_mem: " << shared_mem << " in stream " << m_device.stream() << endl;
+        const array<Index, 3> indices(m_indices[idxX], m_indices[idxY],
+                                      m_indices[idxZ]);
+        const array<Index, 3> kernel_dims(m_kernelImpl.dimensions()[idxX],
+                                          m_kernelImpl.dimensions()[idxY],
+                                          m_kernelImpl.dimensions()[idxZ]);
+        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(
+            m_inputImpl.dimensions(), kernel_dims, indices);
+
+        LAUNCH_GPU_KERNEL((EigenConvolutionKernel3D<TensorEvaluator<InputArgType, GpuDevice>, Index, InputDims>), num_blocks, block_size, shared_mem, m_device, m_inputImpl, indexMapper, m_kernel, numP, numX, maxX, numY, maxY, numZ, maxZ, kernel_size_x, kernel_size_y, kernel_size_z, data);
+        break;
+      }
+
+      default: {
+        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3), THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    eigen_assert(m_buf);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return m_buf[index];
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const
+  {
+    eigen_assert(m_buf);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buf+index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost
+    // model.
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost =
+        TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
+         TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) +
+                          m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized,
+                                       PacketSize));
+  }
+
+ private:
+  // No assignment (copies are needed by the kernels)
+  TensorEvaluator& operator = (const TensorEvaluator&);
+
+  TensorEvaluator<InputArgType, GpuDevice> m_inputImpl;
+  TensorEvaluator<KernelArgType, GpuDevice> m_kernelImpl;
+  KernelArgType m_kernelArg;
+  Indices m_indices;
+  Dimensions m_dimensions;
+  Scalar* m_buf;
+  const Scalar* m_kernel;
+  bool m_local_kernel;
+
+  const GpuDevice& m_device;
+};
+#endif
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
new file mode 100644
index 0000000..033318f
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
@@ -0,0 +1,544 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
+
+namespace Eigen {
+
+/** \class TensorConvolution
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor convolution class.
+ *
+ *
+ */
+
+enum class convolution_type { CONV1D, CONV2D, CONV3D };
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor, convolution_type Conv_Dim>
+struct EigenConvolutionKernel;
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV1D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper;
+  const size_t kernelSize;
+  const cl::sycl::range<2> input_range;
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper_,
+                         const size_t kernelSize_, const cl::sycl::range<2> input_range_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernelSize(kernelSize_),
+        input_range(input_range_) {}
+
+  template <typename BooleanDim2>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim2 boolean_check) {
+    return (boolean_check[0] && boolean_check[1]);
+  }
+  void operator()(cl::sycl::nd_item<2> itemID) {
+    auto buffer_ptr = buffer_acc.get_pointer();
+    auto kernel_ptr = kernel_filter.get_pointer();
+    // the required row to be calculated for the for each plane in shered memory
+    const size_t num_input = (itemID.get_local_range()[0] + kernelSize - 1);
+    const size_t plane_kernel_offset = itemID.get_local_id(1) * num_input;
+    const size_t input_offset = itemID.get_group(0) * itemID.get_local_range()[0];
+    const size_t plane_tensor_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(1));
+    /// fill the shared memory
+    for (size_t i = itemID.get_local_id(0); i < num_input; i += itemID.get_local_range()[0]) {
+      const size_t local_index = i + plane_kernel_offset;
+      const size_t tensor_index =
+          plane_tensor_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + input_offset);
+
+      local_acc[local_index] =
+          (((i + input_offset) < (input_range[0] + kernelSize - 1)) && itemID.get_global_id(1) < input_range[1])
+              ? device_evaluator.coeff(tensor_index)
+              : CoeffReturnType(0);
+    }
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+    // calculate the convolution // output start x
+    const size_t first_output_start = itemID.get_group(0) * (itemID.get_local_range()[0]);
+    if (boundary_check(itemID.get_global_id() < input_range)) {
+      CoeffReturnType result = static_cast<CoeffReturnType>(0);
+      const size_t index = plane_kernel_offset + itemID.get_local_id(0);
+      for (size_t k = 0; k < kernelSize; ++k) {
+        result += (local_acc[k + index] * kernel_ptr[k]);
+      }
+      const size_t tensor_index =
+          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(1)) +
+          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + first_output_start);
+      buffer_ptr[tensor_index] = result;
+    }
+  }
+};
+
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV2D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper;
+  const cl::sycl::range<2> kernel_size;
+  const cl::sycl::range<3> input_range;
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper_,
+                         const cl::sycl::range<2> kernel_size_, const cl::sycl::range<3> input_range_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernel_size(kernel_size_),
+        input_range(input_range_) {}
+  template <typename BooleanDim3>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) {
+    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
+  }
+
+  void operator()(cl::sycl::nd_item<3> itemID) {
+    auto buffer_ptr = buffer_acc.get_pointer();
+    auto kernel_ptr = kernel_filter.get_pointer();
+    // the required row to be calculated for the for each plane in shered memory
+    const auto num_input = cl::sycl::range<2>{
+        (cl::sycl::range<2>(itemID.get_local_range()[0], itemID.get_local_range()[1]) + kernel_size - 1)};
+
+    const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(2));
+    const size_t plane_kernel_offset = itemID.get_local_id(2) * num_input[1];
+
+    const auto input_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
+                                                 itemID.get_group(1) * itemID.get_local_range()[1]};
+      
+    // fill the local memory
+    bool in_range_dim2 = itemID.get_global_id(2) < input_range[2];
+    for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
+      const size_t local_input_offset = num_input[0] * (j + plane_kernel_offset);
+      bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1)); 
+      for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
+        const size_t local_index = i + local_input_offset;
+        const size_t tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
+                                                             i + input_offset[0], j + input_offset[1]);
+        local_acc[local_index] = (((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) &&
+                                  in_range_dim1 && in_range_dim2)
+                                     ? device_evaluator.coeff(tensor_index)
+                                     : CoeffReturnType(0);
+      }
+    }
+
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+    // output offset start for each thread
+    const auto output_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
+                                                  itemID.get_group(1) * itemID.get_local_range()[1]};
+
+    if (boundary_check(itemID.get_global_id() < input_range)) {
+      CoeffReturnType result = static_cast<CoeffReturnType>(0);
+
+      for (size_t j = 0; j < kernel_size[1]; j++) {
+        size_t kernel_offset = kernel_size[0] * j;
+        const size_t index =
+            (num_input[0] * (plane_kernel_offset + j + itemID.get_local_id(1))) + itemID.get_local_id(0);
+        for (size_t i = 0; i < kernel_size[0]; i++) {
+          result += (local_acc[i + index] * kernel_ptr[i + kernel_offset]);
+        }
+      }
+      const size_t tensor_index =
+          indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(2)) +
+          indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + output_offset[0],
+                                                             itemID.get_local_id(1) + output_offset[1]);
+
+      buffer_ptr[tensor_index] = result;
+    }
+  }
+};
+
+template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims,
+          typename Kernel_accessor, typename Buffer_accessor>
+struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor,
+                              Buffer_accessor, convolution_type::CONV3D> {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      Local_accessor;
+  Local_accessor local_acc;
+  Evaluator device_evaluator;
+  Kernel_accessor kernel_filter;
+  Buffer_accessor buffer_acc;
+  internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper;
+  const cl::sycl::range<3> kernel_size;
+  const cl::sycl::range<3> input_range;
+  const size_t numP;
+
+  EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_,
+                         Buffer_accessor buffer_acc_,
+                         internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper_,
+                         const cl::sycl::range<3> kernel_size_, const cl::sycl::range<3> input_range_,
+                         const size_t numP_)
+      : local_acc(local_acc_),
+        device_evaluator(device_evaluator_),
+        kernel_filter(kernel_filter_),
+        buffer_acc(buffer_acc_),
+        indexMapper(indexMapper_),
+        kernel_size(kernel_size_),
+        input_range(input_range_),
+        numP(numP_) {}
+  template <typename BooleanDim3>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) {
+    return (boolean_check[0] && boolean_check[1] && boolean_check[2]);
+  }
+  void operator()(cl::sycl::nd_item<3> itemID) {
+    auto buffer_ptr = buffer_acc.get_pointer();
+    auto kernel_ptr = kernel_filter.get_pointer();
+    const auto num_input = cl::sycl::range<3>{itemID.get_local_range() + kernel_size - 1};
+
+    const auto input_offset = cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range()};
+
+    const auto output_offset =
+          cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};
+
+    for (size_t p = 0; p < numP; p++) {
+      /// fill the shared memory
+      const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p);
+      for (size_t k = itemID.get_local_id(2); k < num_input[2]; k += itemID.get_local_range()[2]) {
+        size_t local_index_dim2 = num_input[0] * num_input[1] * k;
+        bool cond_k_dim = (k + input_offset[2] < (input_range[2] + kernel_size[2] - 1));
+        for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
+          bool cond_j_dim = cond_k_dim && (j + input_offset[1] < (input_range[1] + kernel_size[1] - 1));
+          size_t local_index_dim1 = (num_input[0] * j)  + local_index_dim2;
+          for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
+            bool conds = cond_j_dim && (i + input_offset[0] < (input_range[0] + kernel_size[0] - 1));
+            const size_t local_index = local_index_dim1 + i;
+            const size_t tensor_index =
+                plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
+                                         i + input_offset[0], j + input_offset[1], k + input_offset[2]);
+            local_acc[local_index] = conds ? device_evaluator.coeff(tensor_index) : CoeffReturnType(0);
+          }
+        }
+      }
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+
+      // calculate the convolution
+
+      if (boundary_check(itemID.get_global_id() < input_range)) {
+        CoeffReturnType result = static_cast<CoeffReturnType>(0);
+        for (size_t k = 0; k < kernel_size[2]; k++) {
+          for (size_t j = 0; j < kernel_size[1]; j++) {
+            for (size_t i = 0; i < kernel_size[0]; i++) {
+              const size_t kernel_index = i + kernel_size[0] * (j + kernel_size[1] * k);
+              const size_t local_index =
+                  ((i + itemID.get_local_id(0)) +
+                   num_input[0] * ((j + itemID.get_local_id(1)) + num_input[1] * (k + itemID.get_local_id(2))));
+
+              result += (local_acc[local_index] * kernel_ptr[kernel_index]);
+            }
+          }
+        }
+        const size_t tensor_index =
+            indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p) +
+            indexMapper.mapGpuOutputKernelToTensorOutputOffset(output_offset[0], output_offset[1], output_offset[2]);
+        buffer_ptr[tensor_index] = result;
+      }
+
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+    }
+  }
+};
+
+template <typename Indices, typename InputArgType, typename KernelArgType>
+struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Eigen::SyclDevice> {
+  typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
+
+  static const int NumDims =
+      internal::array_size<typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions>::value;
+  static const int NumKernelDims = internal::array_size<Indices>::value;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions KernelDimensions;
+  typedef const Eigen::SyclDevice Device;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Eigen::SyclDevice>::type PacketReturnType;
+  typedef typename InputArgType::Scalar Scalar;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Eigen::SyclDevice> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<const CoeffReturnType, Eigen::SyclDevice> KernelStorage;
+
+  enum {
+    IsAligned = TensorEvaluator<InputArgType, Eigen::SyclDevice>::IsAligned &
+                TensorEvaluator<KernelArgType, Eigen::SyclDevice>::IsAligned,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType &op, const Eigen::SyclDevice &device)
+      : m_inputImpl(op.inputExpression(), device),
+        m_kernelArg(op.kernelExpression()),
+        m_kernelImpl(op.kernelExpression(), device),
+        m_indices(op.indices()),
+        m_buf(NULL),
+        m_kernel(NULL),
+        m_local_kernel(false),
+        m_device(device) {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout) ==
+                         static_cast<int>(TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Layout)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    const typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions &input_dims = m_inputImpl.dimensions();
+    const typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions &kernel_dims =
+        m_kernelImpl.dimensions();
+
+    m_dimensions = m_inputImpl.dimensions();
+    for (int i = 0; i < NumKernelDims; ++i) {
+      const Index index = op.indices()[i];
+      const Index input_dim = input_dims[index];
+      const Index kernel_dim = kernel_dims[i];
+      const Index result_dim = input_dim - kernel_dim + 1;
+      m_dimensions[index] = result_dim;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC const Dimensions &dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    preloadKernel();
+    m_inputImpl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      executeEval(data);
+      return false;
+    } else {
+      m_buf = (EvaluatorPointerType)m_device.get(
+          (Scalar *)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar)));
+      executeEval(m_buf);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_inputImpl.cleanup();
+    if (m_buf) {
+      m_device.deallocate_temp(m_buf);
+      m_buf = NULL;
+    }
+    if (m_local_kernel) {
+      m_device.deallocate_temp(m_kernel);
+      m_local_kernel = false;
+    }
+    m_kernel = NULL;
+  }
+  /// used by sycl in order to build the sycl buffer
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device &device() const { return m_device; }
+  /// used by sycl in order to build the sycl buffer
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_buf; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
+    // Don't make a local copy of the kernel unless we have to (i.e. it's an
+    // expression that needs to be evaluated)
+    typename KernelStorage::Type in_place = m_kernelImpl.data();
+    if (in_place) {
+      m_kernel = in_place;
+      m_local_kernel = false;
+    } else {
+      ptrdiff_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
+      EvaluatorPointerType local = (EvaluatorPointerType)m_device.get((Scalar *)m_device.allocate_temp(kernel_sz));
+      typedef TensorEvalToOp<const KernelArgType> EvalTo;
+      EvalTo evalToTmp(m_device.get(local), m_kernelArg);
+      const bool PacketAccess = internal::IsVectorizable<Eigen::SyclDevice, KernelArgType>::value;
+      internal::TensorExecutor<const EvalTo, Eigen::SyclDevice, PacketAccess>::run(evalToTmp, m_device);
+      m_kernel = local;
+      m_local_kernel = true;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void executeEval(EvaluatorPointerType data) const {
+    typedef TensorEvaluator<InputArgType, Eigen::SyclDevice> InputEvaluator;
+    typedef typename InputEvaluator::Dimensions InputDims;
+    switch (NumKernelDims) {
+      case 1: {
+        const size_t numX = dimensions()[m_indices[0]];
+        const size_t numP = dimensions().TotalSize() / numX;
+        const auto input_dim = std::array<size_t, 2>{numX, numP};
+        auto global_range = cl::sycl::range<2>{};
+        auto local_range = cl::sycl::range<2>{};
+        const size_t kernel_size = m_kernelImpl.dimensions().TotalSize();
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+        const size_t local_memory_size = (local_range[0] + kernel_size - 1) * (local_range[1]);
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        const array<Index, 1> indices{{m_indices[0]}};
+        const array<Index, 1> kernel_dims{{m_kernelImpl.dimensions()[0]}};
+        internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D>
+            ConvKernel;
+
+        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+            m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size,
+            indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1]));
+        break;
+      }
+
+      case 2: {
+        auto kernel_index = std::array<size_t, 2>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0};
+        auto kernel_size = cl::sycl::range<2>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]]};
+        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
+        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
+        const size_t numP = dimensions().TotalSize() / (numX * numY);
+        auto input_dim = std::array<size_t, 3>{numX, numY, numP};
+
+        auto global_range = cl::sycl::range<3>{};
+        auto local_range = cl::sycl::range<3>{};
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+
+        const size_t local_memory_size =
+            (local_range[0] + kernel_size[0] - 1) * (local_range[1] + kernel_size[1] - 1) * local_range[2];
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        const array<Index, 2> indices{{m_indices[kernel_index[0]], m_indices[kernel_index[1]]}};
+        const array<Index, 2> kernel_dims{
+            {m_kernelImpl.dimensions()[kernel_index[0]], m_kernelImpl.dimensions()[kernel_index[1]]}};
+        internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D>
+            ConvKernel;
+        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+            m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
+            indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]});
+        break;
+      }
+
+      case 3: {
+        auto kernel_index = std::array<size_t, 3>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1,
+                                                  static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0};
+
+        auto kernel_size = cl::sycl::range<3>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[1]],
+                                              (size_t)m_kernelImpl.dimensions()[kernel_index[2]]};
+
+        const size_t numX = dimensions()[m_indices[kernel_index[0]]];
+        const size_t numY = dimensions()[m_indices[kernel_index[1]]];
+        const size_t numZ = dimensions()[m_indices[kernel_index[2]]];
+        auto input_dim = std::array<size_t, 3>{numX, numY, numZ};
+        const size_t numP = dimensions().TotalSize() / (numX * numY * numZ);
+
+        const array<Index, 3> indices{
+            {m_indices[kernel_index[0]], m_indices[kernel_index[1]], m_indices[kernel_index[2]]}};
+        const array<Index, 3> kernel_dims{{m_kernelImpl.dimensions()[kernel_index[0]],
+                                           m_kernelImpl.dimensions()[kernel_index[1]],
+                                           m_kernelImpl.dimensions()[kernel_index[2]]}};
+
+        internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
+
+        auto global_range = cl::sycl::range<3>{};
+        auto local_range = cl::sycl::range<3>{};
+
+        m_device.parallel_for_setup(input_dim, global_range, local_range);
+        auto local_memory_range = (local_range + kernel_size - 1);
+        const size_t local_memory_size = local_memory_range[0] * local_memory_range[1] * local_memory_range[2];
+
+        gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock());
+        typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
+                                       typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D>
+            ConvKernel;
+        m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
+            m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
+            indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP);
+        break;
+      }
+
+      default: {
+        EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3),
+                            THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_buf != NULL);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return m_buf[index];
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const {
+    eigen_assert(m_buf != NULL);
+    eigen_assert(index < m_dimensions.TotalSize());
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buf + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost
+    // model.
+    const double kernel_size = m_kernelImpl.dimensions().TotalSize();
+    // We ignore the use of fused multiply-add.
+    const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
+    const double firstIndex_compute_cost =
+        NumDims *
+        (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>());
+    return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
+           kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) +
+                          TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
+  }
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_kernelImpl.bind(cgh);
+    m_inputImpl.bind(cgh);
+    m_buf.bind(cgh);
+    m_kernel.bind(cgh);
+  }
+
+ private:
+  // No assignment (copies are needed by the kernels)
+  TensorEvaluator &operator=(const TensorEvaluator &);
+  TensorEvaluator<InputArgType, Eigen::SyclDevice> m_inputImpl;
+  KernelArgType m_kernelArg;
+  TensorEvaluator<KernelArgType, Eigen::SyclDevice> m_kernelImpl;
+  Indices m_indices;
+  Dimensions m_dimensions;
+  EvaluatorPointerType m_buf;
+  typename KernelStorage::Type m_kernel;
+  bool m_local_kernel;
+  const Eigen::SyclDevice EIGEN_DEVICE_REF m_device;
+};  // namespace Eigen
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
new file mode 100644
index 0000000..195267c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
@@ -0,0 +1,214 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
+
+namespace Eigen {
+
+/** \class TensorEvaluator
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief A cost model used to limit the number of threads used for evaluating
+  * tensor expression.
+  *
+  */
+
+// Class storing the cost of evaluating a tensor expression in terms of the
+// estimated number of operand bytes loads, bytes stored, and compute cycles.
+class TensorOpCost {
+ public:
+  // TODO(rmlarsen): Fix the scalar op costs in Eigen proper. Even a simple
+  // model based on minimal reciprocal throughput numbers from Intel or
+  // Agner Fog's tables would be better than what is there now.
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int MulCost() {
+    return internal::functor_traits<
+        internal::scalar_product_op<ArgType, ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int AddCost() {
+    return internal::functor_traits<internal::scalar_sum_op<ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int DivCost() {
+    return internal::functor_traits<
+        internal::scalar_quotient_op<ArgType, ArgType> >::Cost;
+  }
+  template <typename ArgType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int ModCost() {
+    return internal::functor_traits<internal::scalar_mod_op<ArgType> >::Cost;
+  }
+  template <typename SrcType, typename TargetType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int CastCost() {
+    return internal::functor_traits<
+        internal::scalar_cast_op<SrcType, TargetType> >::Cost;
+  }
+
+  EIGEN_DEVICE_FUNC
+  TensorOpCost() : bytes_loaded_(0), bytes_stored_(0), compute_cycles_(0) {}
+  EIGEN_DEVICE_FUNC
+  TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles)
+      : bytes_loaded_(bytes_loaded),
+        bytes_stored_(bytes_stored),
+        compute_cycles_(compute_cycles) {}
+
+  EIGEN_DEVICE_FUNC
+  TensorOpCost(double bytes_loaded, double bytes_stored, double compute_cycles,
+               bool vectorized, double packet_size)
+      : bytes_loaded_(bytes_loaded),
+        bytes_stored_(bytes_stored),
+        compute_cycles_(vectorized ? compute_cycles / packet_size
+                                   : compute_cycles) {
+    eigen_assert(bytes_loaded >= 0 && (numext::isfinite)(bytes_loaded));
+    eigen_assert(bytes_stored >= 0 && (numext::isfinite)(bytes_stored));
+    eigen_assert(compute_cycles >= 0 && (numext::isfinite)(compute_cycles));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_loaded() const {
+    return bytes_loaded_;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bytes_stored() const {
+    return bytes_stored_;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double compute_cycles() const {
+    return compute_cycles_;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double total_cost(
+      double load_cost, double store_cost, double compute_cost) const {
+    return load_cost * bytes_loaded_ + store_cost * bytes_stored_ +
+           compute_cost * compute_cycles_;
+  }
+
+  // Drop memory access component. Intended for cases when memory accesses are
+  // sequential or are completely masked by computations.
+  EIGEN_DEVICE_FUNC void dropMemoryCost() {
+    bytes_loaded_ = 0;
+    bytes_stored_ = 0;
+  }
+
+  // TODO(rmlarsen): Define min in terms of total cost, not elementwise.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMin(
+      const TensorOpCost& rhs) const {
+    double bytes_loaded = numext::mini(bytes_loaded_, rhs.bytes_loaded());
+    double bytes_stored = numext::mini(bytes_stored_, rhs.bytes_stored());
+    double compute_cycles = numext::mini(compute_cycles_, rhs.compute_cycles());
+    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
+  }
+
+  // TODO(rmlarsen): Define max in terms of total cost, not elementwise.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax(
+      const TensorOpCost& rhs) const {
+    double bytes_loaded = numext::maxi(bytes_loaded_, rhs.bytes_loaded());
+    double bytes_stored = numext::maxi(bytes_stored_, rhs.bytes_stored());
+    double compute_cycles = numext::maxi(compute_cycles_, rhs.compute_cycles());
+    return TensorOpCost(bytes_loaded, bytes_stored, compute_cycles);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator+=(
+      const TensorOpCost& rhs) {
+    bytes_loaded_ += rhs.bytes_loaded();
+    bytes_stored_ += rhs.bytes_stored();
+    compute_cycles_ += rhs.compute_cycles();
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost& operator*=(double rhs) {
+    bytes_loaded_ *= rhs;
+    bytes_stored_ *= rhs;
+    compute_cycles_ *= rhs;
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator+(
+      TensorOpCost lhs, const TensorOpCost& rhs) {
+    lhs += rhs;
+    return lhs;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
+      TensorOpCost lhs, double rhs) {
+    lhs *= rhs;
+    return lhs;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend TensorOpCost operator*(
+      double lhs, TensorOpCost rhs) {
+    rhs *= lhs;
+    return rhs;
+  }
+
+  friend std::ostream& operator<<(std::ostream& os, const TensorOpCost& tc) {
+    return os << "[bytes_loaded = " << tc.bytes_loaded()
+              << ", bytes_stored = " << tc.bytes_stored()
+              << ", compute_cycles = " << tc.compute_cycles() << "]";
+  }
+
+ private:
+  double bytes_loaded_;
+  double bytes_stored_;
+  double compute_cycles_;
+};
+
+// TODO(rmlarsen): Implement a policy that chooses an "optimal" number of theads
+// in [1:max_threads] instead of just switching multi-threading off for small
+// work units.
+template <typename Device>
+class TensorCostModel {
+ public:
+  // Scaling from Eigen compute cost to device cycles.
+  static const int kDeviceCyclesPerComputeCycle = 1;
+
+ // Costs in device cycles.
+  static const int kStartupCycles = 100000;
+  static const int kPerThreadCycles = 100000;
+  static const int kTaskSize = 40000;
+
+  // Returns the number of threads in [1:max_threads] to use for
+  // evaluating an expression with the given output size and cost per
+  // coefficient.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int numThreads(
+      double output_size, const TensorOpCost& cost_per_coeff, int max_threads) {
+    double cost = totalCost(output_size, cost_per_coeff);
+    double threads = (cost - kStartupCycles) / kPerThreadCycles + 0.9;
+    // Make sure we don't invoke undefined behavior when we convert to an int.
+    threads = numext::mini<double>(threads, GenericNumTraits<int>::highest());
+    return numext::mini(max_threads,
+                        numext::maxi<int>(1, static_cast<int>(threads)));
+  }
+
+  // taskSize assesses parallel task size.
+  // Value of 1.0 means ideal parallel task size. Values < 1.0 mean that task
+  // granularity needs to be increased to mitigate parallelization overheads.
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double taskSize(
+      double output_size, const TensorOpCost& cost_per_coeff) {
+    return totalCost(output_size, cost_per_coeff) / kTaskSize;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double totalCost(
+      double output_size, const TensorOpCost& cost_per_coeff) {
+    // Cost of memory fetches from L2 cache. 64 is typical cache line size.
+    // 11 is L2 cache latency on Haswell.
+    // We don't know whether data is in L1, L2 or L3. But we are most interested
+    // in single-threaded computational time around 100us-10ms (smaller time
+    // is too small for parallelization, larger time is not interesting
+    // either because we are probably using all available threads already).
+    // And for the target time range, L2 seems to be what matters. Data set
+    // fitting into L1 is too small to take noticeable time. Data set fitting
+    // only into L3 presumably will take more than 10ms to load and process.
+    const double kLoadCycles = 1.0 / 64 * 11;
+    const double kStoreCycles = 1.0 / 64 * 11;
+    // Scaling from Eigen compute cost to device cycles.
+    return output_size *
+        cost_per_coeff.total_cost(kLoadCycles, kStoreCycles,
+                                  kDeviceCyclesPerComputeCycle);
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
new file mode 100644
index 0000000..95a8a84
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorCustomOp.h
@@ -0,0 +1,347 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
+
+namespace Eigen {
+
+/** \class TensorCustomUnaryOp
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor custom class.
+  *
+  *
+  */
+namespace internal {
+template<typename CustomUnaryFunc, typename XprType>
+struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::StorageKind StorageKind;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = traits<XprType>::NumDimensions;
+  static const int Layout = traits<XprType>::Layout;
+  typedef typename traits<XprType>::PointerType PointerType;
+};
+
+template<typename CustomUnaryFunc, typename XprType>
+struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
+{
+  typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename CustomUnaryFunc, typename XprType>
+struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType> >
+{
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename CustomUnaryFunc, typename XprType>
+class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorCustomUnaryOp>::type Nested;
+  typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorCustomUnaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func)
+      : m_expr(expr), m_func(func) {}
+
+  EIGEN_DEVICE_FUNC
+  const CustomUnaryFunc& func() const { return m_func; }
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type&
+  expression() const { return m_expr; }
+
+  protected:
+    typename XprType::Nested m_expr;
+    const CustomUnaryFunc m_func;
+};
+
+
+// Eval as rvalue
+template<typename CustomUnaryFunc, typename XprType, typename Device>
+struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
+{
+  typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
+  typedef typename internal::traits<ArgType>::Index Index;
+  static const int NumDims = internal::traits<ArgType>::NumDimensions;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename internal::remove_const<typename ArgType::Scalar>::type Scalar;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<XprType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device)
+      : m_op(op), m_device(device), m_result(NULL)
+  {
+    m_dimensions = op.func().dimensions(op.expression());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
+          m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_result) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_result[index];
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_result.bind(cgh);
+  }
+#endif
+
+ protected:
+  void evalTo(EvaluatorPointerType data) {
+    TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index> > result(m_device.get(data), m_dimensions);
+    m_op.func().eval(m_op.expression(), result, m_device);
+  }
+
+  Dimensions m_dimensions;
+  const ArgType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  EvaluatorPointerType m_result;
+};
+
+
+
+/** \class TensorCustomBinaryOp
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor custom class.
+  *
+  *
+  */
+namespace internal {
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
+{
+  typedef typename internal::promote_storage_type<typename LhsXprType::Scalar,
+                                                  typename RhsXprType::Scalar>::ret Scalar;
+  typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
+                                                  typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
+  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
+                                        typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
+                                      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const int NumDimensions = traits<LhsXprType>::NumDimensions;
+  static const int Layout = traits<LhsXprType>::Layout;
+  typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                                typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType>::type PointerType;
+};
+
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense>
+{
+  typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
+};
+
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> >
+{
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
+class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorCustomBinaryOp>::type Nested;
+  typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorCustomBinaryOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const CustomBinaryFunc& func)
+
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func) {}
+
+  EIGEN_DEVICE_FUNC
+  const CustomBinaryFunc& func() const { return m_func; }
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename LhsXprType::Nested>::type&
+  lhsExpression() const { return m_lhs_xpr; }
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename RhsXprType::Nested>::type&
+  rhsExpression() const { return m_rhs_xpr; }
+
+  protected:
+    typename LhsXprType::Nested m_lhs_xpr;
+    typename RhsXprType::Nested m_rhs_xpr;
+    const CustomBinaryFunc m_func;
+};
+
+
+// Eval as rvalue
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
+struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device>
+{
+  typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
+  typedef typename internal::traits<XprType>::Index Index;
+  static const int NumDims = internal::traits<XprType>::NumDimensions;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<LhsXprType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_op(op), m_device(device), m_result(NULL)
+  {
+    m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (data) {
+      evalTo(data);
+      return false;
+    } else {
+      m_result = static_cast<EvaluatorPointerType>(m_device.get( (CoeffReturnType*)
+        m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))));
+      evalTo(m_result);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_result != NULL) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_result[index];
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_result.bind(cgh);
+  }
+#endif
+
+ protected:
+  void evalTo(EvaluatorPointerType data) {
+    TensorMap<Tensor<CoeffReturnType, NumDims, Layout> > result(m_device.get(data), m_dimensions);
+    m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
+  }
+
+  Dimensions m_dimensions;
+  const XprType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  EvaluatorPointerType m_result;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
new file mode 100644
index 0000000..96fa46c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h
@@ -0,0 +1,137 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
+
+namespace Eigen {
+
+/** \class TensorDevice
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Pseudo expression providing an operator = that will evaluate its argument
+  * on the specified computing 'device' (GPU, thread pool, ...)
+  *
+  * Example:
+  *    C.device(EIGEN_GPU) = A + B;
+  *
+  * Todo: operator *= and /=.
+  */
+
+template <typename ExpressionType, typename DeviceType> class TensorDevice {
+  public:
+    TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
+
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TensorDevice)
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
+      typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+      Assign assign(m_expression, other);
+      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+      return *this;
+    }
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE TensorDevice& operator+=(const OtherDerived& other) {
+      typedef typename OtherDerived::Scalar Scalar;
+      typedef TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const ExpressionType, const OtherDerived> Sum;
+      Sum sum(m_expression, other);
+      typedef TensorAssignOp<ExpressionType, const Sum> Assign;
+      Assign assign(m_expression, sum);
+      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+      return *this;
+    }
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE TensorDevice& operator-=(const OtherDerived& other) {
+      typedef typename OtherDerived::Scalar Scalar;
+      typedef TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const ExpressionType, const OtherDerived> Difference;
+      Difference difference(m_expression, other);
+      typedef TensorAssignOp<ExpressionType, const Difference> Assign;
+      Assign assign(m_expression, difference);
+      internal::TensorExecutor<const Assign, DeviceType>::run(assign, m_device);
+      return *this;
+    }
+
+  protected:
+    const DeviceType& m_device;
+    ExpressionType& m_expression;
+};
+
+/** \class TensorAsyncDevice
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Pseudo expression providing an operator = that will evaluate its
+ * argument asynchronously on the specified device. Currently only
+ * ThreadPoolDevice implements proper asynchronous execution, while the default
+ * and GPU devices just run the expression synchronously and call m_done() on
+ * completion..
+ *
+ * Example:
+ *    auto done = []() { ... expression evaluation done ... };
+ *    C.device(thread_pool_device, std::move(done)) = A + B;
+ */
+
+template <typename ExpressionType, typename DeviceType, typename DoneCallback>
+class TensorAsyncDevice {
+ public:
+  TensorAsyncDevice(const DeviceType& device, ExpressionType& expression,
+                    DoneCallback done)
+      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+    typedef internal::TensorExecutor<const Assign, DeviceType> Executor;
+
+    Assign assign(m_expression, other);
+    Executor::run(assign, m_device);
+    m_done();
+
+    return *this;
+  }
+
+ protected:
+  const DeviceType& m_device;
+  ExpressionType& m_expression;
+  DoneCallback m_done;
+};
+
+
+#ifdef EIGEN_USE_THREADS
+template <typename ExpressionType, typename DoneCallback>
+class TensorAsyncDevice<ExpressionType, ThreadPoolDevice, DoneCallback> {
+ public:
+  TensorAsyncDevice(const ThreadPoolDevice& device, ExpressionType& expression,
+                    DoneCallback done)
+      : m_device(device), m_expression(expression), m_done(std::move(done)) {}
+
+  template <typename OtherDerived>
+  EIGEN_STRONG_INLINE TensorAsyncDevice& operator=(const OtherDerived& other) {
+    typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
+    typedef internal::TensorAsyncExecutor<const Assign, ThreadPoolDevice, DoneCallback> Executor;
+
+    // WARNING: After assignment 'm_done' callback will be in undefined state.
+    Assign assign(m_expression, other);
+    Executor::runAsync(assign, m_device, std::move(m_done));
+
+    return *this;
+  }
+
+ protected:
+  const ThreadPoolDevice& m_device;
+  ExpressionType& m_expression;
+  DoneCallback m_done;
+};
+#endif
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
new file mode 100644
index 0000000..f779239
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h
@@ -0,0 +1,6 @@
+
+#if defined(__clang__) || defined(__GNUC__)
+#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorDeviceGpu.h file"
+#endif
+
+#include "TensorDeviceGpu.h"
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
new file mode 100644
index 0000000..46b9d3a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h
@@ -0,0 +1,104 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
+
+
+namespace Eigen {
+
+// Default device for the machine (typically a single cpu core)
+struct DefaultDevice {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+    return internal::aligned_malloc(num_bytes);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
+    internal::aligned_free(buffer);
+  }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
+    return allocate(num_bytes);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
+    deallocate(buffer);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+    ::memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
+    ::memset(buffer, c, n);
+  }
+  template<typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const { 
+    return data;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t numThreads() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+    // Running on the host CPU
+    return 1;
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return 64;
+#else
+    // Running on a CUDA device
+    return 32;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+    // Running on the host CPU
+    return l1CacheSize();
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return 48*1024; // FIXME : update this number for HIP
+#else
+    // Running on a CUDA device, return the amount of shared memory available.
+    return 48*1024;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+    // Running single threaded on the host CPU
+    return l3CacheSize();
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    return firstLevelCacheSize(); // FIXME : update this number for HIP
+#else
+    // Running on a CUDA device
+    return firstLevelCacheSize();
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+    // Running single threaded on the host CPU
+    // Should return an enum that encodes the ISA supported by the CPU
+    return 1;
+#elif defined(EIGEN_HIP_DEVICE_COMPILE)
+    // Running on a HIP device
+    // return 1 as major for HIP
+    return 1;
+#else
+    // Running on a CUDA device
+    return EIGEN_CUDA_ARCH / 100;
+#endif
+  }
+};
+
+}  // namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_DEFAULT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
new file mode 100644
index 0000000..ec2e3cb
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h
@@ -0,0 +1,389 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
+
+// This header file container defines fo gpu* macros which will resolve to
+// their equivalent hip* or cuda* versions depending on the compiler in use
+// A separate header (included at the end of this file) will undefine all 
+#include "TensorGpuHipCudaDefines.h"
+
+namespace Eigen {
+
+static const int kGpuScratchSize = 1024;
+
+// This defines an interface that GPUDevice can take to use
+// HIP / CUDA streams underneath.
+class StreamInterface {
+ public:
+  virtual ~StreamInterface() {}
+
+  virtual const gpuStream_t& stream() const = 0;
+  virtual const gpuDeviceProp_t& deviceProperties() const = 0;
+
+  // Allocate memory on the actual device where the computation will run
+  virtual void* allocate(size_t num_bytes) const = 0;
+  virtual void deallocate(void* buffer) const = 0;
+
+  // Return a scratchpad buffer of size 1k
+  virtual void* scratchpad() const = 0;
+
+  // Return a semaphore. The semaphore is initially initialized to 0, and
+  // each kernel using it is responsible for resetting to 0 upon completion
+  // to maintain the invariant that the semaphore is always equal to 0 upon
+  // each kernel start.
+  virtual unsigned int* semaphore() const = 0;
+};
+
+class GpuDeviceProperties {
+ public:
+  GpuDeviceProperties() : 
+      initialized_(false), first_(true), device_properties_(nullptr) {}
+ 
+  ~GpuDeviceProperties() {
+    if (device_properties_) {
+      delete[] device_properties_;
+    }
+  }
+  
+  EIGEN_STRONG_INLINE const gpuDeviceProp_t& get(int device) const {
+    return device_properties_[device];
+  }
+
+  EIGEN_STRONG_INLINE bool isInitialized() const {
+    return initialized_;
+  }
+
+  void initialize() {
+    if (!initialized_) {
+      // Attempts to ensure proper behavior in the case of multiple threads
+      // calling this function simultaneously. This would be trivial to
+      // implement if we could use std::mutex, but unfortunately mutex don't
+      // compile with nvcc, so we resort to atomics and thread fences instead.
+      // Note that if the caller uses a compiler that doesn't support c++11 we
+      // can't ensure that the initialization is thread safe.
+      if (first_.exchange(false)) {
+        // We're the first thread to reach this point.
+        int num_devices;
+        gpuError_t status = gpuGetDeviceCount(&num_devices);
+        if (status != gpuSuccess) {
+          std::cerr << "Failed to get the number of GPU devices: "
+                    << gpuGetErrorString(status)
+                    << std::endl;
+          gpu_assert(status == gpuSuccess);
+        }
+        device_properties_ = new gpuDeviceProp_t[num_devices];
+        for (int i = 0; i < num_devices; ++i) {
+          status = gpuGetDeviceProperties(&device_properties_[i], i);
+          if (status != gpuSuccess) {
+            std::cerr << "Failed to initialize GPU device #"
+                      << i
+                      << ": "
+                      << gpuGetErrorString(status)
+                      << std::endl;
+            gpu_assert(status == gpuSuccess);
+          }
+        }
+
+        std::atomic_thread_fence(std::memory_order_release);
+        initialized_ = true;
+      } else {
+        // Wait for the other thread to inititialize the properties.
+        while (!initialized_) {
+          std::atomic_thread_fence(std::memory_order_acquire);
+          std::this_thread::sleep_for(std::chrono::milliseconds(1000));
+        }
+      }
+    }
+  }
+
+ private:
+  volatile bool initialized_;
+  std::atomic<bool> first_;
+  gpuDeviceProp_t* device_properties_;
+};
+
+EIGEN_ALWAYS_INLINE const GpuDeviceProperties& GetGpuDeviceProperties() {
+  static GpuDeviceProperties* deviceProperties = new GpuDeviceProperties();
+  if (!deviceProperties->isInitialized()) {
+    deviceProperties->initialize();
+  }
+  return *deviceProperties;
+}
+
+EIGEN_ALWAYS_INLINE const gpuDeviceProp_t& GetGpuDeviceProperties(int device) {
+  return GetGpuDeviceProperties().get(device);
+}
+
+static const gpuStream_t default_stream = gpuStreamDefault;
+
+class GpuStreamDevice : public StreamInterface {
+ public:
+  // Use the default stream on the current device
+  GpuStreamDevice() : stream_(&default_stream), scratch_(NULL), semaphore_(NULL) {
+    gpuGetDevice(&device_);
+  }
+  // Use the default stream on the specified device
+  GpuStreamDevice(int device) : stream_(&default_stream), device_(device), scratch_(NULL), semaphore_(NULL) {}
+  // Use the specified stream. Note that it's the
+  // caller responsibility to ensure that the stream can run on
+  // the specified device. If no device is specified the code
+  // assumes that the stream is associated to the current gpu device.
+  GpuStreamDevice(const gpuStream_t* stream, int device = -1)
+      : stream_(stream), device_(device), scratch_(NULL), semaphore_(NULL) {
+    if (device < 0) {
+      gpuGetDevice(&device_);
+    } else {
+      int num_devices;
+      gpuError_t err = gpuGetDeviceCount(&num_devices);
+      EIGEN_UNUSED_VARIABLE(err)
+      gpu_assert(err == gpuSuccess);
+      gpu_assert(device < num_devices);
+      device_ = device;
+    }
+  }
+
+  virtual ~GpuStreamDevice() {
+    if (scratch_) {
+      deallocate(scratch_);
+    }
+  }
+
+  const gpuStream_t& stream() const { return *stream_; }
+  const gpuDeviceProp_t& deviceProperties() const {
+    return GetGpuDeviceProperties(device_);
+  }
+  virtual void* allocate(size_t num_bytes) const {
+    gpuError_t err = gpuSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+    void* result;
+    err = gpuMalloc(&result, num_bytes);
+    gpu_assert(err == gpuSuccess);
+    gpu_assert(result != NULL);
+    return result;
+  }
+  virtual void deallocate(void* buffer) const {
+    gpuError_t err = gpuSetDevice(device_);
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+    gpu_assert(buffer != NULL);
+    err = gpuFree(buffer);
+    gpu_assert(err == gpuSuccess);
+  }
+
+  virtual void* scratchpad() const {
+    if (scratch_ == NULL) {
+      scratch_ = allocate(kGpuScratchSize + sizeof(unsigned int));
+    }
+    return scratch_;
+  }
+
+  virtual unsigned int* semaphore() const {
+    if (semaphore_ == NULL) {
+      char* scratch = static_cast<char*>(scratchpad()) + kGpuScratchSize;
+      semaphore_ = reinterpret_cast<unsigned int*>(scratch);
+      gpuError_t err = gpuMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_);
+      EIGEN_UNUSED_VARIABLE(err)
+      gpu_assert(err == gpuSuccess);
+    }
+    return semaphore_;
+  }
+
+ private:
+  const gpuStream_t* stream_;
+  int device_;
+  mutable void* scratch_;
+  mutable unsigned int* semaphore_;
+};
+
+struct GpuDevice {
+  // The StreamInterface is not owned: the caller is
+  // responsible for its initialization and eventual destruction.
+  explicit GpuDevice(const StreamInterface* stream) : stream_(stream), max_blocks_(INT_MAX) {
+    eigen_assert(stream);
+  }
+  explicit GpuDevice(const StreamInterface* stream, int num_blocks) : stream_(stream), max_blocks_(num_blocks) {
+    eigen_assert(stream);
+  }
+  // TODO(bsteiner): This is an internal API, we should not expose it.
+  EIGEN_STRONG_INLINE const gpuStream_t& stream() const {
+    return stream_->stream();
+  }
+
+  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+    return stream_->allocate(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
+    stream_->deallocate(buffer);
+  }
+
+  EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
+    return stream_->allocate(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
+    stream_->deallocate(buffer);
+  }
+
+  template<typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const { 
+    return data;
+  }
+
+  EIGEN_STRONG_INLINE void* scratchpad() const {
+    return stream_->scratchpad();
+  }
+
+  EIGEN_STRONG_INLINE unsigned int* semaphore() const {
+    return stream_->semaphore();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToDevice,
+                                      stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+#else
+    EIGEN_UNUSED_VARIABLE(dst);
+    EIGEN_UNUSED_VARIABLE(src);
+    EIGEN_UNUSED_VARIABLE(n);
+    eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+    gpuError_t err =
+        gpuMemcpyAsync(dst, src, n, gpuMemcpyHostToDevice, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+  }
+
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+    gpuError_t err =
+        gpuMemcpyAsync(dst, src, n, gpuMemcpyDeviceToHost, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuMemsetAsync(buffer, c, n, stream_->stream());
+    EIGEN_UNUSED_VARIABLE(err)
+    gpu_assert(err == gpuSuccess);
+#else
+  eigen_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE size_t numThreads() const {
+    // FIXME
+    return 32;
+  }
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+    // FIXME
+    return 48*1024;
+  }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // We won't try to take advantage of the l2 cache for the time being, and
+    // there is no l3 cache on hip/cuda devices.
+    return firstLevelCacheSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void synchronize() const {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+    gpuError_t err = gpuStreamSynchronize(stream_->stream());
+    if (err != gpuSuccess) {
+      std::cerr << "Error detected in GPU stream: "
+                << gpuGetErrorString(err)
+                << std::endl;
+      gpu_assert(err == gpuSuccess);
+    }
+#else
+    gpu_assert(false && "The default device should be used instead to generate kernel code");
+#endif
+  }
+
+  EIGEN_STRONG_INLINE int getNumGpuMultiProcessors() const {
+    return stream_->deviceProperties().multiProcessorCount;
+  }
+  EIGEN_STRONG_INLINE int maxGpuThreadsPerBlock() const {
+    return stream_->deviceProperties().maxThreadsPerBlock;
+  }
+  EIGEN_STRONG_INLINE int maxGpuThreadsPerMultiProcessor() const {
+    return stream_->deviceProperties().maxThreadsPerMultiProcessor;
+  }
+  EIGEN_STRONG_INLINE int sharedMemPerBlock() const {
+    return stream_->deviceProperties().sharedMemPerBlock;
+  }
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+    return stream_->deviceProperties().major;
+  }
+  EIGEN_STRONG_INLINE int minorDeviceVersion() const {
+    return stream_->deviceProperties().minor;
+  }
+
+  EIGEN_STRONG_INLINE int maxBlocks() const {
+    return max_blocks_;
+  }
+
+  // This function checks if the GPU runtime recorded an error for the
+  // underlying stream device.
+  inline bool ok() const {
+#ifdef EIGEN_GPUCC
+    gpuError_t error = gpuStreamQuery(stream_->stream());
+    return (error == gpuSuccess) || (error == gpuErrorNotReady);
+#else
+    return false;
+#endif
+  }
+
+ private:
+  const StreamInterface* stream_;
+  int max_blocks_;
+};
+
+#if defined(EIGEN_HIPCC)
+
+#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)             \
+  hipLaunchKernelGGL(kernel, dim3(gridsize), dim3(blocksize), (sharedmem), (device).stream(), __VA_ARGS__); \
+  gpu_assert(hipGetLastError() == hipSuccess);
+
+#else
+ 
+#define LAUNCH_GPU_KERNEL(kernel, gridsize, blocksize, sharedmem, device, ...)             \
+  (kernel) <<< (gridsize), (blocksize), (sharedmem), (device).stream() >>> (__VA_ARGS__);   \
+  gpu_assert(cudaGetLastError() == cudaSuccess);
+
+#endif
+ 
+// FIXME: Should be device and kernel specific.
+#ifdef EIGEN_GPUCC
+static EIGEN_DEVICE_FUNC inline void setGpuSharedMemConfig(gpuSharedMemConfig config) {
+#ifndef EIGEN_GPU_COMPILE_PHASE
+  gpuError_t status = gpuDeviceSetSharedMemConfig(config);
+  EIGEN_UNUSED_VARIABLE(status)
+  gpu_assert(status == gpuSuccess);
+#else
+  EIGEN_UNUSED_VARIABLE(config)
+#endif
+}
+#endif
+
+}  // end namespace Eigen
+
+// undefine all the gpu* macros we defined at the beginning of the file
+#include "TensorGpuHipCudaUndefines.h"
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_GPU_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
new file mode 100644
index 0000000..df591c2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
@@ -0,0 +1,1048 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_SYCL) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
+#include <unordered_set>
+
+namespace Eigen {
+
+namespace TensorSycl {
+namespace internal {
+
+/// Cache all the device information needed
+struct SyclDeviceInfo {
+  SyclDeviceInfo(cl::sycl::queue queue)
+      : local_mem_type(
+            queue.get_device()
+                .template get_info<cl::sycl::info::device::local_mem_type>()),
+        max_work_item_sizes(
+            queue.get_device()
+                .template get_info<
+                    cl::sycl::info::device::max_work_item_sizes>()),
+        max_mem_alloc_size(
+            queue.get_device()
+                .template get_info<
+                    cl::sycl::info::device::max_mem_alloc_size>()),
+        max_compute_units(queue.get_device()
+                              .template get_info<
+                                  cl::sycl::info::device::max_compute_units>()),
+        max_work_group_size(
+            queue.get_device()
+                .template get_info<
+                    cl::sycl::info::device::max_work_group_size>()),
+        local_mem_size(
+            queue.get_device()
+                .template get_info<cl::sycl::info::device::local_mem_size>()),
+        platform_name(queue.get_device()
+                          .get_platform()
+                          .template get_info<cl::sycl::info::platform::name>()),
+        device_name(queue.get_device()
+                        .template get_info<cl::sycl::info::device::name>()),
+        device_vendor(
+            queue.get_device()
+                .template get_info<cl::sycl::info::device::vendor>()) {}
+
+  cl::sycl::info::local_mem_type local_mem_type;
+  cl::sycl::id<3> max_work_item_sizes;
+  unsigned long max_mem_alloc_size;
+  unsigned long max_compute_units;
+  unsigned long max_work_group_size;
+  size_t local_mem_size;
+  std::string platform_name;
+  std::string device_name;
+  std::string device_vendor;
+};
+
+}  // end namespace internal
+}  // end namespace TensorSycl
+
+typedef TensorSycl::internal::buffer_data_type_t buffer_scalar_t;
+// All devices (even AMD CPU with intel OpenCL runtime) that support OpenCL and
+// can consume SPIR or SPIRV can use the Eigen SYCL backend and consequently
+// TensorFlow via the Eigen SYCL Backend.
+EIGEN_STRONG_INLINE auto get_sycl_supported_devices()
+    -> decltype(cl::sycl::device::get_devices()) {
+#ifdef EIGEN_SYCL_USE_DEFAULT_SELECTOR
+  return {cl::sycl::device(cl::sycl::default_selector())};
+#else
+  std::vector<cl::sycl::device> supported_devices;
+  auto platform_list = cl::sycl::platform::get_platforms();
+  for (const auto &platform : platform_list) {
+    auto device_list = platform.get_devices();
+    auto platform_name =
+        platform.template get_info<cl::sycl::info::platform::name>();
+    std::transform(platform_name.begin(), platform_name.end(),
+                   platform_name.begin(), ::tolower);
+    for (const auto &device : device_list) {
+      auto vendor = device.template get_info<cl::sycl::info::device::vendor>();
+      std::transform(vendor.begin(), vendor.end(), vendor.begin(), ::tolower);
+      bool unsupported_condition =
+          (device.is_cpu() && platform_name.find("amd") != std::string::npos &&
+           vendor.find("apu") == std::string::npos) ||
+          (platform_name.find("experimental") != std::string::npos) ||
+          device.is_host();
+      if (!unsupported_condition) {
+        supported_devices.push_back(device);
+      }
+    }
+  }
+  return supported_devices;
+#endif
+}
+
+class QueueInterface {
+ public:
+  /// Creating device by using cl::sycl::selector or cl::sycl::device.
+  template <typename DeviceOrSelector>
+  explicit QueueInterface(
+      const DeviceOrSelector &dev_or_sel, cl::sycl::async_handler handler,
+      unsigned num_threads = std::thread::hardware_concurrency())
+      : m_queue(dev_or_sel, handler),
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+        m_prog(m_queue.get_context(), get_sycl_supported_devices()),
+#endif
+        m_thread_pool(num_threads),
+        m_device_info(m_queue) {
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+    m_prog.build_with_kernel_type<DeviceOrSelector>();
+    auto f = [&](cl::sycl::handler &cgh) {
+      cgh.single_task<DeviceOrSelector>(m_prog.get_kernel<DeviceOrSelector>(),
+                                        [=]() {})
+    };
+    EIGEN_SYCL_TRY_CATCH(m_queue.submit(f));
+#endif
+  }
+
+  template <typename DeviceOrSelector>
+  explicit QueueInterface(
+      const DeviceOrSelector &dev_or_sel,
+      unsigned num_threads = std::thread::hardware_concurrency())
+      : QueueInterface(dev_or_sel,
+                       [this](cl::sycl::exception_list l) {
+                         this->exception_caught_ = this->sycl_async_handler(l);
+                       },
+                       num_threads) {}
+
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+  EIGEN_STRONG_INLINE cl::sycl::program &program() const { return m_prog; }
+#endif
+
+  /// Attach an existing buffer to the pointer map, Eigen will not reuse it
+  EIGEN_STRONG_INLINE void *attach_buffer(
+      cl::sycl::buffer<buffer_scalar_t, 1> &buf) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    return static_cast<void *>(pMapper.add_pointer(buf));
+  }
+
+  /// Detach previously attached buffer
+  EIGEN_STRONG_INLINE void detach_buffer(void *p) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    TensorSycl::internal::SYCLfree<false>(p, pMapper);
+  }
+
+  /// Allocating device pointer. This pointer is actually an 8 bytes host
+  /// pointer used as key to access the sycl device buffer. The reason is that
+  /// we cannot use device buffer as a pointer as a m_data in Eigen leafNode
+  /// expressions. So we create a key pointer to be used in Eigen expression
+  /// construction. When we convert the Eigen construction into the sycl
+  /// construction we use this pointer as a key in our buffer_map and we make
+  /// sure that we dedicate only one buffer only for this pointer. The device
+  /// pointer would be deleted by calling deallocate function.
+  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
+#if EIGEN_MAX_ALIGN_BYTES > 0
+    size_t align = num_bytes % EIGEN_MAX_ALIGN_BYTES;
+    if (align > 0) {
+      num_bytes += EIGEN_MAX_ALIGN_BYTES - align;
+    }
+#endif
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
+  }
+
+  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const {
+#if EIGEN_MAX_ALIGN_BYTES > 0
+    size_t align = num_bytes % EIGEN_MAX_ALIGN_BYTES;
+    if (align > 0) {
+      num_bytes += EIGEN_MAX_ALIGN_BYTES - align;
+    }
+#endif
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
+    if (scratch_buffers.empty()) {
+      return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
+      ;
+    } else {
+      for (auto it = scratch_buffers.begin(); it != scratch_buffers.end();) {
+        auto buff = pMapper.get_buffer(*it);
+        if (buff.get_size() >= num_bytes) {
+          auto ptr = *it;
+          scratch_buffers.erase(it);
+          return ptr;
+        } else {
+          ++it;
+        }
+      }
+      return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
+    }
+#else
+    return TensorSycl::internal::SYCLmalloc(num_bytes, pMapper);
+#endif
+  }
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<
+      cl::sycl::access::mode::read_write, data_t>
+  get(data_t *data) const {
+    return get_range_accessor<cl::sycl::access::mode::read_write, data_t>(data);
+  }
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(
+      TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write,
+                                        data_t>
+          data) const {
+    return static_cast<data_t *>(data.get_virtual_pointer());
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void *p) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
+    scratch_buffers.insert(p);
+#else
+    TensorSycl::internal::SYCLfree(p, pMapper);
+#endif
+  }
+  template <cl::sycl::access::mode AcMd, typename T>
+  EIGEN_STRONG_INLINE void deallocate_temp(
+      const TensorSycl::internal::RangeAccess<AcMd, T> &p) const {
+    deallocate_temp(p.get_virtual_pointer());
+  }
+
+  /// This is used to deallocate the device pointer. p is used as a key inside
+  /// the map to find the device buffer and delete it.
+  EIGEN_STRONG_INLINE void deallocate(void *p) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    TensorSycl::internal::SYCLfree(p, pMapper);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_all() const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    TensorSycl::internal::SYCLfreeAll(pMapper);
+#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
+    scratch_buffers.clear();
+#endif
+  }
+
+  /// The memcpyHostToDevice is used to copy the data from host to device
+  /// The destination pointer could be deleted before the copy happend which is
+  /// why a callback function is needed. By default if none is provided, the
+  /// function is blocking.
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(
+      void *dst, const void *src, size_t n,
+      std::function<void()> callback) const {
+    static const auto write_mode = cl::sycl::access::mode::discard_write;
+    static const auto global_access = cl::sycl::access::target::global_buffer;
+    typedef cl::sycl::accessor<buffer_scalar_t, 1, write_mode, global_access>
+        write_accessor;
+    if (n == 0) {
+      if (callback) callback();
+      return;
+    }
+    n /= sizeof(buffer_scalar_t);
+    auto f = [&](cl::sycl::handler &cgh) {
+      write_accessor dst_acc = get_range_accessor<write_mode>(cgh, dst, n);
+      buffer_scalar_t const *ptr = static_cast<buffer_scalar_t const *>(src);
+      auto non_deleter = [](buffer_scalar_t const *) {};
+      std::shared_ptr<const buffer_scalar_t> s_ptr(ptr, non_deleter);
+      cgh.copy(s_ptr, dst_acc);
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
+    synchronize_and_callback(e, callback);
+  }
+
+  /// The memcpyDeviceToHost is used to copy the data from device to host.
+  /// The source pointer could be deleted before the copy happend which is
+  /// why a callback function is needed. By default if none is provided, the
+  /// function is blocking.
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(
+      void *dst, const void *src, size_t n,
+      std::function<void()> callback) const {
+    static const auto read_mode = cl::sycl::access::mode::read;
+    static const auto global_access = cl::sycl::access::target::global_buffer;
+    typedef cl::sycl::accessor<buffer_scalar_t, 1, read_mode, global_access>
+        read_accessor;
+    if (n == 0) {
+      if (callback) callback();
+      return;
+    }
+    n /= sizeof(buffer_scalar_t);
+    auto f = [&](cl::sycl::handler &cgh) {
+      read_accessor src_acc = get_range_accessor<read_mode>(cgh, src, n);
+      buffer_scalar_t *ptr = static_cast<buffer_scalar_t *>(dst);
+      auto non_deleter = [](buffer_scalar_t *) {};
+      std::shared_ptr<buffer_scalar_t> s_ptr(ptr, non_deleter);
+      cgh.copy(src_acc, s_ptr);
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
+    synchronize_and_callback(e, callback);
+  }
+
+  /// The memcpy function.
+  /// No callback is required here as both arguments are on the device
+  /// and SYCL can handle the dependency.
+  EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const {
+    static const auto read_mode = cl::sycl::access::mode::read;
+    static const auto write_mode = cl::sycl::access::mode::discard_write;
+    if (n == 0) {
+      return;
+    }
+    n /= sizeof(buffer_scalar_t);
+    auto f = [&](cl::sycl::handler &cgh) {
+      auto src_acc = get_range_accessor<read_mode>(cgh, src, n);
+      auto dst_acc = get_range_accessor<write_mode>(cgh, dst, n);
+      cgh.copy(src_acc, dst_acc);
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
+    async_synchronize(e);
+  }
+
+  /// the memset function.
+  /// No callback is required here as both arguments are on the device
+  /// and SYCL can handle the dependency.
+  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const {
+    static const auto write_mode = cl::sycl::access::mode::discard_write;
+    if (n == 0) {
+      return;
+    }
+    n /= sizeof(buffer_scalar_t);
+    auto f = [&](cl::sycl::handler &cgh) {
+      auto dst_acc = get_range_accessor<write_mode>(cgh, data, n);
+      // The cast to uint8_t is here to match the behaviour of the standard
+      // memset. The cast to buffer_scalar_t is needed to match the type of the
+      // accessor (in case buffer_scalar_t is not uint8_t)
+      cgh.fill(dst_acc, static_cast<buffer_scalar_t>(static_cast<uint8_t>(c)));
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(f));
+    async_synchronize(e);
+  }
+
+  /// Get a range accessor to the virtual pointer's device memory. This range
+  /// accessor will allow access to the memory from the pointer to the end of
+  /// the buffer.
+  ///
+  /// NOTE: Inside a kernel the range accessor will always be indexed from the
+  /// start of the buffer, so the offset in the accessor is only used by
+  /// methods like handler::copy and will not be available inside a kernel.
+  template <cl::sycl::access::mode AcMd, typename T>
+  EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<AcMd, T>
+  get_range_accessor(const void *ptr) const {
+    static const auto global_access = cl::sycl::access::target::global_buffer;
+    static const auto is_place_holder = cl::sycl::access::placeholder::true_t;
+    typedef TensorSycl::internal::RangeAccess<AcMd, T> ret_type;
+    typedef const TensorSycl::internal::buffer_data_type_t *internal_ptr_t;
+
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+
+    auto original_buffer = pMapper.get_buffer(ptr);
+    const ptrdiff_t offset = pMapper.get_offset(ptr);
+    const ptrdiff_t typed_offset = offset / sizeof(T);
+    eigen_assert(typed_offset >= 0);
+    const auto typed_size = original_buffer.get_size() / sizeof(T);
+    auto buffer = original_buffer.template reinterpret<
+        typename Eigen::internal::remove_const<T>::type>(
+        cl::sycl::range<1>(typed_size));
+    const ptrdiff_t size = buffer.get_count() - typed_offset;
+    eigen_assert(size >= 0);
+    typedef cl::sycl::accessor<typename Eigen::internal::remove_const<T>::type,
+                               1, AcMd, global_access, is_place_holder>
+        placeholder_accessor_t;
+    const auto start_ptr = static_cast<internal_ptr_t>(ptr) - offset;
+    return ret_type(placeholder_accessor_t(buffer, cl::sycl::range<1>(size),
+                                           cl::sycl::id<1>(typed_offset)),
+                    static_cast<size_t>(typed_offset),
+                    reinterpret_cast<std::intptr_t>(start_ptr));
+  }
+
+  /// Get a range accessor to the virtual pointer's device memory with a
+  /// specified size.
+  template <cl::sycl::access::mode AcMd, typename Index>
+  EIGEN_STRONG_INLINE cl::sycl::accessor<
+      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
+  get_range_accessor(cl::sycl::handler &cgh, const void *ptr,
+                     const Index n_bytes) const {
+    static const auto global_access = cl::sycl::access::target::global_buffer;
+    eigen_assert(n_bytes >= 0);
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    auto buffer = pMapper.get_buffer(ptr);
+    const ptrdiff_t offset = pMapper.get_offset(ptr);
+    eigen_assert(offset >= 0);
+    eigen_assert(offset + n_bytes <= buffer.get_size());
+    return buffer.template get_access<AcMd, global_access>(
+        cgh, cl::sycl::range<1>(n_bytes), cl::sycl::id<1>(offset));
+  }
+
+  /// Creation of sycl accessor for a buffer. This function first tries to find
+  /// the buffer in the buffer_map. If found it gets the accessor from it, if
+  /// not, the function then adds an entry by creating a sycl buffer for that
+  /// particular pointer.
+  template <cl::sycl::access::mode AcMd>
+  EIGEN_STRONG_INLINE cl::sycl::accessor<
+      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
+  get_sycl_accessor(cl::sycl::handler &cgh, const void *ptr) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    return pMapper.get_buffer(ptr)
+        .template get_access<AcMd, cl::sycl::access::target::global_buffer>(
+            cgh);
+  }
+
+  EIGEN_STRONG_INLINE cl::sycl::buffer<buffer_scalar_t, 1> get_sycl_buffer(
+      const void *ptr) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    return pMapper.get_buffer(ptr);
+  }
+
+  EIGEN_STRONG_INLINE ptrdiff_t get_offset(const void *ptr) const {
+    std::lock_guard<std::mutex> lock(pmapper_mutex_);
+    return pMapper.get_offset(ptr);
+  }
+
+  template <typename OutScalar, typename sycl_kernel, typename Lhs,
+            typename Rhs, typename OutPtr, typename Range, typename Index,
+            typename... T>
+  EIGEN_ALWAYS_INLINE void binary_kernel_launcher(const Lhs &lhs,
+                                                  const Rhs &rhs, OutPtr outptr,
+                                                  Range thread_range,
+                                                  Index scratchSize,
+                                                  T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      // binding the placeholder accessors to a commandgroup handler
+      lhs.bind(cgh);
+      rhs.bind(cgh);
+      outptr.bind(cgh);
+      typedef cl::sycl::accessor<OutScalar, 1,
+                                 cl::sycl::access::mode::read_write,
+                                 cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+          program().template get_kernel<sycl_kernel>(),
+#endif
+          thread_range, sycl_kernel(scratch, lhs, rhs, outptr, var...));
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
+    async_synchronize(e);
+  }
+
+  template <typename OutScalar, typename sycl_kernel, typename InPtr,
+            typename OutPtr, typename Range, typename Index, typename... T>
+  EIGEN_ALWAYS_INLINE void unary_kernel_launcher(const InPtr &inptr,
+                                                 OutPtr &outptr,
+                                                 Range thread_range,
+                                                 Index scratchSize,
+                                                 T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      // binding the placeholder accessors to a commandgroup handler
+      inptr.bind(cgh);
+      outptr.bind(cgh);
+      typedef cl::sycl::accessor<OutScalar, 1,
+                                 cl::sycl::access::mode::read_write,
+                                 cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+          program().template get_kernel<sycl_kernel>(),
+#endif
+          thread_range, sycl_kernel(scratch, inptr, outptr, var...));
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
+    async_synchronize(e);
+  }
+
+    template <typename OutScalar, typename sycl_kernel, typename InPtr,
+           typename Range, typename Index, typename... T>
+  EIGEN_ALWAYS_INLINE void nullary_kernel_launcher(const InPtr &inptr,
+                                                 Range thread_range,
+                                                 Index scratchSize,
+                                                 T... var) const {
+    auto kernel_functor = [=](cl::sycl::handler &cgh) {
+      // binding the placeholder accessors to a commandgroup handler
+      inptr.bind(cgh);
+      typedef cl::sycl::accessor<OutScalar, 1,
+                                 cl::sycl::access::mode::read_write,
+                                 cl::sycl::access::target::local>
+          LocalAccessor;
+
+      LocalAccessor scratch(cl::sycl::range<1>(scratchSize), cgh);
+      cgh.parallel_for(
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+          program().template get_kernel<sycl_kernel>(),
+#endif
+          thread_range, sycl_kernel(scratch, inptr, var...));
+    };
+    cl::sycl::event e;
+    EIGEN_SYCL_TRY_CATCH(e = m_queue.submit(kernel_functor));
+    async_synchronize(e);
+  }
+
+
+  EIGEN_STRONG_INLINE void synchronize() const {
+#ifdef EIGEN_EXCEPTIONS
+    m_queue.wait_and_throw();
+#else
+    m_queue.wait();
+#endif
+  }
+
+
+  EIGEN_STRONG_INLINE void async_synchronize(cl::sycl::event e) const {
+    set_latest_event(e);
+#ifndef EIGEN_SYCL_ASYNC_EXECUTION
+    synchronize();
+#endif
+  }
+
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize,
+                                              Index &rng, Index &GRange) const {
+    tileSize = static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    tileSize = std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
+                                           EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                        static_cast<Index>(tileSize));
+    rng = n;
+    if (rng == 0) rng = static_cast<Index>(1);
+    GRange = rng;
+    if (tileSize > GRange)
+      tileSize = GRange;
+    else if (GRange > tileSize) {
+      Index xMode = static_cast<Index>(GRange % tileSize);
+      if (xMode != 0) GRange += static_cast<Index>(tileSize - xMode);
+    }
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(
+      const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
+      cl::sycl::range<2> &local_range) const {
+    std::array<Index, 2> input_range = input_dim;
+    Index max_workgroup_Size =
+        static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    max_workgroup_Size =
+        std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
+                                    EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                 static_cast<Index>(max_workgroup_Size));
+    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
+    local_range[1] =
+        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
+    input_range[1] = input_dim[1];
+    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[1] = input_range[1];
+    if (local_range[1] > global_range[1])
+      local_range[1] = global_range[1];
+    else if (global_range[1] > local_range[1]) {
+      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
+      if (xMode != 0)
+        global_range[1] += static_cast<Index>(local_range[1] - xMode);
+    }
+    local_range[0] = static_cast<Index>(max_workgroup_Size / local_range[1]);
+    input_range[0] = input_dim[0];
+    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
+    global_range[0] = input_range[0];
+    if (local_range[0] > global_range[0])
+      local_range[0] = global_range[0];
+    else if (global_range[0] > local_range[0]) {
+      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
+      if (xMode != 0)
+        global_range[0] += static_cast<Index>(local_range[0] - xMode);
+    }
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(
+      const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
+      cl::sycl::range<3> &local_range) const {
+    std::array<Index, 3> input_range = input_dim;
+    Index max_workgroup_Size =
+        static_cast<Index>(getNearestPowerOfTwoWorkGroupSize());
+    max_workgroup_Size =
+        std::min(static_cast<Index>(EIGEN_SYCL_LOCAL_THREAD_DIM0 *
+                                    EIGEN_SYCL_LOCAL_THREAD_DIM1),
+                 static_cast<Index>(max_workgroup_Size));
+    Index pow_of_2 = static_cast<Index>(std::log2(max_workgroup_Size));
+    local_range[2] =
+        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 3)));
+    input_range[2] = input_dim[2];
+    if (input_range[2] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[2] = input_range[2];
+    if (local_range[2] > global_range[2])
+      local_range[2] = global_range[2];
+    else if (global_range[2] > local_range[2]) {
+      Index xMode = static_cast<Index>(global_range[2] % local_range[2]);
+      if (xMode != 0)
+        global_range[2] += static_cast<Index>(local_range[2] - xMode);
+    }
+    pow_of_2 = static_cast<Index>(
+        std::log2(static_cast<Index>(max_workgroup_Size / local_range[2])));
+    local_range[1] =
+        static_cast<Index>(std::pow(2, static_cast<Index>(pow_of_2 / 2)));
+    input_range[1] = input_dim[1];
+    if (input_range[1] == 0) input_range[1] = static_cast<Index>(1);
+    global_range[1] = input_range[1];
+    if (local_range[1] > global_range[1])
+      local_range[1] = global_range[1];
+    else if (global_range[1] > local_range[1]) {
+      Index xMode = static_cast<Index>(global_range[1] % local_range[1]);
+      if (xMode != 0)
+        global_range[1] += static_cast<Index>(local_range[1] - xMode);
+    }
+    local_range[0] = static_cast<Index>(max_workgroup_Size /
+                                        (local_range[1] * local_range[2]));
+    input_range[0] = input_dim[0];
+    if (input_range[0] == 0) input_range[0] = static_cast<Index>(1);
+    global_range[0] = input_range[0];
+    if (local_range[0] > global_range[0])
+      local_range[0] = global_range[0];
+    else if (global_range[0] > local_range[0]) {
+      Index xMode = static_cast<Index>(global_range[0] % local_range[0]);
+      if (xMode != 0)
+        global_range[0] += static_cast<Index>(local_range[0] - xMode);
+    }
+  }
+
+  EIGEN_STRONG_INLINE bool has_local_memory() const {
+#if !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
+    return false;
+#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
+    return true;
+#else
+    return m_device_info.local_mem_type ==
+           cl::sycl::info::local_mem_type::local;
+#endif
+  }
+
+  EIGEN_STRONG_INLINE unsigned long max_buffer_size() const {
+    return m_device_info.max_mem_alloc_size;
+  }
+
+  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const {
+    return m_device_info.max_compute_units;
+  }
+
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const {
+    return m_device_info.max_work_group_size;
+  }
+
+  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const {
+    return m_device_info.max_work_item_sizes;
+  }
+
+  /// No need for sycl it should act the same as CPU version
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const { return 1; }
+
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
+    // OpenCL doesnot have such concept
+    return 2;
+  }
+
+  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const {
+    return m_device_info.local_mem_size;
+  }
+
+  // This function returns the nearest power of 2 Work-group size which is <=
+  // maximum device workgroup size.
+  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
+    return getPowerOfTwo(m_device_info.max_work_group_size, false);
+  }
+
+  EIGEN_STRONG_INLINE std::string getPlatformName() const {
+    return m_device_info.platform_name;
+  }
+
+  EIGEN_STRONG_INLINE std::string getDeviceName() const {
+    return m_device_info.device_name;
+  }
+
+  EIGEN_STRONG_INLINE std::string getDeviceVendor() const {
+    return m_device_info.device_vendor;
+  }
+
+  // This function returns the nearest power of 2
+  // if roundup is true returns result>=wgsize
+  // else it return result <= wgsize
+  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t wGSize, bool roundUp) const {
+    if (roundUp) --wGSize;
+    wGSize |= (wGSize >> 1);
+    wGSize |= (wGSize >> 2);
+    wGSize |= (wGSize >> 4);
+    wGSize |= (wGSize >> 8);
+    wGSize |= (wGSize >> 16);
+#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_ARM64 || EIGEN_OS_WIN64
+    wGSize |= (wGSize >> 32);
+#endif
+    return ((!roundUp) ? (wGSize - (wGSize >> 1)) : ++wGSize);
+  }
+
+  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const { return m_queue; }
+
+  // This function checks if the runtime recorded an error for the
+  // underlying stream device.
+  EIGEN_STRONG_INLINE bool ok() const {
+    if (!exception_caught_) {
+      synchronize();
+    }
+    return !exception_caught_;
+  }
+
+  EIGEN_STRONG_INLINE cl::sycl::event get_latest_event() const {
+#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
+    std::lock_guard<std::mutex> lock(event_mutex_);
+    return latest_events_[std::this_thread::get_id()];
+#else
+    eigen_assert(false);
+    return cl::sycl::event();
+#endif
+  }
+
+  // destructor
+  ~QueueInterface() {
+    pMapper.clear();
+#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
+    scratch_buffers.clear();
+#endif
+  }
+
+ protected:
+  EIGEN_STRONG_INLINE void set_latest_event(cl::sycl::event e) const {
+#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
+    std::lock_guard<std::mutex> lock(event_mutex_);
+    latest_events_[std::this_thread::get_id()] = e;
+#else
+    EIGEN_UNUSED_VARIABLE(e);
+#endif
+  }
+
+  void synchronize_and_callback(cl::sycl::event e,
+                                const std::function<void()> &callback) const {
+    set_latest_event(e);
+    if (callback) {
+      auto callback_ = [=]() {
+#ifdef EIGEN_EXCEPTIONS
+        cl::sycl::event(e).wait_and_throw();
+#else
+        cl::sycl::event(e).wait();
+#endif
+        callback();
+      };
+      m_thread_pool.Schedule(std::move(callback_));
+    } else {
+#ifdef EIGEN_EXCEPTIONS
+      m_queue.wait_and_throw();
+#else
+      m_queue.wait();
+#endif
+    }
+  }
+
+  bool sycl_async_handler(cl::sycl::exception_list exceptions) const {
+    bool exception_caught = false;
+    for (const auto &e : exceptions) {
+      if (e) {
+        exception_caught = true;
+        EIGEN_THROW_X(e);
+      }
+    }
+    return exception_caught;
+  }
+
+  /// class members:
+  bool exception_caught_ = false;
+
+  mutable std::mutex pmapper_mutex_;
+
+#ifdef EIGEN_SYCL_STORE_LATEST_EVENT
+  mutable std::mutex event_mutex_;
+  mutable std::unordered_map<std::thread::id, cl::sycl::event> latest_events_;
+#endif
+
+  /// std::map is the container used to make sure that we create only one buffer
+  /// per pointer. The lifespan of the buffer now depends on the lifespan of
+  /// SyclDevice. If a non-read-only pointer is needed to be accessed on the
+  /// host we should manually deallocate it.
+  mutable TensorSycl::internal::PointerMapper pMapper;
+#ifndef EIGEN_SYCL_NO_REUSE_BUFFERS
+  mutable std::unordered_set<void *> scratch_buffers;
+#endif
+  /// sycl queue
+  mutable cl::sycl::queue m_queue;
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+  mutable cl::sycl::program m_prog;
+#endif
+
+  /// The thread pool is used to wait on events and call callbacks
+  /// asynchronously
+  mutable Eigen::ThreadPool m_thread_pool;
+
+  const TensorSycl::internal::SyclDeviceInfo m_device_info;
+};
+
+struct SyclDeviceBase {
+  /// QueueInterface is not owned. it is the caller's responsibility to destroy
+  /// it
+  const QueueInterface *m_queue_stream;
+  explicit SyclDeviceBase(const QueueInterface *queue_stream)
+      : m_queue_stream(queue_stream) {}
+  EIGEN_STRONG_INLINE const QueueInterface *queue_stream() const {
+    return m_queue_stream;
+  }
+};
+
+// Here is a sycl device struct which accept the sycl queue interface
+// as an input
+struct SyclDevice : public SyclDeviceBase {
+  explicit SyclDevice(const QueueInterface *queue_stream)
+      : SyclDeviceBase(queue_stream) {}
+
+  // this is the accessor used to construct the evaluator
+  template <cl::sycl::access::mode AcMd, typename T>
+  EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<AcMd, T>
+  get_range_accessor(const void *ptr) const {
+    return queue_stream()->template get_range_accessor<AcMd, T>(ptr);
+  }
+
+  // get sycl accessor
+  template <cl::sycl::access::mode AcMd>
+  EIGEN_STRONG_INLINE cl::sycl::accessor<
+      buffer_scalar_t, 1, AcMd, cl::sycl::access::target::global_buffer>
+  get_sycl_accessor(cl::sycl::handler &cgh, const void *ptr) const {
+    return queue_stream()->template get_sycl_accessor<AcMd>(cgh, ptr);
+  }
+
+  /// Accessing the created sycl device buffer for the device pointer
+  EIGEN_STRONG_INLINE cl::sycl::buffer<buffer_scalar_t, 1> get_sycl_buffer(
+      const void *ptr) const {
+    return queue_stream()->get_sycl_buffer(ptr);
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(Index n, Index &tileSize,
+                                              Index &rng, Index &GRange) const {
+    queue_stream()->parallel_for_setup(n, tileSize, rng, GRange);
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(
+      const std::array<Index, 2> &input_dim, cl::sycl::range<2> &global_range,
+      cl::sycl::range<2> &local_range) const {
+    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
+  }
+
+  /// This is used to prepare the number of threads and also the number of
+  /// threads per block for sycl kernels
+  template <typename Index>
+  EIGEN_STRONG_INLINE void parallel_for_setup(
+      const std::array<Index, 3> &input_dim, cl::sycl::range<3> &global_range,
+      cl::sycl::range<3> &local_range) const {
+    queue_stream()->parallel_for_setup(input_dim, global_range, local_range);
+  }
+
+  /// allocate device memory
+  EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
+    return queue_stream()->allocate(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void *allocate_temp(size_t num_bytes) const {
+    return queue_stream()->allocate_temp(num_bytes);
+  }
+
+  /// deallocate device memory
+  EIGEN_STRONG_INLINE void deallocate(void *p) const {
+    queue_stream()->deallocate(p);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void *buffer) const {
+    queue_stream()->deallocate_temp(buffer);
+  }
+  template <cl::sycl::access::mode AcMd, typename T>
+  EIGEN_STRONG_INLINE void deallocate_temp(
+      const TensorSycl::internal::RangeAccess<AcMd, T> &buffer) const {
+    queue_stream()->deallocate_temp(buffer);
+  }
+  EIGEN_STRONG_INLINE void deallocate_all() const {
+    queue_stream()->deallocate_all();
+  }
+
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSycl::internal::RangeAccess<
+      cl::sycl::access::mode::read_write, data_t>
+  get(data_t *data) const {
+    return queue_stream()->get(data);
+  }
+  template <typename data_t>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE data_t *get(
+      TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write,
+                                        data_t>
+          data) const {
+    return queue_stream()->get(data);
+  }
+
+  /// attach existing buffer
+  EIGEN_STRONG_INLINE void *attach_buffer(
+      cl::sycl::buffer<buffer_scalar_t, 1> &buf) const {
+    return queue_stream()->attach_buffer(buf);
+  }
+  /// detach buffer
+  EIGEN_STRONG_INLINE void detach_buffer(void *p) const {
+    queue_stream()->detach_buffer(p);
+  }
+  EIGEN_STRONG_INLINE ptrdiff_t get_offset(const void *ptr) const {
+    return queue_stream()->get_offset(ptr);
+  }
+
+  // some runtime conditions that can be applied here
+  EIGEN_STRONG_INLINE bool isDeviceSuitable() const { return true; }
+
+  /// memcpyHostToDevice
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(
+      Index *dst, const Index *src, size_t n,
+      std::function<void()> callback = {}) const {
+    queue_stream()->memcpyHostToDevice(dst, src, n, callback);
+  }
+  /// memcpyDeviceToHost
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(
+      void *dst, const Index *src, size_t n,
+      std::function<void()> callback = {}) const {
+    queue_stream()->memcpyDeviceToHost(dst, src, n, callback);
+  }
+  /// the memcpy function
+  template <typename Index>
+  EIGEN_STRONG_INLINE void memcpy(void *dst, const Index *src, size_t n) const {
+    queue_stream()->memcpy(dst, src, n);
+  }
+  /// the memset function
+  EIGEN_STRONG_INLINE void memset(void *data, int c, size_t n) const {
+    queue_stream()->memset(data, c, n);
+  }
+  /// returning the sycl queue
+  EIGEN_STRONG_INLINE cl::sycl::queue &sycl_queue() const {
+    return queue_stream()->sycl_queue();
+  }
+#ifdef EIGEN_SYCL_USE_PROGRAM_CLASS
+  EIGEN_STRONG_INLINE cl::sycl::program &program() const {
+    return queue_stream()->program();
+  }
+#endif
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const { return 48 * 1024; }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // We won't try to take advantage of the l2 cache for the time being, and
+    // there is no l3 cache on sycl devices.
+    return firstLevelCacheSize();
+  }
+  EIGEN_STRONG_INLINE unsigned long getNumSyclMultiProcessors() const {
+    return queue_stream()->getNumSyclMultiProcessors();
+  }
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerBlock() const {
+    return queue_stream()->maxSyclThreadsPerBlock();
+  }
+  EIGEN_STRONG_INLINE cl::sycl::id<3> maxWorkItemSizes() const {
+    return queue_stream()->maxWorkItemSizes();
+  }
+  EIGEN_STRONG_INLINE unsigned long maxSyclThreadsPerMultiProcessor() const {
+    // OpenCL doesnot have such concept
+    return queue_stream()->maxSyclThreadsPerMultiProcessor();
+  }
+  EIGEN_STRONG_INLINE size_t sharedMemPerBlock() const {
+    return queue_stream()->sharedMemPerBlock();
+  }
+  EIGEN_STRONG_INLINE size_t getNearestPowerOfTwoWorkGroupSize() const {
+    return queue_stream()->getNearestPowerOfTwoWorkGroupSize();
+  }
+
+  EIGEN_STRONG_INLINE size_t getPowerOfTwo(size_t val, bool roundUp) const {
+    return queue_stream()->getPowerOfTwo(val, roundUp);
+  }
+  /// No need for sycl it should act the same as CPU version
+  EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+    return queue_stream()->majorDeviceVersion();
+  }
+
+  EIGEN_STRONG_INLINE void synchronize() const {
+    queue_stream()->synchronize();
+  }
+  EIGEN_STRONG_INLINE void async_synchronize(
+      cl::sycl::event e = cl::sycl::event()) const {
+    queue_stream()->async_synchronize(e);
+  }
+  EIGEN_STRONG_INLINE cl::sycl::event get_latest_event() const {
+    return queue_stream()->get_latest_event();
+  }
+
+  // This function checks if the runtime recorded an error for the
+  // underlying stream device.
+  EIGEN_STRONG_INLINE bool ok() const { return queue_stream()->ok(); }
+
+  EIGEN_STRONG_INLINE bool has_local_memory() const {
+    return queue_stream()->has_local_memory();
+  }
+  EIGEN_STRONG_INLINE long max_buffer_size() const {
+    return queue_stream()->max_buffer_size();
+  }
+  EIGEN_STRONG_INLINE std::string getPlatformName() const {
+    return queue_stream()->getPlatformName();
+  }
+  EIGEN_STRONG_INLINE std::string getDeviceName() const {
+    return queue_stream()->getDeviceName();
+  }
+  EIGEN_STRONG_INLINE std::string getDeviceVendor() const {
+    return queue_stream()->getDeviceVendor();
+  }
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE void binary_kernel_launcher(T... var) const {
+    queue_stream()->template binary_kernel_launcher<OutScalar, KernelType>(
+        var...);
+  }
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE void unary_kernel_launcher(T... var) const {
+    queue_stream()->template unary_kernel_launcher<OutScalar, KernelType>(
+        var...);
+  }
+
+  template <typename OutScalar, typename KernelType, typename... T>
+  EIGEN_ALWAYS_INLINE void nullary_kernel_launcher(T... var) const {
+    queue_stream()->template nullary_kernel_launcher<OutScalar, KernelType>(
+        var...);
+  }
+};
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
new file mode 100644
index 0000000..e524b53
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h
@@ -0,0 +1,409 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_THREADS) && !defined(EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H)
+#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
+
+namespace Eigen {
+
+// Runs an arbitrary function and then calls Notify() on the passed in
+// Notification.
+template <typename Function, typename... Args> struct FunctionWrapperWithNotification
+{
+  static void run(Notification* n, Function f, Args... args) {
+    f(args...);
+    if (n) {
+      n->Notify();
+    }
+  }
+};
+
+template <typename Function, typename... Args> struct FunctionWrapperWithBarrier
+{
+  static void run(Barrier* b, Function f, Args... args) {
+    f(args...);
+    if (b) {
+      b->Notify();
+    }
+  }
+};
+
+template <typename SyncType>
+static EIGEN_STRONG_INLINE void wait_until_ready(SyncType* n) {
+  if (n) {
+    n->Wait();
+  }
+}
+
+// An abstract interface to a device specific memory allocator.
+class Allocator {
+ public:
+  virtual ~Allocator() {}
+  virtual void* allocate(size_t num_bytes) const = 0;
+  virtual void deallocate(void* buffer) const = 0;
+};
+
+// Build a thread pool device on top the an existing pool of threads.
+struct ThreadPoolDevice {
+  // The ownership of the thread pool remains with the caller.
+  ThreadPoolDevice(ThreadPoolInterface* pool, int num_cores, Allocator* allocator = nullptr)
+      : pool_(pool), num_threads_(num_cores), allocator_(allocator) { }
+
+  EIGEN_STRONG_INLINE void* allocate(size_t num_bytes) const {
+    return allocator_ ? allocator_->allocate(num_bytes)
+        : internal::aligned_malloc(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate(void* buffer) const {
+    if (allocator_) {
+      allocator_->deallocate(buffer);
+    } else {
+      internal::aligned_free(buffer);
+    }
+  }
+
+    EIGEN_STRONG_INLINE void* allocate_temp(size_t num_bytes) const {
+    return allocate(num_bytes);
+  }
+
+  EIGEN_STRONG_INLINE void deallocate_temp(void* buffer) const {
+    deallocate(buffer);
+  }
+
+  template<typename Type>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Type get(Type data) const {
+    return data;
+  }
+
+  EIGEN_STRONG_INLINE void memcpy(void* dst, const void* src, size_t n) const {
+#ifdef __ANDROID__
+    ::memcpy(dst, src, n);
+#else
+    // TODO(rmlarsen): Align blocks on cache lines.
+    // We have observed that going beyond 4 threads usually just wastes
+    // CPU cycles due to the threads competing for memory bandwidth, so we
+    // statically schedule at most 4 block copies here.
+    const size_t kMinBlockSize = 32768;
+    const size_t num_threads = CostModel::numThreads(n, TensorOpCost(1.0, 1.0, 0), 4);
+    if (n <= kMinBlockSize || num_threads < 2) {
+      ::memcpy(dst, src, n);
+    } else {
+      const char* src_ptr = static_cast<const char*>(src);
+      char* dst_ptr = static_cast<char*>(dst);
+      const size_t blocksize = (n + (num_threads - 1)) / num_threads;
+      Barrier barrier(static_cast<int>(num_threads - 1));
+      // Launch the last 3 blocks on worker threads.
+      for (size_t i = 1; i < num_threads; ++i) {
+        enqueue_with_barrier(&barrier, [n, i, src_ptr, dst_ptr, blocksize] {
+          ::memcpy(dst_ptr + i * blocksize, src_ptr + i * blocksize,
+                   numext::mini(blocksize, n - (i * blocksize)));
+        });
+      }
+      // Launch the first block on the main thread.
+      ::memcpy(dst_ptr, src_ptr, blocksize);
+      barrier.Wait();
+    }
+#endif
+  }
+  EIGEN_STRONG_INLINE void memcpyHostToDevice(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+  EIGEN_STRONG_INLINE void memcpyDeviceToHost(void* dst, const void* src, size_t n) const {
+    memcpy(dst, src, n);
+  }
+
+  EIGEN_STRONG_INLINE void memset(void* buffer, int c, size_t n) const {
+    ::memset(buffer, c, n);
+  }
+
+  EIGEN_STRONG_INLINE int numThreads() const {
+    return num_threads_;
+  }
+
+  // Number of theads available in the underlying thread pool. This number can
+  // be different from the value returned by numThreads().
+  EIGEN_STRONG_INLINE int numThreadsInPool() const {
+    return pool_->NumThreads();
+  }
+
+  EIGEN_STRONG_INLINE size_t firstLevelCacheSize() const {
+    return l1CacheSize();
+  }
+
+  EIGEN_STRONG_INLINE size_t lastLevelCacheSize() const {
+    // The l3 cache size is shared between all the cores.
+    return l3CacheSize() / num_threads_;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+    // Should return an enum that encodes the ISA supported by the CPU
+    return 1;
+  }
+
+  template <class Function, class... Args>
+  EIGEN_STRONG_INLINE Notification* enqueue(Function&& f,
+                                            Args&&... args) const {
+    Notification* n = new Notification();
+    pool_->Schedule(
+        std::bind(&FunctionWrapperWithNotification<Function, Args...>::run, n,
+                  std::move(f), args...));
+    return n;
+  }
+
+  template <class Function, class... Args>
+  EIGEN_STRONG_INLINE void enqueue_with_barrier(Barrier* b, Function&& f,
+                                                Args&&... args) const {
+    pool_->Schedule(
+        std::bind(&FunctionWrapperWithBarrier<Function, Args...>::run, b,
+                  std::move(f), args...));
+  }
+
+  template <class Function, class... Args>
+  EIGEN_STRONG_INLINE void enqueueNoNotification(Function&& f,
+                                                 Args&&... args) const {
+    if (sizeof...(args) > 0) {
+      pool_->Schedule(std::bind(std::move(f), args...));
+    } else {
+      pool_->Schedule(std::move(f));
+    }
+  }
+
+  // Returns a logical thread index between 0 and pool_->NumThreads() - 1 if
+  // called from one of the threads in pool_. Returns -1 otherwise.
+  EIGEN_STRONG_INLINE int currentThreadId() const {
+    return pool_->CurrentThreadId();
+  }
+
+  // WARNING: This function is synchronous and will block the calling thread.
+  //
+  // Synchronous parallelFor executes f with [0, n) arguments in parallel and
+  // waits for completion. F accepts a half-open interval [first, last). Block
+  // size is chosen based on the iteration cost and resulting parallel
+  // efficiency. If block_align is not nullptr, it is called to round up the
+  // block size.
+  void parallelFor(Index n, const TensorOpCost& cost,
+                   std::function<Index(Index)> block_align,
+                   std::function<void(Index, Index)> f) const {
+    if (EIGEN_PREDICT_FALSE(n <= 0)){
+      return;
+    // Compute small problems directly in the caller thread.
+    } else if (n == 1 || numThreads() == 1 ||
+               CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
+      f(0, n);
+      return;
+    }
+
+    // Compute block size and total count of blocks.
+    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
+
+    // Recursively divide size into halves until we reach block_size.
+    // Division code rounds mid to block_size, so we are guaranteed to get
+    // block_count leaves that do actual computations.
+    Barrier barrier(static_cast<unsigned int>(block.count));
+    std::function<void(Index, Index)> handleRange;
+    handleRange = [=, &handleRange, &barrier, &f](Index firstIdx,
+                                                  Index lastIdx) {
+      while (lastIdx - firstIdx > block.size) {
+        // Split into halves and schedule the second half on a different thread.
+        const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
+        pool_->Schedule([=, &handleRange]() { handleRange(midIdx, lastIdx); });
+        lastIdx = midIdx;
+      }
+      // Single block or less, execute directly.
+      f(firstIdx, lastIdx);
+      barrier.Notify();
+    };
+
+    if (block.count <= numThreads()) {
+      // Avoid a thread hop by running the root of the tree and one block on the
+      // main thread.
+      handleRange(0, n);
+    } else {
+      // Execute the root in the thread pool to avoid running work on more than
+      // numThreads() threads.
+      pool_->Schedule([=, &handleRange]() { handleRange(0, n); });
+    }
+
+    barrier.Wait();
+  }
+
+  // Convenience wrapper for parallelFor that does not align blocks.
+  void parallelFor(Index n, const TensorOpCost& cost,
+                   std::function<void(Index, Index)> f) const {
+    parallelFor(n, cost, nullptr, std::move(f));
+  }
+
+  // WARNING: This function is asynchronous and will not block the calling thread.
+  //
+  // Asynchronous parallelFor executes f with [0, n) arguments in parallel
+  // without waiting for completion. When the last block finished, it will call
+  // 'done' callback. F accepts a half-open interval [first, last). Block size
+  // is chosen based on the iteration cost and resulting parallel efficiency. If
+  // block_align is not nullptr, it is called to round up the block size.
+  void parallelForAsync(Index n, const TensorOpCost& cost,
+                        std::function<Index(Index)> block_align,
+                        std::function<void(Index, Index)> f,
+                        std::function<void()> done) const {
+    // Compute small problems directly in the caller thread.
+    if (n <= 1 || numThreads() == 1 ||
+        CostModel::numThreads(n, cost, static_cast<int>(numThreads())) == 1) {
+      f(0, n);
+      done();
+      return;
+    }
+
+    // Compute block size and total count of blocks.
+    ParallelForBlock block = CalculateParallelForBlock(n, cost, block_align);
+
+    ParallelForAsyncContext* const ctx =
+        new ParallelForAsyncContext(block.count, std::move(f), std::move(done));
+
+    // Recursively divide size into halves until we reach block_size.
+    // Division code rounds mid to block_size, so we are guaranteed to get
+    // block_count leaves that do actual computations.
+    ctx->handle_range = [this, ctx, block](Index firstIdx, Index lastIdx) {
+      while (lastIdx - firstIdx > block.size) {
+        // Split into halves and schedule the second half on a different thread.
+        const Index midIdx = firstIdx + divup((lastIdx - firstIdx) / 2, block.size) * block.size;
+        pool_->Schedule(
+            [ctx, midIdx, lastIdx]() { ctx->handle_range(midIdx, lastIdx); });
+        lastIdx = midIdx;
+      }
+
+      // Single block or less, execute directly.
+      ctx->f(firstIdx, lastIdx);
+
+      // Delete async context if it was the last block.
+      if (ctx->count.fetch_sub(1) == 1) delete ctx;
+    };
+
+    if (block.count <= numThreads()) {
+      // Avoid a thread hop by running the root of the tree and one block on the
+      // main thread.
+      ctx->handle_range(0, n);
+    } else {
+      // Execute the root in the thread pool to avoid running work on more than
+      // numThreads() threads.
+      pool_->Schedule([ctx, n]() { ctx->handle_range(0, n); });
+    }
+  }
+
+  // Convenience wrapper for parallelForAsync that does not align blocks.
+  void parallelForAsync(Index n, const TensorOpCost& cost,
+                        std::function<void(Index, Index)> f,
+                        std::function<void()> done) const {
+    parallelForAsync(n, cost, nullptr, std::move(f), std::move(done));
+  }
+
+  // Thread pool accessor.
+  ThreadPoolInterface* getPool() const { return pool_; }
+
+  // Allocator accessor.
+  Allocator* allocator() const { return allocator_; }
+
+ private:
+  typedef TensorCostModel<ThreadPoolDevice> CostModel;
+
+  // For parallelForAsync we must keep passed in closures on the heap, and
+  // delete them only after `done` callback finished.
+  struct ParallelForAsyncContext {
+    ParallelForAsyncContext(Index block_count,
+                            std::function<void(Index, Index)> block_f,
+                            std::function<void()> done_callback)
+        : count(block_count),
+          f(std::move(block_f)),
+          done(std::move(done_callback)) {}
+    ~ParallelForAsyncContext() { done(); }
+
+    std::atomic<Index> count;
+    std::function<void(Index, Index)> f;
+    std::function<void()> done;
+
+    std::function<void(Index, Index)> handle_range;
+  };
+
+  struct ParallelForBlock {
+    Index size;   // block size
+    Index count;  // number of blocks
+  };
+
+  // Calculates block size based on (1) the iteration cost and (2) parallel
+  // efficiency. We want blocks to be not too small to mitigate parallelization
+  // overheads; not too large to mitigate tail effect and potential load
+  // imbalance and we also want number of blocks to be evenly dividable across
+  // threads.
+  ParallelForBlock CalculateParallelForBlock(
+      const Index n, const TensorOpCost& cost,
+      std::function<Index(Index)> block_align) const {
+    const double block_size_f = 1.0 / CostModel::taskSize(1, cost);
+    const Index max_oversharding_factor = 4;
+    Index block_size = numext::mini(
+        n, numext::maxi<Index>(
+               divup<Index>(n, max_oversharding_factor * numThreads()),
+               block_size_f));
+    const Index max_block_size = numext::mini(n, 2 * block_size);
+
+    if (block_align) {
+      Index new_block_size = block_align(block_size);
+      eigen_assert(new_block_size >= block_size);
+      block_size = numext::mini(n, new_block_size);
+    }
+
+    Index block_count = divup(n, block_size);
+
+    // Calculate parallel efficiency as fraction of total CPU time used for
+    // computations:
+    double max_efficiency =
+        static_cast<double>(block_count) /
+        (divup<int>(block_count, numThreads()) * numThreads());
+
+    // Now try to increase block size up to max_block_size as long as it
+    // doesn't decrease parallel efficiency.
+    for (Index prev_block_count = block_count;
+         max_efficiency < 1.0 && prev_block_count > 1;) {
+      // This is the next block size that divides size into a smaller number
+      // of blocks than the current block_size.
+      Index coarser_block_size = divup(n, prev_block_count - 1);
+      if (block_align) {
+        Index new_block_size = block_align(coarser_block_size);
+        eigen_assert(new_block_size >= coarser_block_size);
+        coarser_block_size = numext::mini(n, new_block_size);
+      }
+      if (coarser_block_size > max_block_size) {
+        break;  // Reached max block size. Stop.
+      }
+      // Recalculate parallel efficiency.
+      const Index coarser_block_count = divup(n, coarser_block_size);
+      eigen_assert(coarser_block_count < prev_block_count);
+      prev_block_count = coarser_block_count;
+      const double coarser_efficiency =
+          static_cast<double>(coarser_block_count) /
+          (divup<int>(coarser_block_count, numThreads()) * numThreads());
+      if (coarser_efficiency + 0.01 >= max_efficiency) {
+        // Taking it.
+        block_size = coarser_block_size;
+        block_count = coarser_block_count;
+        if (max_efficiency < coarser_efficiency) {
+          max_efficiency = coarser_efficiency;
+        }
+      }
+    }
+
+    return {block_size, block_count};
+  }
+
+  ThreadPoolInterface* pool_;
+  int num_threads_;
+  Allocator* allocator_;
+};
+
+
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_THREAD_POOL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
new file mode 100644
index 0000000..1a30e45
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h
@@ -0,0 +1,236 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
+
+namespace Eigen {
+
+/** \internal
+  *
+  * \class TensorDimensionList
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Special case of tensor index list used to list all the dimensions of a tensor of rank n.
+  *
+  * \sa Tensor
+  */
+
+template <typename Index, std::size_t Rank> struct DimensionList {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  const Index operator[] (const Index i) const { return i; }
+};
+
+namespace internal {
+
+template<typename Index, std::size_t Rank> struct array_size<DimensionList<Index, Rank> > {
+  static const size_t value = Rank;
+};
+template<typename Index, std::size_t Rank> struct array_size<const DimensionList<Index, Rank> > {
+  static const size_t value = Rank;
+};
+
+template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(DimensionList<Index, Rank>&) {
+  return n;
+}
+template<DenseIndex n, typename Index, std::size_t Rank> const Index array_get(const DimensionList<Index, Rank>&) {
+  return n;
+}
+
+
+#if EIGEN_HAS_CONSTEXPR
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex) {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<DimensionList<Index, Rank> > {
+  static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i == value;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i == value;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i != value;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<const DimensionList<Index, Rank> > {
+  static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i != value;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i > value;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i > value;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i < value;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const DenseIndex i, const DenseIndex value) {
+    return i < value;
+  }
+};
+
+#else
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run(const DenseIndex) {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct all_indices_known_statically_impl<const DimensionList<Index, Rank> > {
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE bool run() {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
+    return true;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct indices_statically_known_to_increase_impl<const DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
+    return true;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_eq_impl<const DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex){
+    return false;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_ne_impl<const DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_gt_impl<const DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+template <typename Index, std::size_t Rank>
+struct index_statically_lt_impl<const DimensionList<Index, Rank> > {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const DenseIndex, const DenseIndex) {
+    return false;
+  }
+};
+#endif
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSION_LIST_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
new file mode 100644
index 0000000..f0f1e83
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h
@@ -0,0 +1,490 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
+
+
+namespace Eigen {
+
+/** \internal
+  *
+  * \class TensorDimensions
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Set of classes used to encode and store the dimensions of a Tensor.
+  *
+  * The Sizes class encodes as part of the type the number of dimensions and the
+  * sizes corresponding to each dimension. It uses no storage space since it is
+  * entirely known at compile time.
+  * The DSizes class is its dynamic sibling: the number of dimensions is known
+  * at compile time but the sizes are set during execution.
+  *
+  * \sa Tensor
+  */
+
+// Boilerplate code
+namespace internal {
+
+template<std::ptrdiff_t n, typename Dimension> struct dget {
+  static const std::ptrdiff_t value = get<n, Dimension>::value;
+};
+
+
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct fixed_size_tensor_index_linearization_helper
+{
+  template <typename Dimensions> EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const& indices,
+                          const Dimensions& dimensions)
+  {
+    return array_get<RowMajor ? n - 1 : (NumIndices - n)>(indices) +
+        dget<RowMajor ? n - 1 : (NumIndices - n), Dimensions>::value *
+        fixed_size_tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
+  }
+};
+
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct fixed_size_tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
+{
+  template <typename Dimensions> EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Index run(array<Index, NumIndices> const&, const Dimensions&)
+  {
+    return 0;
+  }
+};
+
+template<typename Index, std::ptrdiff_t n>
+struct fixed_size_tensor_index_extraction_helper
+{
+  template <typename Dimensions> EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Index run(const Index index,
+                          const Dimensions& dimensions)
+  {
+    const Index mult = (index == n-1) ? 1 : 0;
+    return array_get<n-1>(dimensions) * mult +
+        fixed_size_tensor_index_extraction_helper<Index, n - 1>::run(index, dimensions);
+  }
+};
+
+template<typename Index>
+struct fixed_size_tensor_index_extraction_helper<Index, 0>
+{
+  template <typename Dimensions> EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Index run(const Index,
+                          const Dimensions&)
+  {
+    return 0;
+  }
+  };
+
+}  // end namespace internal
+
+
+// Fixed size
+#ifndef EIGEN_EMULATE_CXX11_META_H
+template <typename std::ptrdiff_t... Indices>
+struct Sizes {
+  typedef internal::numeric_list<std::ptrdiff_t, Indices...> Base;
+  const Base t = Base();
+  static const std::ptrdiff_t total_size = internal::arg_prod(Indices...);
+  static const ptrdiff_t count = Base::count;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t rank() const {
+    return Base::count;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t TotalSize() {
+    return internal::arg_prod(Indices...);
+  }
+
+  EIGEN_DEVICE_FUNC Sizes() { }
+  template <typename DenseIndex>
+  explicit EIGEN_DEVICE_FUNC Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
+    // todo: add assertion
+  }
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  template <typename... DenseIndex> EIGEN_DEVICE_FUNC Sizes(DenseIndex...) { }
+  explicit EIGEN_DEVICE_FUNC Sizes(std::initializer_list<std::ptrdiff_t> /*l*/) {
+    // todo: add assertion
+  }
+#endif
+
+  template <typename T> Sizes& operator = (const T& /*other*/) {
+    // add assertion failure if the size of other is different
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t operator[] (const std::ptrdiff_t index) const {
+    return internal::fixed_size_tensor_index_extraction_helper<std::ptrdiff_t, Base::count>::run(index, t);
+  }
+
+  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, t);
+  }
+  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, t);
+  }
+};
+
+namespace internal {
+template <typename std::ptrdiff_t... Indices>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<Indices...>&) {
+  return Sizes<Indices...>::total_size;
+}
+}
+
+#else
+
+template <std::ptrdiff_t n>
+struct non_zero_size {
+  typedef internal::type2val<std::ptrdiff_t, n> type;
+};
+template <>
+struct non_zero_size<0> {
+  typedef internal::null_type type;
+};
+
+template <std::ptrdiff_t V1=0, std::ptrdiff_t V2=0, std::ptrdiff_t V3=0, std::ptrdiff_t V4=0, std::ptrdiff_t V5=0> struct Sizes {
+  typedef typename internal::make_type_list<typename non_zero_size<V1>::type, typename non_zero_size<V2>::type, typename non_zero_size<V3>::type, typename non_zero_size<V4>::type, typename non_zero_size<V5>::type >::type Base;
+  static const std::ptrdiff_t count = Base::count;
+  static const std::ptrdiff_t total_size = internal::arg_prod<Base>::value;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t rank() const {
+    return count;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ptrdiff_t TotalSize() {
+    return internal::arg_prod<Base>::value;
+  }
+
+  Sizes() { }
+  template <typename DenseIndex>
+  explicit Sizes(const array<DenseIndex, Base::count>& /*indices*/) {
+    // todo: add assertion
+  }
+  template <typename T> Sizes& operator = (const T& /*other*/) {
+    // add assertion failure if the size of other is different
+    return *this;
+  }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  template <typename... DenseIndex> Sizes(DenseIndex... /*indices*/) { }
+  explicit Sizes(std::initializer_list<std::ptrdiff_t>) {
+    // todo: add assertion
+  }
+#else
+  EIGEN_DEVICE_FUNC explicit Sizes(const DenseIndex) {
+  }
+  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex) {
+  }
+  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex) {
+  }
+  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) {
+  }
+  EIGEN_DEVICE_FUNC Sizes(const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex, const DenseIndex) {
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index operator[] (const Index index) const {
+    switch (index) {
+      case 0:
+        return internal::get<0, Base>::value;
+      case 1:
+        return internal::get<1, Base>::value;
+      case 2:
+        return internal::get<2, Base>::value;
+      case 3:
+        return internal::get<3, Base>::value;
+      case 4:
+        return internal::get<4, Base>::value;
+      default:
+        eigen_assert(false && "index overflow");
+        return static_cast<Index>(-1);
+    }
+  }
+
+  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ptrdiff_t IndexOfColMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, false>::run(indices, *reinterpret_cast<const Base*>(this));
+  }
+  template <typename DenseIndex> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ptrdiff_t IndexOfRowMajor(const array<DenseIndex, Base::count>& indices) const {
+    return internal::fixed_size_tensor_index_linearization_helper<DenseIndex, Base::count, Base::count, true>::run(indices, *reinterpret_cast<const Base*>(this));
+  }
+};
+
+namespace internal {
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_prod(const Sizes<V1, V2, V3, V4, V5>&) {
+  return Sizes<V1, V2, V3, V4, V5>::total_size;
+}
+}
+
+#endif
+
+// Boilerplate
+namespace internal {
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct tensor_index_linearization_helper
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const& dimensions)
+  {
+    return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
+      array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
+        tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
+  }
+};
+
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Index run(array<Index, NumIndices> const& indices, array<Index, NumIndices> const&)
+  {
+    return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
+  }
+};
+}  // end namespace internal
+
+
+
+// Dynamic size
+template <typename DenseIndex, int NumDims>
+struct DSizes : array<DenseIndex, NumDims> {
+  typedef array<DenseIndex, NumDims> Base;
+  static const int count = NumDims;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const {
+    return NumDims;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex TotalSize() const {
+    return (NumDims == 0) ? 1 : internal::array_prod(*static_cast<const Base*>(this));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DSizes() {
+    for (int i = 0 ; i < NumDims; ++i) {
+      (*this)[i] = 0;
+    }
+  }
+  EIGEN_DEVICE_FUNC explicit DSizes(const array<DenseIndex, NumDims>& a) : Base(a) { }
+
+  EIGEN_DEVICE_FUNC explicit DSizes(const DenseIndex i0) {
+    eigen_assert(NumDims == 1);
+    (*this)[0] = i0;
+  }
+
+  EIGEN_DEVICE_FUNC DSizes(const DimensionList<DenseIndex, NumDims>& a) {
+    for (int i = 0 ; i < NumDims; ++i) {
+      (*this)[i] = a[i];
+    }
+  }
+
+  // Enable DSizes index type promotion only if we are promoting to the
+  // larger type, e.g. allow to promote dimensions of type int to long.
+  template<typename OtherIndex>
+  EIGEN_DEVICE_FUNC
+  explicit DSizes(const array<OtherIndex, NumDims>& other,
+                  // Default template parameters require c++11.
+                  typename internal::enable_if<
+                     internal::is_same<
+                         DenseIndex,
+                         typename internal::promote_index_type<
+                             DenseIndex,
+                             OtherIndex
+                         >::type
+                     >::value, void*>::type = 0) {
+    for (int i = 0; i < NumDims; ++i) {
+      (*this)[i] = static_cast<DenseIndex>(other[i]);
+    }
+  }
+
+#ifdef EIGEN_HAS_INDEX_LIST
+  template <typename FirstType, typename... OtherTypes>
+  EIGEN_DEVICE_FUNC
+  explicit DSizes(const Eigen::IndexList<FirstType, OtherTypes...>& dimensions) {
+    for (int i = 0; i < dimensions.count; ++i) {
+      (*this)[i] = dimensions[i];
+    }
+  }
+#endif
+
+#ifndef EIGEN_EMULATE_CXX11_META_H
+  template <typename std::ptrdiff_t... Indices>
+  EIGEN_DEVICE_FUNC DSizes(const Sizes<Indices...>& a) {
+    for (int i = 0 ; i < NumDims; ++i) {
+      (*this)[i] = a[i];
+    }
+  }
+#else
+  template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5>
+  EIGEN_DEVICE_FUNC DSizes(const Sizes<V1, V2, V3, V4, V5>& a) {
+    for (int i = 0 ; i < NumDims; ++i) {
+      (*this)[i] = a[i];
+    }
+  }
+#endif
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE explicit DSizes(DenseIndex firstDimension, DenseIndex secondDimension, IndexTypes... otherDimensions) : Base({{firstDimension, secondDimension, otherDimensions...}}) {
+    EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 2 == NumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  }
+#else
+  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1) {
+    eigen_assert(NumDims == 2);
+    (*this)[0] = i0;
+    (*this)[1] = i1;
+  }
+  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2) {
+    eigen_assert(NumDims == 3);
+    (*this)[0] = i0;
+    (*this)[1] = i1;
+    (*this)[2] = i2;
+  }
+  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3) {
+    eigen_assert(NumDims == 4);
+    (*this)[0] = i0;
+    (*this)[1] = i1;
+    (*this)[2] = i2;
+    (*this)[3] = i3;
+  }
+  EIGEN_DEVICE_FUNC DSizes(const DenseIndex i0, const DenseIndex i1, const DenseIndex i2, const DenseIndex i3, const DenseIndex i4) {
+    eigen_assert(NumDims == 5);
+    (*this)[0] = i0;
+    (*this)[1] = i1;
+    (*this)[2] = i2;
+    (*this)[3] = i3;
+    (*this)[4] = i4;
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC DSizes& operator = (const array<DenseIndex, NumDims>& other) {
+    *static_cast<Base*>(this) = other;
+    return *this;
+  }
+
+  // A constexpr would be so much better here
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfColMajor(const array<DenseIndex, NumDims>& indices) const {
+    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, false>::run(indices, *static_cast<const Base*>(this));
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex IndexOfRowMajor(const array<DenseIndex, NumDims>& indices) const {
+    return internal::tensor_index_linearization_helper<DenseIndex, NumDims, NumDims - 1, true>::run(indices, *static_cast<const Base*>(this));
+  }
+};
+
+template <typename IndexType, int NumDims>
+std::ostream& operator<<(std::ostream& os,
+                         const DSizes<IndexType, NumDims>& dims) {
+  os << "[";
+  for (int i = 0; i < NumDims; ++i) {
+    if (i > 0) os << ", ";
+    os << dims[i];
+  }
+  os << "]";
+  return os;
+}
+
+// Boilerplate
+namespace internal {
+template<typename Index, std::ptrdiff_t NumIndices, std::ptrdiff_t n, bool RowMajor>
+struct tensor_vsize_index_linearization_helper
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const& dimensions)
+  {
+    return array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
+      array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
+        tensor_vsize_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
+  }
+};
+
+template<typename Index, std::ptrdiff_t NumIndices, bool RowMajor>
+struct tensor_vsize_index_linearization_helper<Index, NumIndices, 0, RowMajor>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Index run(array<Index, NumIndices> const& indices, std::vector<DenseIndex> const&)
+  {
+    return array_get<RowMajor ? 0 : NumIndices - 1>(indices);
+  }
+};
+}  // end namespace internal
+
+
+namespace internal {
+
+template <typename DenseIndex, int NumDims> struct array_size<const DSizes<DenseIndex, NumDims> > {
+  static const ptrdiff_t value = NumDims;
+};
+template <typename DenseIndex, int NumDims> struct array_size<DSizes<DenseIndex, NumDims> > {
+  static const ptrdiff_t value = NumDims;
+};
+#ifndef EIGEN_EMULATE_CXX11_META_H
+template <typename std::ptrdiff_t... Indices> struct array_size<const Sizes<Indices...> > {
+static const std::ptrdiff_t value = Sizes<Indices...>::count;
+};
+template <typename std::ptrdiff_t... Indices> struct array_size<Sizes<Indices...> > {
+static const std::ptrdiff_t value = Sizes<Indices...>::count;
+};
+template <std::ptrdiff_t n, typename std::ptrdiff_t... Indices> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<Indices...>&) {
+  return get<n, internal::numeric_list<std::ptrdiff_t, Indices...> >::value;
+}
+template <std::ptrdiff_t n> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<>&) {
+  eigen_assert(false && "should never be called");
+  return -1;
+}
+#else
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<const Sizes<V1,V2,V3,V4,V5> > {
+  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
+};
+template <std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> struct array_size<Sizes<V1,V2,V3,V4,V5> > {
+  static const ptrdiff_t value = Sizes<V1,V2,V3,V4,V5>::count;
+};
+template <std::ptrdiff_t n, std::ptrdiff_t V1, std::ptrdiff_t V2, std::ptrdiff_t V3, std::ptrdiff_t V4, std::ptrdiff_t V5> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::ptrdiff_t array_get(const Sizes<V1,V2,V3,V4,V5>&) {
+  return get<n, typename Sizes<V1,V2,V3,V4,V5>::Base>::value;
+}
+
+#endif
+
+
+template <typename Dims1, typename Dims2, ptrdiff_t n, ptrdiff_t m>
+struct sizes_match_below_dim {
+  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) {
+    return false;
+  }
+};
+template <typename Dims1, typename Dims2, ptrdiff_t n>
+struct sizes_match_below_dim<Dims1, Dims2, n, n> {
+  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1& dims1, Dims2& dims2) {
+    return (array_get<n-1>(dims1) == array_get<n-1>(dims2)) &&
+        sizes_match_below_dim<Dims1, Dims2, n-1, n-1>::run(dims1, dims2);
+  }
+};
+template <typename Dims1, typename Dims2>
+struct sizes_match_below_dim<Dims1, Dims2, 0, 0> {
+  static EIGEN_DEVICE_FUNC  EIGEN_STRONG_INLINE bool run(Dims1&, Dims2&) {
+    return true;
+  }
+};
+
+} // end namespace internal
+
+
+template <typename Dims1, typename Dims2>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool dimensions_match(Dims1 dims1, Dims2 dims2) {
+  return internal::sizes_match_below_dim<Dims1, Dims2, internal::array_size<Dims1>::value, internal::array_size<Dims2>::value>::run(dims1, dims2);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_DIMENSIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
new file mode 100644
index 0000000..a48d035
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
@@ -0,0 +1,236 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
+
+namespace Eigen {
+
+/** \class TensorForcedEval
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reshaping class.
+  *
+  *
+  */
+namespace internal {
+template<typename XprType, template <class> class MakePointer_>
+struct traits<TensorEvalToOp<XprType, MakePointer_> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename MakePointer_<Scalar>::Type PointerType;
+
+  enum {
+    Flags = 0
+  };
+  template <class T>
+  struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+
+
+  };
+};
+
+template<typename XprType, template <class> class MakePointer_>
+struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
+{
+  typedef const TensorEvalToOp<XprType, MakePointer_>& type;
+};
+
+template<typename XprType, template <class> class MakePointer_>
+struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
+{
+  typedef TensorEvalToOp<XprType, MakePointer_> type;
+};
+
+}  // end namespace internal
+
+
+
+
+template<typename XprType, template <class> class MakePointer_>
+class TensorEvalToOp : public TensorBase<TensorEvalToOp<XprType, MakePointer_>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename MakePointer_<CoeffReturnType>::Type PointerType;
+  typedef typename Eigen::internal::nested<TensorEvalToOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorEvalToOp>::Index Index;
+
+  static const int NumDims = Eigen::internal::traits<TensorEvalToOp>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvalToOp(PointerType buffer, const XprType& expr)
+      : m_xpr(expr), m_buffer(buffer) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_DEVICE_FUNC PointerType buffer() const { return m_buffer; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    PointerType m_buffer;
+};
+
+
+
+template<typename ArgType, typename Device, template <class> class MakePointer_>
+struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
+{
+  typedef TensorEvalToOp<ArgType, MakePointer_> XprType;
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef typename XprType::Index Index;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  enum {
+    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = true,
+    PreferBlockAccess = false,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = true
+  };
+
+  static const int NumDims = internal::traits<ArgType>::NumDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  typedef internal::TensorBlockAssignment<
+      CoeffReturnType, NumDims, typename ArgTensorBlock::XprType, Index>
+      TensorBlockAssignment;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_buffer(device.get(op.buffer())), m_expression(op.expression()){}
+
+
+  EIGEN_STRONG_INLINE ~TensorEvaluator() {
+  }
+
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType scalar) {
+    EIGEN_UNUSED_VARIABLE(scalar);
+    eigen_assert(scalar == NULL);
+    return m_impl.evalSubExprsIfNeeded(m_buffer);
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType scalar, EvalSubExprsCallback done) {
+    EIGEN_UNUSED_VARIABLE(scalar);
+    eigen_assert(scalar == NULL);
+    m_impl.evalSubExprsIfNeededAsync(m_buffer, std::move(done));
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
+    m_buffer[i] = m_impl.coeff(i);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
+    internal::pstoret<CoeffReturnType, PacketReturnType, Aligned>(m_buffer + i, m_impl.template packet<TensorEvaluator<ArgType, Device>::IsAligned ? Aligned : Unaligned>(i));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return m_impl.getResourceRequirements();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
+      TensorBlockDesc& desc, TensorBlockScratch& scratch) {
+    // Add `m_buffer` as destination buffer to the block descriptor.
+    desc.template AddDestinationBuffer<Layout>(
+        /*dst_base=*/m_buffer + desc.offset(),
+        /*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()));
+
+    ArgTensorBlock block =
+        m_impl.block(desc, scratch, /*root_of_expr_ast=*/true);
+
+    // If block was evaluated into a destination buffer, there is no need to do
+    // an assignment.
+    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(
+              desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
+              m_buffer, desc.offset()),
+          block.expr());
+    }
+    block.cleanup();
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_buffer[index];
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here.
+    return m_impl.costPerCoeff(vectorized) +
+        TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_buffer; }
+  ArgType expression() const { return m_expression; }
+  #ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+    m_buffer.bind(cgh);
+  }
+  #endif
+
+
+ private:
+  TensorEvaluator<ArgType, Device> m_impl;
+  EvaluatorPointerType m_buffer;
+  const ArgType m_expression;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_EVAL_TO_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
new file mode 100644
index 0000000..3aff7fa
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
@@ -0,0 +1,983 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
+
+namespace Eigen {
+
+/** \class TensorEvaluator
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor evaluator classes.
+  *
+  * These classes are responsible for the evaluation of the tensor expression.
+  *
+  * TODO: add support for more types of expressions, in particular expressions
+  * leading to lvalues (slicing, reshaping, etc...)
+  */
+
+// Generic evaluator
+template<typename Derived, typename Device>
+struct TensorEvaluator
+{
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef Derived XprType;
+  static const int PacketSize =  PacketType<CoeffReturnType, Device>::size;
+  typedef typename internal::traits<Derived>::template MakePointer<Scalar>::Type TensorPointerType;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  // NumDimensions is -1 for variable dim tensors
+  static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
+                               internal::traits<Derived>::NumDimensions : 0;
+
+  enum {
+    IsAligned          = Derived::IsAligned,
+    PacketAccess       = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess        = internal::is_arithmetic<typename internal::remove_const<Scalar>::type>::value,
+    PreferBlockAccess  = false,
+    Layout             = Derived::Layout,
+    CoordAccess        = NumCoords > 0,
+    RawAccess          = true
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
+      : m_data(device.get((const_cast<TensorPointerType>(m.data())))),
+        m_dims(m.dimensions()),
+        m_device(device)
+  { }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType dest) {
+    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && dest) {
+      m_device.memcpy((void*)(m_device.get(dest)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
+      return false;
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): ThreadPoolDevice memcpy is blockign operation.
+    done(evalSubExprsIfNeeded(dest));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_data != NULL);
+    return m_data[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) {
+    eigen_assert(m_data != NULL);
+    return m_data[index];
+  }
+
+  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketReturnType packet(Index index) const
+  {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  // Return a packet starting at `index` where `umask` specifies which elements
+  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
+  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
+  // float element will be loaded, otherwise 0 will be loaded.
+  // Function has been templatized to enable Sfinae.
+  template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
+  partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
+  {
+    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
+    eigen_assert(m_data != NULL);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return m_data[m_dims.IndexOfColMajor(coords)];
+    } else {
+      return m_data[m_dims.IndexOfRowMajor(coords)];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType&
+  coeffRef(const array<DenseIndex, NumCoords>& coords) {
+    eigen_assert(m_data != NULL);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return m_data[m_dims.IndexOfColMajor(coords)];
+    } else {
+      return m_data[m_dims.IndexOfRowMajor(coords)];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
+                        PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    assert(m_data != NULL);
+    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
+  }
+
+  template<typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlockDesc& desc, const TensorBlock& block) {
+    assert(m_data != NULL);
+
+    typedef typename TensorBlock::XprType TensorBlockExpr;
+    typedef internal::TensorBlockAssignment<Scalar, NumCoords, TensorBlockExpr,
+                                            Index>
+        TensorBlockAssign;
+
+    TensorBlockAssign::Run(
+        TensorBlockAssign::target(desc.dimensions(),
+                                  internal::strides<Layout>(m_dims), m_data,
+                                  desc.offset()),
+        block.expr());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_data.bind(cgh);
+  }
+#endif
+ protected:
+  EvaluatorPointerType m_data;
+  Dimensions m_dims;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+namespace {
+template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T loadConstant(const T* address) {
+  return *address;
+}
+// Use the texture cache on CUDA devices whenever possible
+#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
+template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float loadConstant(const float* address) {
+  return __ldg(address);
+}
+template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double loadConstant(const double* address) {
+  return __ldg(address);
+}
+template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+Eigen::half loadConstant(const Eigen::half* address) {
+  return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
+}
+#endif
+#ifdef EIGEN_USE_SYCL
+// overload of load constant should be implemented here based on range access
+template <cl::sycl::access::mode AcMd, typename T>
+T &loadConstant(const Eigen::TensorSycl::internal::RangeAccess<AcMd, T> &address) {
+  return *address;
+}
+#endif
+}
+
+
+// Default evaluator for rvalues
+template<typename Derived, typename Device>
+struct TensorEvaluator<const Derived, Device>
+{
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef const Derived XprType;
+  typedef typename internal::traits<Derived>::template MakePointer<const Scalar>::Type TensorPointerType;
+  typedef StorageMemory<const Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  // NumDimensions is -1 for variable dim tensors
+  static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
+                               internal::traits<Derived>::NumDimensions : 0;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  enum {
+    IsAligned         = Derived::IsAligned,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = internal::is_arithmetic<ScalarNoConst>::value,
+    PreferBlockAccess = false,
+    Layout            = Derived::Layout,
+    CoordAccess       = NumCoords > 0,
+    RawAccess         = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
+      : m_data(device.get(m.data())), m_dims(m.dimensions()), m_device(device)
+  { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data) {
+      m_device.memcpy((void*)(m_device.get(data)),m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
+      return false;
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType dest, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): ThreadPoolDevice memcpy is a blockign operation.
+    done(evalSubExprsIfNeeded(dest));
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    eigen_assert(m_data != NULL);
+    return loadConstant(m_data+index);
+  }
+
+  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketReturnType packet(Index index) const
+  {
+    return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  // Return a packet starting at `index` where `umask` specifies which elements
+  // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
+  // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
+  // float element will be loaded, otherwise 0 will be loaded.
+  // Function has been templatized to enable Sfinae.
+  template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
+  partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
+  {
+    return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
+    eigen_assert(m_data != NULL);
+    const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
+                        : m_dims.IndexOfRowMajor(coords);
+    return loadConstant(m_data+index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
+                        PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    assert(m_data != NULL);
+    return TensorBlock::materialize(m_data, m_dims, desc, scratch);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_data.bind(cgh);
+  }
+#endif
+ protected:
+  EvaluatorPointerType m_data;
+  Dimensions m_dims;
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+
+
+
+// -------------------- CwiseNullaryOp --------------------
+
+template<typename NullaryOp, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
+{
+  typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;
+
+  TensorEvaluator(const XprType& op, const Device& device)
+      : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper()
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = true,
+    PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess
+    #ifdef EIGEN_USE_SYCL
+    &&  (PacketType<CoeffReturnType, Device>::size >1)
+    #endif
+    ,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    done(true);
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() { }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_wrapper(m_functor, index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
+                        PacketType<CoeffReturnType, Device>::size);
+  }
+
+  EIGEN_DEVICE_FUNC  EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+   // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_argImpl.bind(cgh);
+  }
+#endif
+
+ private:
+  const NullaryOp m_functor;
+  TensorEvaluator<ArgType, Device> m_argImpl;
+  const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
+};
+
+
+
+// -------------------- CwiseUnaryOp --------------------
+
+template<typename UnaryOp, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
+{
+  typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+  enum {
+    IsAligned          = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess       = int(TensorEvaluator<ArgType, Device>::PacketAccess) &
+                         int(internal::functor_traits<UnaryOp>::PacketAccess),
+    BlockAccess        = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess  = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout             = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess        = false,  // to be implemented
+    RawAccess          = false
+  };
+
+  TensorEvaluator(const XprType& op, const Device& device)
+    : m_device(device),
+      m_functor(op.functor()),
+      m_argImpl(op.nestedExpression(), device)
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  static const int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  typedef internal::TensorCwiseUnaryBlock<UnaryOp, ArgTensorBlock>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_argImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_argImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_argImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_functor(m_argImpl.coeff(index));
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
+    return m_argImpl.costPerCoeff(vectorized) +
+        TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
+    return m_argImpl.getResourceRequirements().addCostPerCoeff(
+        {0, 0, functor_cost / PacketSize});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    return TensorBlock(m_argImpl.block(desc, scratch), m_functor);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const{
+    m_argImpl.bind(cgh);
+  }
+#endif
+
+
+ private:
+  const Device EIGEN_DEVICE_REF m_device;
+  const UnaryOp m_functor;
+  TensorEvaluator<ArgType, Device> m_argImpl;
+};
+
+
+// -------------------- CwiseBinaryOp --------------------
+
+template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
+{
+  typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;
+
+  enum {
+    IsAligned         = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
+                        int(TensorEvaluator<RightArgType, Device>::IsAligned),
+    PacketAccess      = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
+                        int(TensorEvaluator<RightArgType, Device>::PacketAccess) &
+                        int(internal::functor_traits<BinaryOp>::PacketAccess),
+    BlockAccess       = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
+                        int(TensorEvaluator<RightArgType, Device>::BlockAccess),
+    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
+                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
+    Layout            = TensorEvaluator<LeftArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  TensorEvaluator(const XprType& op, const Device& device)
+    : m_device(device),
+      m_functor(op.functor()),
+      m_leftImpl(op.lhsExpression(), device),
+      m_rightImpl(op.rhsExpression(), device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static const int NumDims = internal::array_size<
+      typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const LeftArgType, Device>::TensorBlock
+      LeftTensorBlock;
+  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
+      RightTensorBlock;
+
+  typedef internal::TensorCwiseBinaryBlock<BinaryOp, LeftTensorBlock,
+                                           RightTensorBlock>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
+  {
+    // TODO: use right impl instead if right impl dimensions are known at compile time.
+    return m_leftImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    m_rightImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    // TODO(ezhulenev): Evaluate two expression in parallel?
+    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
+      m_rightImpl.evalSubExprsIfNeededAsync(nullptr,
+                                            [done](bool) { done(true); });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
+    return m_leftImpl.costPerCoeff(vectorized) +
+           m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
+    return internal::TensorBlockResourceRequirements::merge(
+               m_leftImpl.getResourceRequirements(),
+               m_rightImpl.getResourceRequirements())
+        .addCostPerCoeff({0, 0, functor_cost / PacketSize});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    desc.DropDestinationBuffer();
+    return TensorBlock(m_leftImpl.block(desc, scratch),
+                         m_rightImpl.block(desc, scratch), m_functor);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  #ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_leftImpl.bind(cgh);
+    m_rightImpl.bind(cgh);
+  }
+  #endif
+ private:
+  const Device EIGEN_DEVICE_REF m_device;
+  const BinaryOp m_functor;
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+};
+
+// -------------------- CwiseTernaryOp --------------------
+
+template<typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
+struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device>
+{
+  typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType;
+
+  enum {
+    IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned & TensorEvaluator<Arg3Type, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<Arg1Type, Device>::PacketAccess &&
+                        TensorEvaluator<Arg2Type, Device>::PacketAccess &&
+                        TensorEvaluator<Arg3Type, Device>::PacketAccess &&
+                        internal::functor_traits<TernaryOp>::PacketAccess,
+    BlockAccess       = false,
+    PreferBlockAccess = TensorEvaluator<Arg1Type, Device>::PreferBlockAccess ||
+                        TensorEvaluator<Arg2Type, Device>::PreferBlockAccess ||
+                        TensorEvaluator<Arg3Type, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<Arg1Type, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  TensorEvaluator(const XprType& op, const Device& device)
+    : m_functor(op.functor()),
+      m_arg1Impl(op.arg1Expression(), device),
+      m_arg2Impl(op.arg2Expression(), device),
+      m_arg3Impl(op.arg3Expression(), device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                         typename internal::traits<Arg2Type>::StorageKind>::value),
+                        STORAGE_KIND_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
+                         typename internal::traits<Arg3Type>::StorageKind>::value),
+                        STORAGE_KIND_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
+                         typename internal::traits<Arg2Type>::Index>::value),
+                        STORAGE_INDEX_MUST_MATCH)
+    EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
+                         typename internal::traits<Arg3Type>::Index>::value),
+                        STORAGE_INDEX_MUST_MATCH)
+
+    eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
+  {
+    // TODO: use arg2 or arg3 dimensions if they are known at compile time.
+    return m_arg1Impl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_arg1Impl.evalSubExprsIfNeeded(NULL);
+    m_arg2Impl.evalSubExprsIfNeeded(NULL);
+    m_arg3Impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_arg1Impl.cleanup();
+    m_arg2Impl.cleanup();
+    m_arg3Impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index),
+                              m_arg2Impl.template packet<LoadMode>(index),
+                              m_arg3Impl.template packet<LoadMode>(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
+    return m_arg1Impl.costPerCoeff(vectorized) +
+           m_arg2Impl.costPerCoeff(vectorized) +
+           m_arg3Impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+   // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_arg1Impl.bind(cgh);
+    m_arg2Impl.bind(cgh);
+    m_arg3Impl.bind(cgh);
+  }
+#endif
+
+ private:
+  const TernaryOp m_functor;
+  TensorEvaluator<Arg1Type, Device> m_arg1Impl;
+  TensorEvaluator<Arg2Type, Device> m_arg2Impl;
+  TensorEvaluator<Arg3Type, Device> m_arg3Impl;
+};
+
+
+// -------------------- SelectOp --------------------
+
+template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
+struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
+{
+  typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  enum {
+    IsAligned         = TensorEvaluator<ThenArgType, Device>::IsAligned &
+                        TensorEvaluator<ElseArgType, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<ThenArgType, Device>::PacketAccess &
+                        TensorEvaluator<ElseArgType, Device>::PacketAccess &
+                        PacketType<Scalar, Device>::HasBlend,
+    BlockAccess       = TensorEvaluator<IfArgType, Device>::BlockAccess &&
+                        TensorEvaluator<ThenArgType, Device>::BlockAccess &&
+                        TensorEvaluator<ElseArgType, Device>::BlockAccess,
+    PreferBlockAccess = TensorEvaluator<IfArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<ThenArgType, Device>::PreferBlockAccess ||
+                        TensorEvaluator<ElseArgType, Device>::PreferBlockAccess,
+    Layout            = TensorEvaluator<IfArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  TensorEvaluator(const XprType& op, const Device& device)
+    : m_condImpl(op.ifExpression(), device),
+      m_thenImpl(op.thenExpression(), device),
+      m_elseImpl(op.elseExpression(), device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
+    eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
+  }
+
+  typedef typename XprType::Index Index;
+  typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  static const int NumDims = internal::array_size<Dimensions>::value;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const IfArgType, Device>::TensorBlock
+      IfArgTensorBlock;
+  typedef typename TensorEvaluator<const ThenArgType, Device>::TensorBlock
+      ThenArgTensorBlock;
+  typedef typename TensorEvaluator<const ElseArgType, Device>::TensorBlock
+      ElseArgTensorBlock;
+
+  struct TensorSelectOpBlockFactory {
+    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
+    struct XprType {
+      typedef TensorSelectOp<const IfArgXprType, const ThenArgXprType, const ElseArgXprType> type;
+    };
+
+    template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
+    typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type expr(
+        const IfArgXprType& if_expr, const ThenArgXprType& then_expr, const ElseArgXprType& else_expr) const {
+      return typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type(if_expr, then_expr, else_expr);
+    }
+  };
+
+  typedef internal::TensorTernaryExprBlock<TensorSelectOpBlockFactory,
+                                           IfArgTensorBlock, ThenArgTensorBlock,
+                                           ElseArgTensorBlock>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
+  {
+    // TODO: use then or else impl instead if they happen to be known at compile time.
+    return m_condImpl.dimensions();
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_condImpl.evalSubExprsIfNeeded(NULL);
+    m_thenImpl.evalSubExprsIfNeeded(NULL);
+    m_elseImpl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_condImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
+      m_thenImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
+        m_elseImpl.evalSubExprsIfNeeded(nullptr, [done](bool) { done(true); });
+      });
+    });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_condImpl.cleanup();
+    m_thenImpl.cleanup();
+    m_elseImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+     internal::Selector<PacketSize> select;
+     EIGEN_UNROLL_LOOP
+     for (Index i = 0; i < PacketSize; ++i) {
+       select.select[i] = m_condImpl.coeff(index+i);
+     }
+     return internal::pblend(select,
+                             m_thenImpl.template packet<LoadMode>(index),
+                             m_elseImpl.template packet<LoadMode>(index));
+
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    return m_condImpl.costPerCoeff(vectorized) +
+           m_thenImpl.costPerCoeff(vectorized)
+        .cwiseMax(m_elseImpl.costPerCoeff(vectorized));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    auto then_req = m_thenImpl.getResourceRequirements();
+    auto else_req = m_elseImpl.getResourceRequirements();
+
+    auto merged_req =
+        internal::TensorBlockResourceRequirements::merge(then_req, else_req);
+    merged_req.cost_per_coeff =
+        then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);
+
+    return internal::TensorBlockResourceRequirements::merge(
+        m_condImpl.getResourceRequirements(), merged_req);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    // It's unsafe to pass destination buffer to underlying expressions, because
+    // output might be aliased with one of the inputs.
+    desc.DropDestinationBuffer();
+
+    return TensorBlock(
+        m_condImpl.block(desc, scratch), m_thenImpl.block(desc, scratch),
+        m_elseImpl.block(desc, scratch), TensorSelectOpBlockFactory());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+ // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_condImpl.bind(cgh);
+    m_thenImpl.bind(cgh);
+    m_elseImpl.bind(cgh);
+  }
+#endif
+ private:
+  TensorEvaluator<IfArgType, Device> m_condImpl;
+  TensorEvaluator<ThenArgType, Device> m_thenImpl;
+  TensorEvaluator<ElseArgType, Device> m_elseImpl;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
new file mode 100644
index 0000000..c52fb77
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -0,0 +1,703 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
+
+namespace Eigen {
+
+/**
+ * \class TensorExecutor
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief The tensor executor class.
+ *
+ * This class is responsible for launch the evaluation of the expression on
+ * the specified computing device.
+ *
+ * @tparam Vectorizable can use packet math (SSE/AVX/etc... registers and
+ *                      instructions)
+ * @tparam Tiling       can use block based tensor evaluation
+ *                      (see TensorBlock.h)
+ */
+namespace internal {
+
+/**
+ * Evaluating TensorBroadcastingOp via coefficient of packet path is extremely
+ * expensive. If expression has at least one broadcast op in it, and it supports
+ * block based evaluation, we always prefer it, even for the small tensors. For
+ * all other tileable ops, block evaluation overhead for small tensors (fits
+ * into L1) is too large, and we fallback on vectorized evaluation.
+ */
+
+// TODO(ezhulenev): Add specializations for all other types of Tensor ops.
+
+template<typename Expression>
+struct ExpressionHasTensorBroadcastingOp {
+  enum { value = false };
+};
+
+template<typename LhsXprType, typename RhsXprType>
+struct ExpressionHasTensorBroadcastingOp<
+    const TensorAssignOp<LhsXprType, RhsXprType> > {
+  enum { value = ExpressionHasTensorBroadcastingOp<RhsXprType>::value };
+};
+
+template<typename UnaryOp, typename XprType>
+struct ExpressionHasTensorBroadcastingOp<
+    const TensorCwiseUnaryOp<UnaryOp, XprType> > {
+  enum { value = ExpressionHasTensorBroadcastingOp<XprType>::value };
+};
+
+template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct ExpressionHasTensorBroadcastingOp<
+    const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> > {
+  enum {
+    value = ExpressionHasTensorBroadcastingOp<LhsXprType>::value ||
+        ExpressionHasTensorBroadcastingOp<RhsXprType>::value
+  };
+};
+
+template<typename Broadcast, typename XprType>
+struct ExpressionHasTensorBroadcastingOp<
+    const TensorBroadcastingOp<Broadcast, XprType> > {
+  enum { value = true };
+};
+
+// -------------------------------------------------------------------------- //
+
+/**
+ * Default strategy: the expression is evaluated sequentially with a single cpu
+ * thread, without vectorization and block evaluation.
+ */
+template <typename Expression, typename Device, bool Vectorizable,
+          TiledEvaluation Tiling>
+class TensorExecutor {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  // Including `unsupported/Eigen/CXX11/Tensor` in different translation units
+  // with/without `EIGEN_USE_THREADS` or `EIGEN_USE_GPU` is a potential ODR
+  // violation. If this template is instantiated with a non-default device, it
+  // means that this header file was included without defining
+  // `EIGEN_USE_THREADS`, `EIGEN_USE_GPU` or `EIGEN_USE_SYCL`.
+  static_assert(std::is_same<Device, DefaultDevice>::value,
+                "Default executor instantiated with non-default device. "
+                "You must #define EIGEN_USE_THREADS, EIGEN_USE_GPU or "
+                "EIGEN_USE_SYCL before including Eigen headers.");
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                                      const Device& device = Device()) {
+    TensorEvaluator<Expression, Device> evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      for (StorageIndex i = 0; i < size; ++i) {
+        evaluator.evalScalar(i);
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Default async execution strategy is not implemented. Currently it's only
+ * available for ThreadPoolDevice (see definition below).
+ */
+template <typename Expression, typename Device, typename DoneCallback,
+          bool Vectorizable, TiledEvaluation Tiling>
+class TensorAsyncExecutor {};
+
+/**
+ * Process all the data with a single cpu thread, using vectorized instructions.
+ */
+template <typename Expression>
+class TensorExecutor<Expression, DefaultDevice, /*Vectorizable=*/true,
+                     /*Tiling=*/TiledEvaluation::Off> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(
+      const Expression& expr, const DefaultDevice& device = DefaultDevice()) {
+    TensorEvaluator<Expression, DefaultDevice> evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      const int PacketSize = unpacket_traits<typename TensorEvaluator<
+          Expression, DefaultDevice>::PacketReturnType>::size;
+
+      // Give compiler a strong possibility to unroll the loop. But don't insist
+      // on unrolling, because if the function is expensive compiler should not
+      // unroll the loop at the expense of inlining.
+      const StorageIndex UnrolledSize =
+          (size / (4 * PacketSize)) * 4 * PacketSize;
+      for (StorageIndex i = 0; i < UnrolledSize; i += 4 * PacketSize) {
+        for (StorageIndex j = 0; j < 4; j++) {
+          evaluator.evalPacket(i + j * PacketSize);
+        }
+      }
+      const StorageIndex VectorizedSize = (size / PacketSize) * PacketSize;
+      for (StorageIndex i = UnrolledSize; i < VectorizedSize; i += PacketSize) {
+        evaluator.evalPacket(i);
+      }
+      for (StorageIndex i = VectorizedSize; i < size; ++i) {
+        evaluator.evalScalar(i);
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Process all the data with a single cpu thread, using blocks of data. By
+ * sizing a block to fit L1 cache we get better cache performance.
+ */
+template <typename Expression, bool Vectorizable>
+class TensorExecutor<Expression, DefaultDevice, Vectorizable,
+                     /*Tiling=*/TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef typename remove_const<Scalar>::type ScalarNoConst;
+
+  typedef TensorEvaluator<Expression, DefaultDevice> Evaluator;
+  typedef typename traits<Expression>::Index StorageIndex;
+
+  static const int NumDims = traits<Expression>::NumDimensions;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                         const DefaultDevice& device = DefaultDevice()) {
+    typedef TensorBlockMapper<NumDims, Evaluator::Layout, StorageIndex>
+        TensorBlockMapper;
+
+    typedef internal::TensorBlockDescriptor<NumDims, StorageIndex>
+        TensorBlockDesc;
+    typedef internal::TensorBlockScratchAllocator<DefaultDevice>
+        TensorBlockScratch;
+
+    Evaluator evaluator(expr, device);
+
+    // TODO(ezhulenev): Do not use tiling for small tensors?
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+
+    if (needs_assign) {
+      // Query expression tree for desired block size/shape.
+      const TensorBlockResourceRequirements requirements =
+          evaluator.getResourceRequirements();
+
+      const TensorBlockMapper block_mapper(
+          typename TensorBlockDesc::Dimensions(evaluator.dimensions()),
+          requirements);
+
+      // Share scratch memory allocator between all blocks.
+      TensorBlockScratch scratch(device);
+
+      const StorageIndex total_block_count = block_mapper.blockCount();
+      for (StorageIndex i = 0; i < total_block_count; ++i) {
+        TensorBlockDesc desc = block_mapper.blockDescriptor(i);
+        evaluator.evalBlock(desc, scratch);
+        scratch.reset();
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+/**
+ * Multicore strategy: the index space is partitioned and each partition is
+ * executed on a single core.
+ *
+ * (1) TensorExecutor will submit work to the ThreadPoolDevice managed thread
+ *     pool, and will block the caller thread until all tasks are finished.
+ *
+ * (2) TensorAsyncExecutor is a non-blocking version, that will submit work to
+ *     the ThreadPoolDevice managed thread pool, and will return immediately.
+ *     It will call 'done' callback after all tasks are finished.
+ */
+#ifdef EIGEN_USE_THREADS
+
+template <typename TensorBlockMapper>
+struct TensorExecutorTilingContext {
+  TensorExecutorTilingContext() = default;
+  TensorExecutorTilingContext(const TensorBlockMapper& b_mapper,
+                              const TensorOpCost& b_cost, size_t b_aligned_size)
+      : block_mapper(b_mapper),
+        cost(b_cost),
+        aligned_blocksize(b_aligned_size) {}
+
+  TensorBlockMapper block_mapper;  // navigate through blocks
+  TensorOpCost cost;               // cost of computing a single block
+  size_t aligned_blocksize;        // block size after memory alignment
+};
+
+// Computes a block evaluation parameters, and allocates temporary memory buffer
+// for blocks. See TensorExecutor/TensorAsyncExecutor (Tiling=On) below.
+template <typename Evaluator, typename TensorBlockMapper, bool Vectorizable>
+TensorExecutorTilingContext<TensorBlockMapper> GetTensorExecutorTilingContext(
+    const Evaluator& evaluator) {
+  // Query expression tree for desired block size/shape.
+  TensorBlockResourceRequirements requirements =
+      evaluator.getResourceRequirements();
+
+  // Update target block size based on cost model.
+  double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(
+      1, requirements.cost_per_coeff);
+  requirements.size = static_cast<size_t>(1.0 / taskSize);
+
+  TensorBlockMapper block_mapper(
+      typename TensorBlockMapper::Dimensions(evaluator.dimensions()),
+      requirements);
+
+  size_t block_size = block_mapper.blockTotalSize();
+  const size_t align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);
+  const size_t aligned_blocksize =
+      align *
+      divup<size_t>(block_size * sizeof(typename Evaluator::Scalar), align);
+
+  return {block_mapper, requirements.cost_per_coeff * block_size,
+          aligned_blocksize};
+}
+
+template <typename Evaluator, typename StorageIndex, bool Vectorizable>
+struct EvalRange {
+  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx,
+                  const StorageIndex lastIdx) {
+    Evaluator evaluator = *evaluator_in;
+    eigen_assert(lastIdx >= firstIdx);
+    for (StorageIndex i = firstIdx; i < lastIdx; ++i) {
+      evaluator.evalScalar(i);
+    }
+  }
+
+  static StorageIndex alignBlockSize(StorageIndex size) { return size; }
+};
+
+template <typename Evaluator, typename StorageIndex>
+struct EvalRange<Evaluator, StorageIndex, /*Vectorizable*/ true> {
+  static const int PacketSize =
+      unpacket_traits<typename Evaluator::PacketReturnType>::size;
+
+  static void run(Evaluator* evaluator_in, const StorageIndex firstIdx,
+                  const StorageIndex lastIdx) {
+    Evaluator evaluator = *evaluator_in;
+    eigen_assert(lastIdx >= firstIdx);
+    StorageIndex i = firstIdx;
+    if (lastIdx - firstIdx >= PacketSize) {
+      eigen_assert(firstIdx % PacketSize == 0);
+      StorageIndex last_chunk_offset = lastIdx - 4 * PacketSize;
+      // Give compiler a strong possibility to unroll the loop. But don't insist
+      // on unrolling, because if the function is expensive compiler should not
+      // unroll the loop at the expense of inlining.
+      for (; i <= last_chunk_offset; i += 4 * PacketSize) {
+        for (StorageIndex j = 0; j < 4; j++) {
+          evaluator.evalPacket(i + j * PacketSize);
+        }
+      }
+      last_chunk_offset = lastIdx - PacketSize;
+      for (; i <= last_chunk_offset; i += PacketSize) {
+        evaluator.evalPacket(i);
+      }
+    }
+    for (; i < lastIdx; ++i) {
+      evaluator.evalScalar(i);
+    }
+  }
+
+  static StorageIndex alignBlockSize(StorageIndex size) {
+    // Align block size to packet size and account for unrolling in run above.
+    if (size >= 16 * PacketSize) {
+      return (size + 4 * PacketSize - 1) & ~(4 * PacketSize - 1);
+    }
+    // Aligning to 4 * PacketSize would increase block size by more than 25%.
+    return (size + PacketSize - 1) & ~(PacketSize - 1);
+  }
+};
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                         const ThreadPoolDevice& device) {
+    typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+    typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
+
+    Evaluator evaluator(expr, device);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+    if (needs_assign) {
+      const StorageIndex size = array_prod(evaluator.dimensions());
+      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
+                         EvalRange::alignBlockSize,
+                         [&evaluator](StorageIndex firstIdx, StorageIndex lastIdx) {
+                           EvalRange::run(&evaluator, firstIdx, lastIdx);
+                         });
+    }
+    evaluator.cleanup();
+  }
+};
+
+template <typename Expression, bool Vectorizable>
+class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable,
+                     /*Tiling=*/TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Index IndexType;
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef typename remove_const<Scalar>::type ScalarNoConst;
+
+  static const int NumDims = traits<Expression>::NumDimensions;
+
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
+  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType>
+      TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice>
+      TensorBlockScratch;
+
+  static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                                      const ThreadPoolDevice& device) {
+    Evaluator evaluator(expr, device);
+
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+    if (needs_assign) {
+      const TilingContext tiling =
+          internal::GetTensorExecutorTilingContext<Evaluator, BlockMapper,
+                                                   Vectorizable>(evaluator);
+
+      auto eval_block = [&device, &evaluator, &tiling](IndexType firstBlockIdx,
+                                                       IndexType lastBlockIdx) {
+        TensorBlockScratch scratch(device);
+
+        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx;
+             ++block_idx) {
+          TensorBlockDesc desc = tiling.block_mapper.blockDescriptor(block_idx);
+          evaluator.evalBlock(desc, scratch);
+          scratch.reset();
+        }
+      };
+
+      // Evaluate small expressions directly as a single block.
+      if (tiling.block_mapper.blockCount() == 1) {
+        TensorBlockScratch scratch(device);
+        TensorBlockDesc desc(0, tiling.block_mapper.blockDimensions());
+        evaluator.evalBlock(desc, scratch);
+      } else {
+        device.parallelFor(tiling.block_mapper.blockCount(), tiling.cost,
+                           eval_block);
+      }
+    }
+    evaluator.cleanup();
+  }
+};
+
+template <typename Expression, typename DoneCallback, bool Vectorizable,
+          TiledEvaluation Tiling>
+class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
+                          Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+
+  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
+                                           const ThreadPoolDevice& device,
+                                           DoneCallback done) {
+    TensorAsyncExecutorContext* const ctx =
+        new TensorAsyncExecutorContext(expr, device, std::move(done));
+
+    const auto on_eval_subexprs = [ctx, &device](bool need_assign) -> void {
+      if (!need_assign) {
+        delete ctx;
+        return;
+      }
+
+      typedef EvalRange<Evaluator, StorageIndex, Vectorizable> EvalRange;
+      const StorageIndex size = array_prod(ctx->evaluator.dimensions());
+      device.parallelForAsync(
+          size, ctx->evaluator.costPerCoeff(Vectorizable),
+          EvalRange::alignBlockSize,
+          [ctx](StorageIndex firstIdx, StorageIndex lastIdx) {
+            EvalRange::run(&ctx->evaluator, firstIdx, lastIdx);
+          },
+          [ctx]() { delete ctx; });
+    };
+
+    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
+  }
+
+ private:
+  struct TensorAsyncExecutorContext {
+    TensorAsyncExecutorContext(const Expression& expr,
+                               const ThreadPoolDevice& thread_pool,
+                               DoneCallback done)
+        : evaluator(expr, thread_pool), on_done(std::move(done)) {}
+
+    ~TensorAsyncExecutorContext() {
+      evaluator.cleanup();
+      on_done();
+    }
+
+    Evaluator evaluator;
+
+   private:
+    DoneCallback on_done;
+  };
+};
+
+template <typename Expression, typename DoneCallback, bool Vectorizable>
+class TensorAsyncExecutor<Expression, ThreadPoolDevice, DoneCallback,
+                          Vectorizable, /*Tileable*/ TiledEvaluation::On> {
+ public:
+  typedef typename traits<Expression>::Index IndexType;
+  typedef typename traits<Expression>::Scalar Scalar;
+  typedef typename remove_const<Scalar>::type ScalarNoConst;
+
+  static const int NumDims = traits<Expression>::NumDimensions;
+
+  typedef TensorEvaluator<Expression, ThreadPoolDevice> Evaluator;
+  typedef TensorBlockMapper<NumDims, Evaluator::Layout, IndexType> BlockMapper;
+  typedef TensorExecutorTilingContext<BlockMapper> TilingContext;
+
+  typedef internal::TensorBlockDescriptor<NumDims, IndexType> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<ThreadPoolDevice>
+      TensorBlockScratch;
+
+  static EIGEN_STRONG_INLINE void runAsync(const Expression& expr,
+                                           const ThreadPoolDevice& device,
+                                           DoneCallback done) {
+
+    TensorAsyncExecutorContext* const ctx =
+        new TensorAsyncExecutorContext(expr, device, std::move(done));
+
+    const auto on_eval_subexprs = [ctx](bool need_assign) -> void {
+      if (!need_assign) {
+        delete ctx;
+        return;
+      }
+
+      ctx->tiling = internal::GetTensorExecutorTilingContext<
+          Evaluator, BlockMapper, Vectorizable>(ctx->evaluator);
+
+      auto eval_block = [ctx](IndexType firstBlockIdx, IndexType lastBlockIdx) {
+        TensorBlockScratch scratch(ctx->device);
+
+        for (IndexType block_idx = firstBlockIdx; block_idx < lastBlockIdx;
+             ++block_idx) {
+          TensorBlockDesc desc =
+              ctx->tiling.block_mapper.blockDescriptor(block_idx);
+          ctx->evaluator.evalBlock(desc, scratch);
+          scratch.reset();
+        }
+      };
+
+      // Evaluate small expressions directly as a single block.
+      if (ctx->tiling.block_mapper.blockCount() == 1) {
+        TensorBlockScratch scratch(ctx->device);
+        TensorBlockDesc desc(0, ctx->tiling.block_mapper.blockDimensions());
+        ctx->evaluator.evalBlock(desc, scratch);
+        delete ctx;
+      } else {
+        ctx->device.parallelForAsync(ctx->tiling.block_mapper.blockCount(),
+                                     ctx->tiling.cost, eval_block,
+                                     [ctx]() { delete ctx; });
+      }
+    };
+
+    ctx->evaluator.evalSubExprsIfNeededAsync(nullptr, on_eval_subexprs);
+  }
+
+ private:
+  struct TensorAsyncExecutorContext {
+    TensorAsyncExecutorContext(const Expression& expr,
+                               const ThreadPoolDevice& thread_pool,
+                               DoneCallback done)
+        : device(thread_pool),
+          evaluator(expr, thread_pool),
+          on_done(std::move(done)) {}
+
+    ~TensorAsyncExecutorContext() {
+      evaluator.cleanup();
+      on_done();
+    }
+
+    const ThreadPoolDevice& device;
+    Evaluator evaluator;
+    TilingContext tiling;
+
+   private:
+    DoneCallback on_done;
+  };
+};
+
+#endif  // EIGEN_USE_THREADS
+
+// GPU: the evaluation of the expression is offloaded to a GPU.
+#if defined(EIGEN_USE_GPU)
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index StorageIndex;
+  static void run(const Expression& expr, const GpuDevice& device);
+};
+
+#if defined(EIGEN_GPUCC)
+template <typename Evaluator, typename StorageIndex, bool Vectorizable>
+struct EigenMetaKernelEval {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {
+    for (StorageIndex i = firstIdx; i < lastIdx; i += step_size) {
+      eval.evalScalar(i);
+    }
+  }
+};
+
+template <typename Evaluator, typename StorageIndex>
+struct EigenMetaKernelEval<Evaluator, StorageIndex, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  void run(Evaluator& eval, StorageIndex firstIdx, StorageIndex lastIdx, StorageIndex step_size) {
+    const StorageIndex PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size;
+    const StorageIndex vectorized_size = (lastIdx / PacketSize) * PacketSize;
+    const StorageIndex vectorized_step_size = step_size * PacketSize;
+
+    // Use the vector path
+    for (StorageIndex i = firstIdx * PacketSize; i < vectorized_size;
+         i += vectorized_step_size) {
+      eval.evalPacket(i);
+    }
+    for (StorageIndex i = vectorized_size + firstIdx; i < lastIdx; i += step_size) {
+      eval.evalScalar(i);
+    }
+  }
+};
+
+template <typename Evaluator, typename StorageIndex>
+__global__ void
+__launch_bounds__(1024)
+EigenMetaKernel(Evaluator eval, StorageIndex size) {
+
+  const StorageIndex first_index = blockIdx.x * blockDim.x + threadIdx.x;
+  const StorageIndex step_size = blockDim.x * gridDim.x;
+
+  const bool vectorizable = Evaluator::PacketAccess & Evaluator::IsAligned;
+  EigenMetaKernelEval<Evaluator, StorageIndex, vectorizable>::run(eval, first_index, size, step_size);
+}
+
+/*static*/
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+EIGEN_STRONG_INLINE void TensorExecutor<Expression, GpuDevice, Vectorizable, Tiling>::run(
+    const Expression& expr, const GpuDevice& device) {
+  TensorEvaluator<Expression, GpuDevice> evaluator(expr, device);
+  const bool needs_assign = evaluator.evalSubExprsIfNeeded(nullptr);
+  if (needs_assign) {
+
+    const int block_size = device.maxGpuThreadsPerBlock();
+    const int max_blocks = device.getNumGpuMultiProcessors() *
+                           device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const StorageIndex size = array_prod(evaluator.dimensions());
+    // Create a least one block to ensure we won't crash when tensorflow calls with tensors of size 0.
+    const int num_blocks = numext::maxi<int>(numext::mini<int>(max_blocks, divup<int>(size, block_size)), 1);
+
+    LAUNCH_GPU_KERNEL(
+        (EigenMetaKernel<TensorEvaluator<Expression, GpuDevice>, StorageIndex>),
+        num_blocks, block_size, 0, device, evaluator, size);
+  }
+  evaluator.cleanup();
+}
+
+#endif  // EIGEN_GPUCC
+#endif  // EIGEN_USE_GPU
+
+// SYCL Executor policy
+#ifdef EIGEN_USE_SYCL
+
+template <typename Evaluator>
+struct ExecExprFunctorKernel {
+  typedef typename Evaluator::Index Index;
+  Evaluator evaluator;
+  const Index range;
+  template <typename Scratch>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ExecExprFunctorKernel(
+      const Scratch, Evaluator evaluator_, const Index range_)
+      : evaluator(evaluator_), range(range_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void operator()(
+      cl::sycl::nd_item<1> itemID) {
+    compute(itemID);
+  }
+  template <bool is_vec = Evaluator::PacketAccess>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if<!is_vec>::type
+  compute(const cl::sycl::nd_item<1>& itemID) {
+    Index gId = static_cast<Index>(itemID.get_global_linear_id());
+    Index total_threads = itemID.get_global_range(0);
+
+    for (Index i = gId; i < range; i += total_threads) {
+      evaluator.evalScalar(i);
+    }
+  }
+  template <bool is_vec = Evaluator::PacketAccess>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if<is_vec>::type
+  compute(const cl::sycl::nd_item<1>& itemID) {
+    const Index vectorizedRange =
+        (range / Evaluator::PacketSize) * Evaluator::PacketSize;
+    Index gId = static_cast<Index>(itemID.get_global_linear_id());
+    const Index step = Evaluator::PacketSize * itemID.get_global_range(0);
+    const Index start = Evaluator::PacketSize * gId;
+    for (Index i = start; i < vectorizedRange; i += step) {
+      evaluator.evalPacket(i);
+    }
+    gId += vectorizedRange;
+    for (Index i = gId; i < range; i += itemID.get_global_range(0)) {
+      evaluator.evalScalar(i);
+    }
+  }
+};
+
+template <typename Expression, bool Vectorizable, TiledEvaluation Tiling>
+class TensorExecutor<Expression, Eigen::SyclDevice, Vectorizable, Tiling> {
+ public:
+  typedef typename Expression::Index Index;
+  static EIGEN_STRONG_INLINE void run(const Expression& expr,
+                                      const Eigen::SyclDevice& dev) {
+    typedef Eigen::TensorEvaluator<Expression, Eigen::SyclDevice> Evaluator;
+    Evaluator evaluator(expr, dev);
+    const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL);
+    if (needs_assign) {
+      Index range, GRange, tileSize;
+      Index total_size = ::Eigen::internal::array_prod(evaluator.dimensions());
+      total_size = (total_size == 0) ? 1 : total_size;
+      const int PacketSize =
+          Eigen::PacketType<typename Evaluator::CoeffReturnType,
+                            Eigen::SyclDevice>::size;
+      Index vectorizable_threads = static_cast<Index>(total_size / PacketSize);
+      dev.parallel_for_setup(vectorizable_threads, tileSize, range, GRange);
+      range = total_size;
+
+      dev.template nullary_kernel_launcher<
+          typename Evaluator::CoeffReturnType,
+          ExecExprFunctorKernel<Evaluator> >(
+          evaluator,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange),
+                                cl::sycl::range<1>(tileSize)),
+          Index(1), range);
+    }
+    evaluator.cleanup();
+  }
+};
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_EXECUTOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
new file mode 100644
index 0000000..c9bccfc
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h
@@ -0,0 +1,388 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
+
+namespace Eigen {
+
+/** \class TensorExpr
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor expression classes.
+  *
+  * The TensorCwiseNullaryOp class applies a nullary operators to an expression.
+  * This is typically used to generate constants.
+  *
+  * The TensorCwiseUnaryOp class represents an expression where a unary operator
+  * (e.g. cwiseSqrt) is applied to an expression.
+  *
+  * The TensorCwiseBinaryOp class represents an expression where a binary
+  * operator (e.g. addition) is applied to a lhs and a rhs expression.
+  *
+  */
+namespace internal {
+template<typename NullaryOp, typename XprType>
+struct traits<TensorCwiseNullaryOp<NullaryOp, XprType> >
+    : traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+  enum {
+    Flags = 0
+  };
+};
+
+}  // end namespace internal
+
+
+
+template<typename NullaryOp, typename XprType>
+class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, XprType>, ReadOnlyAccessors>
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef TensorCwiseNullaryOp<NullaryOp, XprType> Nested;
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const XprType& xpr, const NullaryOp& func = NullaryOp())
+        : m_xpr(xpr), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    nestedExpression() const { return m_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const NullaryOp& functor() const { return m_functor; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const NullaryOp m_functor;
+};
+
+
+
+namespace internal {
+template<typename UnaryOp, typename XprType>
+struct traits<TensorCwiseUnaryOp<UnaryOp, XprType> >
+    : traits<XprType>
+{
+  // TODO(phli): Add InputScalar, InputPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Input or Output.
+  typedef typename result_of<UnaryOp(typename XprType::Scalar)>::type Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar, 
+                                  typename XprTraits::PointerType
+                                  >::type 
+                                  PointerType;
+};
+
+template<typename UnaryOp, typename XprType>
+struct eval<TensorCwiseUnaryOp<UnaryOp, XprType>, Eigen::Dense>
+{
+  typedef const TensorCwiseUnaryOp<UnaryOp, XprType>& type;
+};
+
+template<typename UnaryOp, typename XprType>
+struct nested<TensorCwiseUnaryOp<UnaryOp, XprType>, 1, typename eval<TensorCwiseUnaryOp<UnaryOp, XprType> >::type>
+{
+  typedef TensorCwiseUnaryOp<UnaryOp, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename UnaryOp, typename XprType>
+class TensorCwiseUnaryOp : public TensorBase<TensorCwiseUnaryOp<UnaryOp, XprType>, ReadOnlyAccessors>
+{
+  public:
+    // TODO(phli): Add InputScalar, InputPacket.  Check references to
+    // current Scalar/Packet to see if the intent is Input or Output.
+    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef Scalar CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorCwiseUnaryOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorCwiseUnaryOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
+      : m_xpr(xpr), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC
+    const UnaryOp& functor() const { return m_functor; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    nestedExpression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const UnaryOp m_functor;
+};
+
+
+namespace internal {
+template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct traits<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs
+  // are different.
+  // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
+  // current Scalar/Packet to see if the intent is Inputs or Output.
+  typedef typename result_of<
+      BinaryOp(typename LhsXprType::Scalar,
+               typename RhsXprType::Scalar)>::type Scalar;
+  typedef traits<LhsXprType> XprTraits;
+  typedef typename promote_storage_type<
+      typename traits<LhsXprType>::StorageKind,
+      typename traits<RhsXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<
+      typename traits<LhsXprType>::Index,
+      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar,
+                                  typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
+                                                      typename traits<LhsXprType>::PointerType,
+                                                      typename traits<RhsXprType>::PointerType>::type
+                                  >::type 
+                                  PointerType;
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, Eigen::Dense>
+{
+  typedef const TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>& type;
+};
+
+template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
+struct nested<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, 1, typename eval<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> >::type>
+{
+  typedef TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename BinaryOp, typename LhsXprType, typename RhsXprType>
+class TensorCwiseBinaryOp : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsXprType, RhsXprType>, ReadOnlyAccessors>
+{
+  public:
+    // TODO(phli): Add Lhs/RhsScalar, Lhs/RhsPacket.  Check references to
+    // current Scalar/Packet to see if the intent is Inputs or Output.
+    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef Scalar CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorCwiseBinaryOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorCwiseBinaryOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const BinaryOp& func = BinaryOp())
+        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC
+    const BinaryOp& functor() const { return m_functor; }
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename LhsXprType::Nested>::type&
+    lhsExpression() const { return m_lhs_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename RhsXprType::Nested>::type&
+    rhsExpression() const { return m_rhs_xpr; }
+
+  protected:
+    typename LhsXprType::Nested m_lhs_xpr;
+    typename RhsXprType::Nested m_rhs_xpr;
+    const BinaryOp m_functor;
+};
+
+
+namespace internal {
+template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct traits<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >
+{
+  // Type promotion to handle the case where the types of the args are different.
+  typedef typename result_of<
+      TernaryOp(typename Arg1XprType::Scalar,
+                typename Arg2XprType::Scalar,
+                typename Arg3XprType::Scalar)>::type Scalar;
+  typedef traits<Arg1XprType> XprTraits;
+  typedef typename traits<Arg1XprType>::StorageKind StorageKind;
+  typedef typename traits<Arg1XprType>::Index Index;
+  typedef typename Arg1XprType::Nested Arg1Nested;
+  typedef typename Arg2XprType::Nested Arg2Nested;
+  typedef typename Arg3XprType::Nested Arg3Nested;
+  typedef typename remove_reference<Arg1Nested>::type _Arg1Nested;
+  typedef typename remove_reference<Arg2Nested>::type _Arg2Nested;
+  typedef typename remove_reference<Arg3Nested>::type _Arg3Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename TypeConversion<Scalar,
+                                  typename conditional<Pointer_type_promotion<typename Arg2XprType::Scalar, Scalar>::val,
+                                                      typename traits<Arg2XprType>::PointerType,
+                                                      typename traits<Arg3XprType>::PointerType>::type
+                                  >::type 
+                                  PointerType;
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, Eigen::Dense>
+{
+  typedef const TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>& type;
+};
+
+template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+struct nested<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, 1, typename eval<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> >::type>
+{
+  typedef TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType>
+class TensorCwiseTernaryOp : public TensorBase<TensorCwiseTernaryOp<TernaryOp, Arg1XprType, Arg2XprType, Arg3XprType>, ReadOnlyAccessors>
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef Scalar CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorCwiseTernaryOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorCwiseTernaryOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseTernaryOp(const Arg1XprType& arg1, const Arg2XprType& arg2, const Arg3XprType& arg3, const TernaryOp& func = TernaryOp())
+        : m_arg1_xpr(arg1), m_arg2_xpr(arg2), m_arg3_xpr(arg3), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC
+    const TernaryOp& functor() const { return m_functor; }
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename Arg1XprType::Nested>::type&
+    arg1Expression() const { return m_arg1_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename Arg2XprType::Nested>::type&
+    arg2Expression() const { return m_arg2_xpr; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename Arg3XprType::Nested>::type&
+    arg3Expression() const { return m_arg3_xpr; }
+
+  protected:
+    typename Arg1XprType::Nested m_arg1_xpr;
+    typename Arg2XprType::Nested m_arg2_xpr;
+    typename Arg3XprType::Nested m_arg3_xpr;
+    const TernaryOp m_functor;
+};
+
+
+namespace internal {
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
+    : traits<ThenXprType>
+{
+  typedef typename traits<ThenXprType>::Scalar Scalar;
+  typedef traits<ThenXprType> XprTraits;
+  typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind,
+                                        typename traits<ElseXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<ElseXprType>::Index,
+                                      typename traits<ThenXprType>::Index>::type Index;
+  typedef typename IfXprType::Nested IfNested;
+  typedef typename ThenXprType::Nested ThenNested;
+  typedef typename ElseXprType::Nested ElseNested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename conditional<Pointer_type_promotion<typename ThenXprType::Scalar, Scalar>::val,
+                               typename traits<ThenXprType>::PointerType,
+                               typename traits<ElseXprType>::PointerType>::type PointerType;
+};
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense>
+{
+  typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type;
+};
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1, typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type>
+{
+  typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type;
+};
+
+}  // end namespace internal
+
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, ReadOnlyAccessors>
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType,
+                                                    typename ElseXprType::CoeffReturnType>::ret CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC
+    TensorSelectOp(const IfXprType& a_condition,
+                   const ThenXprType& a_then,
+                   const ElseXprType& a_else)
+      : m_condition(a_condition), m_then(a_then), m_else(a_else)
+    { }
+
+    EIGEN_DEVICE_FUNC
+    const IfXprType& ifExpression() const { return m_condition; }
+
+    EIGEN_DEVICE_FUNC
+    const ThenXprType& thenExpression() const { return m_then; }
+
+    EIGEN_DEVICE_FUNC
+    const ElseXprType& elseExpression() const { return m_else; }
+
+  protected:
+    typename IfXprType::Nested m_condition;
+    typename ThenXprType::Nested m_then;
+    typename ElseXprType::Nested m_else;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_EXPR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
new file mode 100644
index 0000000..4a1a068
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
@@ -0,0 +1,669 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Jianwei Cui <thucjw@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FFT_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FFT_H
+
+namespace Eigen {
+
+/** \class TensorFFT
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor FFT class.
+  *
+  * TODO:
+  * Vectorize the Cooley Tukey and the Bluestein algorithm
+  * Add support for multithreaded evaluation
+  * Improve the performance on GPU
+  */
+
+template <bool NeedUprade> struct MakeComplex {
+  template <typename T>
+  EIGEN_DEVICE_FUNC
+  T operator() (const T& val) const { return val; }
+};
+
+template <> struct MakeComplex<true> {
+  template <typename T>
+  EIGEN_DEVICE_FUNC
+  std::complex<T> operator() (const T& val) const { return std::complex<T>(val, 0); }
+};
+
+template <> struct MakeComplex<false> {
+  template <typename T>
+  EIGEN_DEVICE_FUNC
+  std::complex<T> operator() (const std::complex<T>& val) const { return val; }
+};
+
+template <int ResultType> struct PartOf {
+  template <typename T> T operator() (const T& val) const { return val; }
+};
+
+template <> struct PartOf<RealPart> {
+  template <typename T> T operator() (const std::complex<T>& val) const { return val.real(); }
+};
+
+template <> struct PartOf<ImagPart> {
+  template <typename T> T operator() (const std::complex<T>& val) const { return val.imag(); }
+};
+
+namespace internal {
+template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
+struct traits<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir> > : public traits<XprType> {
+  typedef traits<XprType> XprTraits;
+  typedef typename NumTraits<typename XprTraits::Scalar>::Real RealScalar;
+  typedef typename std::complex<RealScalar> ComplexScalar;
+  typedef typename XprTraits::Scalar InputScalar;
+  typedef typename conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename traits<XprType>::PointerType PointerType;
+};
+
+template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
+struct eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, Eigen::Dense> {
+  typedef const TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>& type;
+};
+
+template <typename FFT, typename XprType, int FFTResultType, int FFTDirection>
+struct nested<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection>, 1, typename eval<TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> >::type> {
+  typedef TensorFFTOp<FFT, XprType, FFTResultType, FFTDirection> type;
+};
+
+}  // end namespace internal
+
+template <typename FFT, typename XprType, int FFTResultType, int FFTDir>
+class TensorFFTOp : public TensorBase<TensorFFTOp<FFT, XprType, FFTResultType, FFTDir>, ReadOnlyAccessors> {
+ public:
+  typedef typename Eigen::internal::traits<TensorFFTOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename std::complex<RealScalar> ComplexScalar;
+  typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
+  typedef OutputScalar CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorFFTOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorFFTOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorFFTOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFFTOp(const XprType& expr, const FFT& fft)
+      : m_xpr(expr), m_fft(fft) {}
+
+  EIGEN_DEVICE_FUNC
+  const FFT& fft() const { return m_fft; }
+
+  EIGEN_DEVICE_FUNC
+  const typename internal::remove_all<typename XprType::Nested>::type& expression() const {
+    return m_xpr;
+  }
+
+ protected:
+  typename XprType::Nested m_xpr;
+  const FFT m_fft;
+};
+
+// Eval as rvalue
+template <typename FFT, typename ArgType, typename Device, int FFTResultType, int FFTDir>
+struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, Device> {
+  typedef TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename std::complex<RealScalar> ComplexScalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  typedef internal::traits<XprType> XprTraits;
+  typedef typename XprTraits::Scalar InputScalar;
+  typedef typename internal::conditional<FFTResultType == RealPart || FFTResultType == ImagPart, RealScalar, ComplexScalar>::type OutputScalar;
+  typedef OutputScalar CoeffReturnType;
+  typedef typename PacketType<OutputScalar, Device>::type PacketReturnType;
+  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+    typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = true,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_fft(op.fft()), m_impl(op.expression(), device), m_data(NULL), m_device(device) {
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      eigen_assert(input_dims[i] > 0);
+      m_dimensions[i] = input_dims[i];
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+      }
+    }
+    m_size = m_dimensions.TotalSize();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_dimensions;
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    if (data) {
+      evalToBuf(data);
+      return false;
+    } else {
+      m_data = (EvaluatorPointerType)m_device.get((CoeffReturnType*)(m_device.allocate_temp(sizeof(CoeffReturnType) * m_size)));
+      evalToBuf(m_data);
+      return true;
+    }
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_data) {
+      m_device.deallocate(m_data);
+      m_data = NULL;
+    }
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const {
+    return m_data[index];
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType
+  packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_data.bind(cgh);
+  }
+#endif
+
+ private:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalToBuf(EvaluatorPointerType data) {
+    const bool write_to_out = internal::is_same<OutputScalar, ComplexScalar>::value;
+    ComplexScalar* buf = write_to_out ? (ComplexScalar*)data : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * m_size);
+
+    for (Index i = 0; i < m_size; ++i) {
+      buf[i] = MakeComplex<internal::is_same<InputScalar, RealScalar>::value>()(m_impl.coeff(i));
+    }
+
+    for (size_t i = 0; i < m_fft.size(); ++i) {
+      Index dim = m_fft[i];
+      eigen_assert(dim >= 0 && dim < NumDims);
+      Index line_len = m_dimensions[dim];
+      eigen_assert(line_len >= 1);
+      ComplexScalar* line_buf = (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * line_len);
+      const bool is_power_of_two = isPowerOfTwo(line_len);
+      const Index good_composite = is_power_of_two ? 0 : findGoodComposite(line_len);
+      const Index log_len = is_power_of_two ? getLog2(line_len) : getLog2(good_composite);
+
+      ComplexScalar* a = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
+      ComplexScalar* b = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * good_composite);
+      ComplexScalar* pos_j_base_powered = is_power_of_two ? NULL : (ComplexScalar*)m_device.allocate(sizeof(ComplexScalar) * (line_len + 1));
+      if (!is_power_of_two) {
+        // Compute twiddle factors
+        //   t_n = exp(sqrt(-1) * pi * n^2 / line_len)
+        // for n = 0, 1,..., line_len-1.
+        // For n > 2 we use the recurrence t_n = t_{n-1}^2 / t_{n-2} * t_1^2
+
+        // The recurrence is correct in exact arithmetic, but causes
+        // numerical issues for large transforms, especially in
+        // single-precision floating point.
+        //
+        // pos_j_base_powered[0] = ComplexScalar(1, 0);
+        // if (line_len > 1) {
+        //   const ComplexScalar pos_j_base = ComplexScalar(
+        //       numext::cos(M_PI / line_len), numext::sin(M_PI / line_len));
+        //   pos_j_base_powered[1] = pos_j_base;
+        //   if (line_len > 2) {
+        //     const ComplexScalar pos_j_base_sq = pos_j_base * pos_j_base;
+        //     for (int i = 2; i < line_len + 1; ++i) {
+        //       pos_j_base_powered[i] = pos_j_base_powered[i - 1] *
+        //           pos_j_base_powered[i - 1] /
+        //           pos_j_base_powered[i - 2] *
+        //           pos_j_base_sq;
+        //     }
+        //   }
+        // }
+        // TODO(rmlarsen): Find a way to use Eigen's vectorized sin
+        // and cosine functions here.
+        for (int j = 0; j < line_len + 1; ++j) {
+          double arg = ((EIGEN_PI * j) * j) / line_len;
+          std::complex<double> tmp(numext::cos(arg), numext::sin(arg));
+          pos_j_base_powered[j] = static_cast<ComplexScalar>(tmp);
+        }
+      }
+
+      for (Index partial_index = 0; partial_index < m_size / line_len; ++partial_index) {
+        const Index base_offset = getBaseOffsetFromIndex(partial_index, dim);
+
+        // get data into line_buf
+        const Index stride = m_strides[dim];
+        if (stride == 1) {
+          m_device.memcpy(line_buf, &buf[base_offset], line_len*sizeof(ComplexScalar));
+        } else {
+          Index offset = base_offset;
+          for (int j = 0; j < line_len; ++j, offset += stride) {
+            line_buf[j] = buf[offset];
+          }
+        }
+
+        // process the line
+        if (is_power_of_two) {
+          processDataLineCooleyTukey(line_buf, line_len, log_len);
+        }
+        else {
+          processDataLineBluestein(line_buf, line_len, good_composite, log_len, a, b, pos_j_base_powered);
+        }
+
+        // write back
+        if (FFTDir == FFT_FORWARD && stride == 1) {
+          m_device.memcpy(&buf[base_offset], line_buf, line_len*sizeof(ComplexScalar));
+        } else {
+          Index offset = base_offset;
+          const ComplexScalar div_factor =  ComplexScalar(1.0 / line_len, 0);
+          for (int j = 0; j < line_len; ++j, offset += stride) {
+             buf[offset] = (FFTDir == FFT_FORWARD) ? line_buf[j] : line_buf[j] * div_factor;
+          }
+        }
+      }
+      m_device.deallocate(line_buf);
+      if (!is_power_of_two) {
+        m_device.deallocate(a);
+        m_device.deallocate(b);
+        m_device.deallocate(pos_j_base_powered);
+      }
+    }
+
+    if(!write_to_out) {
+      for (Index i = 0; i < m_size; ++i) {
+        data[i] = PartOf<FFTResultType>()(buf[i]);
+      }
+      m_device.deallocate(buf);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static bool isPowerOfTwo(Index x) {
+    eigen_assert(x > 0);
+    return !(x & (x - 1));
+  }
+
+  // The composite number for padding, used in Bluestein's FFT algorithm
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index findGoodComposite(Index n) {
+    Index i = 2;
+    while (i < 2 * n - 1) i *= 2;
+    return i;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Index getLog2(Index m) {
+    Index log2m = 0;
+    while (m >>= 1) log2m++;
+    return log2m;
+  }
+
+  // Call Cooley Tukey algorithm directly, data length must be power of 2
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineCooleyTukey(ComplexScalar* line_buf, Index line_len, Index log_len) {
+    eigen_assert(isPowerOfTwo(line_len));
+    scramble_FFT(line_buf, line_len);
+    compute_1D_Butterfly<FFTDir>(line_buf, line_len, log_len);
+  }
+
+  // Call Bluestein's FFT algorithm, m is a good composite number greater than (2 * n - 1), used as the padding length
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void processDataLineBluestein(ComplexScalar* line_buf, Index line_len, Index good_composite, Index log_len, ComplexScalar* a, ComplexScalar* b, const ComplexScalar* pos_j_base_powered) {
+    Index n = line_len;
+    Index m = good_composite;
+    ComplexScalar* data = line_buf;
+
+    for (Index i = 0; i < n; ++i) {
+      if(FFTDir == FFT_FORWARD) {
+        a[i] = data[i] * numext::conj(pos_j_base_powered[i]);
+      }
+      else {
+        a[i] = data[i] * pos_j_base_powered[i];
+      }
+    }
+    for (Index i = n; i < m; ++i) {
+      a[i] = ComplexScalar(0, 0);
+    }
+
+    for (Index i = 0; i < n; ++i) {
+      if(FFTDir == FFT_FORWARD) {
+        b[i] = pos_j_base_powered[i];
+      }
+      else {
+        b[i] = numext::conj(pos_j_base_powered[i]);
+      }
+    }
+    for (Index i = n; i < m - n; ++i) {
+      b[i] = ComplexScalar(0, 0);
+    }
+    for (Index i = m - n; i < m; ++i) {
+      if(FFTDir == FFT_FORWARD) {
+        b[i] = pos_j_base_powered[m-i];
+      }
+      else {
+        b[i] = numext::conj(pos_j_base_powered[m-i]);
+      }
+    }
+
+    scramble_FFT(a, m);
+    compute_1D_Butterfly<FFT_FORWARD>(a, m, log_len);
+
+    scramble_FFT(b, m);
+    compute_1D_Butterfly<FFT_FORWARD>(b, m, log_len);
+
+    for (Index i = 0; i < m; ++i) {
+      a[i] *= b[i];
+    }
+
+    scramble_FFT(a, m);
+    compute_1D_Butterfly<FFT_REVERSE>(a, m, log_len);
+
+    //Do the scaling after ifft
+    for (Index i = 0; i < m; ++i) {
+      a[i] /= m;
+    }
+
+    for (Index i = 0; i < n; ++i) {
+      if(FFTDir == FFT_FORWARD) {
+        data[i] = a[i] * numext::conj(pos_j_base_powered[i]);
+      }
+      else {
+        data[i] = a[i] * pos_j_base_powered[i];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void scramble_FFT(ComplexScalar* data, Index n) {
+    eigen_assert(isPowerOfTwo(n));
+    Index j = 1;
+    for (Index i = 1; i < n; ++i){
+      if (j > i) {
+        std::swap(data[j-1], data[i-1]);
+      }
+      Index m = n >> 1;
+      while (m >= 2 && j > m) {
+        j -= m;
+        m >>= 1;
+      }
+      j += m;
+    }
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_2(ComplexScalar* data) {
+    ComplexScalar tmp = data[1];
+    data[1] = data[0] - data[1];
+    data[0] += tmp;
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_4(ComplexScalar* data) {
+    ComplexScalar tmp[4];
+    tmp[0] = data[0] + data[1];
+    tmp[1] = data[0] - data[1];
+    tmp[2] = data[2] + data[3];
+    if (Dir == FFT_FORWARD) {
+      tmp[3] = ComplexScalar(0.0, -1.0) * (data[2] - data[3]);
+    } else {
+      tmp[3] = ComplexScalar(0.0, 1.0) * (data[2] - data[3]);
+    }
+    data[0] = tmp[0] + tmp[2];
+    data[1] = tmp[1] + tmp[3];
+    data[2] = tmp[0] - tmp[2];
+    data[3] = tmp[1] - tmp[3];
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_8(ComplexScalar* data) {
+    ComplexScalar tmp_1[8];
+    ComplexScalar tmp_2[8];
+
+    tmp_1[0] = data[0] + data[1];
+    tmp_1[1] = data[0] - data[1];
+    tmp_1[2] = data[2] + data[3];
+    if (Dir == FFT_FORWARD) {
+      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, -1);
+    } else {
+      tmp_1[3] = (data[2] - data[3]) * ComplexScalar(0, 1);
+    }
+    tmp_1[4] = data[4] + data[5];
+    tmp_1[5] = data[4] - data[5];
+    tmp_1[6] = data[6] + data[7];
+    if (Dir == FFT_FORWARD) {
+      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, -1);
+    } else {
+      tmp_1[7] = (data[6] - data[7]) * ComplexScalar(0, 1);
+    }
+    tmp_2[0] = tmp_1[0] + tmp_1[2];
+    tmp_2[1] = tmp_1[1] + tmp_1[3];
+    tmp_2[2] = tmp_1[0] - tmp_1[2];
+    tmp_2[3] = tmp_1[1] - tmp_1[3];
+    tmp_2[4] = tmp_1[4] + tmp_1[6];
+// SQRT2DIV2 = sqrt(2)/2
+#define SQRT2DIV2 0.7071067811865476
+    if (Dir == FFT_FORWARD) {
+      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, -SQRT2DIV2);
+      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, -1);
+      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, -SQRT2DIV2);
+    } else {
+      tmp_2[5] = (tmp_1[5] + tmp_1[7]) * ComplexScalar(SQRT2DIV2, SQRT2DIV2);
+      tmp_2[6] = (tmp_1[4] - tmp_1[6]) * ComplexScalar(0, 1);
+      tmp_2[7] = (tmp_1[5] - tmp_1[7]) * ComplexScalar(-SQRT2DIV2, SQRT2DIV2);
+    }
+    data[0] = tmp_2[0] + tmp_2[4];
+    data[1] = tmp_2[1] + tmp_2[5];
+    data[2] = tmp_2[2] + tmp_2[6];
+    data[3] = tmp_2[3] + tmp_2[7];
+    data[4] = tmp_2[0] - tmp_2[4];
+    data[5] = tmp_2[1] - tmp_2[5];
+    data[6] = tmp_2[2] - tmp_2[6];
+    data[7] = tmp_2[3] - tmp_2[7];
+  }
+
+  template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void butterfly_1D_merge(
+      ComplexScalar* data, Index n, Index n_power_of_2) {
+    // Original code:
+    // RealScalar wtemp = std::sin(M_PI/n);
+    // RealScalar wpi =  -std::sin(2 * M_PI/n);
+    const RealScalar wtemp = m_sin_PI_div_n_LUT[n_power_of_2];
+    const RealScalar wpi = (Dir == FFT_FORWARD)
+                               ? m_minus_sin_2_PI_div_n_LUT[n_power_of_2]
+                               : -m_minus_sin_2_PI_div_n_LUT[n_power_of_2];
+
+    const ComplexScalar wp(wtemp, wpi);
+    const ComplexScalar wp_one = wp + ComplexScalar(1, 0);
+    const ComplexScalar wp_one_2 = wp_one * wp_one;
+    const ComplexScalar wp_one_3 = wp_one_2 * wp_one;
+    const ComplexScalar wp_one_4 = wp_one_3 * wp_one;
+    const Index n2 = n / 2;
+    ComplexScalar w(1.0, 0.0);
+    for (Index i = 0; i < n2; i += 4) {
+       ComplexScalar temp0(data[i + n2] * w);
+       ComplexScalar temp1(data[i + 1 + n2] * w * wp_one);
+       ComplexScalar temp2(data[i + 2 + n2] * w * wp_one_2);
+       ComplexScalar temp3(data[i + 3 + n2] * w * wp_one_3);
+       w = w * wp_one_4;
+
+       data[i + n2] = data[i] - temp0;
+       data[i] += temp0;
+
+       data[i + 1 + n2] = data[i + 1] - temp1;
+       data[i + 1] += temp1;
+
+       data[i + 2 + n2] = data[i + 2] - temp2;
+       data[i + 2] += temp2;
+
+       data[i + 3 + n2] = data[i + 3] - temp3;
+       data[i + 3] += temp3;
+    }
+  }
+
+ template <int Dir>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_1D_Butterfly(
+      ComplexScalar* data, Index n, Index n_power_of_2) {
+    eigen_assert(isPowerOfTwo(n));
+    if (n > 8) {
+      compute_1D_Butterfly<Dir>(data, n / 2, n_power_of_2 - 1);
+      compute_1D_Butterfly<Dir>(data + n / 2, n / 2, n_power_of_2 - 1);
+      butterfly_1D_merge<Dir>(data, n, n_power_of_2);
+    } else if (n == 8) {
+      butterfly_8<Dir>(data);
+    } else if (n == 4) {
+      butterfly_4<Dir>(data);
+    } else if (n == 2) {
+      butterfly_2<Dir>(data);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getBaseOffsetFromIndex(Index index, Index omitted_dim) const {
+    Index result = 0;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > omitted_dim; --i) {
+        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
+        const Index idx = index / partial_m_stride;
+        index -= idx * partial_m_stride;
+        result += idx * m_strides[i];
+      }
+      result += index;
+    }
+    else {
+      for (Index i = 0; i < omitted_dim; ++i) {
+        const Index partial_m_stride = m_strides[i] / m_dimensions[omitted_dim];
+        const Index idx = index / partial_m_stride;
+        index -= idx * partial_m_stride;
+        result += idx * m_strides[i];
+      }
+      result += index;
+    }
+    // Value of index_coords[omitted_dim] is not determined to this step
+    return result;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index getIndexFromOffset(Index base, Index omitted_dim, Index offset) const {
+    Index result = base + offset * m_strides[omitted_dim] ;
+    return result;
+  }
+
+ protected:
+  Index m_size;
+  const FFT EIGEN_DEVICE_REF m_fft;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  EvaluatorPointerType m_data;
+  const Device EIGEN_DEVICE_REF m_device;
+
+  // This will support a maximum FFT size of 2^32 for each dimension
+  // m_sin_PI_div_n_LUT[i] = (-2) * std::sin(M_PI / std::pow(2,i)) ^ 2;
+  const RealScalar m_sin_PI_div_n_LUT[32] = {
+    RealScalar(0.0),
+    RealScalar(-2),
+    RealScalar(-0.999999999999999),
+    RealScalar(-0.292893218813453),
+    RealScalar(-0.0761204674887130),
+    RealScalar(-0.0192147195967696),
+    RealScalar(-0.00481527332780311),
+    RealScalar(-0.00120454379482761),
+    RealScalar(-3.01181303795779e-04),
+    RealScalar(-7.52981608554592e-05),
+    RealScalar(-1.88247173988574e-05),
+    RealScalar(-4.70619042382852e-06),
+    RealScalar(-1.17654829809007e-06),
+    RealScalar(-2.94137117780840e-07),
+    RealScalar(-7.35342821488550e-08),
+    RealScalar(-1.83835707061916e-08),
+    RealScalar(-4.59589268710903e-09),
+    RealScalar(-1.14897317243732e-09),
+    RealScalar(-2.87243293150586e-10),
+    RealScalar( -7.18108232902250e-11),
+    RealScalar(-1.79527058227174e-11),
+    RealScalar(-4.48817645568941e-12),
+    RealScalar(-1.12204411392298e-12),
+    RealScalar(-2.80511028480785e-13),
+    RealScalar(-7.01277571201985e-14),
+    RealScalar(-1.75319392800498e-14),
+    RealScalar(-4.38298482001247e-15),
+    RealScalar(-1.09574620500312e-15),
+    RealScalar(-2.73936551250781e-16),
+    RealScalar(-6.84841378126949e-17),
+    RealScalar(-1.71210344531737e-17),
+    RealScalar(-4.28025861329343e-18)
+  };
+
+  // m_minus_sin_2_PI_div_n_LUT[i] = -std::sin(2 * M_PI / std::pow(2,i));
+  const RealScalar m_minus_sin_2_PI_div_n_LUT[32] = {
+    RealScalar(0.0),
+    RealScalar(0.0),
+    RealScalar(-1.00000000000000e+00),
+    RealScalar(-7.07106781186547e-01),
+    RealScalar(-3.82683432365090e-01),
+    RealScalar(-1.95090322016128e-01),
+    RealScalar(-9.80171403295606e-02),
+    RealScalar(-4.90676743274180e-02),
+    RealScalar(-2.45412285229123e-02),
+    RealScalar(-1.22715382857199e-02),
+    RealScalar(-6.13588464915448e-03),
+    RealScalar(-3.06795676296598e-03),
+    RealScalar(-1.53398018628477e-03),
+    RealScalar(-7.66990318742704e-04),
+    RealScalar(-3.83495187571396e-04),
+    RealScalar(-1.91747597310703e-04),
+    RealScalar(-9.58737990959773e-05),
+    RealScalar(-4.79368996030669e-05),
+    RealScalar(-2.39684498084182e-05),
+    RealScalar(-1.19842249050697e-05),
+    RealScalar(-5.99211245264243e-06),
+    RealScalar(-2.99605622633466e-06),
+    RealScalar(-1.49802811316901e-06),
+    RealScalar(-7.49014056584716e-07),
+    RealScalar(-3.74507028292384e-07),
+    RealScalar(-1.87253514146195e-07),
+    RealScalar(-9.36267570730981e-08),
+    RealScalar(-4.68133785365491e-08),
+    RealScalar(-2.34066892682746e-08),
+    RealScalar(-1.17033446341373e-08),
+    RealScalar(-5.85167231706864e-09),
+    RealScalar(-2.92583615853432e-09)
+  };
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_FFT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
new file mode 100644
index 0000000..ca39bb8
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFixedSize.h
@@ -0,0 +1,379 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
+
+namespace Eigen {
+
+/** \class TensorFixedSize
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The fixed sized version of the tensor class.
+  *
+  * The fixed sized equivalent of
+  * Eigen::Tensor<float, 3> t(3, 5, 7);
+  * is
+  * Eigen::TensorFixedSize<float, Sizes<3,5,7>> t;
+  */
+
+template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
+class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> >
+{
+  public:
+    typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self;
+    typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > Base;
+    typedef typename Eigen::internal::nested<Self>::type Nested;
+    typedef typename internal::traits<Self>::StorageKind StorageKind;
+    typedef typename internal::traits<Self>::Index Index;
+    typedef Scalar_ Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+    static const int Options = Options_;
+
+    enum {
+      IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0),
+      PacketAccess = (internal::packet_traits<Scalar>::size > 1),
+      BlockAccess = false,
+      PreferBlockAccess = false,
+      Layout = Options_ & RowMajor ? RowMajor : ColMajor,
+      CoordAccess = true,
+      RawAccess = true
+    };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  typedef Dimensions_ Dimensions;
+  static const std::size_t NumIndices = Dimensions::count;
+
+  protected:
+  TensorStorage<Scalar, Dimensions, Options> m_storage;
+
+  public:
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    rank()                   const { return NumIndices; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions&        dimensions()             const { return m_storage.dimensions(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index                    size()                   const { return m_storage.size(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar                   *data()                        { return m_storage.data(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar             *data()                  const { return m_storage.data(); }
+
+    // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    // work, because that uses base().coeffRef() - and we don't yet
+    // implement a similar class hierarchy
+    inline Self& base()             { return *this; }
+    inline const Self& base() const { return *this; }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
+    {
+      eigen_internal_assert(checkIndexRange(indices));
+      return m_storage.data()[linearizedIndex(indices)];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_storage.data()[index];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& coeff() const
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return m_storage.data()[0];
+    }
+
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
+    {
+      eigen_internal_assert(checkIndexRange(indices));
+      return m_storage.data()[linearizedIndex(indices)];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_storage.data()[index];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return m_storage.data()[0];
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
+    {
+      if (Options&RowMajor) {
+        const Index index = i1 + i0 * m_storage.dimensions()[1];
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + i1 * m_storage.dimensions()[0];
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
+    {
+      if (Options&RowMajor) {
+         const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
+         return m_storage.data()[index];
+      } else {
+         const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
+    {
+      if (Options&RowMajor) {
+        const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
+    {
+      if (Options&RowMajor) {
+        const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
+        return m_storage.data()[index];
+      }
+    }
+#endif
+
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
+    {
+      eigen_assert(checkIndexRange(indices));
+      return coeff(indices);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return coeff(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator()() const
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeff();
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
+    {
+      // The bracket operator is only for vectors, use the parenthesis operator instead.
+      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeff(index);
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
+    {
+      // The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
+    {
+       if (Options&RowMajor) {
+         const Index index = i1 + i0 * m_storage.dimensions()[1];
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + i1 * m_storage.dimensions()[0];
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
+    {
+       if (Options&RowMajor) {
+         const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
+        return m_storage.data()[index];
+      } else {
+         const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
+    {
+      if (Options&RowMajor) {
+        const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
+        return m_storage.data()[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
+    {
+      if (Options&RowMajor) {
+        const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
+        return m_storage.data()[index];
+      } else {
+        const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
+        return m_storage.data()[index];
+      }
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
+    {
+      eigen_assert(checkIndexRange(indices));
+      return coeffRef(indices);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index index)
+    {
+      eigen_assert(index >= 0 && index < size());
+      return coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return coeffRef();
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator[](Index index)
+    {
+      // The bracket operator is only for vectors, use the parenthesis operator instead
+      EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorFixedSize()
+      : m_storage()
+    {
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorFixedSize(const Self& other)
+      : m_storage(other.m_storage)
+    {
+    }
+
+#if EIGEN_HAS_RVALUE_REFERENCES
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(Self&& other)
+      : m_storage(other.m_storage)
+    {
+    }
+#endif
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
+    {
+      typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
+      Assign assign(*this, other.derived());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    }
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other)
+    {
+      typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
+      Assign assign(*this, other.derived());
+      internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
+    }
+
+    // FIXME: check that the dimensions of other match the dimensions of *this.
+    // Unfortunately this isn't possible yet when the rhs is an expression.
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(TensorFixedSize)
+
+
+  protected:
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const
+    {
+      using internal::array_apply_and_reduce;
+      using internal::array_zip_and_reduce;
+      using internal::greater_equal_zero_op;
+      using internal::logical_and_op;
+      using internal::lesser_op;
+
+      return true;
+        // check whether the indices are all >= 0
+          /*       array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
+        // check whether the indices fit in the dimensions
+        array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());*/
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
+    {
+      if (Options&RowMajor) {
+        return m_storage.dimensions().IndexOfRowMajor(indices);
+      } else {
+        return m_storage.dimensions().IndexOfColMajor(indices);
+      }
+    }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
new file mode 100644
index 0000000..e800ded
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h
@@ -0,0 +1,237 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
+
+namespace Eigen {
+
+/** \class TensorForcedEval
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reshaping class.
+  *
+  *
+  */
+namespace internal {
+template<typename XprType>
+struct traits<TensorForcedEvalOp<XprType> >
+{
+  // Type promotion to handle the case where the types of the lhs and the rhs are different.
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename XprType>
+struct eval<TensorForcedEvalOp<XprType>, Eigen::Dense>
+{
+  typedef const TensorForcedEvalOp<XprType>& type;
+};
+
+template<typename XprType>
+struct nested<TensorForcedEvalOp<XprType>, 1, typename eval<TensorForcedEvalOp<XprType> >::type>
+{
+  typedef TensorForcedEvalOp<XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename XprType>
+class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorForcedEvalOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorForcedEvalOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorForcedEvalOp(const XprType& expr)
+      : m_xpr(expr) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+};
+
+namespace internal {
+template <typename Device, typename CoeffReturnType>
+struct non_integral_type_placement_new{
+  template <typename StorageType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index numValues, StorageType m_buffer) {
+   // Initialize non-trivially constructible types.
+    if (!internal::is_arithmetic<CoeffReturnType>::value) {
+      for (Index i = 0; i < numValues; ++i) new (m_buffer + i) CoeffReturnType();
+    }
+}
+};
+
+// SYCL does not support non-integral types 
+// having new (m_buffer + i) CoeffReturnType() causes the following compiler error for SYCL Devices 
+// no matching function for call to 'operator new'
+template <typename CoeffReturnType>
+struct non_integral_type_placement_new<Eigen::SyclDevice, CoeffReturnType> {
+  template <typename StorageType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(Index, StorageType) {
+}
+};
+} // end namespace internal
+
+template<typename ArgType_, typename Device>
+struct TensorEvaluator<const TensorForcedEvalOp<ArgType_>, Device>
+{
+  typedef const typename internal::remove_all<ArgType_>::type ArgType;
+  typedef TensorForcedEvalOp<ArgType> XprType;
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = true,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = internal::is_arithmetic<CoeffReturnType>::value,
+    PreferBlockAccess = false,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess         = true
+  };
+
+  static const int NumDims = internal::traits<ArgType>::NumDimensions;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_op(op.expression()),
+      m_device(device), m_buffer(NULL)
+  { }
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_impl.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    const Index numValues =  internal::array_prod(m_impl.dimensions());
+    m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(numValues * sizeof(CoeffReturnType)));
+
+   internal::non_integral_type_placement_new<Device, CoeffReturnType>()(numValues, m_buffer);
+
+    typedef TensorEvalToOp< const typename internal::remove_const<ArgType>::type > EvalTo;
+    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
+
+    internal::TensorExecutor<
+        const EvalTo, typename internal::remove_const<Device>::type,
+        /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
+        /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
+        run(evalToTmp, m_device);
+
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    const Index numValues = internal::array_prod(m_impl.dimensions());
+    m_buffer = m_device.get((CoeffReturnType*)m_device.allocate_temp(
+        numValues * sizeof(CoeffReturnType)));
+    typedef TensorEvalToOp<const typename internal::remove_const<ArgType>::type>
+        EvalTo;
+    EvalTo evalToTmp(m_device.get(m_buffer), m_op);
+
+    auto on_done = std::bind([](EvalSubExprsCallback done_) { done_(true); },
+                             std::move(done));
+    internal::TensorAsyncExecutor<
+        const EvalTo, typename internal::remove_const<Device>::type,
+        decltype(on_done),
+        /*Vectorizable=*/internal::IsVectorizable<Device, const ArgType>::value,
+        /*Tiling=*/internal::IsTileable<Device, const ArgType>::value>::
+        runAsync(evalToTmp, m_device, std::move(on_done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_device.deallocate_temp(m_buffer);
+    m_buffer = NULL;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_buffer[index];
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_buffer + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    assert(m_buffer != NULL);
+    return TensorBlock::materialize(m_buffer, m_impl.dimensions(), desc, scratch);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  EvaluatorPointerType data() const { return m_buffer; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_buffer.bind(cgh);
+    m_impl.bind(cgh);
+  }
+#endif
+ private:
+  TensorEvaluator<ArgType, Device> m_impl;
+  const ArgType m_op;
+  const Device EIGEN_DEVICE_REF m_device;
+  EvaluatorPointerType m_buffer;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_FORCED_EVAL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
new file mode 100644
index 0000000..246ebe4
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
@@ -0,0 +1,191 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
+
+namespace Eigen {
+
+// MakePointer class is used as a container of the address space of the pointer
+// on the host and on the device. From the host side it generates the T* pointer
+// and when EIGEN_USE_SYCL is used it construct a buffer with a map_allocator to
+// T* m_data on the host. It is always called on the device.
+// Specialisation of MakePointer class for creating the sycl buffer with
+// map_allocator.
+template<typename T> struct MakePointer {
+  typedef T* Type;
+  typedef const T* ConstType;
+};
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T* constCast(const T* data) {
+  return const_cast<T*>(data);
+}
+
+// The StorageMemory class is a container of the device specific pointer
+// used for refering to a Pointer on TensorEvaluator class. While the TensorExpression
+// is a device-agnostic type and need MakePointer class for type conversion,
+// the TensorEvaluator class can be specialized for a device, hence it is possible
+// to construct different types of temproray storage memory in TensorEvaluator
+// for different devices by specializing the following StorageMemory class.
+template<typename T, typename device> struct StorageMemory: MakePointer <T> {};
+
+namespace internal{
+template<typename A, typename B> struct Pointer_type_promotion {
+  static const bool val=false;
+};
+template<typename A> struct Pointer_type_promotion<A, A> {
+  static const bool val = true;
+};
+template<typename A, typename B> struct TypeConversion {
+  typedef A* type;
+};
+}
+
+
+template<typename PlainObjectType, int Options_ = Unaligned, template <class> class MakePointer_ = MakePointer> class TensorMap;
+template<typename Scalar_, int NumIndices_, int Options_ = 0, typename IndexType = DenseIndex> class Tensor;
+template<typename Scalar_, typename Dimensions, int Options_ = 0, typename IndexType = DenseIndex> class TensorFixedSize;
+template<typename PlainObjectType> class TensorRef;
+template<typename Derived, int AccessLevel> class TensorBase;
+
+template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryOp;
+template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
+template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
+template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> class TensorCwiseTernaryOp;
+template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer > class TensorReductionOp;
+template<typename XprType> class TensorIndexTupleOp;
+template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp;
+template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
+template<typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType> class TensorContractionOp;
+template<typename TargetType, typename XprType> class TensorConversionOp;
+template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
+template<typename FFT, typename XprType, int FFTDataType, int FFTDirection> class TensorFFTOp;
+template<typename PatchDim, typename XprType> class TensorPatchOp;
+template<DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorImagePatchOp;
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorVolumePatchOp;
+template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
+template<DenseIndex DimId, typename XprType> class TensorChippingOp;
+template<typename NewDimensions, typename XprType> class TensorReshapingOp;
+template<typename XprType> class TensorLayoutSwapOp;
+template<typename StartIndices, typename Sizes, typename XprType> class TensorSlicingOp;
+template<typename ReverseDimensions, typename XprType> class TensorReverseOp;
+template<typename PaddingDimensions, typename XprType> class TensorPaddingOp;
+template<typename Shuffle, typename XprType> class TensorShufflingOp;
+template<typename Strides, typename XprType> class TensorStridingOp;
+template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> class TensorStridingSlicingOp;
+template<typename Strides, typename XprType> class TensorInflationOp;
+template<typename Generator, typename XprType> class TensorGeneratorOp;
+template<typename LeftXprType, typename RightXprType> class TensorAssignOp;
+template<typename Op, typename XprType> class TensorScanOp;
+template<typename Dims, typename XprType> class TensorTraceOp;
+
+template<typename CustomUnaryFunc, typename XprType> class TensorCustomUnaryOp;
+template<typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> class TensorCustomBinaryOp;
+
+template<typename XprType, template <class> class MakePointer_ = MakePointer> class TensorEvalToOp;
+template<typename XprType> class TensorForcedEvalOp;
+
+template<typename ExpressionType, typename DeviceType> class TensorDevice;
+template<typename ExpressionType, typename DeviceType, typename DoneCallback> class TensorAsyncDevice;
+template<typename Derived, typename Device> struct TensorEvaluator;
+
+struct NoOpOutputKernel;
+
+struct DefaultDevice;
+struct ThreadPoolDevice;
+struct GpuDevice;
+struct SyclDevice;
+
+#ifdef EIGEN_USE_SYCL
+
+template <typename T> struct MakeSYCLPointer {
+  typedef Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T> Type;
+};
+
+template <typename T>
+EIGEN_STRONG_INLINE const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>&
+constCast(const Eigen::TensorSycl::internal::RangeAccess<cl::sycl::access::mode::read_write, T>& data) {
+  return data;
+}
+
+template <typename T>
+struct StorageMemory<T, SyclDevice> : MakeSYCLPointer<T> {};
+template <typename T>
+struct StorageMemory<T, const SyclDevice> : StorageMemory<T, SyclDevice> {};
+
+namespace TensorSycl {
+namespace internal{
+template <typename Evaluator, typename Op> class GenericNondeterministicReducer;
+}
+}
+#endif
+
+
+enum FFTResultType {
+  RealPart = 0,
+  ImagPart = 1,
+  BothParts = 2
+};
+
+enum FFTDirection {
+    FFT_FORWARD = 0,
+    FFT_REVERSE = 1
+};
+
+
+namespace internal {
+
+template <typename Device, typename Expression>
+struct IsVectorizable {
+  static const bool value = TensorEvaluator<Expression, Device>::PacketAccess;
+};
+
+template <typename Expression>
+struct IsVectorizable<GpuDevice, Expression> {
+  static const bool value = TensorEvaluator<Expression, GpuDevice>::PacketAccess &&
+                            TensorEvaluator<Expression, GpuDevice>::IsAligned;
+};
+
+// Tiled evaluation strategy.
+enum TiledEvaluation {
+  Off = 0,    // tiled evaluation is not supported
+  On = 1,     // still work in progress (see TensorBlock.h)
+};
+
+template <typename Device, typename Expression>
+struct IsTileable {
+  // Check that block evaluation is supported and it's a preferred option (at
+  // least one sub-expression has much faster block evaluation, e.g.
+  // broadcasting).
+  static const bool BlockAccess =
+      TensorEvaluator<Expression, Device>::BlockAccess &&
+      TensorEvaluator<Expression, Device>::PreferBlockAccess;
+
+  static const TiledEvaluation value =
+      BlockAccess ? TiledEvaluation::On : TiledEvaluation::Off;
+};
+
+template <typename Expression, typename Device,
+          bool Vectorizable      = IsVectorizable<Device, Expression>::value,
+          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
+class TensorExecutor;
+
+template <typename Expression, typename Device, typename DoneCallback,
+          bool Vectorizable = IsVectorizable<Device, Expression>::value,
+          TiledEvaluation Tiling = IsTileable<Device, Expression>::value>
+class TensorAsyncExecutor;
+
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_FORWARD_DECLARATIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
new file mode 100644
index 0000000..d963032
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h
@@ -0,0 +1,488 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
+
+namespace Eigen {
+namespace internal {
+
+
+/** \internal
+ * \brief Template functor to compute the modulo between an array and a scalar.
+ */
+template <typename Scalar>
+struct scalar_mod_op {
+  EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a % m_divisor; }
+  const Scalar m_divisor;
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod_op<Scalar> >
+{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; };
+
+
+/** \internal
+ * \brief Template functor to compute the modulo between 2 arrays.
+ */
+template <typename Scalar>
+struct scalar_mod2_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_mod2_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a, const Scalar& b) const { return a % b; }
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod2_op<Scalar> >
+{ enum { Cost = scalar_div_cost<Scalar,false>::value, PacketAccess = false }; };
+
+template <typename Scalar>
+struct scalar_fmod_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar
+  operator()(const Scalar& a, const Scalar& b) const {
+    return numext::fmod(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_fmod_op<Scalar> > {
+  enum { Cost = 13,  // Reciprocal throughput of FPREM on Haswell.
+         PacketAccess = false };
+};
+
+template<typename Reducer, typename Device>
+struct reducer_traits {
+  enum {
+    Cost = 1,
+    PacketAccess = false,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+// Standard reduction functors
+template <typename T> struct SumReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    internal::scalar_sum_op<T> sum_op;
+    *accum = sum_op(*accum, t);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    (*accum) = padd<Packet>(*accum, p);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    return accum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_sum_op<T> sum_op;
+    return sum_op(saccum, predux(vaccum));
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<SumReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasAdd,
+    IsStateful = false,
+    IsExactlyAssociative = NumTraits<T>::IsInteger
+  };
+};
+
+template <typename T> struct MeanReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  MeanReducer() : scalarCount_(0), packetCount_(0) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) {
+    internal::scalar_sum_op<T> sum_op;
+    *accum = sum_op(*accum, t);
+    scalarCount_++;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) {
+    (*accum) = padd<Packet>(*accum, p);
+    packetCount_++;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    internal::scalar_quotient_op<T> quotient_op;
+    return quotient_op(accum, T(scalarCount_));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return pdiv(vaccum, pset1<Packet>(T(packetCount_)));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_sum_op<T> sum_op;
+    internal::scalar_quotient_op<T> quotient_op;
+    return quotient_op(
+        sum_op(saccum, predux(vaccum)),
+        T(scalarCount_ + packetCount_ * unpacket_traits<Packet>::size));
+  }
+
+  protected:
+    DenseIndex scalarCount_;
+    DenseIndex packetCount_;
+};
+
+template <typename T, typename Device>
+struct reducer_traits<MeanReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasAdd &&
+                   PacketType<T, Device>::HasDiv && !NumTraits<T>::IsInteger,
+    IsStateful = true,
+    IsExactlyAssociative = NumTraits<T>::IsInteger
+  };
+};
+
+
+template <typename T, bool IsMax = true, bool IsInteger = true>
+struct MinMaxBottomValue {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
+    return Eigen::NumTraits<T>::lowest();
+  }
+};
+template <typename T>
+struct MinMaxBottomValue<T, true, false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
+    return -Eigen::NumTraits<T>::infinity();
+  }
+};
+template <typename T>
+struct MinMaxBottomValue<T, false, true> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
+    return Eigen::NumTraits<T>::highest();
+  }
+};
+template <typename T>
+struct MinMaxBottomValue<T, false, false> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE T bottom_value() {
+    return Eigen::NumTraits<T>::infinity();
+  }
+};
+
+
+template <typename T, int NaNPropagation=PropagateFast> struct MaxReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    *accum = op(t, *accum);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    (*accum) = op.packetOp(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return MinMaxBottomValue<T, /*IsMax=*/true, Eigen::NumTraits<T>::IsInteger>::bottom_value();
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    return accum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    scalar_max_op<T, T, NaNPropagation> op;
+    return op(saccum, op.predux(vaccum));
+  }
+};
+
+template <typename T, typename Device, int NaNPropagation>
+    struct reducer_traits<MaxReducer<T, NaNPropagation>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasMax,
+    IsStateful = false,
+    IsExactlyAssociative = (NaNPropagation!=PropagateFast)
+  };
+};
+
+template <typename T, int NaNPropagation=PropagateFast> struct MinReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    *accum = op(t, *accum);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    (*accum) = op.packetOp(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return MinMaxBottomValue<T, /*IsMax=*/false, Eigen::NumTraits<T>::IsInteger>::bottom_value();
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    return accum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    scalar_min_op<T, T, NaNPropagation> op;
+    return op(saccum, op.predux(vaccum));
+  }
+};
+
+template <typename T, typename Device, int NaNPropagation>
+    struct reducer_traits<MinReducer<T, NaNPropagation>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = PacketType<T, Device>::HasMin,
+    IsStateful = false,
+    IsExactlyAssociative = (NaNPropagation!=PropagateFast)
+  };
+};
+
+template <typename T> struct ProdReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    internal::scalar_product_op<T> prod_op;
+    (*accum) = prod_op(*accum, t);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) const {
+    (*accum) = pmul<Packet>(*accum, p);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    internal::scalar_cast_op<int, T> conv;
+    return conv(1);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return pset1<Packet>(initialize());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
+    return accum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const {
+    internal::scalar_product_op<T> prod_op;
+    return prod_op(saccum, predux_mul(vaccum));
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ProdReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::MulCost,
+    PacketAccess = PacketType<T, Device>::HasMul,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+
+struct AndReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const {
+    *accum = *accum && t;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const {
+    return true;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const {
+    return accum;
+  }
+};
+
+template <typename Device>
+struct reducer_traits<AndReducer, Device> {
+  enum {
+    Cost = 1,
+    PacketAccess = false,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+
+struct OrReducer {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(bool t, bool* accum) const {
+    *accum = *accum || t;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool initialize() const {
+    return false;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool finalize(bool accum) const {
+    return accum;
+  }
+};
+
+template <typename Device>
+struct reducer_traits<OrReducer, Device> {
+  enum {
+    Cost = 1,
+    PacketAccess = false,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+// Argmin/Argmax reducers.  Returns the first occurrence if multiple locations
+// contain the same min/max value.
+template <typename T> struct ArgMaxTupleReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) const {
+    if (t.second < accum->second) {
+      return;
+    } else if (t.second > accum->second || accum->first > t.first ) {
+      *accum = t;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return T(0, NumTraits<typename T::second_type>::lowest());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const {
+    return accum;
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ArgMaxTupleReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = false,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+
+template <typename T> struct ArgMinTupleReducer
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T& t, T* accum) const {
+    if (t.second > accum->second) {
+      return;
+    } else if (t.second < accum->second || accum->first > t.first) {
+      *accum = t;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
+    return T(0, NumTraits<typename T::second_type>::highest());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T& accum) const {
+    return accum;
+  }
+};
+
+template <typename T, typename Device>
+struct reducer_traits<ArgMinTupleReducer<T>, Device> {
+  enum {
+    Cost = NumTraits<T>::AddCost,
+    PacketAccess = false,
+    IsStateful = false,
+    IsExactlyAssociative = true
+  };
+};
+
+
+template <typename T, typename Index, size_t NumDims>
+class GaussianGenerator {
+ public:
+  static const bool PacketAccess = false;
+
+  EIGEN_DEVICE_FUNC GaussianGenerator(const array<T, NumDims>& means,
+                                      const array<T, NumDims>& std_devs)
+      : m_means(means)
+  {
+    EIGEN_UNROLL_LOOP
+    for (size_t i = 0; i < NumDims; ++i) {
+      m_two_sigmas[i] = std_devs[i] * std_devs[i] * 2;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC T operator()(const array<Index, NumDims>& coordinates) const {
+    T tmp = T(0);
+    EIGEN_UNROLL_LOOP
+    for (size_t i = 0; i < NumDims; ++i) {
+      T offset = coordinates[i] - m_means[i];
+      tmp += offset * offset / m_two_sigmas[i];
+    }
+    return numext::exp(-tmp);
+  }
+
+ private:
+  array<T, NumDims> m_means;
+  array<T, NumDims> m_two_sigmas;
+};
+
+template <typename T, typename Index, size_t NumDims>
+struct functor_traits<GaussianGenerator<T, Index, NumDims> > {
+  enum {
+    Cost = NumDims * (2 * NumTraits<T>::AddCost + NumTraits<T>::MulCost +
+                      functor_traits<scalar_quotient_op<T, T> >::Cost) +
+           functor_traits<scalar_exp_op<T> >::Cost,
+    PacketAccess = GaussianGenerator<T, Index, NumDims>::PacketAccess
+  };
+};
+
+template <typename Scalar>
+struct scalar_clamp_op {
+  EIGEN_DEVICE_FUNC inline scalar_clamp_op(const Scalar& _min, const Scalar& _max) : m_min(_min), m_max(_max) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
+  operator()(const Scalar& x) const {
+    return numext::mini(numext::maxi(x, m_min), m_max);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
+  packetOp(const Packet& x) const {
+    return internal::pmin(internal::pmax(x, pset1<Packet>(m_min)), pset1<Packet>(m_max));
+  }
+  const Scalar m_min;
+  const Scalar m_max;
+};
+template<typename Scalar>
+struct functor_traits<scalar_clamp_op<Scalar> >
+{ enum { Cost = 2 * NumTraits<Scalar>::AddCost, PacketAccess = (packet_traits<Scalar>::HasMin && packet_traits<Scalar>::HasMax)}; };
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_FUNCTORS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
new file mode 100644
index 0000000..174bf06
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
@@ -0,0 +1,302 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
+#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
+
+namespace Eigen {
+
+/** \class TensorGeneratorOp
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor generator class.
+  *
+  *
+  */
+namespace internal {
+template<typename Generator, typename XprType>
+struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Generator, typename XprType>
+struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
+{
+  typedef const TensorGeneratorOp<Generator, XprType>& type;
+};
+
+template<typename Generator, typename XprType>
+struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type>
+{
+  typedef TensorGeneratorOp<Generator, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Generator, typename XprType>
+class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
+      : m_xpr(expr), m_generator(generator) {}
+
+    EIGEN_DEVICE_FUNC
+    const Generator& generator() const { return m_generator; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Generator m_generator;
+};
+
+
+// Eval as rvalue
+template<typename Generator, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
+{
+  typedef TensorGeneratorOp<Generator, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
+  static const int NumDims = internal::array_size<Dimensions>::value;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  enum {
+    IsAligned         = false,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = true,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  typedef internal::TensorIntDivisor<Index> IndexDivisor;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      :  m_device(device), m_generator(op.generator())
+  {
+    TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
+    m_dimensions = argImpl.dimensions();
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
+        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    } else {
+      m_strides[NumDims - 1] = 1;
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
+        if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    array<Index, NumDims> coords;
+    extract_coordinates(index, coords);
+    return m_generator(coords);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+packetSize-1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+    for (int i = 0; i < packetSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.firstLevelCacheSize();
+    // TODO(ezhulenev): Generator should have a cost.
+    return internal::TensorBlockResourceRequirements::skewed<Scalar>(
+        target_size);
+  }
+
+  struct BlockIteratorState {
+    Index stride;
+    Index span;
+    Index size;
+    Index count;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    static const bool is_col_major =
+        static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+    // Compute spatial coordinates for the first block element.
+    array<Index, NumDims> coords;
+    extract_coordinates(desc.offset(), coords);
+    array<Index, NumDims> initial_coords = coords;
+
+    // Offset in the output block buffer.
+    Index offset = 0;
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = is_col_major ? i : NumDims - 1 - i;
+      it[i].size = desc.dimension(dim);
+      it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
+      it[i].span = it[i].stride * (it[i].size - 1);
+      it[i].count = 0;
+    }
+    eigen_assert(it[0].stride == 1);
+
+    // Prepare storage for the materialized generator result.
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(desc, scratch);
+
+    CoeffReturnType* block_buffer = block_storage.data();
+
+    static const int packet_size = PacketType<CoeffReturnType, Device>::size;
+
+    static const int inner_dim = is_col_major ? 0 : NumDims - 1;
+    const Index inner_dim_size = it[0].size;
+    const Index inner_dim_vectorized = inner_dim_size - packet_size;
+
+    while (it[NumDims - 1].count < it[NumDims - 1].size) {
+      Index i = 0;
+      // Generate data for the vectorized part of the inner-most dimension.
+      for (; i <= inner_dim_vectorized; i += packet_size) {
+        for (Index j = 0; j < packet_size; ++j) {
+          array<Index, NumDims> j_coords = coords;  // Break loop dependence.
+          j_coords[inner_dim] += j;
+          *(block_buffer + offset + i + j) = m_generator(j_coords);
+        }
+        coords[inner_dim] += packet_size;
+      }
+      // Finalize non-vectorized part of the inner-most dimension.
+      for (; i < inner_dim_size; ++i) {
+        *(block_buffer + offset + i) = m_generator(coords);
+        coords[inner_dim]++;
+      }
+      coords[inner_dim] = initial_coords[inner_dim];
+
+      // For the 1d tensor we need to generate only one inner-most dimension.
+      if (NumDims == 1) break;
+
+      // Update offset.
+      for (i = 1; i < NumDims; ++i) {
+        if (++it[i].count < it[i].size) {
+          offset += it[i].stride;
+          coords[is_col_major ? i : NumDims - 1 - i]++;
+          break;
+        }
+        if (i != NumDims - 1) it[i].count = 0;
+        coords[is_col_major ? i : NumDims - 1 - i] =
+            initial_coords[is_col_major ? i : NumDims - 1 - i];
+        offset -= it[i].span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool) const {
+    // TODO(rmlarsen): This is just a placeholder. Define interface to make
+    // generators return their cost.
+    return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() +
+                                  TensorOpCost::MulCost<Scalar>());
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType  data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[0] = index;
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fast_strides[i];
+        index -= idx * m_strides[i];
+        coords[i] = idx;
+      }
+      coords[NumDims-1] = index;
+    }
+  }
+
+  const Device EIGEN_DEVICE_REF m_device;
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  array<IndexDivisor, NumDims> m_fast_strides;
+  Generator m_generator;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
new file mode 100644
index 0000000..665b861
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGlobalFunctions.h
@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
+
+namespace Eigen {
+
+/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given tensors.
+ *
+ * This function computes the regularized incomplete beta function (integral).
+ *
+ */
+template <typename ADerived, typename BDerived, typename XDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const
+    TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>,
+                         const ADerived, const BDerived, const XDerived>
+    betainc(const ADerived& a, const BDerived& b, const XDerived& x) {
+  return TensorCwiseTernaryOp<
+      internal::scalar_betainc_op<typename XDerived::Scalar>, const ADerived,
+      const BDerived, const XDerived>(
+      a, b, x, internal::scalar_betainc_op<typename XDerived::Scalar>());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_GLOBAL_FUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
new file mode 100644
index 0000000..cb53ce2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h
@@ -0,0 +1,99 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_USE_GPU) && !defined(EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H)
+#define EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
+
+// Note that we are using EIGEN_USE_HIP here instead of EIGEN_HIPCC...this is by design
+// There is code in the Tensorflow codebase that will define EIGEN_USE_GPU,  but
+// for some reason gets sent to the gcc/host compiler instead of the gpu/nvcc/hipcc compiler
+// When compiling such files, gcc will end up trying to pick up the CUDA headers by 
+// default (see the code within "unsupported/Eigen/CXX11/Tensor" that is guarded by EIGEN_USE_GPU)
+// This will obviously not work when trying to compile tensorflow on a system with no CUDA
+// To work around this issue for HIP systems (and leave the default behaviour intact), the
+// HIP tensorflow build defines EIGEN_USE_HIP when compiling all source files, and 
+// "unsupported/Eigen/CXX11/Tensor" has been updated to use HIP header when EIGEN_USE_HIP is
+// defined. In continuation of that requirement, the guard here needs to be EIGEN_USE_HIP as well
+
+#if defined(EIGEN_USE_HIP)
+
+#define gpuStream_t hipStream_t
+#define gpuDeviceProp_t hipDeviceProp_t
+#define gpuError_t hipError_t
+#define gpuSuccess hipSuccess
+#define gpuErrorNotReady hipErrorNotReady
+#define gpuGetDeviceCount hipGetDeviceCount
+#define gpuGetLastError hipGetLastError
+#define gpuPeekAtLastError hipPeekAtLastError
+#define gpuGetErrorName hipGetErrorName
+#define gpuGetErrorString hipGetErrorString
+#define gpuGetDeviceProperties hipGetDeviceProperties
+#define gpuStreamDefault hipStreamDefault
+#define gpuGetDevice hipGetDevice
+#define gpuSetDevice hipSetDevice
+#define gpuMalloc hipMalloc
+#define gpuFree hipFree
+#define gpuMemsetAsync hipMemsetAsync
+#define gpuMemcpyAsync hipMemcpyAsync
+#define gpuMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define gpuMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define gpuMemcpyHostToDevice hipMemcpyHostToDevice
+#define gpuStreamQuery hipStreamQuery
+#define gpuSharedMemConfig hipSharedMemConfig
+#define gpuDeviceSetSharedMemConfig hipDeviceSetSharedMemConfig
+#define gpuStreamSynchronize hipStreamSynchronize
+#define gpuDeviceSynchronize hipDeviceSynchronize
+#define gpuMemcpy hipMemcpy
+
+#else
+
+#define gpuStream_t cudaStream_t
+#define gpuDeviceProp_t cudaDeviceProp
+#define gpuError_t cudaError_t
+#define gpuSuccess cudaSuccess
+#define gpuErrorNotReady cudaErrorNotReady
+#define gpuGetDeviceCount cudaGetDeviceCount
+#define gpuGetLastError cudaGetLastError
+#define gpuPeekAtLastError cudaPeekAtLastError
+#define gpuGetErrorName cudaGetErrorName
+#define gpuGetErrorString cudaGetErrorString
+#define gpuGetDeviceProperties cudaGetDeviceProperties
+#define gpuStreamDefault cudaStreamDefault
+#define gpuGetDevice cudaGetDevice
+#define gpuSetDevice cudaSetDevice
+#define gpuMalloc cudaMalloc
+#define gpuFree cudaFree
+#define gpuMemsetAsync cudaMemsetAsync
+#define gpuMemcpyAsync cudaMemcpyAsync
+#define gpuMemcpyDeviceToDevice cudaMemcpyDeviceToDevice
+#define gpuMemcpyDeviceToHost cudaMemcpyDeviceToHost
+#define gpuMemcpyHostToDevice cudaMemcpyHostToDevice
+#define gpuStreamQuery cudaStreamQuery
+#define gpuSharedMemConfig cudaSharedMemConfig
+#define gpuDeviceSetSharedMemConfig cudaDeviceSetSharedMemConfig
+#define gpuStreamSynchronize cudaStreamSynchronize
+#define gpuDeviceSynchronize cudaDeviceSynchronize
+#define gpuMemcpy cudaMemcpy
+
+#endif
+
+// gpu_assert can be overridden
+#ifndef gpu_assert
+
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+// HIPCC do not support the use of assert on the GPU side.
+#define gpu_assert(COND)
+#else
+#define gpu_assert(COND) assert(COND)
+#endif
+
+#endif // gpu_assert
+
+#endif  // EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h
new file mode 100644
index 0000000..1d142f2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaUndefines.h
@@ -0,0 +1,44 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Deven Desai <deven.desai.amd@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#if defined(EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H)
+
+#ifndef EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
+
+#undef gpuStream_t
+#undef gpuDeviceProp_t 
+#undef gpuError_t
+#undef gpuSuccess
+#undef gpuErrorNotReady
+#undef gpuGetDeviceCount
+#undef gpuGetErrorString
+#undef gpuGetDeviceProperties
+#undef gpuStreamDefault
+#undef gpuGetDevice
+#undef gpuSetDevice
+#undef gpuMalloc
+#undef gpuFree
+#undef gpuMemsetAsync
+#undef gpuMemcpyAsync
+#undef gpuMemcpyDeviceToDevice
+#undef gpuMemcpyDeviceToHost
+#undef gpuMemcpyHostToDevice
+#undef gpuStreamQuery
+#undef gpuSharedMemConfig
+#undef gpuDeviceSetSharedMemConfig
+#undef gpuStreamSynchronize
+#undef gpuDeviceSynchronize
+#undef gpuMemcpy
+
+#endif // EIGEN_PERMANENTLY_ENABLE_GPU_HIP_CUDA_DEFINES
+
+#undef EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
+
+#endif // EIGEN_CXX11_TENSOR_GPU_HIP_CUDA_DEFINES_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
new file mode 100644
index 0000000..a901c5d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIO.h
@@ -0,0 +1,79 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_IO_H
+#define EIGEN_CXX11_TENSOR_TENSOR_IO_H
+
+namespace Eigen {
+
+namespace internal {
+
+// Print the tensor as a 2d matrix
+template <typename Tensor, int Rank>
+struct TensorPrinter {
+  static void run (std::ostream& os, const Tensor& tensor) {
+    typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
+    typedef typename Tensor::Index Index;
+    const Index total_size = internal::array_prod(tensor.dimensions());
+    if (total_size > 0) {
+      const Index first_dim = Eigen::internal::array_get<0>(tensor.dimensions());
+      static const int layout = Tensor::Layout;
+      Map<const Array<Scalar, Dynamic, Dynamic, layout> > matrix(const_cast<Scalar*>(tensor.data()), first_dim, total_size/first_dim);
+      os << matrix;
+    }
+  }
+};
+
+
+// Print the tensor as a vector
+template <typename Tensor>
+struct TensorPrinter<Tensor, 1> {
+  static void run (std::ostream& os, const Tensor& tensor) {
+    typedef typename internal::remove_const<typename Tensor::Scalar>::type Scalar;
+    typedef typename Tensor::Index Index;
+    const Index total_size = internal::array_prod(tensor.dimensions());
+    if (total_size > 0) {
+      Map<const Array<Scalar, Dynamic, 1> > array(const_cast<Scalar*>(tensor.data()), total_size);
+      os << array;
+    }
+  }
+};
+
+
+// Print the tensor as a scalar
+template <typename Tensor>
+struct TensorPrinter<Tensor, 0> {
+  static void run (std::ostream& os, const Tensor& tensor) {
+    os << tensor.coeff(0);
+  }
+};
+}
+
+template <typename T>
+std::ostream& operator << (std::ostream& os, const TensorBase<T, ReadOnlyAccessors>& expr) {
+  typedef TensorEvaluator<const TensorForcedEvalOp<const T>, DefaultDevice> Evaluator;
+  typedef typename Evaluator::Dimensions Dimensions;
+
+  // Evaluate the expression if needed
+  TensorForcedEvalOp<const T> eval = expr.eval();
+  Evaluator tensor(eval, DefaultDevice());
+  tensor.evalSubExprsIfNeeded(NULL);
+
+  // Print the result
+  static const int rank = internal::array_size<Dimensions>::value;
+  internal::TensorPrinter<Evaluator, rank>::run(os, tensor);
+
+  // Cleanup.
+  tensor.cleanup();
+  return os;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_IO_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
new file mode 100644
index 0000000..dd51850
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h
@@ -0,0 +1,603 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
+
+namespace Eigen {
+
+/** \class TensorImagePatch
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Patch extraction specialized for image processing.
+  * This assumes that the input has a least 3 dimensions ordered as follow:
+  *  1st dimension: channels (of size d)
+  *  2nd dimension: rows (of size r)
+  *  3rd dimension: columns (of size c)
+  *  There can be additional dimensions such as time (for video) or batch (for
+  * bulk processing after the first 3.
+  * Calling the image patch code with patch_rows and patch_cols is equivalent
+  * to calling the regular patch extraction code with parameters d, patch_rows,
+  * patch_cols, and 1 for all the additional dimensions.
+  */
+namespace internal {
+
+template<DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
+{
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions + 1;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
+{
+  typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
+};
+
+template<DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
+{
+  typedef TensorImagePatchOp<Rows, Cols, XprType> type;
+};
+
+template <typename Self, bool Vectorizable>
+struct ImagePatchCopyOp {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename Self::Impl Impl;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
+      const Self& self, const Index num_coeff_to_copy, const Index dst_index,
+      Scalar* dst_data, const Index src_index) {
+    const Impl& impl = self.impl();
+    for (Index i = 0; i < num_coeff_to_copy; ++i) {
+      dst_data[dst_index + i] = impl.coeff(src_index + i);
+    }
+  }
+};
+
+template <typename Self>
+struct ImagePatchCopyOp<Self, true> {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename Self::Impl Impl;
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
+      const Self& self, const Index num_coeff_to_copy, const Index dst_index,
+      Scalar* dst_data, const Index src_index) {
+    const Impl& impl = self.impl();
+    const Index packet_size = internal::unpacket_traits<Packet>::size;
+    const Index vectorized_size =
+        (num_coeff_to_copy / packet_size) * packet_size;
+    for (Index i = 0; i < vectorized_size; i += packet_size) {
+      Packet p = impl.template packet<Unaligned>(src_index + i);
+      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p);
+    }
+    for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) {
+      dst_data[dst_index + i] = impl.coeff(src_index + i);
+    }
+  }
+};
+
+template <typename Self>
+struct ImagePatchPaddingOp {
+  typedef typename Self::Index Index;
+  typedef typename Self::Scalar Scalar;
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(
+      const Index num_coeff_to_pad, const Scalar padding_value,
+      const Index dst_index, Scalar* dst_data) {
+    const Index packet_size = internal::unpacket_traits<Packet>::size;
+    const Packet padded_packet = internal::pset1<Packet>(padding_value);
+    const Index vectorized_size =
+        (num_coeff_to_pad / packet_size) * packet_size;
+    for (Index i = 0; i < vectorized_size; i += packet_size) {
+      internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i,
+                                                   padded_packet);
+    }
+    for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) {
+      dst_data[dst_index + i] = padding_value;
+    }
+  }
+};
+
+}  // end namespace internal
+
+template<DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
+                                                           DenseIndex row_strides, DenseIndex col_strides,
+                                                           DenseIndex in_row_strides, DenseIndex in_col_strides,
+                                                           DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
+                                                           PaddingType padding_type, Scalar padding_value)
+                                                           : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
+                                                           m_row_strides(row_strides), m_col_strides(col_strides),
+                                                           m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
+                                                           m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
+                                                           m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
+                                                           m_padding_type(padding_type), m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
+                                                           DenseIndex row_strides, DenseIndex col_strides,
+                                                           DenseIndex in_row_strides, DenseIndex in_col_strides,
+                                                           DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
+                                                           DenseIndex padding_top, DenseIndex padding_bottom,
+                                                           DenseIndex padding_left, DenseIndex padding_right,
+                                                           Scalar padding_value)
+                                                           : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
+                                                           m_row_strides(row_strides), m_col_strides(col_strides),
+                                                           m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
+                                                           m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
+                                                           m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
+                                                           m_padding_left(padding_left), m_padding_right(padding_right),
+                                                           m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
+
+
+    EIGEN_DEVICE_FUNC
+    DenseIndex patch_rows() const { return m_patch_rows; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex patch_cols() const { return m_patch_cols; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex row_strides() const { return m_row_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex col_strides() const { return m_col_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex in_row_strides() const { return m_in_row_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex in_col_strides() const { return m_in_col_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
+    EIGEN_DEVICE_FUNC
+    bool padding_explicit() const { return m_padding_explicit; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_top() const { return m_padding_top; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_bottom() const { return m_padding_bottom; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_left() const { return m_padding_left; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_right() const { return m_padding_right; }
+    EIGEN_DEVICE_FUNC
+    PaddingType padding_type() const { return m_padding_type; }
+    EIGEN_DEVICE_FUNC
+    Scalar padding_value() const { return m_padding_value; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const DenseIndex m_patch_rows;
+    const DenseIndex m_patch_cols;
+    const DenseIndex m_row_strides;
+    const DenseIndex m_col_strides;
+    const DenseIndex m_in_row_strides;
+    const DenseIndex m_in_col_strides;
+    const DenseIndex m_row_inflate_strides;
+    const DenseIndex m_col_inflate_strides;
+    const bool m_padding_explicit;
+    const DenseIndex m_padding_top;
+    const DenseIndex m_padding_bottom;
+    const DenseIndex m_padding_left;
+    const DenseIndex m_padding_right;
+    const PaddingType m_padding_type;
+    const Scalar m_padding_value;
+};
+
+// Eval as rvalue
+template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
+{
+  typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
+                          Device> Self;
+  typedef TensorEvaluator<ArgType, Device> Impl;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = false,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,
+    RawAccess         = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
+      : m_device(device), m_impl(op.expression(), device)
+  {
+    EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    m_paddingValue = op.padding_value();
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    // Caches a few variables.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputDepth = input_dims[0];
+      m_inputRows = input_dims[1];
+      m_inputCols = input_dims[2];
+    } else {
+      m_inputDepth = input_dims[NumInputDims-1];
+      m_inputRows = input_dims[NumInputDims-2];
+      m_inputCols = input_dims[NumInputDims-3];
+    }
+
+    m_row_strides = op.row_strides();
+    m_col_strides = op.col_strides();
+
+    // Input strides and effective input/patch size
+    m_in_row_strides = op.in_row_strides();
+    m_in_col_strides = op.in_col_strides();
+    m_row_inflate_strides = op.row_inflate_strides();
+    m_col_inflate_strides = op.col_inflate_strides();
+    // The "effective" input rows and input cols are the input rows and cols
+    // after inflating them with zeros.
+    // For examples, a 2x3 matrix with row_inflate_strides and
+    // col_inflate_strides of 2 comes from:
+    //   A B C
+    //   D E F
+    //
+    // to a matrix is 3 x 5:
+    //
+    //   A . B . C
+    //   . . . . .
+    //   D . E . F
+
+    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
+    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
+    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
+    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
+
+    if (op.padding_explicit()) {
+      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+      m_rowPaddingTop = op.padding_top();
+      m_colPaddingLeft = op.padding_left();
+    } else {
+      // Computing padding from the type
+      switch (op.padding_type()) {
+        case PADDING_VALID:
+          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+          // Calculate the padding
+          m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
+          m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
+          break;
+        case PADDING_SAME:
+          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
+          // Calculate the padding
+          m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
+          m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
+          // The padding size calculation for PADDING_SAME has been updated to
+          // be consistent with how TensorFlow extracts its paddings.
+          m_rowPaddingTop = numext::maxi<Index>(0, m_rowPaddingTop);
+          m_colPaddingLeft = numext::maxi<Index>(0, m_colPaddingLeft);
+          break;
+        default:
+          eigen_assert(false && "unexpected padding");
+          m_outputCols=0; // silence the uninitialised warning;
+          m_outputRows=0; //// silence the uninitialised warning;
+      }
+    }
+    eigen_assert(m_outputRows > 0);
+    eigen_assert(m_outputCols > 0);
+
+    // Dimensions for result of extraction.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // ColMajor
+      // 0: depth
+      // 1: patch_rows
+      // 2: patch_cols
+      // 3: number of patches
+      // 4 and beyond: anything else (such as batch).
+      m_dimensions[0] = input_dims[0];
+      m_dimensions[1] = op.patch_rows();
+      m_dimensions[2] = op.patch_cols();
+      m_dimensions[3] = m_outputRows * m_outputCols;
+      for (int i = 4; i < NumDims; ++i) {
+        m_dimensions[i] = input_dims[i-1];
+      }
+    } else {
+      // RowMajor
+      // NumDims-1: depth
+      // NumDims-2: patch_rows
+      // NumDims-3: patch_cols
+      // NumDims-4: number of patches
+      // NumDims-5 and beyond: anything else (such as batch).
+      m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
+      m_dimensions[NumDims-2] = op.patch_rows();
+      m_dimensions[NumDims-3] = op.patch_cols();
+      m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
+      for (int i = NumDims-5; i >= 0; --i) {
+        m_dimensions[i] = input_dims[i];
+      }
+    }
+
+    // Strides for moving the patch in various dimensions.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_colStride = m_dimensions[1];
+      m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
+      m_otherStride = m_patchStride * m_dimensions[3];
+    } else {
+      m_colStride = m_dimensions[NumDims-2];
+      m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
+      m_otherStride = m_patchStride * m_dimensions[NumDims-4];
+    }
+
+    // Strides for navigating through the input tensor.
+    m_rowInputStride = m_inputDepth;
+    m_colInputStride = m_inputDepth * m_inputRows;
+    m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
+
+    // Fast representations of different variables.
+    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
+    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
+    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
+    m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
+    m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
+    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
+
+    // Number of patches in the width dimension.
+    m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
+    } else {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    // Patch index corresponding to the passed in index.
+    const Index patchIndex = index / m_fastPatchStride;
+    // Find the offset of the element wrt the location of the first element.
+    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
+
+    // Other ways to index this element.
+    const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
+    const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
+
+    // Calculate col index in the input original tensor.
+    const Index colIndex = patch2DIndex / m_fastOutputRows;
+    const Index colOffset = patchOffset / m_fastColStride;
+    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
+    const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
+    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
+        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate row index in the original input tensor.
+    const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
+    const Index rowOffset = patchOffset - colOffset * m_colStride;
+    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
+    const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
+    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
+        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+
+    const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
+    return m_impl.coeff(inputIndex);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index indices[2] = {index, index + PacketSize - 1};
+    const Index patchIndex = indices[0] / m_fastPatchStride;
+    if (patchIndex != indices[1] / m_fastPatchStride) {
+      return packetWithPossibleZero(index);
+    }
+    const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
+    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
+
+    // Find the offset of the element wrt the location of the first element.
+    const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
+                                   (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
+
+    const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
+    eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
+
+    const Index colIndex = patch2DIndex / m_fastOutputRows;
+    const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
+
+    // Calculate col indices in the original input tensor.
+    const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
+      m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
+    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputCols[0] == inputCols[1]) {
+      const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
+      const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
+      eigen_assert(rowOffsets[0] <= rowOffsets[1]);
+      // Calculate col indices in the original input tensor.
+      const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
+        m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
+
+      if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
+        return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+      }
+
+      if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
+        // no padding
+        const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+        const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+        const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
+        return m_impl.template packet<Unaligned>(inputIndex);
+      }
+    }
+
+    return packetWithPossibleZero(index);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    // We conservatively estimate the cost for the code path where the computed
+    // index is inside the original image and
+    // TensorEvaluator<ArgType, Device>::CoordAccess is false.
+    const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
+                                6 * TensorOpCost::MulCost<Index>() +
+                                8 * TensorOpCost::MulCost<Index>();
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
+  {
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+
+  Index m_otherStride;
+  Index m_patchStride;
+  Index m_colStride;
+  Index m_row_strides;
+  Index m_col_strides;
+
+  Index m_in_row_strides;
+  Index m_in_col_strides;
+  Index m_row_inflate_strides;
+  Index m_col_inflate_strides;
+
+  Index m_input_rows_eff;
+  Index m_input_cols_eff;
+  Index m_patch_rows_eff;
+  Index m_patch_cols_eff;
+
+  internal::TensorIntDivisor<Index> m_fastOtherStride;
+  internal::TensorIntDivisor<Index> m_fastPatchStride;
+  internal::TensorIntDivisor<Index> m_fastColStride;
+  internal::TensorIntDivisor<Index> m_fastInflateRowStride;
+  internal::TensorIntDivisor<Index> m_fastInflateColStride;
+  internal::TensorIntDivisor<Index> m_fastInputColsEff;
+
+  Index m_rowInputStride;
+  Index m_colInputStride;
+  Index m_patchInputStride;
+
+  Index m_inputDepth;
+  Index m_inputRows;
+  Index m_inputCols;
+
+  Index m_outputRows;
+  Index m_outputCols;
+
+  Index m_rowPaddingTop;
+  Index m_colPaddingLeft;
+
+  internal::TensorIntDivisor<Index> m_fastOutputRows;
+  internal::TensorIntDivisor<Index> m_fastOutputDepth;
+
+  Scalar m_paddingValue;
+
+  const Device EIGEN_DEVICE_REF m_device;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
new file mode 100644
index 0000000..2d8c7b9
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h
@@ -0,0 +1,738 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
+
+
+#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES
+
+#define EIGEN_HAS_INDEX_LIST
+
+namespace Eigen {
+
+/** \internal
+  *
+  * \class TensorIndexList
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Set of classes used to encode a set of Tensor dimensions/indices.
+  *
+  * The indices in the list can be known at compile time or at runtime. A mix
+  * of static and dynamic indices can also be provided if needed. The tensor
+  * code will attempt to take advantage of the indices that are known at
+  * compile time to optimize the code it generates.
+  *
+  * This functionality requires a c++11 compliant compiler. If your compiler
+  * is older you need to use arrays of indices instead.
+  *
+  * Several examples are provided in the cxx11_tensor_index_list.cpp file.
+  *
+  * \sa Tensor
+  */
+
+template <Index n>
+struct type2index {
+  static const Index value = n;
+  EIGEN_DEVICE_FUNC constexpr operator Index() const { return n; }
+  EIGEN_DEVICE_FUNC void set(Index val) {
+    eigen_assert(val == n);
+  }
+};
+
+// This can be used with IndexPairList to get compile-time constant pairs,
+// such as IndexPairList<type2indexpair<1,2>, type2indexpair<3,4>>().
+template <Index f, Index s>
+struct type2indexpair {
+  static const Index first = f;
+  static const Index second = s;
+
+  constexpr EIGEN_DEVICE_FUNC operator IndexPair<Index>() const {
+    return IndexPair<Index>(f, s);
+  }
+
+  EIGEN_DEVICE_FUNC void set(const IndexPair<Index>& val) {
+    eigen_assert(val.first == f);
+    eigen_assert(val.second == s);
+  }
+};
+
+
+template<Index n> struct NumTraits<type2index<n> >
+{
+  typedef Index Real;
+  enum {
+    IsComplex = 0,
+    RequireInitialization = false,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+
+  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real epsilon() { return 0; }
+  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real dummy_precision() { return 0; }
+  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real highest() { return n; }
+  EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR EIGEN_STRONG_INLINE Real lowest() { return n; }
+};
+
+namespace internal {
+template <typename T>
+EIGEN_DEVICE_FUNC void update_value(T& val, Index new_val) {
+  val = internal::convert_index<T>(new_val);
+}
+template <Index n>
+EIGEN_DEVICE_FUNC void update_value(type2index<n>& val, Index new_val) {
+  val.set(new_val);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC void update_value(T& val, IndexPair<Index> new_val) {
+  val = new_val;
+}
+template <Index f, Index s>
+EIGEN_DEVICE_FUNC void update_value(type2indexpair<f, s>& val, IndexPair<Index> new_val) {
+  val.set(new_val);
+}
+
+
+template <typename T>
+struct is_compile_time_constant {
+  static constexpr bool value = false;
+};
+
+template <Index idx>
+struct is_compile_time_constant<type2index<idx> > {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<const type2index<idx> > {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<type2index<idx>& > {
+  static constexpr bool value = true;
+};
+template <Index idx>
+struct is_compile_time_constant<const type2index<idx>& > {
+  static constexpr bool value = true;
+};
+
+template <Index f, Index s>
+struct is_compile_time_constant<type2indexpair<f, s> > {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<const type2indexpair<f, s> > {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<type2indexpair<f, s>& > {
+  static constexpr bool value = true;
+};
+template <Index f, Index s>
+struct is_compile_time_constant<const type2indexpair<f, s>& > {
+  static constexpr bool value = true;
+};
+
+
+template<typename... T>
+struct IndexTuple;
+
+template<typename T, typename... O>
+struct IndexTuple<T, O...> {
+  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head(), others() { }
+  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v, const O... o) : head(v), others(o...) { }
+
+  constexpr static int count = 1 + sizeof...(O);
+  T head;
+  IndexTuple<O...> others;
+  typedef T Head;
+  typedef IndexTuple<O...> Other;
+};
+
+template<typename T>
+  struct IndexTuple<T> {
+  EIGEN_DEVICE_FUNC constexpr IndexTuple() : head() { }
+  EIGEN_DEVICE_FUNC constexpr IndexTuple(const T& v) : head(v) { }
+
+  constexpr static int count = 1;
+  T head;
+  typedef T Head;
+};
+
+
+template<int N, typename... T>
+struct IndexTupleExtractor;
+
+template<int N, typename T, typename... O>
+struct IndexTupleExtractor<N, T, O...> {
+
+  typedef typename IndexTupleExtractor<N-1, O...>::ValType ValType;
+
+  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) {
+    return IndexTupleExtractor<N-1, O...>::get_val(val.others);
+  }
+
+  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) {
+    return IndexTupleExtractor<N-1, O...>::get_val(val.others);
+  }
+  template <typename V>
+  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
+    IndexTupleExtractor<N-1, O...>::set_val(val.others, new_val);
+  }
+
+};
+
+template<typename T, typename... O>
+  struct IndexTupleExtractor<0, T, O...> {
+
+  typedef T ValType;
+
+  EIGEN_DEVICE_FUNC static constexpr ValType& get_val(IndexTuple<T, O...>& val) {
+    return val.head;
+  }
+  EIGEN_DEVICE_FUNC static constexpr const ValType& get_val(const IndexTuple<T, O...>& val) {
+    return val.head;
+  }
+  template <typename V>
+  EIGEN_DEVICE_FUNC static void set_val(IndexTuple<T, O...>& val, V& new_val) {
+    val.head = new_val;
+  }
+};
+
+
+
+template <int N, typename T, typename... O>
+EIGEN_DEVICE_FUNC constexpr typename IndexTupleExtractor<N, T, O...>::ValType& array_get(IndexTuple<T, O...>& tuple) {
+  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
+}
+template <int N, typename T, typename... O>
+EIGEN_DEVICE_FUNC constexpr const typename IndexTupleExtractor<N, T, O...>::ValType& array_get(const IndexTuple<T, O...>& tuple) {
+  return IndexTupleExtractor<N, T, O...>::get_val(tuple);
+}
+template <typename T, typename... O>
+  struct array_size<IndexTuple<T, O...> > {
+  static const size_t value = IndexTuple<T, O...>::count;
+};
+template <typename T, typename... O>
+  struct array_size<const IndexTuple<T, O...> > {
+  static const size_t value = IndexTuple<T, O...>::count;
+};
+
+
+
+
+template <Index Idx, typename ValueT>
+struct tuple_coeff {
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index i, const IndexTuple<T...>& t) {
+    //    return array_get<Idx>(t) * (i == Idx) + tuple_coeff<Idx-1>::get(i, t) * (i != Idx);
+    return (i == Idx ? array_get<Idx>(t) : tuple_coeff<Idx-1, ValueT>::get(i, t));
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT& value) {
+    if (i == Idx) {
+      update_value(array_get<Idx>(t), value);
+    } else {
+      tuple_coeff<Idx-1, ValueT>::set(i, t, value);
+    }
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>& t) {
+    return ((i == Idx) & is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value) ||
+        tuple_coeff<Idx-1, ValueT>::value_known_statically(i, t);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>& t) {
+    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+        tuple_coeff<Idx-1, ValueT>::values_up_to_known_statically(t);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>& t) {
+    return is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+           is_compile_time_constant<typename IndexTupleExtractor<Idx, T...>::ValType>::value &&
+           array_get<Idx>(t) > array_get<Idx-1>(t) &&
+           tuple_coeff<Idx-1, ValueT>::values_up_to_statically_known_to_increase(t);
+  }
+};
+
+template <typename ValueT>
+struct tuple_coeff<0, ValueT> {
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr ValueT get(const Index /*i*/, const IndexTuple<T...>& t) {
+    //  eigen_assert (i == 0);  // gcc fails to compile assertions in constexpr
+    return array_get<0>(t)/* * (i == 0)*/;
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static void set(const Index i, IndexTuple<T...>& t, const ValueT value) {
+    eigen_assert (i == 0);
+    update_value(array_get<0>(t), value);
+  }
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool value_known_statically(const Index i, const IndexTuple<T...>&) {
+    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value && (i == 0);
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_known_statically(const IndexTuple<T...>&) {
+    return is_compile_time_constant<typename IndexTupleExtractor<0, T...>::ValType>::value;
+  }
+
+  template <typename... T>
+  EIGEN_DEVICE_FUNC static constexpr bool values_up_to_statically_known_to_increase(const IndexTuple<T...>&) {
+    return true;
+  }
+};
+}  // namespace internal
+
+
+
+template<typename FirstType, typename... OtherTypes>
+struct IndexList : internal::IndexTuple<FirstType, OtherTypes...> {
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index operator[] (const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr Index get(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const Index value) {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::set(i, *this, value);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr IndexList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { }
+  EIGEN_DEVICE_FUNC constexpr IndexList(FirstType& first, OtherTypes... other) : internal::IndexTuple<FirstType, OtherTypes...>(first, other...) { }
+  EIGEN_DEVICE_FUNC constexpr IndexList() : internal::IndexTuple<FirstType, OtherTypes...>() { }
+
+  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::value_known_statically(i, *this);
+  }
+  EIGEN_DEVICE_FUNC constexpr bool all_values_known_statically() const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::values_up_to_known_statically(*this);
+  }
+
+  EIGEN_DEVICE_FUNC constexpr bool values_statically_known_to_increase() const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::values_up_to_statically_known_to_increase(*this);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+std::ostream& operator<<(std::ostream& os,
+                         const IndexList<FirstType, OtherTypes...>& dims) {
+  os << "[";
+  for (size_t i = 0; i < 1 + sizeof...(OtherTypes); ++i) {
+    if (i > 0) os << ", ";
+    os << dims[i];
+  }
+  os << "]";
+  return os;
+}
+
+template<typename FirstType, typename... OtherTypes>
+constexpr IndexList<FirstType, OtherTypes...> make_index_list(FirstType val1, OtherTypes... other_vals) {
+  return IndexList<FirstType, OtherTypes...>(val1, other_vals...);
+}
+
+
+template<typename FirstType, typename... OtherTypes>
+struct IndexPairList : internal::IndexTuple<FirstType, OtherTypes...> {
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC constexpr IndexPair<Index> operator[] (const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, IndexPair<Index>>::get(i, *this);
+  }
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC void set(const Index i, const IndexPair<Index> value) {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...>>::value-1, IndexPair<Index> >::set(i, *this, value);
+  }
+
+  EIGEN_DEVICE_FUNC  constexpr IndexPairList(const internal::IndexTuple<FirstType, OtherTypes...>& other) : internal::IndexTuple<FirstType, OtherTypes...>(other) { }
+  EIGEN_DEVICE_FUNC  constexpr IndexPairList() : internal::IndexTuple<FirstType, OtherTypes...>() { }
+
+  EIGEN_DEVICE_FUNC constexpr bool value_known_statically(const Index i) const {
+    return internal::tuple_coeff<internal::array_size<internal::IndexTuple<FirstType, OtherTypes...> >::value-1, Index>::value_known_statically(i, *this);
+  }
+};
+
+namespace internal {
+
+template<typename FirstType, typename... OtherTypes>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index array_prod(const IndexList<FirstType, OtherTypes...>& sizes) {
+  Index result = 1;
+  EIGEN_UNROLL_LOOP
+  for (size_t i = 0; i < array_size<IndexList<FirstType, OtherTypes...> >::value; ++i) {
+    result *= sizes[i];
+  }
+  return result;
+}
+
+template<typename FirstType, typename... OtherTypes> struct array_size<IndexList<FirstType, OtherTypes...> > {
+  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value;
+};
+template<typename FirstType, typename... OtherTypes> struct array_size<const IndexList<FirstType, OtherTypes...> > {
+  static const size_t value = array_size<IndexTuple<FirstType, OtherTypes...> >::value;
+};
+
+template<typename FirstType, typename... OtherTypes> struct array_size<IndexPairList<FirstType, OtherTypes...> > {
+  static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
+};
+template<typename FirstType, typename... OtherTypes> struct array_size<const IndexPairList<FirstType, OtherTypes...> > {
+  static const size_t value = std::tuple_size<std::tuple<FirstType, OtherTypes...> >::value;
+};
+
+template<Index N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr Index array_get(IndexList<FirstType, OtherTypes...>& a) {
+  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
+}
+template<Index N, typename FirstType, typename... OtherTypes> EIGEN_DEVICE_FUNC constexpr Index array_get(const IndexList<FirstType, OtherTypes...>& a) {
+  return IndexTupleExtractor<N, FirstType, OtherTypes...>::get_val(a);
+}
+
+template <typename T>
+struct index_known_statically_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_known_statically_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_known_statically_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i);
+  }
+};
+
+
+template <typename T>
+struct all_indices_known_statically_impl {
+  static constexpr bool run() {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct all_indices_known_statically_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct all_indices_known_statically_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return IndexList<FirstType, OtherTypes...>().all_values_known_statically();
+  }
+};
+
+
+template <typename T>
+struct indices_statically_known_to_increase_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+  struct indices_statically_known_to_increase_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+  struct indices_statically_known_to_increase_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run() {
+    return Eigen::IndexList<FirstType, OtherTypes...>().values_statically_known_to_increase();
+  }
+};
+
+
+template <typename Tx>
+struct index_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_eq_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_eq_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) == value);
+  }
+};
+
+
+template <typename T>
+struct index_statically_ne_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_ne_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) != value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_ne_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) != value);
+  }
+};
+
+
+template <typename T>
+struct index_statically_gt_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_gt_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) > value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_gt_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) > value);
+  }
+};
+
+
+
+template <typename T>
+struct index_statically_lt_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_lt_impl<IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) < value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_statically_lt_impl<const IndexList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexList<FirstType, OtherTypes...>().get(i) < value);
+  }
+};
+
+
+
+template <typename Tx>
+struct index_pair_first_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_first_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_first_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexPairList<FirstType, OtherTypes...>().operator[](i).first == value);
+  }
+};
+
+
+
+template <typename Tx>
+struct index_pair_second_statically_eq_impl {
+  EIGEN_DEVICE_FUNC static constexpr bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_second_statically_eq_impl<IndexPairList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
+  }
+};
+
+template <typename FirstType, typename... OtherTypes>
+struct index_pair_second_statically_eq_impl<const IndexPairList<FirstType, OtherTypes...> > {
+  EIGEN_DEVICE_FUNC static constexpr bool run(const Index i, const Index value) {
+    return IndexPairList<FirstType, OtherTypes...>().value_known_statically(i) &
+        (IndexPairList<FirstType, OtherTypes...>().operator[](i).second == value);
+  }
+};
+
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#else
+
+namespace Eigen {
+namespace internal {
+
+template <typename T>
+struct index_known_statically_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(const Index) {
+    return false;
+  }
+};
+
+template <typename T>
+struct all_indices_known_statically_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
+    return false;
+  }
+};
+
+template <typename T>
+struct indices_statically_known_to_increase_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run() {
+    return false;
+  }
+};
+
+template <typename T>
+struct index_statically_eq_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename T>
+struct index_statically_ne_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename T>
+struct index_statically_gt_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename T>
+struct index_statically_lt_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename Tx>
+struct index_pair_first_statically_eq_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+template <typename Tx>
+struct index_pair_second_statically_eq_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool run(Index, Index) {
+    return false;
+  }
+};
+
+
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif
+
+
+namespace Eigen {
+namespace internal {
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_known_statically(Index i) {
+  return index_known_statically_impl<T>::run(i);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool all_indices_known_statically() {
+  return all_indices_known_statically_impl<T>::run();
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool indices_statically_known_to_increase() {
+  return indices_statically_known_to_increase_impl<T>::run();
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_eq(Index i, Index value) {
+  return index_statically_eq_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_ne(Index i, Index value) {
+  return index_statically_ne_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_gt(Index i, Index value) {
+  return index_statically_gt_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_statically_lt(Index i, Index value) {
+  return index_statically_lt_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_first_statically_eq(Index i, Index value) {
+  return index_pair_first_statically_eq_impl<T>::run(i, value);
+}
+
+template <typename T>
+static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bool index_pair_second_statically_eq(Index i, Index value) {
+  return index_pair_second_statically_eq_impl<T>::run(i, value);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_INDEX_LIST_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
new file mode 100644
index 0000000..c5cb61a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInflation.h
@@ -0,0 +1,247 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Ke Yang <yangke@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
+
+namespace Eigen {
+
+/** \class TensorInflation
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor inflation class.
+  *
+  *
+  */
+namespace internal {
+template<typename Strides, typename XprType>
+struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Strides, typename XprType>
+struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense>
+{
+  typedef const TensorInflationOp<Strides, XprType>& type;
+};
+
+template<typename Strides, typename XprType>
+struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type>
+{
+  typedef TensorInflationOp<Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename Strides, typename XprType>
+class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
+      : m_xpr(expr), m_strides(strides) {}
+
+    EIGEN_DEVICE_FUNC
+    const Strides& strides() const { return m_strides; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Strides m_strides;
+};
+
+// Eval as rvalue
+template<typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
+{
+  typedef TensorInflationOp<Strides, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_strides(op.strides())
+  {
+    m_dimensions = m_impl.dimensions();
+    // Expand each dimension to the inflated dimension.
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
+    }
+
+    // Remember the strides for fast division.
+    for (int i = 0; i < NumDims; ++i) {
+      m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_outputStrides[0] = 1;
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+      }
+    } else {  // RowMajor
+      m_outputStrides[NumDims-1] = 1;
+      m_inputStrides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  // Computes the input index given the output index. Returns true if the output
+  // index doesn't fall into a hole.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const
+  {
+    eigen_assert(index < dimensions().TotalSize());
+    *inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
+          return false;
+        }
+        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (index != index / m_fastStrides[0] * m_strides[0]) {
+        return false;
+      }
+      *inputIndex += index / m_strides[0];
+      return true;
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        if (idx != idx / m_fastStrides[i] * m_strides[i]) {
+          return false;
+        }
+        *inputIndex += idx / m_strides[i] * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) {
+        return false;
+      }
+      *inputIndex += index / m_strides[NumDims - 1];
+    }
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    Index inputIndex = 0;
+    if (getInputIndex(index, &inputIndex)) {
+     return m_impl.coeff(inputIndex);
+    } else {
+     return Scalar(0);
+    }
+  }
+
+  // TODO(yangke): optimize this function so that we can detect and produce
+  // all-zero packets
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
+                                           3 * TensorOpCost::MulCost<Index>() +
+                                           2 * TensorOpCost::AddCost<Index>());
+    const double input_size = m_impl.dimensions().TotalSize();
+    const double output_size = m_dimensions.TotalSize();
+    if (output_size == 0)
+      return TensorOpCost();
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
+                        compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Strides m_strides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
new file mode 100644
index 0000000..26a3818
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorInitializer.h
@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+
+#include <initializer_list>
+
+namespace Eigen {
+
+/** \class TensorInitializer
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Helper template to initialize Tensors from std::initializer_lists.
+  */
+namespace internal {
+
+template <typename Derived, int N>
+struct Initializer {
+  typedef std::initializer_list<
+    typename Initializer<Derived, N - 1>::InitList> InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
+                  const InitList& vals) {
+    int i = 0;
+    for (const auto& v : vals) {
+      (*indices)[traits<Derived>::NumDimensions - N] = i++;
+      Initializer<Derived, N - 1>::run(tensor, indices, v);
+    }
+  }
+};
+
+template <typename Derived>
+struct Initializer<Derived, 1> {
+  typedef std::initializer_list<typename traits<Derived>::Scalar> InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>* indices,
+                  const InitList& vals) {
+    int i = 0;
+    // There is likely a faster way to do that than iterating.
+    for (const auto& v : vals) {
+      (*indices)[traits<Derived>::NumDimensions - 1] = i++;
+      tensor.coeffRef(*indices) = v;
+    }
+  }
+};
+
+template <typename Derived>
+struct Initializer<Derived, 0> {
+  typedef typename traits<Derived>::Scalar InitList;
+
+  static void run(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions>*,
+                  const InitList& v) {
+    tensor.coeffRef(0) = v;
+  }
+};
+
+
+template <typename Derived, int N>
+void initialize_tensor(TensorEvaluator<Derived, DefaultDevice>& tensor,
+                       const typename Initializer<Derived, traits<Derived>::NumDimensions>::InitList& vals) {
+  Eigen::array<typename traits<Derived>::Index, traits<Derived>::NumDimensions> indices;
+  Initializer<Derived, traits<Derived>::NumDimensions>::run(tensor, &indices, vals);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_HAS_VARIADIC_TEMPLATES
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_INITIALIZER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
new file mode 100644
index 0000000..6d5cce4
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorIntDiv.h
@@ -0,0 +1,263 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
+#define EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
+
+
+namespace Eigen {
+
+/** \internal
+  *
+  * \class TensorIntDiv
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Fast integer division by a constant.
+  *
+  * See the paper from Granlund and Montgomery for explanation.
+  *   (at https://doi.org/10.1145/773473.178249)
+  *
+  * \sa Tensor
+  */
+
+namespace internal {
+
+namespace {
+
+  // Note: result is undefined if val == 0
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  typename internal::enable_if<sizeof(T)==4,int>::type count_leading_zeros(const T val)
+  {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    return __clz(val);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::clz(val);
+#elif EIGEN_COMP_MSVC
+    unsigned long index;
+    _BitScanReverse(&index, val);
+    return 31 - index;
+#else
+    EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return __builtin_clz(static_cast<uint32_t>(val));
+#endif
+  }
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  typename internal::enable_if<sizeof(T)==8,int>::type count_leading_zeros(const T val)
+  {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    return __clzll(val);
+#elif defined(SYCL_DEVICE_ONLY)
+    return static_cast<int>(cl::sycl::clz(val));
+#elif EIGEN_COMP_MSVC && EIGEN_ARCH_x86_64
+    unsigned long index;
+    _BitScanReverse64(&index, val);
+    return 63 - index;
+#elif EIGEN_COMP_MSVC
+    // MSVC's _BitScanReverse64 is not available for 32bits builds.
+    unsigned int lo = (unsigned int)(val&0xffffffff);
+    unsigned int hi = (unsigned int)((val>>32)&0xffffffff);
+    int n;
+    if(hi==0)
+      n = 32 + count_leading_zeros<unsigned int>(lo);
+    else
+      n = count_leading_zeros<unsigned int>(hi);
+    return n;
+#else
+    EIGEN_STATIC_ASSERT(sizeof(unsigned long long) == 8, YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return __builtin_clzll(static_cast<uint64_t>(val));
+#endif
+  }
+
+  template <typename T>
+  struct UnsignedTraits {
+    typedef typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type type;
+  };
+
+  template <typename T>
+  struct DividerTraits {
+    typedef typename UnsignedTraits<T>::type type;
+    static const int N = sizeof(T) * 8;
+  };
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t muluh(const uint32_t a, const T b) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+    return __umulhi(a, b);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::mul_hi(a, static_cast<uint32_t>(b));
+#else
+    return (static_cast<uint64_t>(a) * b) >> 32;
+#endif
+  }
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t muluh(const uint64_t a, const T b) {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+    return __umul64hi(a, b);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::mul_hi(a, static_cast<uint64_t>(b));
+#elif EIGEN_HAS_BUILTIN_INT128
+    __uint128_t v = static_cast<__uint128_t>(a) * static_cast<__uint128_t>(b);
+    return static_cast<uint64_t>(v >> 64);
+#else
+    return (TensorUInt128<static_val<0>, uint64_t>(a) * TensorUInt128<static_val<0>, uint64_t>(b)).upper();
+#endif
+  }
+
+  template <int N, typename T>
+  struct DividerHelper {
+    static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint32_t computeMultiplier(const int log_div, const T divider) {
+      EIGEN_STATIC_ASSERT(N == 32, YOU_MADE_A_PROGRAMMING_MISTAKE);
+      return static_cast<uint32_t>((static_cast<uint64_t>(1) << (N+log_div)) / divider - (static_cast<uint64_t>(1) << N) + 1);
+    }
+  };
+
+  template <typename T>
+  struct DividerHelper<64, T> {
+    static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE uint64_t computeMultiplier(const int log_div, const T divider) {
+#if EIGEN_HAS_BUILTIN_INT128 && !defined(EIGEN_GPU_COMPILE_PHASE) && !defined(SYCL_DEVICE_ONLY)
+      return static_cast<uint64_t>((static_cast<__uint128_t>(1) << (64+log_div)) / static_cast<__uint128_t>(divider) - (static_cast<__uint128_t>(1) << 64) + 1);
+#else
+      const uint64_t shift = 1ULL << log_div;
+      TensorUInt128<uint64_t, uint64_t> result = TensorUInt128<uint64_t, static_val<0> >(shift, 0) / TensorUInt128<static_val<0>, uint64_t>(divider)
+                                               - TensorUInt128<static_val<1>, static_val<0> >(1, 0)
+                                               + TensorUInt128<static_val<0>, static_val<1> >(1);
+      return static_cast<uint64_t>(result);
+#endif
+    }
+  };
+}
+
+
+template <typename T, bool div_gt_one = false>
+struct TensorIntDivisor {
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
+    multiplier = 0;
+    shift1 = 0;
+    shift2 = 0;
+  }
+
+  // Must have 0 < divider < 2^31. This is relaxed to
+  // 0 < divider < 2^63 when using 64-bit indices on platforms that support
+  // the __uint128_t type.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor(const T divider) {
+    const int N = DividerTraits<T>::N;
+    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(divider) < NumTraits<UnsignedType>::highest()/2);
+    eigen_assert(divider > 0);
+
+    // fast ln2
+    const int leading_zeros = count_leading_zeros(static_cast<UnsignedType>(divider));
+    int log_div = N - leading_zeros;
+    // if divider is a power of two then log_div is 1 more than it should be.
+    if ((static_cast<typename UnsignedTraits<T>::type>(1) << (log_div-1)) == static_cast<typename UnsignedTraits<T>::type>(divider))
+      log_div--;
+
+    multiplier = DividerHelper<N, T>::computeMultiplier(log_div, divider);
+    shift1 = log_div > 1 ? 1 : log_div;
+    shift2 = log_div > 1 ? log_div-1 : 0;
+  }
+
+  // Must have 0 <= numerator. On platforms that don't support the __uint128_t
+  // type numerator should also be less than 2^32-1.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T divide(const T numerator) const {
+    eigen_assert(static_cast<typename UnsignedTraits<T>::type>(numerator) < NumTraits<UnsignedType>::highest()/2);
+    //eigen_assert(numerator >= 0); // this is implicitly asserted by the line above
+
+    UnsignedType t1 = muluh(multiplier, numerator);
+    UnsignedType t = (static_cast<UnsignedType>(numerator) - t1) >> shift1;
+    return (t1 + t) >> shift2;
+  }
+
+ private:
+  typedef typename DividerTraits<T>::type UnsignedType;
+  UnsignedType multiplier;
+  int32_t shift1;
+  int32_t shift2;
+};
+
+
+// Optimized version for signed 32 bit integers.
+// Derived from Hacker's Delight.
+// Only works for divisors strictly greater than one
+template <>
+class TensorIntDivisor<int32_t, true> {
+ public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIntDivisor() {
+    magic = 0;
+    shift = 0;
+  }
+  // Must have 2 <= divider
+  EIGEN_DEVICE_FUNC TensorIntDivisor(int32_t divider)  {
+    eigen_assert(divider >= 2);
+    calcMagic(divider);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE int divide(const int32_t n) const {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    return (__umulhi(magic, n) >> shift);
+#elif defined(SYCL_DEVICE_ONLY)
+    return (cl::sycl::mul_hi(magic, static_cast<uint32_t>(n)) >> shift);
+#else
+    uint64_t v = static_cast<uint64_t>(magic) * static_cast<uint64_t>(n);
+    return (static_cast<uint32_t>(v >> 32) >> shift);
+#endif
+  }
+
+private:
+  // Compute the magic numbers. See Hacker's Delight section 10 for an in
+  // depth explanation.
+  EIGEN_DEVICE_FUNC void calcMagic(int32_t d) {
+   const unsigned two31 = 0x80000000;     // 2**31.
+   unsigned ad = d;
+   unsigned t = two31 + (ad >> 31);
+   unsigned anc = t - 1 - t%ad;     // Absolute value of nc.
+   int p = 31;                      // Init. p.
+   unsigned q1 = two31/anc;         // Init. q1 = 2**p/|nc|.
+   unsigned r1 = two31 - q1*anc;    // Init. r1 = rem(2**p, |nc|).
+   unsigned q2 = two31/ad;          // Init. q2 = 2**p/|d|.
+   unsigned r2 = two31 - q2*ad;     // Init. r2 = rem(2**p, |d|).
+   unsigned delta = 0;
+   do {
+      p = p + 1;
+      q1 = 2*q1;           // Update q1 = 2**p/|nc|.
+      r1 = 2*r1;           // Update r1 = rem(2**p, |nc|).
+      if (r1 >= anc) {     // (Must be an unsigned
+         q1 = q1 + 1;      // comparison here).
+         r1 = r1 - anc;}
+      q2 = 2*q2;           // Update q2 = 2**p/|d|.
+      r2 = 2*r2;           // Update r2 = rem(2**p, |d|).
+      if (r2 >= ad) {      // (Must be an unsigned
+         q2 = q2 + 1;      // comparison here).
+         r2 = r2 - ad;}
+      delta = ad - r2;
+   } while (q1 < delta || (q1 == delta && r1 == 0));
+
+   magic = (unsigned)(q2 + 1);
+   shift = p - 32;
+  }
+
+  uint32_t magic;
+  int32_t shift;
+};
+
+
+template <typename T, bool div_gt_one>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator / (const T& numerator, const TensorIntDivisor<T, div_gt_one>& divisor) {
+  return divisor.divide(numerator);
+}
+
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_INTDIV_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
new file mode 100644
index 0000000..80106c1
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
@@ -0,0 +1,216 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
+
+namespace Eigen {
+
+/** \class TensorLayoutSwap
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Swap the layout from col-major to row-major, or row-major
+  * to col-major, and invert the order of the dimensions.
+  *
+  * Beware: the dimensions are reversed by this operation. If you want to
+  * preserve the ordering of the dimensions, you need to combine this
+  * operation with a shuffle.
+  *
+  * \example:
+  * Tensor<float, 2, ColMajor> input(2, 4);
+  * Tensor<float, 2, RowMajor> output = input.swap_layout();
+  * eigen_assert(output.dimension(0) == 4);
+  * eigen_assert(output.dimension(1) == 2);
+  *
+  * array<int, 2> shuffle(1, 0);
+  * output = input.swap_layout().shuffle(shuffle);
+  * eigen_assert(output.dimension(0) == 2);
+  * eigen_assert(output.dimension(1) == 4);
+  *
+  */
+namespace internal {
+template<typename XprType>
+struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = traits<XprType>::NumDimensions;
+  static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename XprType>
+struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
+{
+  typedef const TensorLayoutSwapOp<XprType>& type;
+};
+
+template<typename XprType>
+struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type>
+{
+  typedef TensorLayoutSwapOp<XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename XprType>
+class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
+{
+  public:
+    typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
+    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr)
+        : m_xpr(expr) {}
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
+  protected:
+    typename XprType::Nested m_xpr;
+};
+
+
+// Eval as rvalue
+template<typename ArgType, typename Device>
+struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
+{
+  typedef TensorLayoutSwapOp<ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
+    CoordAccess = false,  // to be implemented
+    RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device)
+  {
+    for(int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] = m_impl.dimensions()[NumDims-1-i];
+    }
+  }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    return m_impl.evalSubExprsIfNeeded(data);
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_impl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const {
+    return constCast(m_impl.data());
+  }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  Dimensions m_dimensions;
+};
+
+
+// Eval as lvalue
+template<typename ArgType, typename Device>
+  struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
+  : public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
+  typedef TensorLayoutSwapOp<ArgType> XprType;
+
+  enum {
+    IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = (static_cast<int>(TensorEvaluator<ArgType, Device>::Layout) == static_cast<int>(ColMajor)) ? RowMajor : ColMajor,
+    CoordAccess = false  // to be implemented
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(index);
+  }
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    this->m_impl.template writePacket<StoreMode>(index, x);
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
new file mode 100644
index 0000000..73ff3d2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMacros.h
@@ -0,0 +1,98 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_META_MACROS_H
+
+
+/** use this macro in sfinae selection in templated functions
+ *
+ *   template<typename T,
+ *            typename std::enable_if< isBanana<T>::value , int >::type = 0
+ *   >
+ *   void foo(){}
+ *
+ *   becomes =>
+ *
+ *   template<typename TopoType,
+ *           SFINAE_ENABLE_IF( isBanana<T>::value )
+ *   >
+ *   void foo(){}
+ */
+
+// SFINAE requires variadic templates
+#if !defined(EIGEN_GPUCC)
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  // SFINAE doesn't work for gcc <= 4.7
+  #ifdef EIGEN_COMP_GNUC
+    #if EIGEN_GNUC_AT_LEAST(4,8)
+      #define EIGEN_HAS_SFINAE
+    #endif
+  #else
+    #define EIGEN_HAS_SFINAE
+  #endif
+#endif
+#endif
+
+#define EIGEN_SFINAE_ENABLE_IF( __condition__ ) \
+    typename internal::enable_if< ( __condition__ ) , int >::type = 0
+
+// Define a macro to use a reference on the host but a value on the device
+#if defined(SYCL_DEVICE_ONLY)
+  #define EIGEN_DEVICE_REF
+#else
+  #define EIGEN_DEVICE_REF &
+#endif
+
+// Define a macro for catching SYCL exceptions if exceptions are enabled
+#define EIGEN_SYCL_TRY_CATCH(X) \
+  do { \
+    EIGEN_TRY {X;} \
+    EIGEN_CATCH(const cl::sycl::exception& e) { \
+      EIGEN_THROW_X(std::runtime_error("SYCL exception at " + \
+                                       std::string(__FILE__) + ":" + \
+                                       std::to_string(__LINE__) + "\n" + \
+                                       e.what())); \
+    } \
+  } while (false)
+
+// Define a macro if local memory flags are unset or one of them is set
+// Setting both flags is the same as unsetting them
+#if (!defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)) || \
+     (defined(EIGEN_SYCL_LOCAL_MEM) &&  defined(EIGEN_SYCL_NO_LOCAL_MEM))
+  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
+  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
+#elif defined(EIGEN_SYCL_LOCAL_MEM) && !defined(EIGEN_SYCL_NO_LOCAL_MEM)
+  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON 1
+#elif !defined(EIGEN_SYCL_LOCAL_MEM) && defined(EIGEN_SYCL_NO_LOCAL_MEM)
+  #define EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF 1
+#endif
+
+#if EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
+  #define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    using Base::operator =; \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
+    template <typename OtherDerived> \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const OtherDerived& other) { Base::operator=(other); return *this; }
+#else
+  #define EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived)
+#endif
+
+/** \internal
+ * \brief Macro to manually inherit assignment operators.
+ * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is defined.
+ * This also inherits template<OtherDerived> operator=(const OtherDerived&) assignments.
+ * With C++11 or later this also default-implements the copy-constructor
+ */
+#define EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(Derived)  \
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
+
+#endif
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
new file mode 100644
index 0000000..6834c97
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
@@ -0,0 +1,327 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
+#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
+
+namespace Eigen {
+
+// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_)
+
+/** \class TensorMap
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief A tensor expression mapping an existing array of data.
+  *
+  */
+/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer.
+/// It is added due to the fact that for our device compiler `T*` is not allowed.
+/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`.
+/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` .
+/// Therefore, by adding the default value, we managed to convert the type and it does not break any
+/// existing code as its default value is `T*`.
+template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
+{
+  public:
+    typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
+    typedef TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > Base;
+  #ifdef EIGEN_USE_SYCL
+    typedef  typename Eigen::internal::remove_reference<typename Eigen::internal::nested<Self>::type>::type Nested;
+  #else
+     typedef typename Eigen::internal::nested<Self>::type Nested;
+  #endif
+   typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
+    typedef typename internal::traits<PlainObjectType>::Index Index;
+    typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef typename PlainObjectType::Base::CoeffReturnType CoeffReturnType;
+
+    typedef typename MakePointer_<Scalar>::Type PointerType;
+    typedef typename MakePointer_<Scalar>::ConstType PointerConstType;
+
+    // WARN: PointerType still can be a pointer to const (const Scalar*), for
+    // example in TensorMap<Tensor<const Scalar, ...>> expression. This type of
+    // expression should be illegal, but adding this restriction is not possible
+    // in practice (see https://bitbucket.org/eigen/eigen/pull-requests/488).
+    typedef typename internal::conditional<
+        bool(internal::is_lvalue<PlainObjectType>::value),
+        PointerType,      // use simple pointer in lvalue expressions
+        PointerConstType  // use const pointer in rvalue expressions
+        >::type StoragePointerType;
+
+    // If TensorMap was constructed over rvalue expression (e.g. const Tensor),
+    // we should return a reference to const from operator() (and others), even
+    // if TensorMap itself is not const.
+    typedef typename internal::conditional<
+        bool(internal::is_lvalue<PlainObjectType>::value),
+        Scalar&,
+        const Scalar&
+        >::type StorageRefType;
+
+    static const int Options = Options_;
+
+    static const Index NumIndices = PlainObjectType::NumIndices;
+    typedef typename PlainObjectType::Dimensions Dimensions;
+
+    enum {
+      IsAligned = ((int(Options_)&Aligned)==Aligned),
+      Layout = PlainObjectType::Layout,
+      CoordAccess = true,
+      RawAccess = true
+    };
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr) : m_data(dataPtr), m_dimensions() {
+      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
+      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
+      // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
+      EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
+      EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
+      EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
+      EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
+      EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    }
+#endif
+
+   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const array<Index, NumIndices>& dimensions)
+      : m_data(dataPtr), m_dimensions(dimensions)
+    { }
+
+    template <typename Dimensions>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const Dimensions& dimensions)
+      : m_data(dataPtr), m_dimensions(dimensions)
+    { }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
+      : m_data(tensor.data()), m_dimensions(tensor.dimensions())
+    { }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StoragePointerType data() { return m_data; }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StoragePointerType data() const { return m_data; }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) const
+    {
+      //      eigen_assert(checkIndexRange(indices));
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = m_dimensions.IndexOfRowMajor(indices);
+        return m_data[index];
+      } else {
+        const Index index = m_dimensions.IndexOfColMajor(indices);
+        return m_data[index];
+      }
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()() const
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return m_data[0];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index index) const
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_data[index];
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
+    {
+      EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+        return m_data[index];
+      } else {
+        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
+        return m_data[index];
+      }
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1) const
+    {
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = i1 + i0 * m_dimensions[1];
+        return m_data[index];
+      } else {
+        const Index index = i0 + i1 * m_dimensions[0];
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2) const
+    {
+      if (PlainObjectType::Options&RowMajor) {
+         const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
+         return m_data[index];
+      } else {
+         const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3) const
+    {
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
+        return m_data[index];
+      } else {
+        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
+    {
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
+        return m_data[index];
+      } else {
+        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
+        return m_data[index];
+      }
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices)
+    {
+      //      eigen_assert(checkIndexRange(indices));
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = m_dimensions.IndexOfRowMajor(indices);
+        return m_data[index];
+      } else {
+        const Index index = m_dimensions.IndexOfColMajor(indices);
+        return m_data[index];
+      }
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()()
+    {
+      EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
+      return m_data[0];
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index index)
+    {
+      eigen_internal_assert(index >= 0 && index < size());
+      return m_data[index];
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
+    {
+      static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+       eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
+      const std::size_t NumDims = sizeof...(otherIndices) + 2;
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
+        return m_data[index];
+      } else {
+        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
+        return m_data[index];
+      }
+    }
+#else
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1)
+    {
+       if (PlainObjectType::Options&RowMajor) {
+         const Index index = i1 + i0 * m_dimensions[1];
+        return m_data[index];
+      } else {
+        const Index index = i0 + i1 * m_dimensions[0];
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2)
+    {
+       if (PlainObjectType::Options&RowMajor) {
+         const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
+        return m_data[index];
+      } else {
+         const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3)
+    {
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
+        return m_data[index];
+      } else {
+        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
+        return m_data[index];
+      }
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
+    {
+      if (PlainObjectType::Options&RowMajor) {
+        const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
+        return m_data[index];
+      } else {
+        const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
+        return m_data[index];
+      }
+    }
+#endif
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorMap)
+
+  private:
+    StoragePointerType m_data;
+    Dimensions m_dimensions;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
new file mode 100644
index 0000000..a6181d3
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMeta.h
@@ -0,0 +1,311 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_META_H
+#define EIGEN_CXX11_TENSOR_TENSOR_META_H
+
+namespace Eigen {
+
+template<bool cond> struct Cond {};
+
+template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+const T1& choose(Cond<true>, const T1& first, const T2&) {
+  return first;
+}
+
+template<typename T1, typename T2> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+const T2& choose(Cond<false>, const T1&, const T2& second) {
+  return second;
+}
+
+
+template <typename T, typename X, typename Y>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T divup(const X x, const Y y) {
+  return static_cast<T>((x + y - 1) / y);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T divup(const T x, const T y) {
+  return static_cast<T>((x + y - 1) / y);
+}
+
+template <size_t n> struct max_n_1 {
+  static const size_t size = n;
+};
+template <> struct max_n_1<0> {
+  static const size_t size = 1;
+};
+
+
+// Default packet types
+template <typename Scalar, typename Device>
+struct PacketType : internal::packet_traits<Scalar> {
+  typedef typename internal::packet_traits<Scalar>::type type;
+};
+
+// For CUDA packet types when using a GpuDevice
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_HAS_GPU_FP16)
+
+typedef ulonglong2 Packet4h2;
+template<>
+struct PacketType<half, GpuDevice> {
+  typedef Packet4h2 type;
+  static const int size = 8;
+  enum {
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasNegate = 1,
+    HasAbs    = 1,
+    HasArg    = 0,
+    HasAbs2   = 0,
+    HasMin    = 1,
+    HasMax    = 1,
+    HasConj   = 0,
+    HasSetLinear = 0,
+    HasBlend  = 0,
+
+    HasDiv    = 1,
+    HasSqrt   = 1,
+    HasRsqrt  = 1,
+    HasExp    = 1,
+    HasExpm1  = 0,
+    HasLog    = 1,
+    HasLog1p  = 0,
+    HasLog10  = 0,
+    HasPow    = 1,
+  };
+};
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+
+namespace TensorSycl {
+namespace internal {
+
+template <typename Index, Index A, Index B> struct PlusOp {
+  static constexpr Index Value = A + B;
+};
+
+template <typename Index, Index A, Index B> struct DivOp {
+  static constexpr Index Value = A / B;
+};
+
+template <typename Index, Index start, Index end, Index step,
+          template <class Indx, Indx...> class StepOp>
+struct static_for {
+  template <typename UnaryOperator>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator op) {
+    op(start);
+    static_for<Index, StepOp<Index, start, step>::Value, end, step,
+               StepOp>::loop(op);
+  }
+};
+template <typename Index, Index end, Index step,
+          template <class Indx, Indx...> class StepOp>
+struct static_for<Index, end, end, step, StepOp> {
+  template <typename UnaryOperator>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void loop(UnaryOperator) {}
+};
+
+template <typename OutScalar, typename Device, bool Vectorizable>
+struct Vectorise {
+  static const int PacketSize = 1;
+  typedef OutScalar PacketReturnType;
+};
+
+template <typename OutScalar, typename Device>
+struct Vectorise<OutScalar, Device, true> {
+  static const int PacketSize = Eigen::PacketType<OutScalar, Device>::size;
+  typedef typename Eigen::PacketType<OutScalar, Device>::type PacketReturnType;
+};
+
+static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index roundUp(Index x, Index y) {
+  return ((((x) + (y)-1) / (y)) * (y));
+}
+
+} // namespace internal
+} // namespace TensorSycl
+
+template <>
+  struct PacketType<half, SyclDevice> {
+  typedef half type;
+  static const int size = 1;
+  enum {
+    HasAdd    = 0,
+    HasSub    = 0,
+    HasMul    = 0,
+    HasNegate = 0,
+    HasAbs    = 0,
+    HasArg    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasConj   = 0,
+    HasSetLinear = 0,
+    HasBlend  = 0
+  };
+};
+template <typename Scalar>
+struct PacketType<Scalar, SyclDevice> : internal::default_packet_traits {
+  typedef Scalar type;
+  typedef Scalar half;
+  enum {
+    Vectorizable = 0,
+    size = 1,
+    AlignedOnScalar = 0,
+    HasHalfPacket = 0
+  };
+  enum {
+    HasAdd    = 0,
+    HasSub    = 0,
+    HasMul    = 0,
+    HasNegate = 0,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasConj   = 0,
+    HasSetLinear = 0
+  };
+
+};
+
+template <typename Scalar>
+struct PacketType<Scalar, const SyclDevice> : PacketType<Scalar, SyclDevice>{};
+
+#ifndef EIGEN_DONT_VECTORIZE_SYCL
+#define PACKET_TYPE(CVQual, Type, val, lengths, DEV)\
+template<> struct PacketType<CVQual Type, DEV> : internal::sycl_packet_traits<val, lengths> \
+{\
+  typedef typename internal::packet_traits<Type>::type type;\
+  typedef typename internal::packet_traits<Type>::half half;\
+};
+
+
+PACKET_TYPE(const, float, 1, 4, SyclDevice)
+PACKET_TYPE(, float, 1, 4, SyclDevice)
+PACKET_TYPE(const, float, 1, 4, const SyclDevice)
+PACKET_TYPE(, float, 1, 4, const SyclDevice)
+
+PACKET_TYPE(const, double, 0, 2, SyclDevice)
+PACKET_TYPE(, double, 0, 2, SyclDevice)
+PACKET_TYPE(const, double, 0, 2, const SyclDevice)
+PACKET_TYPE(, double, 0, 2, const SyclDevice)
+#undef PACKET_TYPE
+
+template<> struct PacketType<half, const SyclDevice>: PacketType<half, SyclDevice>{};
+template<> struct PacketType<const half, const SyclDevice>: PacketType<half, SyclDevice>{};
+#endif
+#endif
+
+// Tuple mimics std::pair but works on e.g. nvcc.
+template <typename U, typename V> struct Tuple {
+ public:
+  U first;
+  V second;
+
+  typedef U first_type;
+  typedef V second_type;
+
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Tuple() : first(), second() {}
+
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Tuple(const U& f, const V& s) : first(f), second(s) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void swap(Tuple& rhs) {
+    using numext::swap;
+    swap(first, rhs.first);
+    swap(second, rhs.second);
+  }
+};
+
+template <typename U, typename V>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+bool operator==(const Tuple<U, V>& x, const Tuple<U, V>& y) {
+  return (x.first == y.first && x.second == y.second);
+}
+
+template <typename U, typename V>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+bool operator!=(const Tuple<U, V>& x, const Tuple<U, V>& y) {
+  return !(x == y);
+}
+
+
+// Can't use std::pairs on cuda devices
+template <typename Idx> struct IndexPair {
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair() : first(0), second(0) {}
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE IndexPair(Idx f, Idx s) : first(f), second(s) {}
+
+  EIGEN_DEVICE_FUNC void set(IndexPair<Idx> val) {
+    first = val.first;
+    second = val.second;
+  }
+
+  Idx first;
+  Idx second;
+};
+
+
+#ifdef EIGEN_HAS_SFINAE
+namespace internal {
+
+  template<typename IndexType, typename Index, Index... Is>
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  array<Index, sizeof...(Is)> customIndices2Array(IndexType& idx, numeric_list<Index, Is...>) {
+    return { idx[Is]... };
+  }
+  template<typename IndexType, typename Index>
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  array<Index, 0> customIndices2Array(IndexType&, numeric_list<Index>) {
+    return array<Index, 0>();
+  }
+
+  /** Make an array (for index/dimensions) out of a custom index */
+  template<typename Index, std::size_t NumIndices, typename IndexType>
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  array<Index, NumIndices> customIndices2Array(IndexType& idx) {
+    return customIndices2Array(idx, typename gen_numeric_list<Index, NumIndices>::type{});
+  }
+
+
+  template <typename B, typename D>
+  struct is_base_of
+  {
+
+    typedef char (&yes)[1];
+    typedef char (&no)[2];
+
+    template <typename BB, typename DD>
+    struct Host
+    {
+      operator BB*() const;
+      operator DD*();
+    };
+
+    template<typename T>
+    static yes check(D*, T);
+    static no check(B*, int);
+
+    static const bool value = sizeof(check(Host<B,D>(), int())) == sizeof(yes);
+  };
+
+}
+#endif
+
+
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_META_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
new file mode 100644
index 0000000..b3f00f7
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
@@ -0,0 +1,1102 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
+
+namespace Eigen {
+
+/** \class TensorReshaping
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reshaping class.
+  *
+  *
+  */
+namespace internal {
+template<typename NewDimensions, typename XprType>
+struct traits<TensorReshapingOp<NewDimensions, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = array_size<NewDimensions>::value;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename NewDimensions, typename XprType>
+struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense>
+{
+  typedef const TensorReshapingOp<NewDimensions, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename NewDimensions, typename XprType>
+struct nested<TensorReshapingOp<NewDimensions, XprType>, 1, typename eval<TensorReshapingOp<NewDimensions, XprType> >::type>
+{
+  typedef TensorReshapingOp<NewDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename NewDimensions, typename XprType>
+class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors>
+{
+  public:
+  typedef TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> Base;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar;
+  typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+    EIGEN_DEVICE_FUNC
+    const NewDimensions& dimensions() const { return m_dims; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReshapingOp)
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const NewDimensions m_dims;
+};
+
+
+// Eval as rvalue
+template<typename NewDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
+{
+  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
+  typedef NewDimensions Dimensions;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage;
+
+  static const int NumOutputDims = internal::array_size<Dimensions>::value;
+  static const int NumInputDims  = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+
+  enum ReshapingKind {
+    // We do not use layout information to determine reshaping kind.
+    // Depending on the layout `N` can be inner or outer dimension.
+    OneByN = 0,  // expr.reshape(1, N)
+    NByOne = 1,  // expr.reshape(N, 1)
+    Runtime = 2  // Reshape dimensions are dynamic (specified at runtime).
+  };
+
+  // clang-format off
+  static const ReshapingKind kind =
+#if defined(EIGEN_HAS_INDEX_LIST)
+        (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/0, /*value=*/1)) ? OneByN
+      : (NumOutputDims == 2 && internal::index_statically_eq<NewDimensions>(/*index=*/1, /*value=*/1)) ? NByOne
+      : Runtime;
+#else
+        Runtime;
+#endif
+  // clang-format on
+
+  enum {
+    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    // For trivial reshapes with raw access to underlying data we will provide
+    // zero overhead block access.
+    // TODO(ezhulenev): Consider adding block access without raw access?
+    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess &&
+                        NumInputDims > 0 && NumOutputDims > 0,
+    PreferBlockAccess = false,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumOutputDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef
+      typename internal::TensorMaterializedBlock<ScalarNoConst, NumOutputDims,
+                                                 Layout, Index>
+          TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_dimensions(op.dimensions())
+  {
+    // The total size of the reshaped tensor must be equal to the total size
+    // of the input tensor.
+    eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions()));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType data, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(data, std::move(done));
+  }
+#endif
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    return m_impl.evalSubExprsIfNeeded(data);
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    return m_impl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    return internal::TensorBlockResourceRequirements::any();
+  }
+
+  // required in block(OutputTensorBlock* output_block) const
+  // For C++03 compatibility this must be defined outside the method
+  struct BlockIteratorState {
+    Index stride;
+    Index span;
+    Index size;
+    Index count;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    eigen_assert(m_impl.data() != NULL);
+    eigen_assert((kind == Runtime) ||
+                 (kind == OneByN && desc.dimensions()[0] == 1) ||
+                 (kind == NByOne && desc.dimensions()[1] == 1));
+
+    if (kind == OneByN || kind == NByOne) {
+      // We can guarantee at compile time that block is just a contiguous slice
+      // of the underlying expression memory buffer.
+      return TensorBlock(internal::TensorBlockKind::kView,
+                           m_impl.data() + desc.offset(), desc.dimensions());
+    } else {
+      // This will do additional runtime checks, and in the end it might be also
+      // a view, or it might be a block materialized in the temporary buffer.
+      return TensorBlock::materialize(m_impl.data(), m_dimensions, desc,
+                                        scratch);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const {
+    return constCast(m_impl.data());
+  }
+
+  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+  #ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+  #endif
+ protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  NewDimensions m_dimensions;
+};
+
+
+// Eval as lvalue
+template<typename NewDimensions, typename ArgType, typename Device>
+  struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device>
+  : public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device>
+
+{
+  typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base;
+  typedef TensorReshapingOp<NewDimensions, ArgType> XprType;
+  typedef NewDimensions Dimensions;
+
+  enum {
+    IsAligned         = TensorEvaluator<ArgType, Device>::IsAligned,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = false,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<TensorEvaluator::NumOutputDims, Index>
+      TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(index);
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    this->m_impl.template writePacket<StoreMode>(index, x);
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlockDesc& desc, const TensorBlock& block) {
+    assert(this->m_impl.data() != NULL);
+
+    typedef typename TensorBlock::XprType TensorBlockExpr;
+    typedef internal::TensorBlockAssignment<
+        Scalar, TensorEvaluator::NumOutputDims, TensorBlockExpr, Index>
+        TensorBlockAssign;
+
+    TensorBlockAssign::Run(
+        TensorBlockAssign::target(desc.dimensions(),
+                                  internal::strides<Layout>(this->dimensions()),
+                                  this->m_impl.data(), desc.offset()),
+        block.expr());
+  }
+};
+
+
+/** \class TensorSlicing
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor slicing class.
+  *
+  *
+  */
+namespace internal {
+template<typename StartIndices, typename Sizes, typename XprType>
+struct traits<TensorSlicingOp<StartIndices, Sizes, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = array_size<StartIndices>::value;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename StartIndices, typename Sizes, typename XprType>
+struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense>
+{
+  typedef const TensorSlicingOp<StartIndices, Sizes, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename StartIndices, typename Sizes, typename XprType>
+struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1, typename eval<TensorSlicingOp<StartIndices, Sizes, XprType> >::type>
+{
+  typedef TensorSlicingOp<StartIndices, Sizes, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename StartIndices, typename Sizes, typename XprType>
+class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> >
+{
+  public:
+  typedef TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> > Base;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices, const Sizes& sizes)
+      : m_xpr(expr), m_indices(indices), m_sizes(sizes) {}
+
+    EIGEN_DEVICE_FUNC
+    const StartIndices& startIndices() const { return m_indices; }
+    EIGEN_DEVICE_FUNC
+    const Sizes& sizes() const { return m_sizes; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorSlicingOp)
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const StartIndices m_indices;
+    const Sizes m_sizes;
+};
+
+
+// Fixme: figure out the exact threshold
+namespace {
+template <typename Index, typename Device, bool BlockAccess> struct MemcpyTriggerForSlicing {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const Device& device) : threshold_(2 * device.numThreads()) { }
+  EIGEN_DEVICE_FUNC bool operator ()(Index total, Index contiguous) const {
+    const bool prefer_block_evaluation = BlockAccess && total > 32*1024;
+    return !prefer_block_evaluation && contiguous > threshold_;
+  }
+
+ private:
+  Index threshold_;
+};
+
+// It is very expensive to start the memcpy kernel on GPU: we therefore only
+// use it for large copies.
+#ifdef EIGEN_USE_GPU
+template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, GpuDevice, BlockAccess>  {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const GpuDevice&) { }
+  EIGEN_DEVICE_FUNC bool operator ()(Index, Index contiguous) const { return contiguous > 4*1024*1024; }
+};
+#endif
+
+// It is very expensive to start the memcpy kernel on GPU: we therefore only
+// use it for large copies.
+#ifdef EIGEN_USE_SYCL
+template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, Eigen::SyclDevice, BlockAccess>  {
+  EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const SyclDevice&) { }
+  EIGEN_DEVICE_FUNC bool operator ()(Index, Index contiguous) const { return contiguous > 4*1024*1024; }
+};
+#endif
+
+}
+
+// Eval as rvalue
+template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
+{
+  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
+  static const int NumDims = internal::array_size<Sizes>::value;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Sizes Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets and sizes.
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess &&
+                        // FIXME: Temporary workaround for bug in slicing of bool tensors.
+                        !internal::is_same<typename internal::remove_const<Scalar>::type, bool>::value,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  // Tensor slicing does not change the block type.
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices())
+  {
+    m_is_identity = true;
+    for (int i = 0; i < internal::array_size<Dimensions>::value; ++i) {
+      eigen_assert(m_impl.dimensions()[i] >=
+                   op.sizes()[i] + op.startIndices()[i]);
+      if (m_impl.dimensions()[i] != op.sizes()[i] ||
+          op.startIndices()[i] != 0) {
+        m_is_identity = false;
+      }
+    }
+
+    // No strides for scalars.
+    if (NumDims == 0) return;
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const Sizes& output_dims = op.sizes();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+      }
+
+     // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    } else {
+      m_inputStrides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+      }
+
+     // Don't initialize m_fastOutputStrides[NumDims-1] since it won't ever be accessed.
+      m_outputStrides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization
+        && data && m_impl.data()) {
+      Index contiguous_values = 1;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        for (int i = 0; i < NumDims; ++i) {
+          contiguous_values *= dimensions()[i];
+          if (dimensions()[i] != m_impl.dimensions()[i]) {
+            break;
+          }
+        }
+      } else {
+        for (int i = NumDims-1; i >= 0; --i) {
+          contiguous_values *= dimensions()[i];
+          if (dimensions()[i] != m_impl.dimensions()[i]) {
+            break;
+          }
+        }
+      }
+      // Use memcpy if it's going to be faster than using the regular evaluation.
+      const MemcpyTriggerForSlicing<Index, Device, BlockAccess> trigger(m_device);
+      if (trigger(internal::array_prod(dimensions()), contiguous_values)) {
+        EvaluatorPointerType src = (EvaluatorPointerType)m_impl.data();
+        for (Index i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) {
+          Index offset = srcCoeff(i);
+          m_device.memcpy((void*)(m_device.get(data + i)), m_device.get(src+offset), contiguous_values * sizeof(Scalar));
+        }
+        return false;
+      }
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType /*data*/, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+packetSize-1 < internal::array_prod(dimensions()));
+
+    if (m_is_identity) {
+      return m_impl.template packet<LoadMode>(index);
+    }
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + packetSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
+        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + m_offsets[0]);
+      inputIndices[1] += (indices[1] + m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i];
+        inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + m_offsets[NumDims-1]);
+      inputIndices[1] += (indices[1] + m_offsets[NumDims-1]);
+    }
+    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    }
+    else {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[packetSize-1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < packetSize-1; ++i) {
+        values[i] = coeff(index+i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
+        m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    TensorBlockDesc arg_desc = desc.WithOffset(srcCoeff(desc.offset()));
+    TensorBlock block = m_impl.block(arg_desc, scratch);
+    if (!arg_desc.HasDestinationBuffer()) desc.DropDestinationBuffer();
+    return block;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
+    typename Storage::Type result = constCast(m_impl.data());
+    if (result) {
+      Index offset = 0;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        for (int i = 0; i < NumDims; ++i) {
+          if (m_dimensions[i] != m_impl.dimensions()[i]) {
+            offset += m_offsets[i] * m_inputStrides[i];
+            for (int j = i+1; j < NumDims; ++j) {
+              if (m_dimensions[j] > 1) {
+                return NULL;
+              }
+              offset += m_offsets[j] * m_inputStrides[j];
+            }
+            break;
+          }
+        }
+      } else {
+        for (int i = NumDims - 1; i >= 0; --i) {
+          if (m_dimensions[i] != m_impl.dimensions()[i]) {
+            offset += m_offsets[i] * m_inputStrides[i];
+            for (int j = i-1; j >= 0; --j) {
+              if (m_dimensions[j] > 1) {
+                return NULL;
+              }
+              offset += m_offsets[j] * m_inputStrides[j];
+            }
+            break;
+          }
+        }
+      }
+      return result + offset;
+    }
+    return NULL;
+  }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
+  {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += (index + m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += (idx + m_offsets[i]) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += (index + m_offsets[NumDims-1]);
+    }
+    return inputIndex;
+  }
+
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  Dimensions m_dimensions;
+  bool m_is_identity;
+  const StartIndices m_offsets;
+};
+
+
+// Eval as lvalue
+template<typename StartIndices, typename Sizes, typename ArgType, typename Device>
+struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
+  : public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base;
+  typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType;
+  static const int NumDims = internal::array_size<Sizes>::value;
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Sizes Dimensions;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::BlockAccess,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,
+    RawAccess         = (NumDims == 1) & TensorEvaluator<ArgType, Device>::RawAccess
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+    { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    if (this->m_is_identity) {
+      return this->m_impl.coeffRef(index);
+    } else {
+      return this->m_impl.coeffRef(this->srcCoeff(index));
+    }
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    if (this->m_is_identity) {
+      this->m_impl.template writePacket<StoreMode>(index, x);
+      return;
+    }
+
+    const int packetSize = PacketType<CoeffReturnType, Device>::size;
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + packetSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
+        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + this->m_offsets[0]);
+      inputIndices[1] += (indices[1] + this->m_offsets[0]);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / this->m_fastOutputStrides[i];
+        const Index idx1 = indices[1] / this->m_fastOutputStrides[i];
+        inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i];
+        inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += (indices[0] + this->m_offsets[NumDims-1]);
+      inputIndices[1] += (indices[1] + this->m_offsets[NumDims-1]);
+    }
+    if (inputIndices[1] - inputIndices[0] == packetSize - 1) {
+      this->m_impl.template writePacket<StoreMode>(inputIndices[0], x);
+    }
+    else {
+      EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
+      internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+      this->m_impl.coeffRef(inputIndices[0]) = values[0];
+      this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1];
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < packetSize-1; ++i) {
+        this->coeffRef(index+i) = values[i];
+      }
+    }
+  }
+
+  template<typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlockDesc& desc, const TensorBlock& block) {
+    TensorBlockDesc arg_desc = desc.WithOffset(this->srcCoeff(desc.offset()));
+    this->m_impl.writeBlock(arg_desc, block);
+  }
+};
+
+namespace internal {
+template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct traits<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = array_size<StartIndices>::value;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, Eigen::Dense>
+{
+  typedef const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+struct nested<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, 1, typename eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >::type>
+{
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+
+template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>
+class TensorStridingSlicingOp : public TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >
+{
+  public:
+  typedef TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > Base;
+  typedef typename internal::traits<TensorStridingSlicingOp>::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename internal::nested<TensorStridingSlicingOp>::type Nested;
+  typedef typename internal::traits<TensorStridingSlicingOp>::StorageKind StorageKind;
+  typedef typename internal::traits<TensorStridingSlicingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp(
+    const XprType& expr, const StartIndices& startIndices,
+    const StopIndices& stopIndices, const Strides& strides)
+      : m_xpr(expr), m_startIndices(startIndices), m_stopIndices(stopIndices),
+        m_strides(strides) {}
+
+    EIGEN_DEVICE_FUNC
+    const StartIndices& startIndices() const { return m_startIndices; }
+    EIGEN_DEVICE_FUNC
+    const StartIndices& stopIndices() const { return m_stopIndices; }
+    EIGEN_DEVICE_FUNC
+    const StartIndices& strides() const { return m_strides; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingSlicingOp)
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const StartIndices m_startIndices;
+    const StopIndices m_stopIndices;
+    const Strides m_strides;
+};
+
+// Eval as rvalue
+template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
+{
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
+  static const int NumDims = internal::array_size<Strides>::value;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef Strides Dimensions;
+
+  enum {
+    // Alignment can't be guaranteed at compile time since it depends on the
+    // slice offsets and sizes.
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device),
+        m_device(device),
+        m_strides(op.strides())
+  {
+    // Handle degenerate intervals by gracefully clamping and allowing m_dimensions to be zero
+    DSizes<Index, NumDims> startIndicesClamped, stopIndicesClamped;
+    for (ptrdiff_t i = 0; i < internal::array_size<Dimensions>::value; ++i) {
+      eigen_assert(m_strides[i] != 0 && "0 stride is invalid");
+      if (m_strides[i] > 0) {
+        startIndicesClamped[i] =
+            clamp(op.startIndices()[i], 0, m_impl.dimensions()[i]);
+        stopIndicesClamped[i] =
+            clamp(op.stopIndices()[i], 0, m_impl.dimensions()[i]);
+      } else {
+        /* implies m_strides[i] < 0 by assert */
+        startIndicesClamped[i] =
+            clamp(op.startIndices()[i], -1, m_impl.dimensions()[i] - 1);
+        stopIndicesClamped[i] =
+            clamp(op.stopIndices()[i], -1, m_impl.dimensions()[i] - 1);
+      }
+      m_startIndices[i] = startIndicesClamped[i];
+    }
+
+    typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+    const InputDimensions& input_dims = m_impl.dimensions();
+
+    // compute output tensor shape
+    m_is_identity = true;
+    for (int i = 0; i < NumDims; i++) {
+      Index interval = stopIndicesClamped[i] - startIndicesClamped[i];
+      if (interval == 0 || ((interval < 0) != (m_strides[i] < 0))) {
+        m_dimensions[i] = 0;
+      } else {
+        m_dimensions[i] =
+            (interval / m_strides[i]) + (interval % m_strides[i] != 0 ? 1 : 0);
+        eigen_assert(m_dimensions[i] >= 0);
+      }
+      if (m_strides[i] != 1 || interval != m_impl.dimensions()[i]) {
+        m_is_identity = false;
+      }
+    }
+
+    Strides output_dims = m_dimensions;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = m_strides[0];
+      m_offsets[0] = startIndicesClamped[0];
+      Index previousDimProduct = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        previousDimProduct *= input_dims[i-1];
+        m_inputStrides[i] = previousDimProduct * m_strides[i];
+        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
+      }
+
+      // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed.
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    } else {
+      m_inputStrides[NumDims-1] = m_strides[NumDims-1];
+      m_offsets[NumDims-1] = startIndicesClamped[NumDims-1];
+      Index previousDimProduct = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        previousDimProduct *= input_dims[i+1];
+        m_inputStrides[i] = previousDimProduct * m_strides[i];
+        m_offsets[i] = startIndicesClamped[i] * previousDimProduct;
+      }
+
+      m_outputStrides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : 1);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const {
+    return NULL;
+  }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
+  {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i >= 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
+        index -= idx * m_outputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i] + m_offsets[i];
+        index -= idx * m_outputStrides[i];
+      }
+    }
+    return inputIndex;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max) {
+#ifndef SYCL_DEVICE_ONLY
+    return numext::maxi(min, numext::mini(max,value));
+#else
+    return cl::sycl::clamp(value, min, max);
+#endif
+  }
+
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  bool m_is_identity;
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  DSizes<Index, NumDims> m_startIndices; // clamped startIndices
+  DSizes<Index, NumDims> m_dimensions;
+  DSizes<Index, NumDims> m_offsets; // offset in a flattened shape
+  const Strides m_strides;
+};
+
+// Eval as lvalue
+template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
+  : public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> Base;
+  typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType;
+  static const int NumDims = internal::array_size<Strides>::value;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : Base(op, device)
+    { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef Strides Dimensions;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    if (this->m_is_identity) {
+      return this->m_impl.coeffRef(index);
+    } else {
+      return this->m_impl.coeffRef(this->srcCoeff(index));
+    }
+  }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
new file mode 100644
index 0000000..ee44382
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
@@ -0,0 +1,708 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
+
+namespace Eigen {
+
+/** \class TensorPadding
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor padding class.
+  * At the moment only padding with a constant value is supported.
+  *
+  */
+namespace internal {
+template<typename PaddingDimensions, typename XprType>
+struct traits<TensorPaddingOp<PaddingDimensions, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename PaddingDimensions, typename XprType>
+struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense>
+{
+  typedef const TensorPaddingOp<PaddingDimensions, XprType>& type;
+};
+
+template<typename PaddingDimensions, typename XprType>
+struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type>
+{
+  typedef TensorPaddingOp<PaddingDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename PaddingDimensions, typename XprType>
+class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims, const Scalar padding_value)
+      : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {}
+
+    EIGEN_DEVICE_FUNC
+    const PaddingDimensions& padding() const { return m_padding_dims; }
+    EIGEN_DEVICE_FUNC
+    Scalar padding_value() const { return m_padding_value; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const PaddingDimensions m_padding_dims;
+    const Scalar m_padding_value;
+};
+
+
+// Eval as rvalue
+template<typename PaddingDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device>
+{
+  typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<PaddingDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = true,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = true,
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device)
+  {
+    // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead
+    // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector
+    // of 1 element first and then pad.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Compute dimensions
+    m_dimensions = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] += m_padding[i].first + m_padding[i].second;
+    }
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
+      }
+      m_outputStrides[NumDims] = m_outputStrides[NumDims-1] * m_dimensions[NumDims-1];
+    } else {
+      m_inputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+        m_outputStrides[i+1] = m_outputStrides[i+2] * m_dimensions[i+1];
+      }
+      m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    eigen_assert(index < dimensions().TotalSize());
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        if (isPaddingAtIndexForDim(idx, i)) {
+          return m_paddingValue;
+        }
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (isPaddingAtIndexForDim(index, 0)) {
+        return m_paddingValue;
+      }
+      inputIndex += (index - m_padding[0].first);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i+1];
+        if (isPaddingAtIndexForDim(idx, i)) {
+          return m_paddingValue;
+        }
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i+1];
+      }
+      if (isPaddingAtIndexForDim(index, NumDims-1)) {
+        return m_paddingValue;
+      }
+      inputIndex += (index - m_padding[NumDims-1].first);
+    }
+    return m_impl.coeff(inputIndex);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      return packetColMajor(index);
+    }
+    return packetRowMajor(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    TensorOpCost cost = m_impl.costPerCoeff(vectorized);
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims; ++i)
+        updateCostPerDimension(cost, i, i == 0);
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i >= 0; --i)
+        updateCostPerDimension(cost, i, i == NumDims - 1);
+    }
+    return cost;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    return internal::TensorBlockResourceRequirements::merge(
+        internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
+        m_impl.getResourceRequirements());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    // If one of the dimensions is zero, return empty block view.
+    if (desc.size() == 0) {
+      return TensorBlock(internal::TensorBlockKind::kView, NULL,
+                           desc.dimensions());
+    }
+
+    static const bool IsColMajor = Layout == static_cast<int>(ColMajor);
+    const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1;
+
+    Index offset = desc.offset();
+
+    // Compute offsets in the output tensor corresponding to the desc.offset().
+    DSizes<Index, NumDims> output_offsets;
+    for (int i = NumDims - 1; i > 0; --i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      const int stride_dim = IsColMajor ? dim : dim + 1;
+      output_offsets[dim] = offset / m_outputStrides[stride_dim];
+      offset -= output_offsets[dim] * m_outputStrides[stride_dim];
+    }
+    output_offsets[inner_dim_idx] = offset;
+
+    // Offsets in the input corresponding to output offsets.
+    DSizes<Index, NumDims> input_offsets = output_offsets;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      input_offsets[dim] = input_offsets[dim] - m_padding[dim].first;
+    }
+
+    // Compute offset in the input buffer (at this point it might be illegal and
+    // point outside of the input buffer, because we don't check for negative
+    // offsets, it will be autocorrected in the block iteration loop below).
+    Index input_offset = 0;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      input_offset += input_offsets[dim] * m_inputStrides[dim];
+    }
+
+    // Destination buffer and scratch buffer both indexed from 0 and have the
+    // same dimensions as the requested block (for destination buffer this
+    // property is guaranteed by `desc.destination()`).
+    Index output_offset = 0;
+    const DSizes<Index, NumDims> output_strides =
+        internal::strides<Layout>(desc.dimensions());
+
+    // NOTE(ezhulenev): We initialize bock iteration state for `NumDims - 1`
+    // dimensions, skipping innermost dimension. In theory it should be possible
+    // to squeeze matching innermost dimensions, however in practice that did
+    // not show any improvements in benchmarks. Also in practice first outer
+    // dimension usually has padding, and will prevent squeezing.
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims - 1> it;
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const int dim = IsColMajor ? i + 1 : NumDims - i - 2;
+      it[i].count = 0;
+      it[i].size = desc.dimension(dim);
+
+      it[i].input_stride = m_inputStrides[dim];
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      it[i].output_stride = output_strides[dim];
+      it[i].output_span = it[i].output_stride * (it[i].size - 1);
+    }
+
+    const Index input_inner_dim_size =
+        static_cast<Index>(m_impl.dimensions()[inner_dim_idx]);
+
+    // Total output size.
+    const Index output_size = desc.size();
+
+    // We will fill inner dimension of this size in the output. It might be
+    // larger than the inner dimension in the input, so we might have to pad
+    // before/after we copy values from the input inner dimension.
+    const Index output_inner_dim_size = desc.dimension(inner_dim_idx);
+
+    // How many values to fill with padding BEFORE reading from the input inner
+    // dimension.
+    const Index output_inner_pad_before_size =
+        input_offsets[inner_dim_idx] < 0
+            ? numext::mini(numext::abs(input_offsets[inner_dim_idx]),
+                           output_inner_dim_size)
+            : 0;
+
+    // How many values we can actually copy from the input inner dimension.
+    const Index output_inner_copy_size = numext::mini(
+        // Want to copy from input.
+        (output_inner_dim_size - output_inner_pad_before_size),
+        // Can copy from input.
+        numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] +
+                                             output_inner_pad_before_size),
+                     Index(0)));
+
+    eigen_assert(output_inner_copy_size >= 0);
+
+    // How many values to fill with padding AFTER reading from the input inner
+    // dimension.
+    const Index output_inner_pad_after_size =
+        (output_inner_dim_size - output_inner_copy_size -
+         output_inner_pad_before_size);
+
+    // Sanity check, sum of all sizes must be equal to the output size.
+    eigen_assert(output_inner_dim_size ==
+                 (output_inner_pad_before_size + output_inner_copy_size +
+                  output_inner_pad_after_size));
+
+    // Keep track of current coordinates and padding in the output.
+    DSizes<Index, NumDims> output_coord = output_offsets;
+    DSizes<Index, NumDims> output_padded;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = IsColMajor ? i : NumDims - i - 1;
+      output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+    }
+
+    typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy;
+
+    // Prepare storage for the materialized padding result.
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(desc, scratch);
+
+    // TODO(ezhulenev): Squeeze multiple non-padded inner dimensions into a
+    // single logical inner dimension.
+
+    // When possible we squeeze writes for the innermost (only if non-padded)
+    // dimension with the first padded dimension. This allows to reduce the
+    // number of calls to LinCopy and better utilize vector instructions.
+    const bool squeeze_writes =
+        NumDims > 1 &&
+        // inner dimension is not padded
+        (input_inner_dim_size == m_dimensions[inner_dim_idx]) &&
+        // and equal to the block inner dimension
+        (input_inner_dim_size == output_inner_dim_size);
+
+    const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1;
+
+    // Maximum coordinate on a squeeze dimension that we can write to.
+    const Index squeeze_max_coord =
+        squeeze_writes ? numext::mini(
+                             // max non-padded element in the input
+                             static_cast<Index>(m_dimensions[squeeze_dim] -
+                                                m_padding[squeeze_dim].second),
+                             // max element in the output buffer
+                             static_cast<Index>(output_offsets[squeeze_dim] +
+                                                desc.dimension(squeeze_dim)))
+                       : static_cast<Index>(0);
+
+    // Iterate copying data from `m_impl.data()` to the output buffer.
+    for (Index size = 0; size < output_size;) {
+      // Detect if we are in the padded region (exclude innermost dimension).
+      bool is_padded = false;
+      for (int j = 1; j < NumDims; ++j) {
+        const int dim = IsColMajor ? j : NumDims - j - 1;
+        is_padded = output_padded[dim];
+        if (is_padded) break;
+      }
+
+      if (is_padded) {
+        // Fill single innermost dimension with padding value.
+        size += output_inner_dim_size;
+
+        LinCopy::template Run<LinCopy::Kind::FillLinear>(
+            typename LinCopy::Dst(output_offset, 1, block_storage.data()),
+            typename LinCopy::Src(0, 0, &m_paddingValue),
+            output_inner_dim_size);
+
+
+      } else if (squeeze_writes) {
+        // Squeeze multiple reads from innermost dimensions.
+        const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim];
+        size += output_inner_dim_size * squeeze_num;
+
+        // Copy `squeeze_num` inner dimensions from input to output.
+        LinCopy::template Run<LinCopy::Kind::Linear>(
+            typename LinCopy::Dst(output_offset, 1, block_storage.data()),
+            typename LinCopy::Src(input_offset, 1, m_impl.data()),
+            output_inner_dim_size * squeeze_num);
+
+        // Update iteration state for only `squeeze_num - 1` processed inner
+        // dimensions, because we have another iteration state update at the end
+        // of the loop that will update iteration state for the last inner
+        // processed dimension.
+        it[0].count += (squeeze_num - 1);
+        input_offset += it[0].input_stride * (squeeze_num - 1);
+        output_offset += it[0].output_stride * (squeeze_num - 1);
+        output_coord[squeeze_dim] += (squeeze_num - 1);
+
+      } else {
+        // Single read from innermost dimension.
+        size += output_inner_dim_size;
+
+        {  // Fill with padding before copying from input inner dimension.
+          const Index out = output_offset;
+
+          LinCopy::template Run<LinCopy::Kind::FillLinear>(
+              typename LinCopy::Dst(out, 1, block_storage.data()),
+              typename LinCopy::Src(0, 0, &m_paddingValue),
+              output_inner_pad_before_size);
+        }
+
+        {  // Copy data from input inner dimension.
+          const Index out = output_offset + output_inner_pad_before_size;
+          const Index in = input_offset + output_inner_pad_before_size;
+
+          eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
+
+          LinCopy::template Run<LinCopy::Kind::Linear>(
+              typename LinCopy::Dst(out, 1, block_storage.data()),
+              typename LinCopy::Src(in, 1, m_impl.data()),
+              output_inner_copy_size);
+        }
+
+        {  // Fill with padding after copying from input inner dimension.
+          const Index out = output_offset + output_inner_pad_before_size +
+                            output_inner_copy_size;
+
+          LinCopy::template Run<LinCopy::Kind::FillLinear>(
+              typename LinCopy::Dst(out, 1, block_storage.data()),
+              typename LinCopy::Src(0, 0, &m_paddingValue),
+              output_inner_pad_after_size);
+        }
+      }
+
+      for (int j = 0; j < NumDims - 1; ++j) {
+        const int dim = IsColMajor ? j + 1 : NumDims - j - 2;
+
+        if (++it[j].count < it[j].size) {
+          input_offset += it[j].input_stride;
+          output_offset += it[j].output_stride;
+          output_coord[dim] += 1;
+          output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+          break;
+        }
+        it[j].count = 0;
+        input_offset -= it[j].input_span;
+        output_offset -= it[j].output_span;
+        output_coord[dim] -= it[j].size - 1;
+        output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim);
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState()
+        : count(0),
+          size(0),
+          input_stride(0),
+          input_span(0),
+          output_stride(0),
+          output_span(0) {}
+
+    Index count;
+    Index size;
+    Index input_stride;
+    Index input_span;
+    Index output_stride;
+    Index output_span;
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim(
+      Index index, int dim_index) const {
+#if defined(EIGEN_HAS_INDEX_LIST)
+    return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) &&
+            index < m_padding[dim_index].first) ||
+        (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) &&
+         index >= m_dimensions[dim_index] - m_padding[dim_index].second);
+#else
+    return (index < m_padding[dim_index].first) ||
+           (index >= m_dimensions[dim_index] - m_padding[dim_index].second);
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero(
+      int dim_index) const {
+#if defined(EIGEN_HAS_INDEX_LIST)
+    return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0);
+#else
+    EIGEN_UNUSED_VARIABLE(dim_index);
+    return false;
+#endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero(
+      int dim_index) const {
+#if defined(EIGEN_HAS_INDEX_LIST)
+    return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0);
+#else
+    EIGEN_UNUSED_VARIABLE(dim_index);
+    return false;
+#endif
+  }
+
+
+  void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const {
+    const double in = static_cast<double>(m_impl.dimensions()[i]);
+    const double out = in + m_padding[i].first + m_padding[i].second;
+    if (out == 0)
+      return;
+    const double reduction = in / out;
+    cost *= reduction;
+    if (first) {
+      cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
+                    reduction * (1 * TensorOpCost::AddCost<Index>()));
+    } else {
+      cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() +
+                                 2 * TensorOpCost::MulCost<Index>() +
+                    reduction * (2 * TensorOpCost::MulCost<Index>() +
+                                 1 * TensorOpCost::DivCost<Index>()));
+    }
+  }
+
+ protected:
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    const Index initialIndex = index;
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = NumDims - 1; i > 0; --i) {
+      const Index firstIdx = index;
+      const Index lastIdx = index + PacketSize - 1;
+      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i];
+      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i];
+      const Index lastPaddedRight = m_outputStrides[i+1];
+
+      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      }
+      else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      }
+      else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+        // all the coefficient are between the 2 padding zones.
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      else {
+        // Every other case
+        return packetWithPossibleZero(initialIndex);
+      }
+    }
+
+    const Index lastIdx = index + PacketSize - 1;
+    const Index firstIdx = index;
+    const Index lastPaddedLeft = m_padding[0].first;
+    const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second);
+    const Index lastPaddedRight = m_outputStrides[1];
+
+    if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    }
+    else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    }
+    else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+      // all the coefficient are between the 2 padding zones.
+      inputIndex += (index - m_padding[0].first);
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+    // Every other case
+    return packetWithPossibleZero(initialIndex);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    const Index initialIndex = index;
+    Index inputIndex = 0;
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < NumDims - 1; ++i) {
+      const Index firstIdx = index;
+      const Index lastIdx = index + PacketSize - 1;
+      const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i+1];
+      const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i+1];
+      const Index lastPaddedRight = m_outputStrides[i];
+
+      if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      }
+      else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+        // all the coefficient are in the padding zone.
+        return internal::pset1<PacketReturnType>(m_paddingValue);
+      }
+      else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+        // all the coefficient are between the 2 padding zones.
+        const Index idx = index / m_outputStrides[i+1];
+        inputIndex += (idx - m_padding[i].first) * m_inputStrides[i];
+        index -= idx * m_outputStrides[i+1];
+      }
+      else {
+        // Every other case
+        return packetWithPossibleZero(initialIndex);
+      }
+    }
+
+    const Index lastIdx = index + PacketSize - 1;
+    const Index firstIdx = index;
+    const Index lastPaddedLeft = m_padding[NumDims-1].first;
+    const Index firstPaddedRight = (m_dimensions[NumDims-1] - m_padding[NumDims-1].second);
+    const Index lastPaddedRight = m_outputStrides[NumDims-1];
+
+    if (!isLeftPaddingCompileTimeZero(NumDims-1) && lastIdx < lastPaddedLeft) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    }
+    else if (!isRightPaddingCompileTimeZero(NumDims-1) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) {
+      // all the coefficient are in the padding zone.
+      return internal::pset1<PacketReturnType>(m_paddingValue);
+    }
+    else if ((isLeftPaddingCompileTimeZero(NumDims-1) && isRightPaddingCompileTimeZero(NumDims-1)) || (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) {
+      // all the coefficient are between the 2 padding zones.
+      inputIndex += (index - m_padding[NumDims-1].first);
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+    // Every other case
+    return packetWithPossibleZero(initialIndex);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
+  {
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+  array<Index, NumDims+1> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  PaddingDimensions m_padding;
+
+  Scalar m_paddingValue;
+
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
new file mode 100644
index 0000000..413d25d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h
@@ -0,0 +1,291 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
+
+namespace Eigen {
+
+/** \class TensorPatch
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor patch class.
+  *
+  *
+  */
+namespace internal {
+template<typename PatchDim, typename XprType>
+struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions + 1;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename PatchDim, typename XprType>
+struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense>
+{
+  typedef const TensorPatchOp<PatchDim, XprType>& type;
+};
+
+template<typename PatchDim, typename XprType>
+struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type>
+{
+  typedef TensorPatchOp<PatchDim, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename PatchDim, typename XprType>
+class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
+      : m_xpr(expr), m_patch_dims(patch_dims) {}
+
+    EIGEN_DEVICE_FUNC
+    const PatchDim& patch_dims() const { return m_patch_dims; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const PatchDim m_patch_dims;
+};
+
+
+// Eval as rvalue
+template<typename PatchDim, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
+{
+  typedef TensorPatchOp<PatchDim, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,
+    RawAccess = false
+ };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device)
+  {
+    Index num_patches = 1;
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const PatchDim& patch_dims = op.patch_dims();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < NumDims-1; ++i) {
+        m_dimensions[i] = patch_dims[i];
+        num_patches *= (input_dims[i] - patch_dims[i] + 1);
+      }
+      m_dimensions[NumDims-1] = num_patches;
+
+      m_inputStrides[0] = 1;
+      m_patchStrides[0] = 1;
+      for (int i = 1; i < NumDims-1; ++i) {
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+        m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1);
+      }
+      m_outputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
+      }
+    } else {
+      for (int i = 0; i < NumDims-1; ++i) {
+        m_dimensions[i+1] = patch_dims[i];
+        num_patches *= (input_dims[i] - patch_dims[i] + 1);
+      }
+      m_dimensions[0] = num_patches;
+
+      m_inputStrides[NumDims-2] = 1;
+      m_patchStrides[NumDims-2] = 1;
+      for (int i = NumDims-3; i >= 0; --i) {
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+        m_patchStrides[i] = m_patchStrides[i+1] * (input_dims[i+1] - patch_dims[i+1] + 1);
+      }
+      m_outputStrides[NumDims-1] = 1;
+      for (int i = NumDims-2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
+    // Find the location of the first element of the patch.
+    Index patchIndex = index / m_outputStrides[output_stride_index];
+    // Find the offset of the element wrt the location of the first element.
+    Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index];
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i > 0; --i) {
+        const Index patchIdx = patchIndex / m_patchStrides[i];
+        patchIndex -= patchIdx * m_patchStrides[i];
+        const Index offsetIdx = patchOffset / m_outputStrides[i];
+        patchOffset -= offsetIdx * m_outputStrides[i];
+        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 2; ++i) {
+        const Index patchIdx = patchIndex / m_patchStrides[i];
+        patchIndex -= patchIdx * m_patchStrides[i];
+        const Index offsetIdx = patchOffset / m_outputStrides[i+1];
+        patchOffset -= offsetIdx * m_outputStrides[i+1];
+        inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
+      }
+    }
+    inputIndex += (patchIndex + patchOffset);
+    return m_impl.coeff(inputIndex);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
+    Index indices[2] = {index, index + PacketSize - 1};
+    Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index],
+                             indices[1] / m_outputStrides[output_stride_index]};
+    Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
+                             indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]};
+
+    Index inputIndices[2] = {0, 0};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 2; i > 0; --i) {
+        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
+                                   patchIndices[1] / m_patchStrides[i]};
+        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
+        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
+
+        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i],
+                                    patchOffsets[1] / m_outputStrides[i]};
+        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
+        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];
+
+        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
+        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 2; ++i) {
+        const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
+                                   patchIndices[1] / m_patchStrides[i]};
+        patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
+        patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
+
+        const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i+1],
+                                    patchOffsets[1] / m_outputStrides[i+1]};
+        patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i+1];
+        patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i+1];
+
+        inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
+        inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
+      }
+    }
+    inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
+    inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
+
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    }
+    else {
+      EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize-1; ++i) {
+        values[i] = coeff(index+i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() +
+                                           TensorOpCost::MulCost<Index>() +
+                                           2 * TensorOpCost::AddCost<Index>());
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh); 
+  }
+#endif
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims-1> m_inputStrides;
+  array<Index, NumDims-1> m_patchStrides;
+
+  TensorEvaluator<ArgType, Device> m_impl;
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
new file mode 100644
index 0000000..37c1d1c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRandom.h
@@ -0,0 +1,322 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2018 Mehdi Goli <eigen@codeplay.com> Codeplay Software Ltd.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
+#define EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
+
+namespace Eigen {
+namespace internal {
+
+namespace {
+
+EIGEN_DEVICE_FUNC uint64_t get_random_seed() {
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+  // We don't support 3d kernels since we currently only use 1 and
+  // 2d kernels.
+  gpu_assert(threadIdx.z == 0);
+  return blockIdx.x * blockDim.x + threadIdx.x 
+         + gridDim.x * blockDim.x * (blockIdx.y * blockDim.y + threadIdx.y);
+#else
+  // Rely on Eigen's random implementation.
+  return random<uint64_t>();
+#endif
+}
+
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned PCG_XSH_RS_generator(uint64_t* state, uint64_t stream) {
+  // TODO: Unify with the implementation in the non blocking thread pool.
+  uint64_t current = *state;
+  // Update the internal state
+  *state = current * 6364136223846793005ULL + (stream << 1 | 1);
+  // Generate the random output (using the PCG-XSH-RS scheme)
+  return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61)));
+}
+
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t PCG_XSH_RS_state(uint64_t seed) {
+  seed = seed ? seed : get_random_seed();
+  return seed * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
+}
+
+}  // namespace
+
+
+template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+T RandomToTypeUniform(uint64_t* state, uint64_t stream) {
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  return static_cast<T>(rnd);
+}
+
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Eigen::half RandomToTypeUniform<Eigen::half>(uint64_t* state, uint64_t stream) {
+  // Generate 10 random bits for the mantissa, merge with exponent.
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x3ffu) | (static_cast<uint16_t>(15) << 10);
+  Eigen::half result = Eigen::numext::bit_cast<Eigen::half>(half_bits);
+  // Return the final result
+  return result - Eigen::half(1.0f);
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Eigen::bfloat16 RandomToTypeUniform<Eigen::bfloat16>(uint64_t* state, uint64_t stream) {
+
+  // Generate 7 random bits for the mantissa, merge with exponent.
+  unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x7fu) | (static_cast<uint16_t>(127) << 7);
+  Eigen::bfloat16 result = Eigen::numext::bit_cast<Eigen::bfloat16>(half_bits);
+  // Return the final result
+  return result - Eigen::bfloat16(1.0f);
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float RandomToTypeUniform<float>(uint64_t* state, uint64_t stream) {
+  typedef union {
+    uint32_t raw;
+    float fp;
+  } internal;
+  internal result;
+  // Generate 23 random bits for the mantissa mantissa
+  const unsigned rnd = PCG_XSH_RS_generator(state, stream);
+  result.raw = rnd & 0x7fffffu;
+  // Set the exponent
+  result.raw |= (static_cast<uint32_t>(127) << 23);
+  // Return the final result
+  return result.fp - 1.0f;
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double RandomToTypeUniform<double>(uint64_t* state, uint64_t stream) {
+  typedef union {
+    uint64_t raw;
+    double dp;
+  } internal;
+  internal result;
+  result.raw = 0;
+  // Generate 52 random bits for the mantissa
+  // First generate the upper 20 bits
+  unsigned rnd1 = PCG_XSH_RS_generator(state, stream) & 0xfffffu;
+  // The generate the lower 32 bits
+  unsigned rnd2 = PCG_XSH_RS_generator(state, stream);
+  result.raw = (static_cast<uint64_t>(rnd1) << 32) | rnd2;
+  // Set the exponent
+  result.raw |= (static_cast<uint64_t>(1023) << 52);
+  // Return the final result
+  return result.dp - 1.0;
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<float> RandomToTypeUniform<std::complex<float> >(uint64_t* state, uint64_t stream) {
+  return std::complex<float>(RandomToTypeUniform<float>(state, stream),
+                             RandomToTypeUniform<float>(state, stream));
+}
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<double> RandomToTypeUniform<std::complex<double> >(uint64_t* state, uint64_t stream) {
+  return std::complex<double>(RandomToTypeUniform<double>(state, stream),
+                              RandomToTypeUniform<double>(state, stream));
+}
+
+template <typename T> class UniformRandomGenerator {
+ public:
+  static const bool PacketAccess = true;
+
+  // Uses the given "seed" if non-zero, otherwise uses a random seed.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(
+      uint64_t seed = 0) {
+    m_state = PCG_XSH_RS_state(seed);
+    #ifdef EIGEN_USE_SYCL
+    // In SYCL it is not possible to build PCG_XSH_RS_state in one step.
+    // Therefor, we need two step to initializate the m_state.
+    // IN SYCL, the constructor of the functor is s called on the CPU
+    // and we get the clock seed here from the CPU. However, This seed is
+    //the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
+    // and only  available on the Operator() function (which is called on the GPU).
+    // Thus for CUDA (((CLOCK  + global_thread_id)* 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to each thread
+    // but for SYCL ((CLOCK * 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to each thread and each thread adds
+    // the  (global_thread_id* 6364136223846793005ULL) for itself only once, in order to complete the construction
+    // similar to CUDA Therefore, the thread Id injection is not available at this stage.
+    //However when the operator() is called the thread ID will be avilable. So inside the opeator,
+    // we add the thrreadID, BlockId,... (which is equivalent of i)
+    //to the seed and construct the unique m_state per thead similar to cuda.
+    m_exec_once =false;
+   #endif
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(
+      const UniformRandomGenerator& other) {
+    m_state = other.m_state;
+    #ifdef EIGEN_USE_SYCL
+     m_exec_once =other.m_exec_once;
+    #endif
+  }
+
+  template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T operator()(Index i) const {
+    #ifdef EIGEN_USE_SYCL
+      if(!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      // The (i * 6364136223846793005ULL) is the remaining part of the PCG_XSH_RS_state on the GPU side
+       m_state += (i * 6364136223846793005ULL);
+       m_exec_once =true;
+      }
+    #endif
+    T result = RandomToTypeUniform<T>(&m_state, i);
+    return result;
+  }
+
+  template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet packetOp(Index i) const {
+    const int packetSize = internal::unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_MAX T values[packetSize];
+      #ifdef EIGEN_USE_SYCL
+      if(!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+       m_state += (i * 6364136223846793005ULL);
+       m_exec_once =true;
+      }
+    #endif
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < packetSize; ++j) {
+      values[j] = RandomToTypeUniform<T>(&m_state, i);
+    }
+    return internal::pload<Packet>(values);
+  }
+
+ private:
+  mutable uint64_t m_state;
+  #ifdef EIGEN_USE_SYCL
+  mutable bool m_exec_once;
+  #endif
+};
+
+template <typename Scalar>
+struct functor_traits<UniformRandomGenerator<Scalar> > {
+  enum {
+    // Rough estimate for floating point, multiplied by ceil(sizeof(T) / sizeof(float)).
+    Cost = 12 * NumTraits<Scalar>::AddCost *
+           ((sizeof(Scalar) + sizeof(float) - 1) / sizeof(float)),
+    PacketAccess = UniformRandomGenerator<Scalar>::PacketAccess
+  };
+};
+
+
+
+template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+T RandomToTypeNormal(uint64_t* state, uint64_t stream) {
+  // Use the ratio of uniform method to generate numbers following a normal
+  // distribution. See for example Numerical Recipes chapter 7.3.9 for the
+  // details.
+  T u, v, q;
+  do {
+    u = RandomToTypeUniform<T>(state, stream);
+    v = T(1.7156) * (RandomToTypeUniform<T>(state, stream) - T(0.5));
+    const T x = u - T(0.449871);
+    const T y = numext::abs(v) + T(0.386595);
+    q = x*x + y * (T(0.196)*y - T(0.25472)*x);
+  } while (q > T(0.27597) &&
+           (q > T(0.27846) || v*v > T(-4) * numext::log(u) * u*u));
+
+  return v/u;
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<float> RandomToTypeNormal<std::complex<float> >(uint64_t* state, uint64_t stream) {
+  return std::complex<float>(RandomToTypeNormal<float>(state, stream),
+                             RandomToTypeNormal<float>(state, stream));
+}
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<double> RandomToTypeNormal<std::complex<double> >(uint64_t* state, uint64_t stream) {
+  return std::complex<double>(RandomToTypeNormal<double>(state, stream),
+                              RandomToTypeNormal<double>(state, stream));
+}
+
+
+template <typename T> class NormalRandomGenerator {
+ public:
+  static const bool PacketAccess = true;
+
+  // Uses the given "seed" if non-zero, otherwise uses a random seed.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(uint64_t seed = 0) {
+    m_state = PCG_XSH_RS_state(seed);
+    #ifdef EIGEN_USE_SYCL
+    // In SYCL it is not possible to build PCG_XSH_RS_state in one step.
+    // Therefor, we need two steps to initializate the m_state.
+    // IN SYCL, the constructor of the functor is s called on the CPU
+    // and we get the clock seed here from the CPU. However, This seed is
+    //the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
+    // and only  available on the Operator() function (which is called on the GPU).
+    // Therefore, the thread Id injection is not available at this stage. However when the operator()
+    //is called the thread ID will be avilable. So inside the opeator,
+    // we add the thrreadID, BlockId,... (which is equivalent of i)
+    //to the seed and construct the unique m_state per thead similar to cuda.
+    m_exec_once =false;
+   #endif
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(
+      const NormalRandomGenerator& other) {
+    m_state = other.m_state;
+#ifdef EIGEN_USE_SYCL
+    m_exec_once=other.m_exec_once;
+#endif
+  }
+
+ template<typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T operator()(Index i) const {
+    #ifdef EIGEN_USE_SYCL
+    if(!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once =true;
+    }
+    #endif
+    T result = RandomToTypeNormal<T>(&m_state, i);
+    return result;
+  }
+
+  template<typename Packet, typename Index> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet packetOp(Index i) const {
+    const int packetSize = internal::unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_MAX T values[packetSize];
+    #ifdef EIGEN_USE_SYCL
+    if(!m_exec_once) {
+      // This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
+      m_state += (i * 6364136223846793005ULL);
+      m_exec_once =true;
+    }
+    #endif
+    EIGEN_UNROLL_LOOP
+    for (int j = 0; j < packetSize; ++j) {
+      values[j] = RandomToTypeNormal<T>(&m_state, i);
+    }
+    return internal::pload<Packet>(values);
+  }
+
+ private:
+  mutable uint64_t m_state;
+   #ifdef EIGEN_USE_SYCL
+  mutable bool m_exec_once;
+  #endif
+};
+
+
+template <typename Scalar>
+struct functor_traits<NormalRandomGenerator<Scalar> > {
+  enum {
+    // On average, we need to generate about 3 random numbers
+    // 15 mul, 8 add, 1.5 logs
+    Cost = 3 * functor_traits<UniformRandomGenerator<Scalar> >::Cost +
+           15 * NumTraits<Scalar>::AddCost + 8 * NumTraits<Scalar>::AddCost +
+           3 * functor_traits<scalar_log_op<Scalar> >::Cost / 2,
+    PacketAccess = NormalRandomGenerator<Scalar>::PacketAccess
+  };
+};
+
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
new file mode 100644
index 0000000..583f462
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
@@ -0,0 +1,998 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
+
+// clang is incompatible with the CUDA syntax wrt making a kernel a class friend,
+// so we'll use a macro to make clang happy.
+#ifndef KERNEL_FRIEND
+#if defined(__clang__) && (defined(__CUDA__) || defined(__HIP__))
+#define KERNEL_FRIEND friend __global__ EIGEN_HIP_LAUNCH_BOUNDS_1024
+#else
+#define KERNEL_FRIEND friend
+#endif
+#endif
+
+
+namespace Eigen {
+
+
+/** \class TensorReduction
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reduction class.
+  *
+  */
+
+namespace internal {
+  template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ >
+  struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> >
+ : traits<XprType>
+{
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::Scalar Scalar;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+
+  template <class T> struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
+};
+
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense>
+{
+  typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type;
+};
+
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type>
+{
+  typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type;
+};
+
+
+template <typename OutputDims> struct DimInitializer {
+  template <typename InputDims, typename ReducedDims> EIGEN_DEVICE_FUNC
+  static void run(const InputDims& input_dims,
+                  const array<bool, internal::array_size<InputDims>::value>& reduced,
+                  OutputDims* output_dims, ReducedDims* reduced_dims) {
+    const int NumInputDims = internal::array_size<InputDims>::value;
+    int outputIndex = 0;
+    int reduceIndex = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (reduced[i]) {
+        (*reduced_dims)[reduceIndex] = input_dims[i];
+        ++reduceIndex;
+      } else {
+        (*output_dims)[outputIndex] = input_dims[i];
+        ++outputIndex;
+      }
+    }
+  }
+};
+
+template <> struct DimInitializer<Sizes<> > {
+  template <typename InputDims, typename Index, size_t Rank> EIGEN_DEVICE_FUNC
+  static void run(const InputDims& input_dims, const array<bool, Rank>&,
+                  Sizes<>*, array<Index, Rank>* reduced_dims) {
+    const int NumInputDims = internal::array_size<InputDims>::value;
+    for (int i = 0; i < NumInputDims; ++i) {
+      (*reduced_dims)[i] = input_dims[i];
+    }
+  }
+};
+
+
+template <typename ReducedDims, int NumTensorDims, int Layout>
+struct are_inner_most_dims {
+  static const bool value = false;
+};
+template <typename ReducedDims, int NumTensorDims, int Layout>
+struct preserve_inner_most_dims {
+  static const bool value = false;
+};
+
+#if EIGEN_HAS_CONSTEXPR && EIGEN_HAS_VARIADIC_TEMPLATES
+template <typename ReducedDims, int NumTensorDims>
+struct are_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_eq<ReducedDims>(0, 0);
+  static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value-1, array_size<ReducedDims>::value-1);
+  static const bool value = tmp1 & tmp2 & tmp3;
+};
+template <typename ReducedDims, int NumTensorDims>
+struct are_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_eq<ReducedDims>(0, NumTensorDims - array_size<ReducedDims>::value);
+  static const bool tmp3 = index_statically_eq<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
+  static const bool value = tmp1 & tmp2 & tmp3;
+
+};
+template <typename ReducedDims, int NumTensorDims>
+struct preserve_inner_most_dims<ReducedDims, NumTensorDims, ColMajor>{
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_gt<ReducedDims>(0, 0);
+  static const bool value = tmp1 & tmp2;
+
+};
+template <typename ReducedDims, int NumTensorDims>
+struct preserve_inner_most_dims<ReducedDims, NumTensorDims, RowMajor>{
+  static const bool tmp1 = indices_statically_known_to_increase<ReducedDims>();
+  static const bool tmp2 = index_statically_lt<ReducedDims>(array_size<ReducedDims>::value - 1, NumTensorDims - 1);
+  static const bool value = tmp1 & tmp2;
+};
+#endif
+
+
+template <int DimIndex, typename Self, typename Op>
+struct GenericDimReducer {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) {
+    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    for (int j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
+      GenericDimReducer<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum);
+    }
+  }
+};
+template <typename Self, typename Op>
+struct GenericDimReducer<0, Self, Op> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::CoeffReturnType* accum) {
+    for (int j = 0; j < self.m_reducedDims[0]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
+      reducer.reduce(self.m_impl.coeff(input), accum);
+    }
+  }
+};
+template <typename Self, typename Op>
+struct GenericDimReducer<-1, Self, Op> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index index, Op& reducer, typename Self::CoeffReturnType* accum) {
+    reducer.reduce(self.m_impl.coeff(index), accum);
+  }
+};
+
+template <typename Self, typename Op, bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess),
+          bool UseTreeReduction = (!Self::ReducerTraits::IsStateful &&
+                                   !Self::ReducerTraits::IsExactlyAssociative)>
+struct InnerMostDimReducer {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    for (typename Self::Index j = 0; j < numValuesToReduce; ++j) {
+      reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+    }
+    return reducer.finalize(accum);
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, true, false> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType reduce(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer) {
+    const typename Self::Index packetSize = internal::unpacket_traits<typename Self::PacketReturnType>::size;
+    const typename Self::Index VectorizedSize = (numValuesToReduce / packetSize) * packetSize;
+    typename Self::PacketReturnType paccum = reducer.template initializePacket<typename Self::PacketReturnType>();
+    for (typename Self::Index j = 0; j < VectorizedSize; j += packetSize) {
+      reducer.reducePacket(self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum);
+    }
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    for (typename Self::Index j = VectorizedSize; j < numValuesToReduce; ++j) {
+      reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+    }
+    return reducer.finalizeBoth(accum, paccum);
+  }
+};
+
+#if !defined(EIGEN_HIPCC) 
+static const int kLeafSize = 1024;
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, false, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType
+  reduce(const Self& self, typename Self::Index firstIndex,
+         typename Self::Index numValuesToReduce, Op& reducer) {
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    if (numValuesToReduce > kLeafSize) {
+      const typename Self::Index half = numValuesToReduce / 2;
+      reducer.reduce(reduce(self, firstIndex, half, reducer), &accum);
+      reducer.reduce(
+          reduce(self, firstIndex + half, numValuesToReduce - half, reducer),
+          &accum);
+    } else {
+      for (typename Self::Index j = 0; j < numValuesToReduce; ++j) {
+        reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+      }
+    }
+    return reducer.finalize(accum);
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimReducer<Self, Op, true, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Self::CoeffReturnType
+  reduce(const Self& self, typename Self::Index firstIndex,
+         typename Self::Index numValuesToReduce, Op& reducer) {
+    const typename Self::Index packetSize =
+        internal::unpacket_traits<typename Self::PacketReturnType>::size;
+    typename Self::CoeffReturnType accum = reducer.initialize();
+    if (numValuesToReduce > packetSize * kLeafSize) {
+      // Make sure the split point is aligned on a packet boundary.
+      const typename Self::Index split =
+          packetSize *
+          divup(firstIndex + divup(numValuesToReduce, typename Self::Index(2)),
+                packetSize);
+      const typename Self::Index num_left =
+          numext::mini(split - firstIndex, numValuesToReduce);
+      reducer.reduce(reduce(self, firstIndex, num_left, reducer), &accum);
+      if (num_left < numValuesToReduce) {
+        reducer.reduce(
+            reduce(self, split, numValuesToReduce - num_left, reducer), &accum);
+      }
+      return reducer.finalize(accum);
+    } else {
+      const typename Self::Index UnrollSize =
+          (numValuesToReduce / (2*packetSize)) * 2*packetSize;
+      const typename Self::Index VectorizedSize =
+          (numValuesToReduce / packetSize) * packetSize;
+      typename Self::PacketReturnType paccum =
+          reducer.template initializePacket<typename Self::PacketReturnType>();
+      typename Self::PacketReturnType paccum2 =
+          reducer.template initializePacket<typename Self::PacketReturnType>();
+      for (typename Self::Index j = 0; j < UnrollSize; j += packetSize * 2) {
+        reducer.reducePacket(
+            self.m_impl.template packet<Unaligned>(firstIndex + j), &paccum);
+        reducer.reducePacket(
+            self.m_impl.template packet<Unaligned>(firstIndex + j + packetSize),
+            &paccum2);
+      }
+      for (typename Self::Index j = UnrollSize; j < VectorizedSize; j+= packetSize) {
+        reducer.reducePacket(self.m_impl.template packet<Unaligned>(
+                                 firstIndex + j), &paccum);
+      }
+      reducer.reducePacket(paccum2, &paccum);
+      for (typename Self::Index j = VectorizedSize; j < numValuesToReduce;
+           ++j) {
+        reducer.reduce(self.m_impl.coeff(firstIndex + j), &accum);
+      }
+      return reducer.finalizeBoth(accum, paccum);
+    }
+  }
+};
+#endif
+ 
+template <int DimIndex, typename Self, typename Op, bool vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
+struct InnerMostDimPreserver {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) {
+    eigen_assert(false && "should never be called");
+  }
+};
+
+template <int DimIndex, typename Self, typename Op>
+struct InnerMostDimPreserver<DimIndex, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) {
+    EIGEN_STATIC_ASSERT((DimIndex > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    for (typename Self::Index j = 0; j < self.m_reducedDims[DimIndex]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[DimIndex];
+      InnerMostDimPreserver<DimIndex-1, Self, Op>::reduce(self, input, reducer, accum);
+    }
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerMostDimPreserver<0, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self& self, typename Self::Index firstIndex, Op& reducer, typename Self::PacketReturnType* accum) {
+    for (typename Self::Index j = 0; j < self.m_reducedDims[0]; ++j) {
+      const typename Self::Index input = firstIndex + j * self.m_reducedStrides[0];
+      reducer.reducePacket(self.m_impl.template packet<Unaligned>(input), accum);
+    }
+  }
+};
+template <typename Self, typename Op>
+struct InnerMostDimPreserver<-1, Self, Op, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const Self&, typename Self::Index, Op&, typename Self::PacketReturnType*) {
+    eigen_assert(false && "should never be called");
+  }
+};
+
+// Default full reducer
+template <typename Self, typename Op, typename Device, bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
+struct FullReducer {
+  static const bool HasOptimizedImplementation = false;
+
+  static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::EvaluatorPointerType output) {
+    const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions());
+    *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
+  }
+};
+
+
+#ifdef EIGEN_USE_THREADS
+// Multithreaded full reducers
+template <typename Self, typename Op,
+          bool Vectorizable = (Self::InputPacketAccess && Self::ReducerTraits::PacketAccess)>
+struct FullReducerShard {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Self& self, typename Self::Index firstIndex,
+                  typename Self::Index numValuesToReduce, Op& reducer,
+                  typename Self::CoeffReturnType* output) {
+    *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(
+        self, firstIndex, numValuesToReduce, reducer);
+  }
+};
+
+// Multithreaded full reducer
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> {
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful;
+  static const Index PacketSize =
+      unpacket_traits<typename Self::PacketReturnType>::size;
+
+  // launch one reducer per thread and accumulate the result.
+  static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device,
+                  typename Self::CoeffReturnType* output) {
+    typedef typename Self::Index Index;
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    if (num_coeffs == 0) {
+      *output = reducer.finalize(reducer.initialize());
+      return;
+    }
+    const TensorOpCost cost =
+        self.m_impl.costPerCoeff(Vectorizable) +
+        TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable,
+                     PacketSize);
+    const int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
+        num_coeffs, cost, device.numThreads());
+    if (num_threads == 1) {
+      *output =
+          InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer);
+      return;
+    }
+    const Index blocksize =
+        std::floor<Index>(static_cast<float>(num_coeffs) / num_threads);
+    const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0;
+    eigen_assert(num_coeffs >= numblocks * blocksize);
+
+    Barrier barrier(internal::convert_index<unsigned int>(numblocks));
+    MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize());
+    for (Index i = 0; i < numblocks; ++i) {
+      device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, Vectorizable>::run,
+                                  self, i * blocksize, blocksize, reducer,
+                                  &shards[i]);
+    }
+    typename Self::CoeffReturnType finalShard;
+    if (numblocks * blocksize < num_coeffs) {
+      finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(
+          self, numblocks * blocksize, num_coeffs - numblocks * blocksize,
+          reducer);
+    } else {
+      finalShard = reducer.initialize();
+    }
+    barrier.Wait();
+
+    for (Index i = 0; i < numblocks; ++i) {
+      reducer.reduce(shards[i], &finalShard);
+    }
+    *output = reducer.finalize(finalShard);
+  }
+};
+
+#endif
+
+
+// Default inner reducer
+template <typename Self, typename Op, typename Device>
+struct InnerReducer {
+  static const bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+
+// Default outer reducer
+template <typename Self, typename Op, typename Device>
+struct OuterReducer {
+  static const bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+
+#ifdef EIGEN_USE_SYCL
+// Default Generic reducer
+template <typename Self, typename Op, typename Device>
+struct GenericReducer {
+  static const bool HasOptimizedImplementation = false;
+
+  EIGEN_DEVICE_FUNC static bool run(const Self&, Op&, const Device&, typename Self::CoeffReturnType*, typename Self::Index, typename Self::Index) {
+    eigen_assert(false && "Not implemented");
+    return true;
+  }
+};
+#endif
+
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+template <int B, int N, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*);
+
+
+#if defined(EIGEN_HAS_GPU_FP16)
+template <typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(R, const S, I_, internal::packet_traits<half>::type*);
+template <int B, int N, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<half>::type*);
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
+
+#endif
+
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+
+template <int NPT, typename S, typename R, typename I_>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+#endif
+
+/**
+ * For SYCL, the return type of the reduction is deduced from the initialize method of the given Op.
+ * This allows the reduction to have a different type for the accumulator than the input data type.
+ * If this is the case, the functor needs to have two reduce method: one for reducing an element of the input
+ * with the accumulator and the other for reducing two accumulators.
+ * Such a reducer can be useful for instance when the accumulator is a boolean or a bitset that checks for
+ * some properties of the input.
+ */
+template <typename Op, typename CoeffReturnType>
+struct ReductionReturnType {
+#if defined(EIGEN_USE_SYCL)
+  typedef typename remove_const<decltype(std::declval<Op>().initialize())>::type type;
+#else
+  typedef typename remove_const<CoeffReturnType>::type type;
+#endif
+};
+
+}  // end namespace internal
+
+
+template <typename Op, typename Dims, typename XprType,  template <class> class MakePointer_>
+class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> {
+  public:
+    typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorReductionOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorReductionOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorReductionOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReductionOp(const XprType& expr, const Dims& dims) : m_expr(expr), m_dims(dims)
+    { }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    TensorReductionOp(const XprType& expr, const Dims& dims, const Op& reducer) : m_expr(expr), m_dims(dims), m_reducer(reducer)
+    { }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const XprType& expression() const { return m_expr; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Dims& dims() const { return m_dims; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Op& reducer() const { return m_reducer; }
+
+  protected:
+    typename XprType::Nested m_expr;
+    const Dims m_dims;
+    const Op m_reducer;
+};
+
+template<typename ArgType, typename Device>
+struct TensorReductionEvaluatorBase;
+
+// Eval as rvalue
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
+{
+  typedef internal::reducer_traits<Op, Device> ReducerTraits;
+  typedef Dims ReducedDims;
+  typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType;
+  typedef typename XprType::Index Index;
+  typedef ArgType ChildType;
+  typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
+  static const int NumInputDims = internal::array_size<InputDimensions>::value;
+  static const int NumReducedDims = internal::array_size<Dims>::value;
+  static const int NumOutputDims = NumInputDims - NumReducedDims;
+  typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self;
+  static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
+  typedef typename internal::ReductionReturnType<Op, typename XprType::CoeffReturnType>::type CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const Index PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+    // Subset of strides of the input tensor for the non-reduced dimensions.
+  // Indexed by output dimensions.
+  static const int NumPreservedStrides = max_n_1<NumOutputDims>::size;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = Self::InputPacketAccess && ReducerTraits::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = true,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  static const bool ReducingInnerMostDims = internal::are_inner_most_dims<Dims, NumInputDims, Layout>::value;
+  static const bool PreservingInnerMostDims = internal::preserve_inner_most_dims<Dims, NumInputDims, Layout>::value;
+  static const bool RunningFullReduction = (NumOutputDims==0);
+
+  EIGEN_STRONG_INLINE TensorReductionEvaluatorBase(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device)
+  {
+    EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
+                        YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Build the bitmap indicating if an input dimension is reduced or not.
+    for (int i = 0; i < NumInputDims; ++i) {
+      m_reduced[i] = false;
+    }
+    for (int i = 0; i < NumReducedDims; ++i) {
+      eigen_assert(op.dims()[i] >= 0);
+      eigen_assert(op.dims()[i] < NumInputDims);
+      m_reduced[op.dims()[i]] = true;
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    internal::DimInitializer<Dimensions>::run(input_dims, m_reduced, &m_dimensions, &m_reducedDims);
+
+    // Precompute output strides.
+    if (NumOutputDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        m_outputStrides[0] = 1;
+        for (int i = 1; i < NumOutputDims; ++i) {
+          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
+        }
+      } else {
+        m_outputStrides[NumOutputDims - 1] = 1;
+        for (int i = NumOutputDims - 2; i >= 0; --i) {
+          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+          m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
+        }
+      }
+    }
+
+    // Precompute input strides.
+    if (NumInputDims > 0) {
+      array<Index, NumInputDims> input_strides;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        input_strides[0] = 1;
+        for (int i = 1; i < NumInputDims; ++i) {
+          input_strides[i] = input_strides[i-1] * input_dims[i-1];
+        }
+      } else {
+        input_strides.back() = 1;
+        for (int i = NumInputDims - 2; i >= 0; --i) {
+          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
+        }
+      }
+
+      int outputIndex = 0;
+      int reduceIndex = 0;
+      for (int i = 0; i < NumInputDims; ++i) {
+        if (m_reduced[i]) {
+          m_reducedStrides[reduceIndex] = input_strides[i];
+          ++reduceIndex;
+        } else {
+          m_preservedStrides[outputIndex] = input_strides[i];
+          m_output_to_input_dim_map[outputIndex] = i;
+          ++outputIndex;
+        }
+      }
+    }
+
+    // Special case for full reductions
+    if (NumOutputDims == 0) {
+      m_preservedStrides[0] = internal::array_prod(input_dims);
+    }
+
+    m_numValuesToReduce =
+        NumOutputDims == 0
+            ? internal::array_prod(input_dims)
+            : (static_cast<int>(Layout) == static_cast<int>(ColMajor))
+                  ? m_preservedStrides[0]
+                  : m_preservedStrides[NumOutputDims - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE
+  bool evalSubExprsIfNeededCommon(EvaluatorPointerType data) {
+    // Use the FullReducer if possible.
+    if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction &&
+        internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation &&
+        ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) ||
+         !RunningOnGPU))) {
+      bool need_assign = false;
+      if (!data) {
+        m_result = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType))));
+        data = m_result;
+        need_assign = true;
+      }
+      Op reducer(m_reducer);
+      internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data);
+      return need_assign;
+    }
+
+    // Attempt to use an optimized reduction.
+    else if ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || (RunningOnSycl)) {
+      bool reducing_inner_dims = true;
+      for (int i = 0; i < NumReducedDims; ++i) {
+        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+          reducing_inner_dims &= m_reduced[i];
+        } else {
+          reducing_inner_dims &= m_reduced[NumInputDims - 1 - i];
+        }
+      }
+      if (internal::InnerReducer<Self, Op, Device>::HasOptimizedImplementation &&
+          (reducing_inner_dims || ReducingInnerMostDims)) {
+        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+        if (!data) {
+          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 128) || (RunningOnSycl)) {
+            data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+            m_result = data;
+          }
+          else {
+            return true;
+          }
+        }
+        Op reducer(m_reducer);
+        // For SYCL this if always return false
+        if (internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) {
+          if (m_result) {
+            m_device.deallocate_temp(m_result);
+            m_result = NULL;
+          }
+          return true;
+        } else {
+          return (m_result != NULL);
+        }
+      }
+
+      bool preserving_inner_dims = true;
+      for (int i = 0; i < NumReducedDims; ++i) {
+        if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+          preserving_inner_dims &= m_reduced[NumInputDims - 1 - i];
+        } else {
+          preserving_inner_dims &= m_reduced[i];
+        }
+      }
+      if (internal::OuterReducer<Self, Op, Device>::HasOptimizedImplementation &&
+          preserving_inner_dims) {
+        const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+        const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+        if (!data) {
+          if ((num_coeffs_to_preserve < 1024 && num_values_to_reduce > num_coeffs_to_preserve && num_values_to_reduce > 32) || (RunningOnSycl)) {
+            data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+            m_result = data;
+          }
+          else {
+            return true;
+          }
+        }
+        Op reducer(m_reducer);
+        // For SYCL this if always return false
+        if (internal::OuterReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve)) {
+          if (m_result) {
+            m_device.deallocate_temp(m_result);
+            m_result = NULL;
+          }
+          return true;
+        } else {
+          return (m_result != NULL);
+        }
+      }
+      #if defined(EIGEN_USE_SYCL)
+      // If there is no Optimised version for SYCL, the reduction expression 
+      // must break into two subexpression and use the SYCL generic Reducer on the device.
+      if(RunningOnSycl) {
+         const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+         const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+         if (!data) {
+           data = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(sizeof(CoeffReturnType) * num_coeffs_to_preserve)));
+           m_result = data;
+         }
+         Op reducer(m_reducer);
+         internal::GenericReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve);
+         return (m_result != NULL);
+       }
+      #endif
+    }
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE
+      void
+      evalSubExprsIfNeededAsync(EvaluatorPointerType data,
+                                EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(NULL, [this, data, done](bool) {
+      done(evalSubExprsIfNeededCommon(data));
+    });
+  }
+#endif
+
+  EIGEN_STRONG_INLINE
+  bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return evalSubExprsIfNeededCommon(data);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+    if (m_result) {
+      m_device.deallocate_temp(m_result);
+      m_result = NULL;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    if (( RunningFullReduction || RunningOnGPU) && m_result ) {
+      return *(m_result + index);
+    }
+    Op reducer(m_reducer);
+    if (ReducingInnerMostDims || RunningFullReduction) {
+      const Index num_values_to_reduce =
+        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
+      return internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstInput(index),
+                                                             num_values_to_reduce, reducer);
+    } else {
+      typename Self::CoeffReturnType accum = reducer.initialize();
+      internal::GenericDimReducer<NumReducedDims-1, Self, Op>::reduce(*this, firstInput(index), reducer, &accum);
+      return reducer.finalize(accum);
+    }
+  }
+
+  // TODO(bsteiner): provide a more efficient implementation.
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index + PacketSize - 1 < Index(internal::array_prod(dimensions())));
+
+    if (RunningOnGPU && m_result) {
+      return internal::pload<PacketReturnType>(m_result + index);
+    }
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    if (ReducingInnerMostDims) {
+      const Index num_values_to_reduce =
+        (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_preservedStrides[0] : m_preservedStrides[NumPreservedStrides - 1];
+      const Index firstIndex = firstInput(index);
+      for (Index i = 0; i < PacketSize; ++i) {
+        Op reducer(m_reducer);
+        values[i] = internal::InnerMostDimReducer<Self, Op>::reduce(*this, firstIndex + i * num_values_to_reduce,
+                                                                    num_values_to_reduce, reducer);
+      }
+    } else if (PreservingInnerMostDims) {
+      const Index firstIndex = firstInput(index);
+      const int innermost_dim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : NumOutputDims - 1;
+      // TBD: extend this the the n innermost dimensions that we preserve.
+      if (((firstIndex % m_dimensions[innermost_dim]) + PacketSize - 1) < m_dimensions[innermost_dim]) {
+        Op reducer(m_reducer);
+        typename Self::PacketReturnType accum = reducer.template initializePacket<typename Self::PacketReturnType>();
+        internal::InnerMostDimPreserver<NumReducedDims-1, Self, Op>::reduce(*this, firstIndex, reducer, &accum);
+        return reducer.finalizePacket(accum);
+      } else {
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = coeff(index + i);
+        }
+      }
+    } else {
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = coeff(index + i);
+      }
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  // Must be called after evalSubExprsIfNeeded().
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    if (RunningFullReduction && m_result) {
+      return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
+    } else {
+      const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+      const double compute_cost = num_values_to_reduce * internal::functor_traits<Op>::Cost;
+      return m_impl.costPerCoeff(vectorized) * num_values_to_reduce +
+          TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }
+  EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+  EIGEN_DEVICE_FUNC const Device& device() const { return m_device; }
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+    m_result.bind(cgh);
+  }
+#endif
+
+  private:
+  template <int, typename, typename> friend struct internal::GenericDimReducer;
+  template <typename, typename, bool, bool> friend struct internal::InnerMostDimReducer;
+  template <int, typename, typename, bool> friend struct internal::InnerMostDimPreserver;
+  template <typename S, typename O, typename D, bool V> friend struct internal::FullReducer;
+#ifdef EIGEN_USE_THREADS
+  template <typename S, typename O, bool V> friend struct internal::FullReducerShard;
+#endif
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+  template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernel(R, const S, I_, typename S::CoeffReturnType*, unsigned int*);
+#if defined(EIGEN_HAS_GPU_FP16)
+  template <typename S, typename R, typename I_> KERNEL_FRIEND void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I_, internal::packet_traits<Eigen::half>::type*);
+  template <int B, int N, typename S, typename R, typename I_> KERNEL_FRIEND void internal::FullReductionKernelHalfFloat(R, const S, I_, half*, internal::packet_traits<Eigen::half>::type*);
+  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernelHalfFloat(R, const S, I_, I_, half*);
+#endif
+  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::InnerReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+
+  template <int NPT, typename S, typename R, typename I_> KERNEL_FRIEND void internal::OuterReductionKernel(R, const S, I_, I_, typename S::CoeffReturnType*);
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+ template < typename Evaluator_, typename Op__> friend class TensorSycl::internal::GenericNondeterministicReducer;
+ // SYCL need the Generic reducer for the case the recution algorithm is neither inner, outer, and full reducer
+ template <typename, typename, typename> friend struct internal::GenericReducer;
+#endif
+
+
+  template <typename S, typename O, typename D> friend struct internal::InnerReducer;
+
+  struct BlockIteratorState {
+    Index input_dim;
+    Index output_size;
+    Index output_count;
+  };
+
+  // Returns the Index in the input tensor of the first value that needs to be
+  // used to compute the reduction at output index "index".
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    if (ReducingInnerMostDims) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        return index * m_preservedStrides[0];
+      } else {
+        return index * m_preservedStrides[NumPreservedStrides - 1];
+      }
+    }
+    // TBD: optimize the case where we preserve the innermost dimensions.
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumOutputDims - 1; i > 0; --i) {
+        // This is index_i in the output tensor.
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (PreservingInnerMostDims) {
+        eigen_assert(m_preservedStrides[0] == 1);
+        startInput += index;
+      } else {
+        startInput += index * m_preservedStrides[0];
+      }
+    } else {
+      for (int i = 0; i < NumOutputDims - 1; ++i) {
+        // This is index_i in the output tensor.
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      if (PreservingInnerMostDims) {
+        eigen_assert(m_preservedStrides[NumPreservedStrides - 1] == 1);
+        startInput += index;
+      } else {
+        startInput += index * m_preservedStrides[NumPreservedStrides - 1];
+      }
+    }
+    return startInput;
+  }
+
+  // Bitmap indicating if an input dimension is reduced or not.
+  array<bool, NumInputDims> m_reduced;
+  // Dimensions of the output of the operation.
+  Dimensions m_dimensions;
+  // Precomputed strides for the output tensor.
+  array<Index, NumOutputDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumOutputDims> m_fastOutputStrides;
+  array<Index, NumPreservedStrides> m_preservedStrides;
+  // Map from output to input dimension index.
+  array<Index, NumOutputDims> m_output_to_input_dim_map;
+  // How many values go into each reduction
+  Index m_numValuesToReduce;
+
+  // Subset of strides of the input tensor for the reduced dimensions.
+  // Indexed by reduced dimensions.
+  array<Index, NumReducedDims> m_reducedStrides;
+  // Size of the input dimensions that are reduced.
+  // Indexed by reduced dimensions.
+  array<Index, NumReducedDims> m_reducedDims;
+
+  // Evaluator for the input expression.
+  TensorEvaluator<ArgType, Device> m_impl;
+
+  // Operation to apply for computing the reduction.
+  Op m_reducer;
+
+  // For full reductions
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+  static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value;
+  static const bool RunningOnSycl = false;
+#elif defined(EIGEN_USE_SYCL)
+static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value;
+static const bool RunningOnGPU = false;
+#else
+  static const bool RunningOnGPU = false;
+  static const bool RunningOnSycl = false;
+#endif
+  EvaluatorPointerType m_result;
+
+  const Device EIGEN_DEVICE_REF m_device;
+};
+
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
+: public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> {
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Base;
+  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Device& device) : Base(op, device){}
+};
+
+
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_>
+struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice>
+: public TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> {
+
+  typedef TensorReductionEvaluatorBase<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Eigen::SyclDevice> Base;
+  EIGEN_STRONG_INLINE TensorEvaluator(const typename Base::XprType& op, const Eigen::SyclDevice& device) : Base(op, device){}
+  // The coeff function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel
+  //Therefore the coeff function should be overridden by for SYCL kernel
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::CoeffReturnType coeff(typename Base::Index index) const {
+    return *(this->data() + index);
+  }
+  // The packet function in the base the recursive method which is not an standard layout and cannot be used in the SYCL kernel
+  //Therefore the packet function should be overridden by for SYCL kernel
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Base::PacketReturnType packet(typename Base::Index index) const {
+    return internal::pload<typename Base::PacketReturnType>(this->data() + index);
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
new file mode 100644
index 0000000..68780cd
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
@@ -0,0 +1,6 @@
+
+#if defined(__clang__) || defined(__GNUC__)
+#warning "Deprecated header file, please either include the main Eigen/CXX11/Tensor header or the respective TensorReductionGpu.h file"
+#endif
+
+#include "TensorReductionGpu.h"
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
new file mode 100644
index 0000000..db4e8d8
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionGpu.h
@@ -0,0 +1,966 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
+
+namespace Eigen {
+namespace internal {
+
+
+#if defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+// Full reducers for GPU, don't vectorize for now
+
+// Reducer function that enables multiple gpu thread to safely accumulate at the same
+// output address. It basically reads the current value of the output variable, and
+// attempts to update it with the new value. If in the meantime another gpu thread
+// updated the content of the output address it will try again.
+template <typename T, typename R>
+__device__ EIGEN_ALWAYS_INLINE void atomicReduce(T* output, T accum, R& reducer) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  if (sizeof(T) == 4)
+  {
+    unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
+    unsigned int newval = oldval;
+    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+    unsigned int readback;
+    while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
+      oldval = readback;
+      newval = oldval;
+      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+      if (newval == oldval) {
+        return;
+      }
+    }
+  }
+  else if (sizeof(T) == 8) {
+    unsigned long long oldval = *reinterpret_cast<unsigned long long*>(output);
+    unsigned long long newval = oldval;
+    reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+    unsigned long long readback;
+    while ((readback = atomicCAS((unsigned long long*)output, oldval, newval)) != oldval) {
+      oldval = readback;
+      newval = oldval;
+      reducer.reduce(accum, reinterpret_cast<T*>(&newval));
+      if (newval == oldval) {
+        return;
+      }
+    }
+  }
+  else {
+    gpu_assert(0 && "Wordsize not supported");
+  }
+#else // EIGEN_CUDA_ARCH >= 300
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif // EIGEN_CUDA_ARCH >= 300
+}
+
+// We extend atomicExch to support extra data types
+template <typename Type>
+__device__ inline Type atomicExchCustom(Type* address, Type val) {
+  return atomicExch(address, val);
+}
+
+template <>
+__device__ inline double atomicExchCustom(double* address, double val) {
+  unsigned long long int* address_as_ull = reinterpret_cast<unsigned long long int*>(address);
+  return __longlong_as_double(atomicExch(address_as_ull, __double_as_longlong(val)));
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename R>
+__device__ inline void atomicReduce(half2* output, half2 accum, R& reducer) {
+  unsigned int oldval = *reinterpret_cast<unsigned int*>(output);
+  unsigned int newval = oldval;
+  reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
+  if (newval == oldval) {
+    return;
+  }
+  unsigned int readback;
+  while ((readback = atomicCAS((unsigned int*)output, oldval, newval)) != oldval) {
+    oldval = readback;
+    newval = oldval;
+    reducer.reducePacket(accum, reinterpret_cast<half2*>(&newval));
+    if (newval == oldval) {
+      return;
+    }
+  }
+}
+// reduction should be associative since reduction is not atomic in wide vector but atomic in half2 operations
+template <typename R>
+__device__ inline void atomicReduce(Packet4h2* output, Packet4h2 accum, R& reducer) {
+  half2* houtput=reinterpret_cast<half2*>(output);
+  half2* haccum=reinterpret_cast<half2*>(&accum);
+  for(int i=0;i<4;++i){
+    atomicReduce(houtput+i,*(haccum+i),reducer);
+  }
+}
+#endif  // EIGEN_HAS_GPU_FP16
+
+template <>
+__device__ inline void atomicReduce(float* output, float accum, SumReducer<float>&) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  atomicAdd(output, accum);
+#else // EIGEN_CUDA_ARCH >= 300
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif // EIGEN_CUDA_ARCH >= 300
+}
+
+
+template <typename CoeffType, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernel(const CoeffType val, Index num_preserved_coeffs, CoeffType* output) {
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  const Index num_threads = blockDim.x * gridDim.x;
+  for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+    output[i] = val;
+  }
+}
+
+
+template <int BlockSize, int NumPerThread, typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernel(Reducer reducer, const Self input, Index num_coeffs,
+                                    typename Self::CoeffReturnType* output, unsigned int* semaphore) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  // Initialize the output value
+  const Index first_index = blockIdx.x * BlockSize * NumPerThread + threadIdx.x;
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      *output = reducer.initialize();
+    }
+  }
+  else {
+    if (threadIdx.x == 0) {
+      unsigned int block = atomicCAS(semaphore, 0u, 1u);
+      if (block == 0) {
+        // We're the first block to run, initialize the output value
+        atomicExchCustom(output, reducer.initialize());
+        __threadfence();
+        atomicExch(semaphore, 2u);
+      }
+      else {
+        // Wait for the first block to initialize the output value.
+        // Use atomicCAS here to ensure that the reads aren't cached
+        unsigned int val;
+        do {
+          val = atomicCAS(semaphore, 2u, 2u);
+        }
+        while (val < 2u);
+      }
+    }
+  }
+
+  __syncthreads();
+
+  eigen_assert(gridDim.x == 1 || *semaphore >= 2u);
+
+  typename Self::CoeffReturnType accum = reducer.initialize();
+  Index max_iter = numext::mini<Index>(num_coeffs - first_index, NumPerThread*BlockSize);
+  for (Index i = 0; i < max_iter; i+=BlockSize) {
+    const Index index = first_index + i;
+    eigen_assert(index < num_coeffs);
+    typename Self::CoeffReturnType val = input.m_impl.coeff(index);
+    reducer.reduce(val, &accum);
+  }
+
+#pragma unroll
+  for (int offset = warpSize/2; offset > 0; offset /= 2) {
+  #if defined(EIGEN_HIPCC)
+    // use std::is_floating_point to determine the type of reduced_val 
+    // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambguous" error 
+    // and list the float and int versions of __shfl_down as the candidate functions. 
+    if (std::is_floating_point<typename Self::CoeffReturnType>::value) {
+      reducer.reduce(__shfl_down(static_cast<float>(accum), offset, warpSize), &accum);
+    } else {
+      reducer.reduce(__shfl_down(static_cast<int>(accum), offset, warpSize), &accum);
+    }
+  #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+    reducer.reduce(__shfl_down(accum, offset, warpSize), &accum);
+  #else
+    reducer.reduce(__shfl_down_sync(0xFFFFFFFF, accum, offset, warpSize), &accum);
+  #endif
+  }
+
+  if ((threadIdx.x & (warpSize - 1)) == 0) {
+    atomicReduce(output, accum, reducer);
+  }
+
+  if (gridDim.x > 1 && threadIdx.x == 0) {
+    // Let the last block reset the semaphore
+    atomicInc(semaphore, gridDim.x + 1);
+#if defined(EIGEN_HIPCC)
+    __threadfence_system();
+#endif
+  }
+#else // EIGEN_CUDA_ARCH >= 300
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif // EIGEN_CUDA_ARCH >= 300
+}
+
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitFullReduxKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
+                                                      packet_traits<Eigen::half>::type* scratch) {
+  eigen_assert(blockDim.x == 1);
+  eigen_assert(gridDim.x == 1);
+  typedef packet_traits<Eigen::half>::type packet_type;
+  Index packet_remainder =
+      num_coeffs % Index(unpacket_traits<packet_type>::size);
+  if (packet_remainder != 0) {
+    half2* h2scratch = reinterpret_cast<half2*>(scratch);
+    for (Index i = num_coeffs - packet_remainder; i + 2 <= num_coeffs; i += 2) {
+      *h2scratch =
+          __halves2half2(input.m_impl.coeff(i), input.m_impl.coeff(i + 1));
+      h2scratch++;
+    }
+    if ((num_coeffs & 1) != 0) {
+      half lastCoeff = input.m_impl.coeff(num_coeffs - 1);
+      *h2scratch = __halves2half2(lastCoeff, reducer.initialize());
+    }
+  } else {
+    *scratch = reducer.template initializePacket<packet_type>();
+  }
+}
+
+template <typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionInitKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs, half* output) {
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  const Index num_threads = blockDim.x * gridDim.x;
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+
+  const Index num_packets =
+      num_coeffs / Index(unpacket_traits<PacketType>::size);
+  PacketType* p_output = reinterpret_cast<PacketType*>(output);
+  for (Index i = thread_id; i < num_packets; i += num_threads) {
+    p_output[i] = reducer.template initializePacket<PacketType>();
+  }
+  Index packet_remainder =
+      num_coeffs % Index(unpacket_traits<PacketType>::size);
+  if (thread_id < packet_remainder) {
+    output[num_coeffs - packet_remainder + thread_id] = reducer.initialize();
+  }
+}
+
+template <int BlockSize, int NumPerThread, typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void FullReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs,
+                                    half* output, packet_traits<Eigen::half>::type* scratch) {
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+  const int packet_width = unpacket_traits<PacketType>::size;
+  eigen_assert(NumPerThread % packet_width == 0);
+  const Index first_index =
+      blockIdx.x * BlockSize * NumPerThread + packet_width * threadIdx.x;
+
+  // Initialize the output value if it wasn't initialized by the ReductionInitKernel
+
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      int rem = num_coeffs % packet_width;
+      if (rem != 0) {
+        half2* p_scratch = reinterpret_cast<half2*>(scratch);
+        *scratch = reducer.template initializePacket<PacketType>();
+        for (int i = 0; i < rem / 2; i++) {
+          *p_scratch = __halves2half2(
+              input.m_impl.coeff(num_coeffs - packet_width + 2 * i),
+              input.m_impl.coeff(num_coeffs - packet_width + 2 * i + 1));
+          p_scratch++;
+        }
+        if ((num_coeffs & 1) != 0) {
+          half last = input.m_impl.coeff(num_coeffs - 1);
+          *p_scratch = __halves2half2(last, reducer.initialize());
+        }
+      } else {
+        *scratch = reducer.template initializePacket<PacketType>();
+      }
+    }
+    __syncthreads();
+  }
+
+  PacketType accum = reducer.template initializePacket<PacketType>();
+  const Index max_iter =
+      numext::mini<Index>((num_coeffs - first_index) / packet_width,
+                          NumPerThread * BlockSize / packet_width);
+  for (Index i = 0; i < max_iter; i += BlockSize) {
+    const Index index = first_index + packet_width * i;
+    eigen_assert(index + packet_width < num_coeffs);
+    PacketType val = input.m_impl.template packet<Unaligned>(index);
+    reducer.reducePacket(val, &accum);
+  }
+
+#pragma unroll
+  for (int offset = warpSize/2; offset > 0; offset /= 2) {
+  #if defined(EIGEN_HIPCC)
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
+      union { int i; half2 h; } wka_in, wka_out;
+      wka_in.h = hacc[i];
+      wka_out.i = __shfl_down(wka_in.i, offset, warpSize);
+      hr[i] = wka_out.h;
+    }
+    reducer.reducePacket(r1, &accum);
+  #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      hr[i] = __shfl_down(hacc[i], offset, warpSize);
+    }
+    reducer.reducePacket(r1, &accum);
+  #else
+    PacketType r1;
+    half2* hr = reinterpret_cast<half2*>(&r1);
+    half2* hacc = reinterpret_cast<half2*>(&accum);
+    for (int i = 0; i < packet_width / 2; i++) {
+      hr[i] = __shfl_down_sync(0xFFFFFFFF, hacc[i], (unsigned)offset, warpSize);
+    }
+    reducer.reducePacket(r1, &accum);
+
+  #endif
+  }
+
+  if ((threadIdx.x & (warpSize - 1)) == 0) {
+    atomicReduce(scratch, accum, reducer);
+  }
+
+  __syncthreads();
+  half2* rv1 = reinterpret_cast<half2*>(scratch);
+  if (packet_width > 2) {
+    reducer.reducePacket(rv1[2], rv1);
+    reducer.reducePacket(rv1[3], rv1 + 1);
+    reducer.reducePacket(rv1[1], rv1);
+  }
+  if (gridDim.x == 1) {
+    if (first_index == 0) {
+      half tmp = __low2half(*rv1);
+      reducer.reduce(__high2half(*rv1), &tmp);
+      *output = tmp;
+    }
+  }
+}
+
+template <typename Op>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ReductionCleanupKernelHalfFloat(Op reducer, half* output, packet_traits<Eigen::half>::type* scratch) {
+  eigen_assert(threadIdx.x == 1);
+  half2* pscratch = reinterpret_cast<half2*>(scratch);
+  half tmp = __float2half(0.f);
+  typedef packet_traits<Eigen::half>::type packet_type;
+  for (int i = 0; i < unpacket_traits<packet_type>::size; i += 2) {
+    reducer.reduce(__low2half(*pscratch), &tmp);
+    reducer.reduce(__high2half(*pscratch), &tmp);
+    pscratch++;
+  }
+  *output = tmp;
+}
+
+#endif // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
+struct FullReductionLauncher {
+  static void run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index) {
+    gpu_assert(false && "Should only be called on doubles, floats and half floats");
+  }
+};
+
+// Specialization for float and double
+template <typename Self, typename Op, typename OutputType, bool PacketAccess>
+struct FullReductionLauncher<
+    Self, Op, OutputType, PacketAccess,
+    typename internal::enable_if<
+      internal::is_same<float, OutputType>::value ||
+      internal::is_same<double, OutputType>::value,
+    void>::type> {
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs) {
+
+    typedef typename Self::Index Index;
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
+
+    unsigned int* semaphore = NULL;
+    if (num_blocks > 1) {
+      semaphore = device.semaphore();
+    }
+
+    LAUNCH_GPU_KERNEL((FullReductionKernel<block_size, num_per_thread, Self, Op, Index>),
+                       num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, semaphore);
+  }
+};
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self, typename Op>
+struct FullReductionLauncher<Self, Op, Eigen::half, false> {
+  static void run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index) {
+    gpu_assert(false && "Should not be called since there is no packet accessor");
+  }
+};
+
+template <typename Self, typename Op>
+struct FullReductionLauncher<Self, Op, Eigen::half, true> {
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs) {
+    typedef typename Self::Index Index;
+    typedef typename packet_traits<Eigen::half>::type PacketType;
+
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int num_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
+    PacketType* scratch = static_cast<PacketType*>(device.scratchpad());
+    // half2* scratch = static_cast<half2*>(device.scratchpad());
+
+    if (num_blocks > 1) {
+      // We initialize the output and the scrathpad outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      LAUNCH_GPU_KERNEL((ReductionInitFullReduxKernelHalfFloat<Self, Op, Index>),
+                         1, 1, 0, device, reducer, self, num_coeffs, scratch);
+    }
+
+    LAUNCH_GPU_KERNEL((FullReductionKernelHalfFloat<block_size, num_per_thread, Self, Op, Index>),
+                       num_blocks, block_size, 0, device, reducer, self, num_coeffs, output, scratch);
+
+    if (num_blocks > 1) {
+      LAUNCH_GPU_KERNEL((ReductionCleanupKernelHalfFloat<Op>),
+                         1, 1, 0, device, reducer, output, scratch);
+    }
+  }
+};
+#endif // EIGEN_HAS_GPU_FP16
+
+
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, GpuDevice, Vectorizable> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple cases
+  // of doubles, floats and half floats
+#ifdef EIGEN_HAS_GPU_FP16
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
+      (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+       internal::is_same<typename Self::CoeffReturnType, double>::value ||
+       (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
+#else // EIGEN_HAS_GPU_FP16
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
+                                                (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                                 internal::is_same<typename Self::CoeffReturnType, double>::value);
+#endif // EIGEN_HAS_GPU_FP16
+
+  template <typename OutputType>
+  static void run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output) {
+    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    // Don't crash when we're called with an input tensor of size 0.
+    if (num_coeffs == 0) {
+      return;
+    }
+
+    FullReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs);
+  }
+};
+
+
+template <int NumPerThread, typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
+                                         typename Self::CoeffReturnType* output) {
+#if (defined(EIGEN_HIP_DEVICE_COMPILE) && defined(__HIP_ARCH_HAS_WARP_SHUFFLE__)) || (EIGEN_CUDA_ARCH >= 300)
+  typedef typename Self::CoeffReturnType Type;
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.y == 1);
+  eigen_assert(gridDim.z == 1);
+
+  const int unroll_times = 16;
+  eigen_assert(NumPerThread % unroll_times == 0);
+
+  const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread);
+  const Index num_input_blocks = input_col_blocks * num_preserved_coeffs;
+
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
+    const Index row = i / input_col_blocks;
+
+    if (row < num_preserved_coeffs) {
+      const Index col_block = i % input_col_blocks;
+      const Index col_begin = col_block * blockDim.x * NumPerThread + threadIdx.x;
+
+      Type reduced_val = reducer.initialize();
+
+      for (Index j = 0; j < NumPerThread; j += unroll_times) {
+        const Index last_col = col_begin + blockDim.x * (j + unroll_times - 1);
+        if (last_col >= num_coeffs_to_reduce) {
+          for (Index col = col_begin + blockDim.x * j; col < num_coeffs_to_reduce; col += blockDim.x) {
+            const Type val = input.m_impl.coeff(row * num_coeffs_to_reduce + col);
+            reducer.reduce(val, &reduced_val);
+          }
+          break;
+        } else {
+          // Faster version of the loop with no branches after unrolling.
+#pragma unroll
+          for (int k = 0; k < unroll_times; ++k) {
+            const Index col = col_begin + blockDim.x * (j + k);
+            reducer.reduce(input.m_impl.coeff(row * num_coeffs_to_reduce + col), &reduced_val);
+          }
+        }
+      }
+
+#pragma unroll
+      for (int offset = warpSize/2; offset > 0; offset /= 2) {
+      #if defined(EIGEN_HIPCC)
+        // use std::is_floating_point to determine the type of reduced_val 
+       // This is needed because when Type == double, hipcc will give a "call to __shfl_down is ambguous" error 
+       // and list the float and int versions of __shfl_down as the candidate functions. 
+        if (std::is_floating_point<Type>::value) {
+          reducer.reduce(__shfl_down(static_cast<float>(reduced_val), offset), &reduced_val);
+        } else {
+          reducer.reduce(__shfl_down(static_cast<int>(reduced_val), offset), &reduced_val);
+        }
+      #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+        reducer.reduce(__shfl_down(reduced_val, offset), &reduced_val);
+      #else
+        reducer.reduce(__shfl_down_sync(0xFFFFFFFF, reduced_val, offset), &reduced_val);
+      #endif
+      }
+
+      if ((threadIdx.x & (warpSize - 1)) == 0) {
+        atomicReduce(&(output[row]), reduced_val, reducer);
+      }
+    }
+  }
+#else // EIGEN_CUDA_ARCH >= 300
+  gpu_assert(0 && "Shouldn't be called on unsupported device");
+#endif // EIGEN_CUDA_ARCH >= 300
+}
+
+#ifdef EIGEN_HAS_GPU_FP16
+
+template <int NumPerThread, typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void InnerReductionKernelHalfFloat(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
+                                              half* output) {
+  eigen_assert(blockDim.y == 1);
+  eigen_assert(blockDim.z == 1);
+  eigen_assert(gridDim.y == 1);
+  eigen_assert(gridDim.z == 1);
+
+  typedef typename packet_traits<Eigen::half>::type PacketType;
+  const int packet_width = unpacket_traits<PacketType>::size;
+  const int unroll_times = 16 / packet_width;
+  eigen_assert(NumPerThread % unroll_times == 0);
+  eigen_assert(unroll_times % 2 == 0);
+
+  const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread * 2);
+  const Index num_input_blocks = divup<Index>(input_col_blocks * num_preserved_coeffs, 2);
+
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    Index i = packet_width * thread_id;
+    for (; i + packet_width <= num_preserved_coeffs;
+         i += packet_width * num_threads) {
+      PacketType* poutput = reinterpret_cast<PacketType*>(output + i);
+      *poutput = reducer.template initializePacket<PacketType>();
+    }
+    if (i < num_preserved_coeffs) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) {
+    const Index row = 2 * (i / input_col_blocks);  // everybody takes 2 rows
+
+    if (row + 1 < num_preserved_coeffs) {
+      const Index col_block = i % input_col_blocks;
+      const Index col_begin =
+          packet_width * (col_block * blockDim.x * NumPerThread + threadIdx.x);
+
+      PacketType reduced_val1 = reducer.template initializePacket<PacketType>();
+      PacketType reduced_val2 = reducer.template initializePacket<PacketType>();
+
+      for (Index j = 0; j < NumPerThread; j += unroll_times) {
+        const Index last_col =
+            col_begin + blockDim.x * (j + unroll_times - 1) * packet_width;
+        if (last_col >= num_coeffs_to_reduce) {
+          Index col = col_begin + blockDim.x * j;
+          for (; col + packet_width <= num_coeffs_to_reduce;
+               col += blockDim.x) {
+            const PacketType val1 = input.m_impl.template packet<Unaligned>(
+                row * num_coeffs_to_reduce + col);
+            reducer.reducePacket(val1, &reduced_val1);
+            const PacketType val2 = input.m_impl.template packet<Unaligned>(
+                (row + 1) * num_coeffs_to_reduce + col);
+            reducer.reducePacket(val2, &reduced_val2);
+          }
+          if (col < num_coeffs_to_reduce) {
+            PacketType r1 = reducer.template initializePacket<PacketType>();
+            PacketType r2 = reducer.template initializePacket<PacketType>();
+            half2* hr1 = reinterpret_cast<half2*>(&r1);
+            half2* hr2 = reinterpret_cast<half2*>(&r2);
+            while (col + 1 < num_coeffs_to_reduce) {
+              *hr1 = __halves2half2(
+                  input.m_impl.coeff(row * num_coeffs_to_reduce + col),
+                  input.m_impl.coeff(row * num_coeffs_to_reduce + col + 1));
+              *hr2 = __halves2half2(
+                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col),
+                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col +
+                                     1));
+              hr1++;
+              hr2++;
+              col += 2;
+            }
+            if (col < num_coeffs_to_reduce) {
+              // Peel;
+              const half last1 =
+                  input.m_impl.coeff(row * num_coeffs_to_reduce + col);
+              *hr1 = __halves2half2(last1, reducer.initialize());
+              const half last2 =
+                  input.m_impl.coeff((row + 1) * num_coeffs_to_reduce + col);
+              *hr2 = __halves2half2(last2, reducer.initialize());
+            }
+            reducer.reducePacket(r1, &reduced_val1);
+            reducer.reducePacket(r2, &reduced_val2);
+          }
+          break;
+        } else {
+          // Faster version of the loop with no branches after unrolling.
+#pragma unroll
+          for (int k = 0; k < unroll_times; ++k) {
+            const Index col = col_begin + blockDim.x * (j + k) * packet_width;
+            reducer.reducePacket(input.m_impl.template packet<Unaligned>(
+                                     row * num_coeffs_to_reduce + col),
+                                 &reduced_val1);
+            reducer.reducePacket(input.m_impl.template packet<Unaligned>(
+                                     (row + 1) * num_coeffs_to_reduce + col),
+                                 &reduced_val2);
+          }
+        }
+      }
+
+#pragma unroll
+      for (int offset = warpSize/2; offset > 0; offset /= 2) {
+      #if defined(EIGEN_HIPCC)
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int i = 0; i < packet_width / 2; i++) {
+	  // FIXME : remove this workaround once we have native half/half2 support for __shfl_down
+	  union { int i; half2 h; } wka_in1, wka_out1;
+	  wka_in1.h = rv1[i];
+	  wka_out1.i = __shfl_down(wka_in1.i, offset, warpSize);
+	  hr1[i] = wka_out1.h;
+
+	  union { int i; half2 h; } wka_in2, wka_out2;
+	  wka_in2.h = rv2[i];
+	  wka_out2.i = __shfl_down(wka_in2.i, offset, warpSize);
+	  hr2[i] = wka_out2.h;
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+      #elif defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int i = 0; i < packet_width / 2; i++) {
+          hr1[i] = __shfl_down(rv1[i], offset, warpSize);
+          hr2[i] = __shfl_down(rv2[i], offset, warpSize);
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+      #else
+        PacketType r1;
+        PacketType r2;
+        half2* hr1 = reinterpret_cast<half2*>(&r1);
+        half2* hr2 = reinterpret_cast<half2*>(&r2);
+        half2* rr1 = reinterpret_cast<half2*>(&reduced_val1);
+        half2* rr2 = reinterpret_cast<half2*>(&reduced_val2);
+        for (int i = 0; i < packet_width / 2; i++) {
+          hr1[i] =
+              __shfl_down_sync(0xFFFFFFFF, rr1[i], (unsigned)offset, warpSize);
+          hr2[i] =
+              __shfl_down_sync(0xFFFFFFFF, rr2[i], (unsigned)offset, warpSize);
+        }
+        reducer.reducePacket(r1, &reduced_val1);
+        reducer.reducePacket(r2, &reduced_val2);
+
+      #endif
+      }
+      half2* rv1 = reinterpret_cast<half2*>(&reduced_val1);
+      half2* rv2 = reinterpret_cast<half2*>(&reduced_val2);
+      half2 val;
+      if (packet_width > 2) {
+        reducer.reducePacket(rv1[2], rv1);
+        reducer.reducePacket(rv1[3], rv1 + 1);
+        reducer.reducePacket(rv1[1], rv1);
+        reducer.reducePacket(rv2[2], rv2);
+        reducer.reducePacket(rv2[3], rv2 + 1);
+        reducer.reducePacket(rv2[1], rv2);
+      }
+      half val1 = __low2half(*rv1);
+      reducer.reduce(__high2half(*rv1), &val1);
+      half val2 = __low2half(*rv2);
+      reducer.reduce(__high2half(*rv2), &val2);
+      val = __halves2half2(val1, val2);
+      if ((threadIdx.x & (warpSize - 1)) == 0) {
+        half* loc = output + row;
+        atomicReduce((half2*)loc, val, reducer);
+      }
+    }
+  }
+}
+
+#endif // EIGEN_HAS_GPU_FP16
+
+template <typename Self, typename Op, typename OutputType, bool PacketAccess, typename Enabled = void>
+struct InnerReductionLauncher {
+  static EIGEN_DEVICE_FUNC bool run(const Self&, Op&, const GpuDevice&, OutputType*, typename Self::Index, typename Self::Index) {
+    gpu_assert(false && "Should only be called to reduce doubles, floats and half floats on a gpu device");
+    return true;
+  }
+};
+
+// Specialization for float and double
+template <typename Self, typename Op, typename OutputType, bool PacketAccess>
+struct InnerReductionLauncher<
+  Self, Op, OutputType, PacketAccess,
+  typename internal::enable_if<
+    internal::is_same<float, OutputType>::value ||
+    internal::is_same<double, OutputType>::value,
+  void>::type> {
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = 256;
+    const int num_per_thread = 128;
+    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() *
+                           device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      const int dyn_blocks = divup<int>(num_preserved_vals, 1024);
+      const int max_blocks = device.getNumGpuMultiProcessors() *
+                           device.maxGpuThreadsPerMultiProcessor() / 1024;
+      const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+      LAUNCH_GPU_KERNEL((ReductionInitKernel<OutputType, Index>),
+                         num_blocks, 1024, 0, device, reducer.initialize(),
+                         num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((InnerReductionKernel<num_per_thread, Self, Op, Index>),
+                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+
+#ifdef EIGEN_HAS_GPU_FP16
+template <typename Self, typename Op>
+struct InnerReductionLauncher<Self, Op, Eigen::half, false> {
+  static bool run(const Self&, Op&, const GpuDevice&, half*, typename Self::Index, typename Self::Index) {
+    gpu_assert(false && "Should not be called since there is no packet accessor");
+    return true;
+  }
+};
+
+template <typename Self, typename Op>
+struct InnerReductionLauncher<Self, Op, Eigen::half, true> {
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    if (num_preserved_vals % 2 != 0) {
+      // Not supported yet, revert to the slower code path
+      return true;
+    }
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = /*256*/128;
+    const int num_per_thread = /*128*/64;
+    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() *
+                           device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs outside the reduction kernel when we can't be sure that there
+      // won't be a race conditions between multiple thread blocks.
+      LAUNCH_GPU_KERNEL((ReductionInitKernelHalfFloat<Self, Op, Index>),
+                         1, 1, 0, device, reducer, self, num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((InnerReductionKernelHalfFloat<num_per_thread, Self, Op, Index>),
+                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+#endif // EIGEN_HAS_GPU_FP16
+
+
+template <typename Self, typename Op>
+struct InnerReducer<Self, Op, GpuDevice> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple case
+  // of floats and half floats.
+#ifdef EIGEN_HAS_GPU_FP16
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
+      (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+       internal::is_same<typename Self::CoeffReturnType, double>::value ||
+       (internal::is_same<typename Self::CoeffReturnType, Eigen::half>::value && reducer_traits<Op, GpuDevice>::PacketAccess));
+#else // EIGEN_HAS_GPU_FP16
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
+                                                 (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                                  internal::is_same<typename Self::CoeffReturnType, double>::value);
+#endif // EIGEN_HAS_GPU_FP16
+
+  template <typename OutputType>
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, OutputType* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    gpu_assert(HasOptimizedImplementation && "Should only be called on doubles, floats or half floats");
+    const Index num_coeffs = array_prod(self.m_impl.dimensions());
+    // Don't crash when we're called with an input tensor of size 0.
+    if (num_coeffs == 0) {
+      return true;
+    }
+    // It's faster to use the usual code.
+    if (num_coeffs_to_reduce <= 128) {
+      return true;
+    }
+
+    return InnerReductionLauncher<Self, Op, OutputType, reducer_traits<Op, GpuDevice>::PacketAccess>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals);
+  }
+};
+
+template <int NumPerThread, typename Self,
+          typename Reducer, typename Index>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void OuterReductionKernel(Reducer reducer, const Self input, Index num_coeffs_to_reduce, Index num_preserved_coeffs,
+                                     typename Self::CoeffReturnType* output) {
+  const Index num_threads = blockDim.x * gridDim.x;
+  const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x;
+  // Initialize the output values if they weren't initialized by the ReductionInitKernel
+  if (gridDim.x == 1) {
+    for (Index i = thread_id; i < num_preserved_coeffs; i += num_threads) {
+      output[i] = reducer.initialize();
+    }
+    __syncthreads();
+  }
+
+  // Do the reduction.
+  const Index max_iter = num_preserved_coeffs * divup<Index>(num_coeffs_to_reduce, NumPerThread);
+  for (Index i = thread_id; i < max_iter; i += num_threads) {
+    const Index input_col = i % num_preserved_coeffs;
+    const Index input_row = (i / num_preserved_coeffs) * NumPerThread;
+    typename Self::CoeffReturnType reduced_val = reducer.initialize();
+    const Index max_row = numext::mini(input_row + NumPerThread, num_coeffs_to_reduce);
+    for (Index j = input_row; j < max_row; j++) {
+      typename Self::CoeffReturnType val = input.m_impl.coeff(j * num_preserved_coeffs + input_col);
+      reducer.reduce(val, &reduced_val);
+    }
+    atomicReduce(&(output[input_col]), reduced_val, reducer);
+  }
+}
+
+
+template <typename Self, typename Op>
+struct OuterReducer<Self, Op, GpuDevice> {
+  // Unfortunately nvidia doesn't support well exotic types such as complex,
+  // so reduce the scope of the optimized version of the code to the simple case
+  // of floats.
+  static const bool HasOptimizedImplementation = !Self::ReducerTraits::IsStateful &&
+                                                 (internal::is_same<typename Self::CoeffReturnType, float>::value ||
+                                                  internal::is_same<typename Self::CoeffReturnType, double>::value);
+  template <typename Device, typename OutputType>
+  static
+    #if !defined(EIGEN_HIPCC)
+    // FIXME :  leaving this EIGEN_DEVICE_FUNC in, results in the following runtime error
+    //          (in the cxx11_tensor_reduction_gpu test)
+    //
+    // terminate called after throwing an instance of 'std::runtime_error'
+    //   what():  No device code available for function: _ZN5Eigen8internal20OuterReductionKernelIL...
+    //
+    // don't know why this happens (and why is it a runtime error instead of a compile time error)
+    //
+    // this will be fixed by HIP PR#457
+    EIGEN_DEVICE_FUNC
+    #endif
+    bool run(const Self&, Op&, const Device&, OutputType*, typename Self::Index, typename Self::Index) {
+    gpu_assert(false && "Should only be called to reduce doubles or floats on a gpu device");
+    return true;
+  }
+
+  static bool run(const Self& self, Op& reducer, const GpuDevice& device, float* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) {
+    typedef typename Self::Index Index;
+
+    // It's faster to use the usual code.
+    if (num_coeffs_to_reduce <= 32) {
+      return true;
+    }
+
+    const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals;
+    const int block_size = 256;
+    const int num_per_thread = 16;
+    const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread);
+    const int max_blocks = device.getNumGpuMultiProcessors() *
+                           device.maxGpuThreadsPerMultiProcessor() / block_size;
+    const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+
+    if (num_blocks > 1) {
+      // We initialize the outputs in the reduction kernel itself when we don't have to worry
+      // about race conditions between multiple thread blocks.
+      const int dyn_blocks = divup<int>(num_preserved_vals, 1024);
+      const int max_blocks = device.getNumGpuMultiProcessors() *
+                             device.maxGpuThreadsPerMultiProcessor() / 1024;
+      const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks);
+      LAUNCH_GPU_KERNEL((ReductionInitKernel<float, Index>),
+                         num_blocks, 1024, 0, device, reducer.initialize(),
+                         num_preserved_vals, output);
+    }
+
+    LAUNCH_GPU_KERNEL((OuterReductionKernel<num_per_thread, Self, Op, Index>),
+                       num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output);
+
+    return false;
+  }
+};
+
+#endif // defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC)
+
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
new file mode 100644
index 0000000..474eba0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
@@ -0,0 +1,582 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorReductionSycl.h
+ *
+ * \brief:
+ *  This is the specialization of the reduction operation. Two phase reduction approach 
+ * is used since the GPU does not have Global Synchronization for global memory among 
+ * different work-group/thread block. To solve the problem, we need to create two kernels 
+ * to reduce the data, where the first kernel reduce the data locally and each local 
+ * workgroup/thread-block save the input data into global memory. In the second phase (global reduction)
+ * one work-group uses one work-group/thread-block to reduces the intermediate data into one single element. 
+ * Here is an NVIDIA presentation explaining the optimized two phase reduction algorithm on GPU:
+ * https://developer.download.nvidia.com/assets/cuda/files/reduction.pdf
+ *
+ *****************************************************************/
+
+#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+namespace Eigen {
+namespace TensorSycl {
+namespace internal {
+
+template <typename Op, typename CoeffReturnType, typename Index, bool Vectorizable>
+struct OpDefiner {
+  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, Vectorizable>::PacketReturnType PacketReturnType;
+  typedef Op type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Op &op) { return op; }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
+                                                                            const Index &) {
+    return accumulator;
+  }
+};
+
+template <typename CoeffReturnType, typename Index>
+struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, false> {
+  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
+    return type();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType finalise_op(const CoeffReturnType &accumulator,
+                                                                           const Index &scale) {
+    ::Eigen::internal::scalar_quotient_op<CoeffReturnType> quotient_op;
+    return quotient_op(accumulator, CoeffReturnType(scale));
+  }
+};
+
+template <typename CoeffReturnType, typename Index>
+struct OpDefiner<Eigen::internal::MeanReducer<CoeffReturnType>, CoeffReturnType, Index, true> {
+  typedef typename Vectorise<CoeffReturnType, Eigen::SyclDevice, true>::PacketReturnType PacketReturnType;
+  typedef Eigen::internal::SumReducer<CoeffReturnType> type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE type get_op(Eigen::internal::MeanReducer<CoeffReturnType> &) {
+    return type();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType finalise_op(const PacketReturnType &accumulator,
+                                                                            const Index &scale) {
+    return ::Eigen::internal::pdiv(accumulator, ::Eigen::internal::pset1<PacketReturnType>(CoeffReturnType(scale)));
+  }
+};
+
+template <typename CoeffReturnType, typename OpType, typename InputAccessor, typename OutputAccessor, typename Index,
+          Index local_range>
+struct SecondStepFullReducer {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, true> OpDef;
+  typedef typename OpDef::type Op;
+  LocalAccessor scratch;
+  InputAccessor aI;
+  OutputAccessor outAcc;
+  Op op;
+  SecondStepFullReducer(LocalAccessor scratch_, InputAccessor aI_, OutputAccessor outAcc_, OpType op_)
+      : scratch(scratch_), aI(aI_), outAcc(outAcc_), op(OpDef::get_op(op_)) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) {
+    // Our empirical research shows that the best performance will be achieved
+    // when there is only one element per thread to reduce in the second step.
+    // in this step the second step reduction time is almost negligible.
+    // Hence, in the second step of reduction the input size is fixed to the
+    // local size, thus, there is only one element read per thread. The
+    // algorithm must be changed if the number of reduce per thread in the
+    // second step is greater than 1. Otherwise, the result will be wrong.
+    const Index localid = itemID.get_local_id(0);
+    auto aInPtr = aI.get_pointer() + localid;
+    auto aOutPtr = outAcc.get_pointer();
+    CoeffReturnType *scratchptr = scratch.get_pointer();
+    CoeffReturnType accumulator = *aInPtr;
+
+    scratchptr[localid] = op.finalize(accumulator);
+    for (Index offset = itemID.get_local_range(0) / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.reduce(scratchptr[localid + offset], &accumulator);
+        scratchptr[localid] = op.finalize(accumulator);
+      }
+    }
+    if (localid == 0) *aOutPtr = op.finalize(accumulator);
+  }
+};
+
+// Full reduction first phase. In this version the vectorization is true and the reduction accept 
+// any generic reducerOp  e.g( max, min, sum, mean, iamax, iamin, etc ). 
+template <typename Evaluator, typename OpType, typename Evaluator::Index local_range>
+class FullReductionKernelFunctor {
+ public:
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, typename Evaluator::CoeffReturnType, Index,
+                    (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+      OpDef;
+
+  typedef typename OpDef::type Op;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::PacketReturnType PacketReturnType;
+  typedef
+      typename ::Eigen::internal::conditional<(Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess),
+                                              PacketReturnType, CoeffReturnType>::type OutType;
+  typedef cl::sycl::accessor<OutType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  LocalAccessor scratch;
+  Evaluator evaluator;
+  EvaluatorPointerType final_output;
+  Index rng;
+  Op op;
+
+  FullReductionKernelFunctor(LocalAccessor scratch_, Evaluator evaluator_, EvaluatorPointerType final_output_,
+                             Index rng_, OpType op_)
+      : scratch(scratch_), evaluator(evaluator_), final_output(final_output_), rng(rng_), op(OpDef::get_op(op_)) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) { compute_reduction(itemID); }
+
+  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<Vect>::type compute_reduction(
+      const cl::sycl::nd_item<1> &itemID) {
+    auto output_ptr = final_output.get_pointer();
+    Index VectorizedRange = (rng / Evaluator::PacketSize) * Evaluator::PacketSize;
+    Index globalid = itemID.get_global_id(0);
+    Index localid = itemID.get_local_id(0);
+    Index step = Evaluator::PacketSize * itemID.get_global_range(0);
+    Index start = Evaluator::PacketSize * globalid;
+    // vectorizable parts
+    PacketReturnType packetAccumulator = op.template initializePacket<PacketReturnType>();
+    for (Index i = start; i < VectorizedRange; i += step) {
+      op.template reducePacket<PacketReturnType>(evaluator.impl().template packet<Unaligned>(i), &packetAccumulator);
+    }
+    globalid += VectorizedRange;
+    // non vectorizable parts
+    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      op.template reducePacket<PacketReturnType>(
+          ::Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, Evaluator::PacketSize>::convert_to_packet_type(
+              evaluator.impl().coeff(i), op.initialize()),
+          &packetAccumulator);
+    }
+    scratch[localid] = packetAccumulator =
+        OpDef::finalise_op(op.template finalizePacket<PacketReturnType>(packetAccumulator), rng);
+    // reduction parts // Local size is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.template reducePacket<PacketReturnType>(scratch[localid + offset], &packetAccumulator);
+        scratch[localid] = op.template finalizePacket<PacketReturnType>(packetAccumulator);
+      }
+    }
+    if (localid == 0) {
+      output_ptr[itemID.get_group(0)] =
+          op.finalizeBoth(op.initialize(), op.template finalizePacket<PacketReturnType>(packetAccumulator));
+    }
+  }
+
+  template <bool Vect = (Evaluator::ReducerTraits::PacketAccess & Evaluator::InputPacketAccess)>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename ::Eigen::internal::enable_if<!Vect>::type compute_reduction(
+      const cl::sycl::nd_item<1> &itemID) {
+    auto output_ptr = final_output.get_pointer();
+    Index globalid = itemID.get_global_id(0);
+    Index localid = itemID.get_local_id(0);
+    // vectorizable parts
+    CoeffReturnType accumulator = op.initialize();
+    // non vectorizable parts
+    for (Index i = globalid; i < rng; i += itemID.get_global_range(0)) {
+      op.reduce(evaluator.impl().coeff(i), &accumulator);
+    }
+    scratch[localid] = accumulator = OpDef::finalise_op(op.finalize(accumulator), rng);
+
+    // reduction parts. the local size is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = local_range / 2; offset > 0; offset /= 2) {
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      if (localid < offset) {
+        op.reduce(scratch[localid + offset], &accumulator);
+        scratch[localid] = op.finalize(accumulator);
+      }
+    }
+    if (localid == 0) {
+      output_ptr[itemID.get_group(0)] = op.finalize(accumulator);
+    }
+  }
+};
+
+template <typename Evaluator, typename OpType>
+class GenericNondeterministicReducer {
+ public:
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  template <typename Scratch>
+  GenericNondeterministicReducer(Scratch, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType functor_,
+                       Index range_, Index num_values_to_reduce_)
+      : evaluator(evaluator_),
+        output_accessor(output_accessor_),
+        functor(OpDef::get_op(functor_)),
+        range(range_),
+        num_values_to_reduce(num_values_to_reduce_) {}
+
+  void operator()(cl::sycl::nd_item<1> itemID) {
+    auto output_accessor_ptr = output_accessor.get_pointer();
+    /// const cast added as a naive solution to solve the qualifier drop error
+    Index globalid = static_cast<Index>(itemID.get_global_linear_id());
+    if (globalid < range) {
+      CoeffReturnType accum = functor.initialize();
+      Eigen::internal::GenericDimReducer<Evaluator::NumReducedDims - 1, Evaluator, Op>::reduce(
+          evaluator, evaluator.firstInput(globalid), functor, &accum);
+      output_accessor_ptr[globalid] = OpDef::finalise_op(functor.finalize(accum), num_values_to_reduce);
+    }
+  }
+
+ private:
+  Evaluator evaluator;
+  EvaluatorPointerType output_accessor;
+  Op functor;
+  Index range;
+  Index num_values_to_reduce;
+};
+
+enum class reduction_dim { inner_most, outer_most };
+// default is preserver
+template <typename Evaluator, typename OpType, typename PannelParameters, reduction_dim rt>
+struct PartialReductionKernel {
+  typedef typename Evaluator::CoeffReturnType CoeffReturnType;
+  typedef typename Evaluator::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Evaluator::Index Index;
+  typedef OpDefiner<OpType, CoeffReturnType, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      ScratchAcc;
+  ScratchAcc scratch;
+  Evaluator evaluator;
+  EvaluatorPointerType output_accessor;
+  Op op;
+  const Index preserve_elements_num_groups;
+  const Index reduce_elements_num_groups;
+  const Index num_coeffs_to_preserve;
+  const Index num_coeffs_to_reduce;
+
+  PartialReductionKernel(ScratchAcc scratch_, Evaluator evaluator_, EvaluatorPointerType output_accessor_, OpType op_,
+                         const Index preserve_elements_num_groups_, const Index reduce_elements_num_groups_,
+                         const Index num_coeffs_to_preserve_, const Index num_coeffs_to_reduce_)
+      : scratch(scratch_),
+        evaluator(evaluator_),
+        output_accessor(output_accessor_),
+        op(OpDef::get_op(op_)),
+        preserve_elements_num_groups(preserve_elements_num_groups_),
+        reduce_elements_num_groups(reduce_elements_num_groups_),
+        num_coeffs_to_preserve(num_coeffs_to_preserve_),
+        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void element_wise_reduce(Index globalRId, Index globalPId,
+                                                                 CoeffReturnType &accumulator) {
+    if (globalPId >= num_coeffs_to_preserve) {
+      return;
+    }
+    Index global_offset = rt == reduction_dim::outer_most ? globalPId + (globalRId * num_coeffs_to_preserve)
+                                                          : globalRId + (globalPId * num_coeffs_to_reduce);
+    Index localOffset = globalRId;
+
+    const Index per_thread_local_stride = PannelParameters::LocalThreadSizeR * reduce_elements_num_groups;
+    const Index per_thread_global_stride =
+        rt == reduction_dim::outer_most ? num_coeffs_to_preserve * per_thread_local_stride : per_thread_local_stride;
+    for (Index i = globalRId; i < num_coeffs_to_reduce; i += per_thread_local_stride) {
+      op.reduce(evaluator.impl().coeff(global_offset), &accumulator);
+      localOffset += per_thread_local_stride;
+      global_offset += per_thread_global_stride;
+    }
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    const Index linearLocalThreadId = itemID.get_local_id(0);
+    Index pLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId % PannelParameters::LocalThreadSizeP
+                                                           : linearLocalThreadId / PannelParameters::LocalThreadSizeR;
+    Index rLocalThreadId = rt == reduction_dim::outer_most ? linearLocalThreadId / PannelParameters::LocalThreadSizeP
+                                                           : linearLocalThreadId % PannelParameters::LocalThreadSizeR;
+    const Index pGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) % preserve_elements_num_groups
+                                                           : itemID.get_group(0) / reduce_elements_num_groups;
+    const Index rGroupId = rt == reduction_dim::outer_most ? itemID.get_group(0) / preserve_elements_num_groups
+                                                           : itemID.get_group(0) % reduce_elements_num_groups;
+
+    Index globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
+    const Index globalRId = rGroupId * PannelParameters::LocalThreadSizeR + rLocalThreadId;
+    auto scratchPtr = scratch.get_pointer().get();
+    auto outPtr =
+        output_accessor.get_pointer() + (reduce_elements_num_groups > 1 ? rGroupId * num_coeffs_to_preserve : 0);
+    CoeffReturnType accumulator = op.initialize();
+
+    element_wise_reduce(globalRId, globalPId, accumulator);
+
+    accumulator = OpDef::finalise_op(op.finalize(accumulator), num_coeffs_to_reduce);
+    scratchPtr[pLocalThreadId + rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)] =
+        accumulator;
+    if (rt == reduction_dim::inner_most) {
+      pLocalThreadId = linearLocalThreadId % PannelParameters::LocalThreadSizeP;
+      rLocalThreadId = linearLocalThreadId / PannelParameters::LocalThreadSizeP;
+      globalPId = pGroupId * PannelParameters::LocalThreadSizeP + pLocalThreadId;
+    }
+
+    /* Apply the reduction operation between the current local
+     * id and the one on the other half of the vector. */
+    auto out_scratch_ptr =
+        scratchPtr + (pLocalThreadId + (rLocalThreadId * (PannelParameters::LocalThreadSizeP + PannelParameters::BC)));
+    itemID.barrier(cl::sycl::access::fence_space::local_space);
+    if (rt == reduction_dim::inner_most) {
+      accumulator = *out_scratch_ptr;
+    }
+    // The Local LocalThreadSizeR is always power of 2
+    EIGEN_UNROLL_LOOP
+    for (Index offset = PannelParameters::LocalThreadSizeR >> 1; offset > 0; offset >>= 1) {
+      if (rLocalThreadId < offset) {
+        op.reduce(out_scratch_ptr[(PannelParameters::LocalThreadSizeP + PannelParameters::BC) * offset], &accumulator);
+        // The result has already been divided for mean reducer in the
+        // previous reduction so no need to divide furthermore
+        *out_scratch_ptr = op.finalize(accumulator);
+      }
+      /* All threads collectively read from global memory into local.
+       * The barrier ensures all threads' IO is resolved before
+       * execution continues (strictly speaking, all threads within
+       * a single work-group - there is no co-ordination between
+       * work-groups, only work-items). */
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+    }
+
+    if (rLocalThreadId == 0 && (globalPId < num_coeffs_to_preserve)) {
+      outPtr[globalPId] = op.finalize(accumulator);
+    }
+  }
+};
+
+template <typename OutScalar, typename Index, typename InputAccessor, typename OutputAccessor, typename OpType>
+struct SecondStepPartialReduction {
+  typedef OpDefiner<OpType, OutScalar, Index, false> OpDef;
+  typedef typename OpDef::type Op;
+  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      ScratchAccessor;
+  InputAccessor input_accessor;
+  OutputAccessor output_accessor;
+  Op op;
+  const Index num_coeffs_to_preserve;
+  const Index num_coeffs_to_reduce;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SecondStepPartialReduction(ScratchAccessor, InputAccessor input_accessor_,
+                                                                   OutputAccessor output_accessor_, OpType op_,
+                                                                   const Index num_coeffs_to_preserve_,
+                                                                   const Index num_coeffs_to_reduce_)
+      : input_accessor(input_accessor_),
+        output_accessor(output_accessor_),
+        op(OpDef::get_op(op_)),
+        num_coeffs_to_preserve(num_coeffs_to_preserve_),
+        num_coeffs_to_reduce(num_coeffs_to_reduce_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    const Index globalId = itemID.get_global_id(0);
+
+    if (globalId >= num_coeffs_to_preserve) return;
+
+    auto in_ptr = input_accessor.get_pointer() + globalId;
+
+    OutScalar accumulator = op.initialize();
+// num_coeffs_to_reduce is not bigger that 256
+    for (Index i = 0; i < num_coeffs_to_reduce; i++) {
+      op.reduce(*in_ptr, &accumulator);
+      in_ptr += num_coeffs_to_preserve;
+    }
+    output_accessor.get_pointer()[globalId] = op.finalize(accumulator);
+  }
+};  // namespace internal
+
+template <typename Index, Index LTP, Index LTR, bool BC_>
+struct ReductionPannel {
+  static EIGEN_CONSTEXPR Index LocalThreadSizeP = LTP;
+  static EIGEN_CONSTEXPR Index LocalThreadSizeR = LTR;
+  static EIGEN_CONSTEXPR bool BC = BC_;
+};
+
+template <typename Self, typename Op, TensorSycl::internal::reduction_dim rt>
+struct PartialReducerLauncher {
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::Storage Storage;
+  typedef typename Self::Index Index;
+  typedef ReductionPannel<typename Self::Index, EIGEN_SYCL_LOCAL_THREAD_DIM0, EIGEN_SYCL_LOCAL_THREAD_DIM1, true>
+      PannelParameters;
+
+  typedef PartialReductionKernel<Self, Op, PannelParameters, rt> SyclReducerKerneType;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType output,
+                  Index num_coeffs_to_reduce, Index num_coeffs_to_preserve) {
+    Index roundUpP = roundUp(num_coeffs_to_preserve, PannelParameters::LocalThreadSizeP);
+
+    // getPowerOfTwo makes sure local range is power of 2 and <=
+    // maxSyclThreadPerBlock this will help us to avoid extra check on the
+    // kernel
+    static_assert(!((PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR) &
+                    (PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR - 1)),
+                  "The Local thread size must be a power of 2 for the reduction "
+                  "operation");
+
+    EIGEN_CONSTEXPR Index localRange = PannelParameters::LocalThreadSizeP * PannelParameters::LocalThreadSizeR;
+    // In this step, we force the code not to be more than 2-step reduction:
+    // Our empirical research shows that if each thread reduces at least 64
+    // elemnts individually, we get better performance. However, this can change
+    // on different platforms. In this step we force the code not to be
+    // morthan step reduction: Our empirical research shows that for inner_most
+    // dim reducer, it is better to have 8 group in a reduce dimension for sizes
+    // > 1024 to achieve the best performance.
+    const Index reductionPerThread = 64;
+    Index cu = dev.getPowerOfTwo(dev.getNumSyclMultiProcessors(), true);
+    const Index pNumGroups = roundUpP / PannelParameters::LocalThreadSizeP;
+    Index rGroups = (cu + pNumGroups - 1) / pNumGroups;
+    const Index rNumGroups = num_coeffs_to_reduce > reductionPerThread * localRange ? std::min(rGroups, localRange) : 1;
+    const Index globalRange = pNumGroups * rNumGroups * localRange;
+
+    EIGEN_CONSTEXPR Index scratchSize =
+        PannelParameters::LocalThreadSizeR * (PannelParameters::LocalThreadSizeP + PannelParameters::BC);
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
+    if (rNumGroups > 1) {
+      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
+          dev.allocate_temp(num_coeffs_to_preserve * rNumGroups * sizeof(CoeffReturnType)));
+      EvaluatorPointerType temp_accessor = dev.get(temp_pointer);
+      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
+          self, temp_accessor, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
+          num_coeffs_to_reduce);
+
+      typedef SecondStepPartialReduction<CoeffReturnType, Index, EvaluatorPointerType, EvaluatorPointerType, Op>
+          SecondStepPartialReductionKernel;
+
+      dev.template unary_kernel_launcher<CoeffReturnType, SecondStepPartialReductionKernel>(
+          temp_accessor, output,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(pNumGroups * localRange), cl::sycl::range<1>(localRange)), Index(1),
+          reducer, num_coeffs_to_preserve, rNumGroups);
+
+      self.device().deallocate_temp(temp_pointer);
+    } else {
+      dev.template unary_kernel_launcher<CoeffReturnType, SyclReducerKerneType>(
+          self, output, thread_range, scratchSize, reducer, pNumGroups, rNumGroups, num_coeffs_to_preserve,
+          num_coeffs_to_reduce);
+    }
+    return false;
+  }
+};
+}  // namespace internal
+}  // namespace TensorSycl
+
+namespace internal {
+
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, Eigen::SyclDevice, Vectorizable> {
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
+  static EIGEN_CONSTEXPR int PacketSize = Self::PacketAccess ? Self::PacketSize : 1;
+  static void run(const Self &self, Op &reducer, const Eigen::SyclDevice &dev, EvaluatorPointerType data) {
+    typedef typename conditional<Self::PacketAccess, typename Self::PacketReturnType, CoeffReturnType>::type OutType;
+    static_assert(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
+                    (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
+                  "The Local thread size must be a power of 2 for the reduction "
+                  "operation");
+    EIGEN_CONSTEXPR Index local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;
+
+    typename Self::Index inputSize = self.impl().dimensions().TotalSize();
+    // In this step we force the code not to be more than 2-step reduction:
+    // Our empirical research shows that if each thread reduces at least 512
+    // elemnts individually, we get better performance.
+    const Index reductionPerThread = 2048;
+    // const Index num_work_group =
+    Index reductionGroup = dev.getPowerOfTwo(
+        (inputSize + (reductionPerThread * local_range - 1)) / (reductionPerThread * local_range), true);
+    const Index num_work_group = std::min(reductionGroup, local_range);
+    // 1
+    // ? local_range
+    // : 1);
+    const Index global_range = num_work_group * local_range;
+
+    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+    typedef TensorSycl::internal::FullReductionKernelFunctor<Self, Op, local_range> reduction_kernel_t;
+    if (num_work_group > 1) {
+      CoeffReturnType *temp_pointer =
+          static_cast<CoeffReturnType *>(dev.allocate_temp(num_work_group * sizeof(CoeffReturnType)));
+      typename Self::EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
+      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, tmp_global_accessor, thread_range,
+                                                                      local_range, inputSize, reducer);
+
+      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
+                                                          EvaluatorPointerType, Index, local_range>
+          GenericRKernel;
+      dev.template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
+          tmp_global_accessor, data,
+          cl::sycl::nd_range<1>(cl::sycl::range<1>(num_work_group), cl::sycl::range<1>(num_work_group)), num_work_group,
+          reducer);
+
+      dev.deallocate_temp(temp_pointer);
+    } else {
+      dev.template unary_kernel_launcher<OutType, reduction_kernel_t>(self, data, thread_range, local_range, inputSize,
+                                                                      reducer);
+    }
+  }
+};
+// vectorizable inner_most most dim preserver
+// col reduction
+template <typename Self, typename Op>
+struct OuterReducer<Self, Op, Eigen::SyclDevice> {
+  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
+        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::outer_most>::run(self, reducer, dev, output,
+                                                                                 num_coeffs_to_reduce,
+                                                                                 num_coeffs_to_preserve);
+  }
+};
+// row reduction
+template <typename Self, typename Op>
+struct InnerReducer<Self, Op, Eigen::SyclDevice> {
+  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = true;
+
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_coeffs_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    return ::Eigen::TensorSycl::internal::PartialReducerLauncher<
+        Self, Op, ::Eigen::TensorSycl::internal::reduction_dim::inner_most>::run(self, reducer, dev, output,
+                                                                                 num_coeffs_to_reduce,
+                                                                                 num_coeffs_to_preserve);
+  }
+};
+
+// ArmgMax uses this kernel for partial reduction//
+// TODO(@mehdi.goli) come up with a better kernel
+// generic partial reduction
+template <typename Self, typename Op>
+struct GenericReducer<Self, Op, Eigen::SyclDevice> {
+  static EIGEN_CONSTEXPR bool HasOptimizedImplementation = false;
+  static bool run(const Self &self, const Op &reducer, const Eigen::SyclDevice &dev,
+                  typename Self::EvaluatorPointerType output, typename Self::Index num_values_to_reduce,
+                  typename Self::Index num_coeffs_to_preserve) {
+    typename Self::Index range, GRange, tileSize;
+    dev.parallel_for_setup(num_coeffs_to_preserve, tileSize, range, GRange);
+
+    dev.template unary_kernel_launcher<typename Self::CoeffReturnType,
+                                       TensorSycl::internal::GenericNondeterministicReducer<Self, Op>>(
+        self, output, cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), Index(1),
+        reducer, range, (num_values_to_reduce != 0) ? num_values_to_reduce : static_cast<Index>(1));
+    return false;
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
new file mode 100644
index 0000000..a27d364
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorRef.h
@@ -0,0 +1,454 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REF_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REF_H
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Dimensions, typename Scalar>
+class TensorLazyBaseEvaluator {
+ public:
+  TensorLazyBaseEvaluator() : m_refcount(0) { }
+  virtual ~TensorLazyBaseEvaluator() { }
+
+  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const = 0;
+  EIGEN_DEVICE_FUNC virtual const Scalar* data() const = 0;
+
+  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const = 0;
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) = 0;
+
+  void incrRefCount() { ++m_refcount; }
+  void decrRefCount() { --m_refcount; }
+  int refCount() const { return m_refcount; }
+
+ private:
+  // No copy, no assignment;
+  TensorLazyBaseEvaluator(const TensorLazyBaseEvaluator& other);
+  TensorLazyBaseEvaluator& operator = (const TensorLazyBaseEvaluator& other);
+
+  int m_refcount;
+};
+
+
+template <typename Dimensions, typename Expr, typename Device>
+class TensorLazyEvaluatorReadOnly : public TensorLazyBaseEvaluator<Dimensions, typename TensorEvaluator<Expr, Device>::Scalar> {
+ public:
+  //  typedef typename TensorEvaluator<Expr, Device>::Dimensions Dimensions;
+  typedef typename TensorEvaluator<Expr, Device>::Scalar Scalar;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+  typedef  TensorEvaluator<Expr, Device> EvalType;
+
+  TensorLazyEvaluatorReadOnly(const Expr& expr, const Device& device) : m_impl(expr, device), m_dummy(Scalar(0)) {
+    m_dims = m_impl.dimensions();
+    m_impl.evalSubExprsIfNeeded(NULL);
+  }
+  virtual ~TensorLazyEvaluatorReadOnly() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC virtual const Dimensions& dimensions() const {
+    return m_dims;
+  }
+  EIGEN_DEVICE_FUNC virtual const Scalar* data() const {
+    return m_impl.data();
+  }
+
+  EIGEN_DEVICE_FUNC virtual const Scalar coeff(DenseIndex index) const {
+    return m_impl.coeff(index);
+  }
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex /*index*/) {
+    eigen_assert(false && "can't reference the coefficient of a rvalue");
+    return m_dummy;
+  };
+
+ protected:
+  TensorEvaluator<Expr, Device> m_impl;
+  Dimensions m_dims;
+  Scalar m_dummy;
+};
+
+template <typename Dimensions, typename Expr, typename Device>
+class TensorLazyEvaluatorWritable : public TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> {
+ public:
+  typedef TensorLazyEvaluatorReadOnly<Dimensions, Expr, Device> Base;
+  typedef typename Base::Scalar Scalar;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  TensorLazyEvaluatorWritable(const Expr& expr, const Device& device) : Base(expr, device) {
+  }
+  virtual ~TensorLazyEvaluatorWritable() {
+  }
+
+  EIGEN_DEVICE_FUNC virtual Scalar& coeffRef(DenseIndex index) {
+    return this->m_impl.coeffRef(index);
+  }
+};
+
+template <typename Dimensions, typename Expr, typename Device>
+class TensorLazyEvaluator : public internal::conditional<bool(internal::is_lvalue<Expr>::value),
+                            TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
+                            TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type {
+ public:
+  typedef typename internal::conditional<bool(internal::is_lvalue<Expr>::value),
+                                         TensorLazyEvaluatorWritable<Dimensions, Expr, Device>,
+                                         TensorLazyEvaluatorReadOnly<Dimensions, const Expr, Device> >::type Base;
+  typedef typename Base::Scalar Scalar;
+
+  TensorLazyEvaluator(const Expr& expr, const Device& device) : Base(expr, device) {
+  }
+  virtual ~TensorLazyEvaluator() {
+  }
+};
+
+}  // namespace internal
+
+
+/** \class TensorRef
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief A reference to a tensor expression
+  * The expression will be evaluated lazily (as much as possible).
+  *
+  */
+template<typename PlainObjectType> class TensorRef : public TensorBase<TensorRef<PlainObjectType> >
+{
+  public:
+    typedef TensorRef<PlainObjectType> Self;
+    typedef typename PlainObjectType::Base Base;
+    typedef typename Eigen::internal::nested<Self>::type Nested;
+    typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
+    typedef typename internal::traits<PlainObjectType>::Index Index;
+    typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+    typedef Scalar* PointerType;
+    typedef PointerType PointerArgType;
+
+    static const Index NumIndices = PlainObjectType::NumIndices;
+    typedef typename PlainObjectType::Dimensions Dimensions;
+
+    enum {
+      IsAligned = false,
+      PacketAccess = false,
+      BlockAccess = false,
+      PreferBlockAccess = false,
+      Layout = PlainObjectType::Layout,
+      CoordAccess = false,  // to be implemented
+      RawAccess = false
+    };
+
+    //===- Tensor block evaluation strategy (see TensorBlock.h) -----------===//
+    typedef internal::TensorBlockNotImplemented TensorBlock;
+    //===------------------------------------------------------------------===//
+
+    EIGEN_STRONG_INLINE TensorRef() : m_evaluator(NULL) {
+    }
+
+    template <typename Expression>
+    EIGEN_STRONG_INLINE TensorRef(const Expression& expr) : m_evaluator(new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice())) {
+      m_evaluator->incrRefCount();
+    }
+
+    template <typename Expression>
+    EIGEN_STRONG_INLINE TensorRef& operator = (const Expression& expr) {
+      unrefEvaluator();
+      m_evaluator = new internal::TensorLazyEvaluator<Dimensions, Expression, DefaultDevice>(expr, DefaultDevice());
+      m_evaluator->incrRefCount();
+      return *this;
+    }
+
+    ~TensorRef() {
+      unrefEvaluator();
+    }
+
+    TensorRef(const TensorRef& other) : m_evaluator(other.m_evaluator) {
+      eigen_assert(m_evaluator->refCount() > 0);
+      m_evaluator->incrRefCount();
+    }
+
+    TensorRef& operator = (const TensorRef& other) {
+      if (this != &other) {
+        unrefEvaluator();
+        m_evaluator = other.m_evaluator;
+        eigen_assert(m_evaluator->refCount() > 0);
+        m_evaluator->incrRefCount();
+      }
+      return *this;
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index rank() const { return m_evaluator->dimensions().size(); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_evaluator->dimensions()[n]; }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_evaluator->dimensions(); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index size() const { return m_evaluator->dimensions().TotalSize(); }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar* data() const { return m_evaluator->data(); }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index index) const
+    {
+      return m_evaluator->coeff(index);
+    }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index firstIndex, IndexTypes... otherIndices) const
+    {
+      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
+      return coeff(indices);
+    }
+    template<typename... IndexTypes> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
+    {
+      const std::size_t num_indices = (sizeof...(otherIndices) + 1);
+      const array<Index, num_indices> indices{{firstIndex, otherIndices...}};
+      return coeffRef(indices);
+    }
+#else
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1) const
+    {
+      array<Index, 2> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      return coeff(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2) const
+    {
+      array<Index, 3> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      return coeff(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3) const
+    {
+      array<Index, 4> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      indices[3] = i3;
+      return coeff(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
+    {
+      array<Index, 5> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      indices[3] = i3;
+      indices[4] = i4;
+      return coeff(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1)
+    {
+      array<Index, 2> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      return coeffRef(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2)
+    {
+      array<Index, 3> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      return coeffRef(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
+    {
+      array<Index, 4> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      indices[3] = i3;
+      return coeffRef(indices);
+    }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index i0, Index i1, Index i2, Index i3, Index i4)
+    {
+      array<Index, 5> indices;
+      indices[0] = i0;
+      indices[1] = i1;
+      indices[2] = i2;
+      indices[3] = i3;
+      indices[4] = i4;
+      return coeffRef(indices);
+    }
+#endif
+
+    template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar coeff(const array<Index, NumIndices>& indices) const
+    {
+      const Dimensions& dims = this->dimensions();
+      Index index = 0;
+      if (PlainObjectType::Options & RowMajor) {
+        index += indices[0];
+        for (size_t i = 1; i < NumIndices; ++i) {
+          index = index * dims[i] + indices[i];
+        }
+      } else {
+        index += indices[NumIndices-1];
+        for (int i = NumIndices-2; i >= 0; --i) {
+          index = index * dims[i] + indices[i];
+        }
+      }
+      return m_evaluator->coeff(index);
+    }
+    template <std::size_t NumIndices> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
+    {
+      const Dimensions& dims = this->dimensions();
+      Index index = 0;
+      if (PlainObjectType::Options & RowMajor) {
+        index += indices[0];
+        for (size_t i = 1; i < NumIndices; ++i) {
+          index = index * dims[i] + indices[i];
+        }
+      } else {
+        index += indices[NumIndices-1];
+        for (int i = NumIndices-2; i >= 0; --i) {
+          index = index * dims[i] + indices[i];
+        }
+      }
+      return m_evaluator->coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar coeff(Index index) const
+    {
+      return m_evaluator->coeff(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
+    {
+      return m_evaluator->coeffRef(index);
+    }
+
+  private:
+    EIGEN_STRONG_INLINE void unrefEvaluator() {
+      if (m_evaluator) {
+        m_evaluator->decrRefCount();
+        if (m_evaluator->refCount() == 0) {
+          delete m_evaluator;
+        }
+      }
+    }
+
+  internal::TensorLazyBaseEvaluator<Dimensions, Scalar>* m_evaluator;
+};
+
+
+// evaluator for rvalues
+template<typename Derived, typename Device>
+struct TensorEvaluator<const TensorRef<Derived>, Device>
+{
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorRef<Derived>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const TensorRef<Derived>& m, const Device&)
+      : m_ref(m)
+  { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_ref.dimensions(); }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
+    return m_ref.coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return m_ref.coeffRef(index);
+  }
+
+  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_ref.data(); }
+
+ protected:
+  TensorRef<Derived> m_ref;
+};
+
+
+// evaluator for lvalues
+template<typename Derived, typename Device>
+struct TensorEvaluator<TensorRef<Derived>, Device> : public TensorEvaluator<const TensorRef<Derived>, Device>
+{
+  typedef typename Derived::Index Index;
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Scalar CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename Derived::Dimensions Dimensions;
+
+  typedef TensorEvaluator<const TensorRef<Derived>, Device> Base;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = false,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(TensorRef<Derived>& m, const Device& d) : Base(m, d)
+  { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return this->m_ref.coeffRef(index);
+  }
+};
+
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_REF_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
new file mode 100644
index 0000000..586ce68
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
@@ -0,0 +1,465 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
+//                    Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
+namespace Eigen {
+
+/** \class TensorReverse
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor reverse elements class.
+  *
+  */
+namespace internal {
+template<typename ReverseDimensions, typename XprType>
+struct traits<TensorReverseOp<ReverseDimensions,
+                              XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename ReverseDimensions, typename XprType>
+struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
+{
+  typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
+};
+
+template<typename ReverseDimensions, typename XprType>
+struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
+            typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
+{
+  typedef TensorReverseOp<ReverseDimensions, XprType> type;
+};
+
+}  // end namespace internal
+
+template<typename ReverseDimensions, typename XprType>
+class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
+                                          XprType>, WriteAccessors>
+{
+  public:
+    typedef TensorBase<TensorReverseOp<ReverseDimensions, XprType>, WriteAccessors>Base;
+    typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
+                                                                      StorageKind;
+    typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(
+      const XprType& expr, const ReverseDimensions& reverse_dims)
+      : m_xpr(expr), m_reverse_dims(reverse_dims) { }
+
+    EIGEN_DEVICE_FUNC
+    const ReverseDimensions& reverse() const { return m_reverse_dims; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReverseOp)
+
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const ReverseDimensions m_reverse_dims;
+};
+
+// Eval as rvalue
+template<typename ReverseDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
+{
+  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<ReverseDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess       = NumDims > 0,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  typedef internal::TensorIntDivisor<Index> IndexDivisor;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
+      ArgTensorBlock;
+
+  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device),
+        m_reverse(op.reverse()),
+        m_device(device)
+  {
+    // Reversing a scalar isn't supported yet. It would be a no-op anyway.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    // Compute strides
+    m_dimensions = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
+        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
+      }
+    } else {
+      m_strides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
+        if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]);
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex(
+      Index index) const {
+    eigen_assert(index < dimensions().TotalSize());
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        Index idx = index / m_fastStrides[i];
+        index -= idx * m_strides[i];
+        if (m_reverse[i]) {
+          idx = m_dimensions[i] - idx - 1;
+        }
+        inputIndex += idx * m_strides[i] ;
+      }
+      if (m_reverse[0]) {
+        inputIndex += (m_dimensions[0] - index - 1);
+      } else {
+        inputIndex += index;
+      }
+    } else {
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        Index idx = index / m_fastStrides[i];
+        index -= idx * m_strides[i];
+        if (m_reverse[i]) {
+          idx = m_dimensions[i] - idx - 1;
+        }
+        inputIndex += idx * m_strides[i] ;
+      }
+      if (m_reverse[NumDims-1]) {
+        inputIndex += (m_dimensions[NumDims-1] - index - 1);
+      } else {
+        inputIndex += index;
+      }
+    }
+    return inputIndex;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(
+      Index index) const  {
+    return m_impl.coeff(reverseIndex(index));
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    // TODO(ndjaitly): write a better packing routine that uses
+    // local structure.
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type
+                                                            values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    const size_t target_size = m_device.lastLevelCacheSize();
+    // Block evaluation reads underlying memory in reverse order, and default
+    // cost model does not properly catch this in bytes stored/loaded.
+    return internal::TensorBlockResourceRequirements::skewed<Scalar>(
+               target_size)
+        .addCostPerCoeff({0, 0, 24});
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool /*root_of_expr_ast*/ = false) const {
+    // TODO(ezhulenev): If underlying tensor expression supports and prefers
+    // block evaluation we must use it. Currently we use coeff and packet
+    // access into the underlying tensor expression.
+    // static const bool useBlockAccessForArgType =
+    //     TensorEvaluator<ArgType, Device>::BlockAccess &&
+    //     TensorEvaluator<ArgType, Device>::PreferBlockAccess;
+
+    static const bool isColMajor =
+        static_cast<int>(Layout) == static_cast<int>(ColMajor);
+
+    static const Index inner_dim_idx = isColMajor ? 0 : NumDims - 1;
+    const bool inner_dim_reversed = m_reverse[inner_dim_idx];
+
+    // Offset in the output block.
+    Index block_offset = 0;
+
+    // Offset in the input Tensor.
+    Index input_offset = reverseIndex(desc.offset());
+
+    // Initialize output block iterator state. Dimension in this array are
+    // always in inner_most -> outer_most order (col major layout).
+    array<BlockIteratorState, NumDims> it;
+    for (int i = 0; i < NumDims; ++i) {
+      const int dim = isColMajor ? i : NumDims - 1 - i;
+      it[i].size = desc.dimension(dim);
+      it[i].count = 0;
+      it[i].reverse = m_reverse[dim];
+
+      it[i].block_stride =
+          i == 0 ? 1 : (it[i - 1].size * it[i - 1].block_stride);
+      it[i].block_span = it[i].block_stride * (it[i].size - 1);
+
+      it[i].input_stride = m_strides[dim];
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      if (it[i].reverse) {
+        it[i].input_stride = -1 * it[i].input_stride;
+        it[i].input_span = -1 * it[i].input_span;
+      }
+    }
+
+    // If multiple inner dimensions have the same reverse flag, check if we can
+    // merge them into a single virtual inner dimension.
+    int effective_inner_dim = 0;
+    for (int i = 1; i < NumDims; ++i) {
+      if (it[i].reverse != it[effective_inner_dim].reverse) break;
+      if (it[i].block_stride != it[effective_inner_dim].size) break;
+      if (it[i].block_stride != numext::abs(it[i].input_stride)) break;
+
+      it[i].size = it[effective_inner_dim].size * it[i].size;
+
+      it[i].block_stride = 1;
+      it[i].input_stride = (inner_dim_reversed ? -1 : 1);
+
+      it[i].block_span = it[i].block_stride * (it[i].size - 1);
+      it[i].input_span = it[i].input_stride * (it[i].size - 1);
+
+      effective_inner_dim = i;
+    }
+
+    eigen_assert(it[effective_inner_dim].block_stride == 1);
+    eigen_assert(it[effective_inner_dim].input_stride ==
+                 (inner_dim_reversed ? -1 : 1));
+
+    const Index inner_dim_size = it[effective_inner_dim].size;
+
+    // Prepare storage for the materialized reverse result.
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(desc, scratch);
+    CoeffReturnType* block_buffer = block_storage.data();
+
+    while (it[NumDims - 1].count < it[NumDims - 1].size) {
+      // Copy inner-most dimension data from reversed location in input.
+      Index dst = block_offset;
+      Index src = input_offset;
+
+      // NOTE(ezhulenev): Adding vectorized path with internal::preverse showed
+      // worse results in benchmarks than a simple coefficient loop.
+      if (inner_dim_reversed) {
+        for (Index i = 0; i < inner_dim_size; ++i) {
+          block_buffer[dst] = m_impl.coeff(src);
+          ++dst;
+          --src;
+        }
+      } else {
+        for (Index i = 0; i < inner_dim_size; ++i) {
+          block_buffer[dst] = m_impl.coeff(src);
+          ++dst;
+          ++src;
+        }
+      }
+
+      // For the 1d tensor we need to generate only one inner-most dimension.
+      if ((NumDims - effective_inner_dim) == 1) break;
+
+      // Update offset.
+      for (Index i = effective_inner_dim + 1; i < NumDims; ++i) {
+        if (++it[i].count < it[i].size) {
+          block_offset += it[i].block_stride;
+          input_offset += it[i].input_stride;
+          break;
+        }
+        if (i != NumDims - 1) it[i].count = 0;
+        block_offset -= it[i].block_span;
+        input_offset -= it[i].input_span;
+      }
+    }
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
+                                     2 * TensorOpCost::MulCost<Index>() +
+                                     TensorOpCost::DivCost<Index>());
+    for (int i = 0; i < NumDims; ++i) {
+      if (m_reverse[i]) {
+        compute_cost += 2 * TensorOpCost::AddCost<Index>();
+      }
+    }
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_strides;
+  array<IndexDivisor, NumDims> m_fastStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+  ReverseDimensions m_reverse;
+  const Device EIGEN_DEVICE_REF m_device;
+
+ private:
+  struct BlockIteratorState {
+    BlockIteratorState()
+        : size(0),
+          count(0),
+          reverse(false),
+          block_stride(0),
+          block_span(0),
+          input_stride(0),
+          input_span(0) {}
+
+    Index size;
+    Index count;
+    bool reverse;
+    Index block_stride;
+    Index block_span;
+    Index input_stride;
+    Index input_span;
+  };
+};
+
+// Eval as lvalue
+
+template <typename ReverseDimensions, typename ArgType, typename Device>
+struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device>
+    : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
+                             Device> {
+  typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
+                          Device> Base;
+  typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<ReverseDimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : Base(op, device) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Dimensions& dimensions() const { return this->m_dimensions; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
+    return this->m_impl.coeffRef(this->reverseIndex(index));
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x) {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    // This code is pilfered from TensorMorphing.h
+    EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      this->coeffRef(index+i) = values[i];
+    }
+  }
+};
+
+
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
new file mode 100644
index 0000000..beae854
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
@@ -0,0 +1,528 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
+#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Op, typename XprType>
+struct traits<TensorScanOp<Op, XprType> >
+    : public traits<XprType> {
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Op, typename XprType>
+struct eval<TensorScanOp<Op, XprType>, Eigen::Dense>
+{
+  typedef const TensorScanOp<Op, XprType>& type;
+};
+
+template<typename Op, typename XprType>
+struct nested<TensorScanOp<Op, XprType>, 1,
+            typename eval<TensorScanOp<Op, XprType> >::type>
+{
+  typedef TensorScanOp<Op, XprType> type;
+};
+} // end namespace internal
+
+/** \class TensorScan
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor scan class.
+  */
+template <typename Op, typename XprType>
+class TensorScanOp
+    : public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> {
+public:
+  typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorScanOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(
+      const XprType& expr, const Index& axis, bool exclusive = false, const Op& op = Op())
+      : m_expr(expr), m_axis(axis), m_accumulator(op), m_exclusive(exclusive) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Index axis() const { return m_axis; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const XprType& expression() const { return m_expr; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Op accumulator() const { return m_accumulator; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  bool exclusive() const { return m_exclusive; }
+
+protected:
+  typename XprType::Nested m_expr;
+  const Index m_axis;
+  const Op m_accumulator;
+  const bool m_exclusive;
+};
+
+
+namespace internal {
+
+template <typename Self>
+EIGEN_STRONG_INLINE void ReduceScalar(Self& self, Index offset,
+                                      typename Self::CoeffReturnType* data) {
+  // Compute the scan along the axis, starting at the given offset
+  typename Self::CoeffReturnType accum = self.accumulator().initialize();
+  if (self.stride() == 1) {
+    if (self.exclusive()) {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      }
+    } else {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  } else {
+    if (self.exclusive()) {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        Index curr = offset + idx3 * self.stride();
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      }
+    } else {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        Index curr = offset + idx3 * self.stride();
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  }
+}
+
+template <typename Self>
+EIGEN_STRONG_INLINE void ReducePacket(Self& self, Index offset,
+                                      typename Self::CoeffReturnType* data) {
+  using Scalar = typename Self::CoeffReturnType;
+  using Packet = typename Self::PacketReturnType;
+  // Compute the scan along the axis, starting at the calculated offset
+  Packet accum = self.accumulator().template initializePacket<Packet>();
+  if (self.stride() == 1) {
+    if (self.exclusive()) {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+      }
+    } else {
+      for (Index curr = offset; curr < offset + self.size(); ++curr) {
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+      }
+    }
+  } else {
+    if (self.exclusive()) {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        const Index curr = offset + idx3 * self.stride();
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+      }
+    } else {
+      for (Index idx3 = 0; idx3 < self.size(); idx3++) {
+        const Index curr = offset + idx3 * self.stride();
+        self.accumulator().reducePacket(self.inner().template packet<Unaligned>(curr), &accum);
+        internal::pstoreu<Scalar, Packet>(data + curr, self.accumulator().finalizePacket(accum));
+      }
+    }
+  }
+}
+
+template <typename Self, bool Vectorize, bool Parallel>
+struct ReduceBlock {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
+                                      typename Self::CoeffReturnType* data) {
+    for (Index idx2 = 0; idx2 < self.stride(); idx2++) {
+      // Calculate the starting offset for the scan
+      Index offset = idx1 + idx2;
+      ReduceScalar(self, offset, data);
+    }
+  }
+};
+
+// Specialization for vectorized reduction.
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/false> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
+                                      typename Self::CoeffReturnType* data) {
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    Index idx2 = 0;
+    for (; idx2 + PacketSize <= self.stride(); idx2 += PacketSize) {
+      // Calculate the starting offset for the packet scan
+      Index offset = idx1 + idx2;
+      ReducePacket(self, offset, data);
+    }
+    for (; idx2 < self.stride(); idx2++) {
+      // Calculate the starting offset for the scan
+      Index offset = idx1 + idx2;
+      ReduceScalar(self, offset, data);
+    }
+  }
+};
+
+// Single-threaded CPU implementation of scan
+template <typename Self, typename Reducer, typename Device,
+          bool Vectorize =
+              (TensorEvaluator<typename Self::ChildTypeNoConst, Device>::PacketAccess &&
+               internal::reducer_traits<Reducer, Device>::PacketAccess)>
+struct ScanLauncher {
+  void operator()(Self& self, typename Self::CoeffReturnType* data) {
+    Index total_size = internal::array_prod(self.dimensions());
+
+    // We fix the index along the scan axis to 0 and perform a
+    // scan per remaining entry. The iteration is split into two nested
+    // loops to avoid an integer division by keeping track of each idx1 and
+    // idx2.
+    for (Index idx1 = 0; idx1 < total_size; idx1 += self.stride() * self.size()) {
+      ReduceBlock<Self, Vectorize, /*Parallel=*/false> block_reducer;
+      block_reducer(self, idx1, data);
+    }
+  }
+};
+
+#ifdef EIGEN_USE_THREADS
+
+// Adjust block_size to avoid false sharing of cachelines among
+// threads. Currently set to twice the cache line size on Intel and ARM
+// processors.
+EIGEN_STRONG_INLINE Index AdjustBlockSize(Index item_size, Index block_size) {
+  EIGEN_CONSTEXPR Index kBlockAlignment = 128;
+  const Index items_per_cacheline =
+      numext::maxi<Index>(1, kBlockAlignment / item_size);
+  return items_per_cacheline * divup(block_size, items_per_cacheline);
+}
+
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/true, /*Parallel=*/true> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
+                                      typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    Index num_scalars = self.stride();
+    Index num_packets = 0;
+    if (self.stride() >= PacketSize) {
+      num_packets = self.stride() / PacketSize;
+      self.device().parallelFor(
+          num_packets,
+        TensorOpCost(PacketSize * self.size(), PacketSize * self.size(),
+                     16 * PacketSize * self.size(), true, PacketSize),
+        // Make the shard size large enough that two neighboring threads
+        // won't write to the same cacheline of `data`.
+        [=](Index blk_size) {
+          return AdjustBlockSize(PacketSize * sizeof(Scalar), blk_size);
+        },
+        [&](Index first, Index last) {
+          for (Index packet = first; packet < last; ++packet) {
+            const Index idx2 = packet * PacketSize;
+            ReducePacket(self, idx1 + idx2, data);
+          }
+        });
+      num_scalars -= num_packets * PacketSize;
+    }
+    self.device().parallelFor(
+        num_scalars, TensorOpCost(self.size(), self.size(), 16 * self.size()),
+        // Make the shard size large enough that two neighboring threads
+        // won't write to the same cacheline of `data`.
+        [=](Index blk_size) {
+          return AdjustBlockSize(sizeof(Scalar), blk_size);
+        },
+        [&](Index first, Index last) {
+          for (Index scalar = first; scalar < last; ++scalar) {
+            const Index idx2 = num_packets * PacketSize + scalar;
+            ReduceScalar(self, idx1 + idx2, data);
+          }
+        });
+  }
+};
+
+template <typename Self>
+struct ReduceBlock<Self, /*Vectorize=*/false, /*Parallel=*/true> {
+  EIGEN_STRONG_INLINE void operator()(Self& self, Index idx1,
+                                      typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    self.device().parallelFor(
+        self.stride(), TensorOpCost(self.size(), self.size(), 16 * self.size()),
+        // Make the shard size large enough that two neighboring threads
+        // won't write to the same cacheline of `data`.
+        [=](Index blk_size) {
+          return AdjustBlockSize(sizeof(Scalar), blk_size);
+        },
+        [&](Index first, Index last) {
+          for (Index idx2 = first; idx2 < last; ++idx2) {
+            ReduceScalar(self, idx1 + idx2, data);
+          }
+        });
+  }
+};
+
+// Specialization for multi-threaded execution.
+template <typename Self, typename Reducer, bool Vectorize>
+struct ScanLauncher<Self, Reducer, ThreadPoolDevice, Vectorize> {
+  void operator()(Self& self, typename Self::CoeffReturnType* data) {
+    using Scalar = typename Self::CoeffReturnType;
+    using Packet = typename Self::PacketReturnType;
+    const int PacketSize = internal::unpacket_traits<Packet>::size;
+    const Index total_size = internal::array_prod(self.dimensions());
+    const Index inner_block_size = self.stride() * self.size();
+    bool parallelize_by_outer_blocks = (total_size >= (self.stride() * inner_block_size));
+
+    if ((parallelize_by_outer_blocks && total_size <= 4096) ||
+        (!parallelize_by_outer_blocks && self.stride() < PacketSize)) {
+      ScanLauncher<Self, Reducer, DefaultDevice, Vectorize> launcher;
+      launcher(self, data);
+      return;
+    }
+
+    if (parallelize_by_outer_blocks) {
+      // Parallelize over outer blocks.
+      const Index num_outer_blocks = total_size / inner_block_size;
+      self.device().parallelFor(
+          num_outer_blocks,
+          TensorOpCost(inner_block_size, inner_block_size,
+                       16 * PacketSize * inner_block_size, Vectorize,
+                       PacketSize),
+          [=](Index blk_size) {
+            return AdjustBlockSize(inner_block_size * sizeof(Scalar), blk_size);
+          },
+          [&](Index first, Index last) {
+            for (Index idx1 = first; idx1 < last; ++idx1) {
+              ReduceBlock<Self, Vectorize, /*Parallelize=*/false> block_reducer;
+              block_reducer(self, idx1 * inner_block_size, data);
+            }
+          });
+    } else {
+      // Parallelize over inner packets/scalars dimensions when the reduction
+      // axis is not an inner dimension.
+      ReduceBlock<Self, Vectorize, /*Parallelize=*/true> block_reducer;
+      for (Index idx1 = 0; idx1 < total_size;
+           idx1 += self.stride() * self.size()) {
+        block_reducer(self, idx1, data);
+      }
+    }
+  }
+};
+#endif  // EIGEN_USE_THREADS
+
+#if defined(EIGEN_USE_GPU) && (defined(EIGEN_GPUCC))
+
+// GPU implementation of scan
+// TODO(ibab) This placeholder implementation performs multiple scans in
+// parallel, but it would be better to use a parallel scan algorithm and
+// optimize memory access.
+template <typename Self, typename Reducer>
+__global__ EIGEN_HIP_LAUNCH_BOUNDS_1024 void ScanKernel(Self self, Index total_size, typename Self::CoeffReturnType* data) {
+  // Compute offset as in the CPU version
+  Index val = threadIdx.x + blockIdx.x * blockDim.x;
+  Index offset = (val / self.stride()) * self.stride() * self.size() + val % self.stride();
+
+  if (offset + (self.size() - 1) * self.stride() < total_size) {
+    // Compute the scan along the axis, starting at the calculated offset
+    typename Self::CoeffReturnType accum = self.accumulator().initialize();
+    for (Index idx = 0; idx < self.size(); idx++) {
+      Index curr = offset + idx * self.stride();
+      if (self.exclusive()) {
+        data[curr] = self.accumulator().finalize(accum);
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+      } else {
+        self.accumulator().reduce(self.inner().coeff(curr), &accum);
+        data[curr] = self.accumulator().finalize(accum);
+      }
+    }
+  }
+  __syncthreads();
+
+}
+
+template <typename Self, typename Reducer, bool Vectorize>
+struct ScanLauncher<Self, Reducer, GpuDevice, Vectorize> {
+  void operator()(const Self& self, typename Self::CoeffReturnType* data) {
+     Index total_size = internal::array_prod(self.dimensions());
+     Index num_blocks = (total_size / self.size() + 63) / 64;
+     Index block_size = 64;
+
+     LAUNCH_GPU_KERNEL((ScanKernel<Self, Reducer>), num_blocks, block_size, 0, self.device(), self, total_size, data);
+  }
+};
+#endif  // EIGEN_USE_GPU && (EIGEN_GPUCC)
+
+}  // namespace internal
+
+// Eval as rvalue
+template <typename Op, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {
+
+  typedef TensorScanOp<Op, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef const ArgType ChildTypeNoConst;
+  typedef const ArgType ChildType;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> Self;
+  typedef StorageMemory<Scalar, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess = false,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,
+    RawAccess = true
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device),
+        m_device(device),
+        m_exclusive(op.exclusive()),
+        m_accumulator(op.accumulator()),
+        m_size(m_impl.dimensions()[op.axis()]),
+        m_stride(1), m_consume_dim(op.axis()),
+        m_output(NULL) {
+
+    // Accumulating a scalar isn't supported.
+    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(op.axis() >= 0 && op.axis() < NumDims);
+
+    // Compute stride of scan axis
+    const Dimensions& dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < op.axis(); ++i) {
+        m_stride = m_stride * dims[i];
+      }
+    } else {
+      // dims can only be indexed through unsigned integers,
+      // so let's use an unsigned type to let the compiler knows.
+      // This prevents stupid warnings: ""'*((void*)(& evaluator)+64)[18446744073709551615]' may be used uninitialized in this function"
+      unsigned int axis = internal::convert_index<unsigned int>(op.axis());
+      for (unsigned int i = NumDims - 1; i > axis; --i) {
+        m_stride = m_stride * dims[i];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_impl.dimensions();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& stride() const {
+    return m_stride;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& consume_dim() const {
+    return m_consume_dim;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Index& size() const {
+    return m_size;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Op& accumulator() const {
+    return m_accumulator;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool exclusive() const {
+    return m_exclusive;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& inner() const {
+    return m_impl;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const {
+    return m_device;
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    internal::ScanLauncher<Self, Op, Device> launcher;
+    if (data) {
+      launcher(*this, data);
+      return false;
+    }
+
+    const Index total_size = internal::array_prod(dimensions());
+    m_output = static_cast<EvaluatorPointerType>(m_device.get((Scalar*) m_device.allocate_temp(total_size * sizeof(Scalar))));
+    launcher(*this, m_output);
+    return true;
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
+    return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const
+  {
+    return m_output;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_output[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
+    return TensorOpCost(sizeof(CoeffReturnType), 0, 0);
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    if (m_output) {
+      m_device.deallocate_temp(m_output);
+      m_output = NULL;
+    }
+    m_impl.cleanup();
+  }
+
+#ifdef EIGEN_USE_SYCL
+ // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+    m_output.bind(cgh);
+  }
+#endif
+protected:
+  TensorEvaluator<ArgType, Device> m_impl;
+  const Device EIGEN_DEVICE_REF m_device;
+  const bool m_exclusive;
+  Op m_accumulator;
+  const Index m_size;
+  Index m_stride;
+  Index m_consume_dim;
+  EvaluatorPointerType m_output;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
new file mode 100644
index 0000000..7f68ecb
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorScanSycl.h
@@ -0,0 +1,513 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorScanSycl.h
+ *
+ * \brief:
+ *  Tensor Scan Sycl implement the extend  version of
+ * "Efficient parallel scan algorithms for GPUs." .for Tensor operations.
+ * The algorithm requires up to 3 stage (consequently 3 kernels) depending on
+ * the size of the tensor. In the first kernel (ScanKernelFunctor), each
+ * threads within the work-group individually reduces the allocated elements per
+ * thread in order to reduces the total number of blocks. In the next step all
+ * thread within the work-group will reduce the associated blocks into the
+ * temporary buffers. In the next kernel(ScanBlockKernelFunctor), the temporary
+ * buffer is given as an input and all the threads within a work-group scan and
+ * reduces the boundaries between the blocks (generated from the previous
+ * kernel). and write the data on the temporary buffer. If the second kernel is
+ * required, the third and final kerenl (ScanAdjustmentKernelFunctor) will
+ * adjust the final result into the output buffer.
+ * The original algorithm for the parallel prefix sum can be found here:
+ *
+ * Sengupta, Shubhabrata, Mark Harris, and Michael Garland. "Efficient parallel
+ * scan algorithms for GPUs." NVIDIA, Santa Clara, CA, Tech. Rep. NVR-2008-003
+ *1, no. 1 (2008): 1-17.
+ *****************************************************************/
+
+#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
+#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
+
+namespace Eigen {
+namespace TensorSycl {
+namespace internal {
+
+#ifndef EIGEN_SYCL_MAX_GLOBAL_RANGE
+#define EIGEN_SYCL_MAX_GLOBAL_RANGE (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 * 4)
+#endif
+
+template <typename index_t>
+struct ScanParameters {
+  // must be power of 2
+  static EIGEN_CONSTEXPR index_t ScanPerThread = 8;
+  const index_t total_size;
+  const index_t non_scan_size;
+  const index_t scan_size;
+  const index_t non_scan_stride;
+  const index_t scan_stride;
+  const index_t panel_threads;
+  const index_t group_threads;
+  const index_t block_threads;
+  const index_t elements_per_group;
+  const index_t elements_per_block;
+  const index_t loop_range;
+
+  ScanParameters(index_t total_size_, index_t non_scan_size_, index_t scan_size_, index_t non_scan_stride_,
+                 index_t scan_stride_, index_t panel_threads_, index_t group_threads_, index_t block_threads_,
+                 index_t elements_per_group_, index_t elements_per_block_, index_t loop_range_)
+      : total_size(total_size_),
+        non_scan_size(non_scan_size_),
+        scan_size(scan_size_),
+        non_scan_stride(non_scan_stride_),
+        scan_stride(scan_stride_),
+        panel_threads(panel_threads_),
+        group_threads(group_threads_),
+        block_threads(block_threads_),
+        elements_per_group(elements_per_group_),
+        elements_per_block(elements_per_block_),
+        loop_range(loop_range_) {}
+};
+
+enum class scan_step { first, second };
+template <typename Evaluator, typename CoeffReturnType, typename OutAccessor, typename Op, typename Index,
+          scan_step stp>
+struct ScanKernelFunctor {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  static EIGEN_CONSTEXPR int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
+
+  LocalAccessor scratch;
+  Evaluator dev_eval;
+  OutAccessor out_accessor;
+  OutAccessor temp_accessor;
+  const ScanParameters<Index> scanParameters;
+  Op accumulator;
+  const bool inclusive;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanKernelFunctor(LocalAccessor scratch_, const Evaluator dev_eval_,
+                                                          OutAccessor out_accessor_, OutAccessor temp_accessor_,
+                                                          const ScanParameters<Index> scanParameters_, Op accumulator_,
+                                                          const bool inclusive_)
+      : scratch(scratch_),
+        dev_eval(dev_eval_),
+        out_accessor(out_accessor_),
+        temp_accessor(temp_accessor_),
+        scanParameters(scanParameters_),
+        accumulator(accumulator_),
+        inclusive(inclusive_) {}
+
+  template <scan_step sst = stp, typename Input>
+  typename ::Eigen::internal::enable_if<sst == scan_step::first, CoeffReturnType>::type EIGEN_DEVICE_FUNC
+      EIGEN_STRONG_INLINE
+      read(const Input &inpt, Index global_id) {
+    return inpt.coeff(global_id);
+  }
+
+  template <scan_step sst = stp, typename Input>
+  typename ::Eigen::internal::enable_if<sst != scan_step::first, CoeffReturnType>::type EIGEN_DEVICE_FUNC
+      EIGEN_STRONG_INLINE
+      read(const Input &inpt, Index global_id) {
+    return inpt[global_id];
+  }
+
+  template <scan_step sst = stp, typename InclusiveOp>
+  typename ::Eigen::internal::enable_if<sst == scan_step::first>::type EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  first_step_inclusive_Operation(InclusiveOp inclusive_op) {
+    inclusive_op();
+  }
+
+  template <scan_step sst = stp, typename InclusiveOp>
+  typename ::Eigen::internal::enable_if<sst != scan_step::first>::type EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  first_step_inclusive_Operation(InclusiveOp) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    auto out_ptr = out_accessor.get_pointer();
+    auto tmp_ptr = temp_accessor.get_pointer();
+    auto scratch_ptr = scratch.get_pointer().get();
+
+    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
+      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
+      Index tmp = data_offset % scanParameters.panel_threads;
+      const Index panel_id = data_offset / scanParameters.panel_threads;
+      const Index group_id = tmp / scanParameters.group_threads;
+      tmp = tmp % scanParameters.group_threads;
+      const Index block_id = tmp / scanParameters.block_threads;
+      const Index local_id = tmp % scanParameters.block_threads;
+      // we put one element per packet in scratch_mem
+      const Index scratch_stride = scanParameters.elements_per_block / PacketSize;
+      const Index scratch_offset = (itemID.get_local_id(0) / scanParameters.block_threads) * scratch_stride;
+      CoeffReturnType private_scan[ScanParameters<Index>::ScanPerThread];
+      CoeffReturnType inclusive_scan;
+      // the actual panel size is scan_size * non_scan_size.
+      // elements_per_panel is roundup to power of 2 for binary tree
+      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
+      const Index group_offset = group_id * scanParameters.non_scan_stride;
+      // This will be effective when the size is bigger than elements_per_block
+      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
+      const Index thread_offset = (ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride);
+      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
+      Index next_elements = 0;
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        private_scan[i] = ((((block_id * scanParameters.elements_per_block) +
+                             (ScanParameters<Index>::ScanPerThread * local_id) + i) < scanParameters.scan_size) &&
+                           (global_id < scanParameters.total_size))
+                              ? read(dev_eval, global_id)
+                              : accumulator.initialize();
+        next_elements += scanParameters.scan_stride;
+      }
+      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
+        if (inclusive) {
+          inclusive_scan = private_scan[ScanParameters<Index>::ScanPerThread - 1];
+        }
+      });
+      // This for loop must be 2
+      EIGEN_UNROLL_LOOP
+      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
+        Index private_offset = 1;
+        // build sum in place up the tree
+        EIGEN_UNROLL_LOOP
+        for (Index d = PacketSize >> 1; d > 0; d >>= 1) {
+          EIGEN_UNROLL_LOOP
+          for (Index l = 0; l < d; l++) {
+            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
+            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
+            CoeffReturnType accum = accumulator.initialize();
+            accumulator.reduce(private_scan[ai], &accum);
+            accumulator.reduce(private_scan[bi], &accum);
+            private_scan[bi] = accumulator.finalize(accum);
+          }
+          private_offset *= 2;
+        }
+        scratch_ptr[2 * local_id + (packetIndex / PacketSize) + scratch_offset] =
+            private_scan[PacketSize - 1 + packetIndex];
+        private_scan[PacketSize - 1 + packetIndex] = accumulator.initialize();
+        // traverse down tree & build scan
+        EIGEN_UNROLL_LOOP
+        for (Index d = 1; d < PacketSize; d *= 2) {
+          private_offset >>= 1;
+          EIGEN_UNROLL_LOOP
+          for (Index l = 0; l < d; l++) {
+            Index ai = private_offset * (2 * l + 1) - 1 + packetIndex;
+            Index bi = private_offset * (2 * l + 2) - 1 + packetIndex;
+            CoeffReturnType accum = accumulator.initialize();
+            accumulator.reduce(private_scan[ai], &accum);
+            accumulator.reduce(private_scan[bi], &accum);
+            private_scan[ai] = private_scan[bi];
+            private_scan[bi] = accumulator.finalize(accum);
+          }
+        }
+      }
+
+      Index offset = 1;
+      // build sum in place up the tree
+      for (Index d = scratch_stride >> 1; d > 0; d >>= 1) {
+        // Synchronise
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (local_id < d) {
+          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
+          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
+          CoeffReturnType accum = accumulator.initialize();
+          accumulator.reduce(scratch_ptr[ai], &accum);
+          accumulator.reduce(scratch_ptr[bi], &accum);
+          scratch_ptr[bi] = accumulator.finalize(accum);
+        }
+        offset *= 2;
+      }
+      // Synchronise
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      // next step optimisation
+      if (local_id == 0) {
+        if (((scanParameters.elements_per_group / scanParameters.elements_per_block) > 1)) {
+          const Index temp_id = panel_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) *
+                                    scanParameters.non_scan_size +
+                                group_id * (scanParameters.elements_per_group / scanParameters.elements_per_block) +
+                                block_id;
+          tmp_ptr[temp_id] = scratch_ptr[scratch_stride - 1 + scratch_offset];
+        }
+        // clear the last element
+        scratch_ptr[scratch_stride - 1 + scratch_offset] = accumulator.initialize();
+      }
+      // traverse down tree & build scan
+      for (Index d = 1; d < scratch_stride; d *= 2) {
+        offset >>= 1;
+        // Synchronise
+        itemID.barrier(cl::sycl::access::fence_space::local_space);
+        if (local_id < d) {
+          Index ai = offset * (2 * local_id + 1) - 1 + scratch_offset;
+          Index bi = offset * (2 * local_id + 2) - 1 + scratch_offset;
+          CoeffReturnType accum = accumulator.initialize();
+          accumulator.reduce(scratch_ptr[ai], &accum);
+          accumulator.reduce(scratch_ptr[bi], &accum);
+          scratch_ptr[ai] = scratch_ptr[bi];
+          scratch_ptr[bi] = accumulator.finalize(accum);
+        }
+      }
+      // Synchronise
+      itemID.barrier(cl::sycl::access::fence_space::local_space);
+      // This for loop must be 2
+      EIGEN_UNROLL_LOOP
+      for (int packetIndex = 0; packetIndex < ScanParameters<Index>::ScanPerThread; packetIndex += PacketSize) {
+        EIGEN_UNROLL_LOOP
+        for (Index i = 0; i < PacketSize; i++) {
+          CoeffReturnType accum = private_scan[packetIndex + i];
+          accumulator.reduce(scratch_ptr[2 * local_id + (packetIndex / PacketSize) + scratch_offset], &accum);
+          private_scan[packetIndex + i] = accumulator.finalize(accum);
+        }
+      }
+      first_step_inclusive_Operation([&]() EIGEN_DEVICE_FUNC {
+        if (inclusive) {
+          accumulator.reduce(private_scan[ScanParameters<Index>::ScanPerThread - 1], &inclusive_scan);
+          private_scan[0] = accumulator.finalize(inclusive_scan);
+        }
+      });
+      next_elements = 0;
+      // right the first set of private param
+      EIGEN_UNROLL_LOOP
+      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
+             scanParameters.scan_size) &&
+            (global_id < scanParameters.total_size)) {
+          Index private_id = (i * !inclusive) + (((i + 1) % ScanParameters<Index>::ScanPerThread) * (inclusive));
+          out_ptr[global_id] = private_scan[private_id];
+        }
+        next_elements += scanParameters.scan_stride;
+      }
+    }  // end for loop
+  }
+};
+
+template <typename CoeffReturnType, typename InAccessor, typename OutAccessor, typename Op, typename Index>
+struct ScanAdjustmentKernelFunctor {
+  typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local>
+      LocalAccessor;
+  static EIGEN_CONSTEXPR int PacketSize = ScanParameters<Index>::ScanPerThread / 2;
+  InAccessor in_accessor;
+  OutAccessor out_accessor;
+  const ScanParameters<Index> scanParameters;
+  Op accumulator;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScanAdjustmentKernelFunctor(LocalAccessor, InAccessor in_accessor_,
+                                                                    OutAccessor out_accessor_,
+                                                                    const ScanParameters<Index> scanParameters_,
+                                                                    Op accumulator_)
+      : in_accessor(in_accessor_),
+        out_accessor(out_accessor_),
+        scanParameters(scanParameters_),
+        accumulator(accumulator_) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) {
+    auto in_ptr = in_accessor.get_pointer();
+    auto out_ptr = out_accessor.get_pointer();
+
+    for (Index loop_offset = 0; loop_offset < scanParameters.loop_range; loop_offset++) {
+      Index data_offset = (itemID.get_global_id(0) + (itemID.get_global_range(0) * loop_offset));
+      Index tmp = data_offset % scanParameters.panel_threads;
+      const Index panel_id = data_offset / scanParameters.panel_threads;
+      const Index group_id = tmp / scanParameters.group_threads;
+      tmp = tmp % scanParameters.group_threads;
+      const Index block_id = tmp / scanParameters.block_threads;
+      const Index local_id = tmp % scanParameters.block_threads;
+
+      // the actual panel size is scan_size * non_scan_size.
+      // elements_per_panel is roundup to power of 2 for binary tree
+      const Index panel_offset = panel_id * scanParameters.scan_size * scanParameters.non_scan_size;
+      const Index group_offset = group_id * scanParameters.non_scan_stride;
+      // This will be effective when the size is bigger than elements_per_block
+      const Index block_offset = block_id * scanParameters.elements_per_block * scanParameters.scan_stride;
+      const Index thread_offset = ScanParameters<Index>::ScanPerThread * local_id * scanParameters.scan_stride;
+
+      const Index global_offset = panel_offset + group_offset + block_offset + thread_offset;
+      const Index block_size = scanParameters.elements_per_group / scanParameters.elements_per_block;
+      const Index in_id = (panel_id * block_size * scanParameters.non_scan_size) + (group_id * block_size) + block_id;
+      CoeffReturnType adjust_val = in_ptr[in_id];
+
+      Index next_elements = 0;
+      EIGEN_UNROLL_LOOP
+      for (Index i = 0; i < ScanParameters<Index>::ScanPerThread; i++) {
+        Index global_id = global_offset + next_elements;
+        if ((((block_id * scanParameters.elements_per_block) + (ScanParameters<Index>::ScanPerThread * local_id) + i) <
+             scanParameters.scan_size) &&
+            (global_id < scanParameters.total_size)) {
+          CoeffReturnType accum = adjust_val;
+          accumulator.reduce(out_ptr[global_id], &accum);
+          out_ptr[global_id] = accumulator.finalize(accum);
+        }
+        next_elements += scanParameters.scan_stride;
+      }
+    }
+  }
+};
+
+template <typename Index>
+struct ScanInfo {
+  const Index &total_size;
+  const Index &scan_size;
+  const Index &panel_size;
+  const Index &non_scan_size;
+  const Index &scan_stride;
+  const Index &non_scan_stride;
+
+  Index max_elements_per_block;
+  Index block_size;
+  Index panel_threads;
+  Index group_threads;
+  Index block_threads;
+  Index elements_per_group;
+  Index elements_per_block;
+  Index loop_range;
+  Index global_range;
+  Index local_range;
+  const Eigen::SyclDevice &dev;
+  EIGEN_STRONG_INLINE ScanInfo(const Index &total_size_, const Index &scan_size_, const Index &panel_size_,
+                               const Index &non_scan_size_, const Index &scan_stride_, const Index &non_scan_stride_,
+                               const Eigen::SyclDevice &dev_)
+      : total_size(total_size_),
+        scan_size(scan_size_),
+        panel_size(panel_size_),
+        non_scan_size(non_scan_size_),
+        scan_stride(scan_stride_),
+        non_scan_stride(non_scan_stride_),
+        dev(dev_) {
+    // must be power of 2
+    local_range = std::min(Index(dev.getNearestPowerOfTwoWorkGroupSize()),
+                           Index(EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1));
+
+    max_elements_per_block = local_range * ScanParameters<Index>::ScanPerThread;
+
+    elements_per_group =
+        dev.getPowerOfTwo(Index(roundUp(Index(scan_size), ScanParameters<Index>::ScanPerThread)), true);
+    const Index elements_per_panel = elements_per_group * non_scan_size;
+    elements_per_block = std::min(Index(elements_per_group), Index(max_elements_per_block));
+    panel_threads = elements_per_panel / ScanParameters<Index>::ScanPerThread;
+    group_threads = elements_per_group / ScanParameters<Index>::ScanPerThread;
+    block_threads = elements_per_block / ScanParameters<Index>::ScanPerThread;
+    block_size = elements_per_group / elements_per_block;
+#ifdef EIGEN_SYCL_MAX_GLOBAL_RANGE
+    const Index max_threads = std::min(Index(panel_threads * panel_size), Index(EIGEN_SYCL_MAX_GLOBAL_RANGE));
+#else
+    const Index max_threads = panel_threads * panel_size;
+#endif
+    global_range = roundUp(max_threads, local_range);
+    loop_range = Index(
+        std::ceil(double(elements_per_panel * panel_size) / (global_range * ScanParameters<Index>::ScanPerThread)));
+  }
+  inline ScanParameters<Index> get_scan_parameter() {
+    return ScanParameters<Index>(total_size, non_scan_size, scan_size, non_scan_stride, scan_stride, panel_threads,
+                                 group_threads, block_threads, elements_per_group, elements_per_block, loop_range);
+  }
+  inline cl::sycl::nd_range<1> get_thread_range() {
+    return cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
+  }
+};
+
+template <typename EvaluatorPointerType, typename CoeffReturnType, typename Reducer, typename Index>
+struct SYCLAdjustBlockOffset {
+  EIGEN_STRONG_INLINE static void adjust_scan_block_offset(EvaluatorPointerType in_ptr, EvaluatorPointerType out_ptr,
+                                                           Reducer &accumulator, const Index total_size,
+                                                           const Index scan_size, const Index panel_size,
+                                                           const Index non_scan_size, const Index scan_stride,
+                                                           const Index non_scan_stride, const Eigen::SyclDevice &dev) {
+    auto scan_info =
+        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
+
+    typedef ScanAdjustmentKernelFunctor<CoeffReturnType, EvaluatorPointerType, EvaluatorPointerType, Reducer, Index>
+        AdjustFuctor;
+    dev.template unary_kernel_launcher<CoeffReturnType, AdjustFuctor>(in_ptr, out_ptr, scan_info.get_thread_range(),
+                                                                      scan_info.max_elements_per_block,
+                                                                      scan_info.get_scan_parameter(), accumulator);
+  }
+};
+
+template <typename CoeffReturnType, scan_step stp>
+struct ScanLauncher_impl {
+  template <typename Input, typename EvaluatorPointerType, typename Reducer, typename Index>
+  EIGEN_STRONG_INLINE static void scan_block(Input in_ptr, EvaluatorPointerType out_ptr, Reducer &accumulator,
+                                             const Index total_size, const Index scan_size, const Index panel_size,
+                                             const Index non_scan_size, const Index scan_stride,
+                                             const Index non_scan_stride, const bool inclusive,
+                                             const Eigen::SyclDevice &dev) {
+    auto scan_info =
+        ScanInfo<Index>(total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride, dev);
+    const Index temp_pointer_size = scan_info.block_size * non_scan_size * panel_size;
+    const Index scratch_size = scan_info.max_elements_per_block / (ScanParameters<Index>::ScanPerThread / 2);
+    CoeffReturnType *temp_pointer =
+        static_cast<CoeffReturnType *>(dev.allocate_temp(temp_pointer_size * sizeof(CoeffReturnType)));
+    EvaluatorPointerType tmp_global_accessor = dev.get(temp_pointer);
+
+    typedef ScanKernelFunctor<Input, CoeffReturnType, EvaluatorPointerType, Reducer, Index, stp> ScanFunctor;
+    dev.template binary_kernel_launcher<CoeffReturnType, ScanFunctor>(
+        in_ptr, out_ptr, tmp_global_accessor, scan_info.get_thread_range(), scratch_size,
+        scan_info.get_scan_parameter(), accumulator, inclusive);
+
+    if (scan_info.block_size > 1) {
+      ScanLauncher_impl<CoeffReturnType, scan_step::second>::scan_block(
+          tmp_global_accessor, tmp_global_accessor, accumulator, temp_pointer_size, scan_info.block_size, panel_size,
+          non_scan_size, Index(1), scan_info.block_size, false, dev);
+
+      SYCLAdjustBlockOffset<EvaluatorPointerType, CoeffReturnType, Reducer, Index>::adjust_scan_block_offset(
+          tmp_global_accessor, out_ptr, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride,
+          non_scan_stride, dev);
+    }
+    dev.deallocate_temp(temp_pointer);
+  }
+};
+
+}  // namespace internal
+}  // namespace TensorSycl
+namespace internal {
+template <typename Self, typename Reducer, bool vectorize>
+struct ScanLauncher<Self, Reducer, Eigen::SyclDevice, vectorize> {
+  typedef typename Self::Index Index;
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  typedef typename Self::Storage Storage;
+  typedef typename Self::EvaluatorPointerType EvaluatorPointerType;
+  void operator()(Self &self, EvaluatorPointerType data) {
+    const Index total_size = internal::array_prod(self.dimensions());
+    const Index scan_size = self.size();
+    const Index scan_stride = self.stride();
+    // this is the scan op (can be sum or ...)
+    auto accumulator = self.accumulator();
+    auto inclusive = !self.exclusive();
+    auto consume_dim = self.consume_dim();
+    auto dev = self.device();
+
+    auto dims = self.inner().dimensions();
+
+    Index non_scan_size = 1;
+    Index panel_size = 1;
+    if (static_cast<int>(Self::Layout) == static_cast<int>(ColMajor)) {
+      for (int i = 0; i < consume_dim; i++) {
+        non_scan_size *= dims[i];
+      }
+      for (int i = consume_dim + 1; i < Self::NumDims; i++) {
+        panel_size *= dims[i];
+      }
+    } else {
+      for (int i = Self::NumDims - 1; i > consume_dim; i--) {
+        non_scan_size *= dims[i];
+      }
+      for (int i = consume_dim - 1; i >= 0; i--) {
+        panel_size *= dims[i];
+      }
+    }
+    const Index non_scan_stride = (scan_stride > 1) ? 1 : scan_size;
+    auto eval_impl = self.inner();
+    TensorSycl::internal::ScanLauncher_impl<CoeffReturnType, TensorSycl::internal::scan_step::first>::scan_block(
+        eval_impl, data, accumulator, total_size, scan_size, panel_size, non_scan_size, scan_stride, non_scan_stride,
+        inclusive, dev);
+  }
+};
+} // namespace internal
+}  // namespace Eigen
+
+#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_SYCL_SYCL_HPP
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
new file mode 100644
index 0000000..e5e5efd
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
@@ -0,0 +1,471 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
+
+namespace Eigen {
+
+/** \class TensorShuffling
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor shuffling class.
+  *
+  *
+  */
+namespace internal {
+template<typename Shuffle, typename XprType>
+struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Shuffle, typename XprType>
+struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
+{
+  typedef const TensorShufflingOp<Shuffle, XprType>& type;
+};
+
+template<typename Shuffle, typename XprType>
+struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
+{
+  typedef TensorShufflingOp<Shuffle, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Shuffle, typename XprType>
+class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
+{
+  public:
+    typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base;
+    typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl)
+      : m_xpr(expr), m_shuffle(shfl) {}
+
+    EIGEN_DEVICE_FUNC
+    const Shuffle& shufflePermutation() const { return m_shuffle; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp)
+
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Shuffle m_shuffle;
+};
+
+
+// Eval as rvalue
+template<typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self;
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess       = false,  // to be implemented
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
+
+  typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims,
+                                                     Layout, Index>
+      TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_device(device),
+        m_impl(op.expression(), device)
+  {
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    const Shuffle& shuffle = op.shufflePermutation();
+    m_is_identity = true;
+    for (int i = 0; i < NumDims; ++i) {
+      m_shuffle[i] = static_cast<int>(shuffle[i]);
+      m_dimensions[i] = input_dims[shuffle[i]];
+      m_inverseShuffle[shuffle[i]] = i;
+      if (m_is_identity && shuffle[i] != i) {
+        m_is_identity = false;
+      }
+    }
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_unshuffledInputStrides[0] = 1;
+      m_outputStrides[0] = 1;
+
+      for (int i = 1; i < NumDims; ++i) {
+        m_unshuffledInputStrides[i] =
+            m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
+        m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(
+                  m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
+      }
+    } else {
+      m_unshuffledInputStrides[NumDims - 1] = 1;
+      m_outputStrides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_unshuffledInputStrides[i] =
+            m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
+        m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(
+                  m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
+      }
+    }
+
+    for (int i = 0; i < NumDims; ++i) {
+      m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+#ifdef EIGEN_USE_THREADS
+  template <typename EvalSubExprsCallback>
+  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
+      EvaluatorPointerType, EvalSubExprsCallback done) {
+    m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
+  }
+#endif  // EIGEN_USE_THREADS
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    if (m_is_identity) {
+      return m_impl.coeff(index);
+    } else {
+      return m_impl.coeff(srcCoeff(index));
+    }
+  }
+
+  template <int LoadMode, typename Self, bool ImplPacketAccess>
+  struct PacketLoader {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    static PacketReturnType Run(const Self& self, Index index) {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < PacketSize; ++i) {
+        values[i] = self.coeff(index + i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  };
+
+  template<int LoadMode, typename Self>
+  struct PacketLoader<LoadMode, Self, true> {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    static PacketReturnType Run(const Self& self, Index index) {
+      if (self.m_is_identity) {
+        return self.m_impl.template packet<LoadMode>(index);
+      } else {
+        EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+        EIGEN_UNROLL_LOOP
+        for (int i = 0; i < PacketSize; ++i) {
+          values[i] = self.coeff(index + i);
+        }
+        PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+        return rslt;
+      }
+    }
+  };
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+        eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+    return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  internal::TensorBlockResourceRequirements getResourceRequirements() const {
+    static const int inner_dim =
+        Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+
+    const size_t target_size = m_device.firstLevelCacheSize();
+    const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
+
+    // Shuffled inner dimensions leads to a random memory access, which is not
+    // captured by default cost model bytes loaded/stored. We add this cost
+    // explicitly. The number of cycles picked based on the benchmarks.
+    // TODO(ezhulenev): This number was picked based on a very questionable
+    // benchmarks, add benchmarks that are representative of real workloads.
+    using BlockRequirements = internal::TensorBlockResourceRequirements;
+    if (inner_dim_shuffled) {
+      return BlockRequirements::uniform<Scalar>(target_size)
+          .addCostPerCoeff({0, 0, NumDims * 28});
+    } else {
+      return BlockRequirements::skewed<Scalar>(target_size);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
+          bool root_of_expr_ast = false) const {
+    assert(m_impl.data() != NULL);
+
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout>
+        TensorBlockIO;
+    typedef typename TensorBlockIO::Dst TensorBlockIODst;
+    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+    const typename TensorBlock::Storage block_storage =
+        TensorBlock::prepareStorage(
+            desc, scratch, /*allow_strided_storage=*/root_of_expr_ast);
+
+    typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides);
+    TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset()));
+
+    TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(),
+                         block_storage.data());
+
+    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle);
+    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
+
+    return block_storage.AsTensorMaterializedBlock();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    const double compute_cost = m_is_identity ? TensorOpCost::AddCost<Index>() :
+                                NumDims * (2 * TensorOpCost::AddCost<Index>() +
+                                           2 * TensorOpCost::MulCost<Index>() +
+                                           TensorOpCost::DivCost<Index>());
+    return m_impl.costPerCoeff(vectorized) +
+           TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+   // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex(
+      Index input_index,
+      const DSizes<Index, NumDims>& input_block_strides,
+      const DSizes<Index, NumDims>& output_block_strides,
+      const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const {
+    Index output_index = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = input_index / fast_input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index *
+          output_block_strides[m_inverseShuffle[0]];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = input_index / fast_input_block_strides[i];
+        output_index += idx * output_block_strides[m_inverseShuffle[i]];
+        input_index -= idx * input_block_strides[i];
+      }
+      return output_index + input_index *
+          output_block_strides[m_inverseShuffle[NumDims - 1]];
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[0];
+    } else {
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_fastOutputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      return inputIndex + index * m_inputStrides[NumDims - 1];
+    }
+  }
+
+  Dimensions m_dimensions;
+  bool m_is_identity;
+  array<int, NumDims> m_shuffle;
+  array<Index, NumDims> m_inverseShuffle;  // TODO(ezhulenev): Make it int type.
+  array<Index, NumDims> m_outputStrides;
+  array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
+  array<Index, NumDims> m_inputStrides;
+  array<Index, NumDims> m_unshuffledInputStrides;
+
+  const Device EIGEN_DEVICE_REF m_device;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+
+// Eval as lvalue
+template<typename Shuffle, typename ArgType, typename Device>
+struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
+    : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
+{
+  typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
+
+  typedef TensorShufflingOp<Shuffle, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  enum {
+    IsAligned         = false,
+    PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
+    BlockAccess       = TensorEvaluator<ArgType, Device>::RawAccess,
+    PreferBlockAccess = true,
+    Layout            = TensorEvaluator<ArgType, Device>::Layout,
+    RawAccess         = false
+  };
+
+  typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : Base(op, device)
+  { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode> EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      this->coeffRef(index+i) = values[i];
+    }
+  }
+
+  template <typename TensorBlock>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
+      const TensorBlockDesc& desc, const TensorBlock& block) {
+    eigen_assert(this->m_impl.data() != NULL);
+
+    typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout>
+        TensorBlockIO;
+    typedef typename TensorBlockIO::Dst TensorBlockIODst;
+    typedef typename TensorBlockIO::Src TensorBlockIOSrc;
+
+    const Scalar* block_buffer = block.data();
+
+    // TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen
+    // expression with coefficient and packet access as `src`.
+    void* mem = NULL;
+    if (block_buffer == NULL) {
+      mem = this->m_device.allocate(desc.size() * sizeof(Scalar));
+      ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem);
+
+      typedef internal::TensorBlockAssignment<
+          ScalarNoConst, NumDims, typename TensorBlock::XprType, Index>
+          TensorBlockAssignment;
+
+      TensorBlockAssignment::Run(
+          TensorBlockAssignment::target(
+              desc.dimensions(), internal::strides<Layout>(desc.dimensions()),
+              buf),
+          block.expr());
+
+      block_buffer = buf;
+    }
+
+    // Read from block.
+    TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()),
+                         block_buffer);
+
+    // Write to the output buffer.
+    typename TensorBlockIO::Dimensions output_strides(
+        this->m_unshuffledInputStrides);
+    typename TensorBlockIO::Dimensions output_dimensions;
+    for (int i = 0; i < NumDims; ++i) {
+      output_dimensions[this->m_shuffle[i]] = desc.dimension(i);
+    }
+    TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(),
+                         this->srcCoeff(desc.offset()));
+
+    // Reorder dimensions according to the shuffle.
+    typename TensorBlockIO::DimensionsMap dst_to_src_dim_map;
+    for (int i = 0; i < NumDims; ++i) {
+      dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]);
+    }
+    TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
+
+    // Deallocate temporary buffer used for the block materialization.
+    if (mem != NULL) this->m_device.deallocate(mem);
+  }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
new file mode 100644
index 0000000..5ff0880
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h
@@ -0,0 +1,161 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+// Copyright (C) 2014-2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
+#define EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
+
+#ifdef EIGEN_TENSOR_STORAGE_CTOR_PLUGIN
+  #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN EIGEN_TENSOR_STORAGE_CTOR_PLUGIN;
+#else
+  #define EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN
+#endif
+
+namespace Eigen {
+
+/** \internal
+  *
+  * \class TensorStorage
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Stores the data of a tensor
+  *
+  * This class stores the data of fixed-size, dynamic-size or mixed tensors
+  * in a way as compact as possible.
+  *
+  * \sa Tensor
+  */
+template<typename T, typename Dimensions, int Options> class TensorStorage;
+
+
+// Pure fixed-size storage
+template<typename T, typename FixedDimensions, int Options_>
+class TensorStorage
+{
+ private:
+  static const std::size_t Size = FixedDimensions::total_size;
+
+  // Allocate an array of size at least one to prevent compiler warnings.
+  static const std::size_t MinSize = max_n_1<Size>::size;
+  EIGEN_ALIGN_MAX T m_data[MinSize];
+
+ public:
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE TensorStorage() {
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T *data() { return m_data; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T *data() const { return m_data; }
+
+  static EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const FixedDimensions& dimensions()
+  {
+    static const FixedDimensions* singleton_dimensions = new FixedDimensions();
+    return *singleton_dimensions;
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE DenseIndex size() const { return Size; }
+};
+
+// pure dynamic
+template<typename T, typename IndexType, int NumIndices_, int Options_>
+class TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_>
+{
+  public:
+    typedef IndexType Index;
+    typedef DSizes<IndexType, NumIndices_> Dimensions;
+    typedef TensorStorage<T, DSizes<IndexType, NumIndices_>, Options_> Self;
+
+    EIGEN_DEVICE_FUNC TensorStorage() : m_data(0), m_dimensions() {
+      if (NumIndices_ == 0) {
+	m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1);
+      }
+    }
+    EIGEN_DEVICE_FUNC TensorStorage(internal::constructor_without_unaligned_array_assert)
+      : m_data(0), m_dimensions(internal::template repeat<NumIndices_, Index>(0)) {}
+    EIGEN_DEVICE_FUNC TensorStorage(Index size, const array<Index, NumIndices_>& dimensions)
+        : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size)), m_dimensions(dimensions)
+      { EIGEN_INTERNAL_TENSOR_STORAGE_CTOR_PLUGIN }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    template <typename... DenseIndex>
+    EIGEN_DEVICE_FUNC TensorStorage(DenseIndex... indices) : m_dimensions(indices...) {
+      m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(m_dimensions));
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC TensorStorage(const Self& other)
+      : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(internal::array_prod(other.m_dimensions)))
+      , m_dimensions(other.m_dimensions)
+    {
+      internal::smart_copy(other.m_data, other.m_data+internal::array_prod(other.m_dimensions), m_data);
+    }
+    EIGEN_DEVICE_FUNC Self& operator=(const Self& other)
+    {
+      if (this != &other) {
+        Self tmp(other);
+        this->swap(tmp);
+      }
+      return *this;
+    }
+
+#if EIGEN_HAS_RVALUE_REFERENCES
+    EIGEN_DEVICE_FUNC TensorStorage(Self&& other) : TensorStorage()
+    {
+      *this = std::move(other);
+    }
+    
+    EIGEN_DEVICE_FUNC Self& operator=(Self&& other)
+    {
+      numext::swap(m_data, other.m_data);
+      numext::swap(m_dimensions, other.m_dimensions);
+      return *this;
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC  ~TensorStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, internal::array_prod(m_dimensions)); }
+    EIGEN_DEVICE_FUNC  void swap(Self& other)
+    { numext::swap(m_data,other.m_data); numext::swap(m_dimensions,other.m_dimensions); }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {return m_dimensions;}
+
+    EIGEN_DEVICE_FUNC void resize(Index size, const array<Index, NumIndices_>& nbDimensions)
+    {
+      const Index currentSz = internal::array_prod(m_dimensions);
+      if(size != currentSz)
+      {
+        internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, currentSz);
+        if (size)
+          m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
+        else if (NumIndices_ == 0) {
+	  m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(1);
+	}
+	else 
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      }
+      m_dimensions = nbDimensions;
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T *data() { return m_data; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T *data() const { return m_data; }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
+
+ private:
+  T *m_data;
+  Dimensions m_dimensions;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSORSTORAGE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
new file mode 100644
index 0000000..2f62a66
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorStriding.h
@@ -0,0 +1,346 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
+#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
+
+namespace Eigen {
+
+/** \class TensorStriding
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor striding class.
+  *
+  *
+  */
+namespace internal {
+template<typename Strides, typename XprType>
+struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+};
+
+template<typename Strides, typename XprType>
+struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense>
+{
+  typedef const TensorStridingOp<Strides, XprType>EIGEN_DEVICE_REF type;
+};
+
+template<typename Strides, typename XprType>
+struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
+{
+  typedef TensorStridingOp<Strides, XprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename Strides, typename XprType>
+class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
+{
+  public:
+    typedef TensorBase<TensorStridingOp<Strides, XprType> > Base;
+    typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
+      : m_xpr(expr), m_dims(dims) {}
+
+    EIGEN_DEVICE_FUNC
+    const Strides& strides() const { return m_dims; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+    EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingOp)
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Strides m_dims;
+};
+
+
+// Eval as rvalue
+template<typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
+{
+  typedef TensorStridingOp<Strides, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device)
+  {
+    m_dimensions = m_impl.dimensions();
+    for (int i = 0; i < NumDims; ++i) {
+      m_dimensions[i] =Eigen::numext::ceil(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
+    }
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_outputStrides[0] = 1;
+      m_inputStrides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
+        m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
+        m_inputStrides[i-1] *= op.strides()[i-1];
+      }
+      m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
+    } else {  // RowMajor
+      m_outputStrides[NumDims-1] = 1;
+      m_inputStrides[NumDims-1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
+        m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
+        m_inputStrides[i+1] *= op.strides()[i+1];
+      }
+      m_inputStrides[0] *= op.strides()[0];
+    }
+  }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType/*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    return m_impl.coeff(srcCoeff(index));
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + PacketSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / m_outputStrides[i];
+        const Index idx1 = indices[1] / m_outputStrides[i];
+        inputIndices[0] += idx0 * m_inputStrides[i];
+        inputIndices[1] += idx1 * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * m_inputStrides[0];
+      inputIndices[1] += indices[1] * m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / m_outputStrides[i];
+        const Index idx1 = indices[1] / m_outputStrides[i];
+        inputIndices[0] += idx0 * m_inputStrides[i];
+        inputIndices[1] += idx1 * m_inputStrides[i];
+        indices[0] -= idx0 * m_outputStrides[i];
+        indices[1] -= idx1 * m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
+      inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
+    }
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
+      return rslt;
+    }
+    else {
+      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+      values[0] = m_impl.coeff(inputIndices[0]);
+      values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize-1; ++i) {
+        values[i] = coeff(index+i);
+      }
+      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+      return rslt;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
+    double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() +
+                                           TensorOpCost::MulCost<Index>() +
+                                           TensorOpCost::DivCost<Index>()) +
+        TensorOpCost::MulCost<Index>();
+    if (vectorized) {
+      compute_cost *= 2;  // packet() computes two indices
+    }
+    const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
+    return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
+        // Computation is not vectorized per se, but it is done once per packet.
+        TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
+  {
+    Index inputIndex = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += index * m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        inputIndex += idx * m_inputStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      inputIndex += index * m_inputStrides[NumDims-1];
+    }
+    return inputIndex;
+  }
+
+  Dimensions m_dimensions;
+  array<Index, NumDims> m_outputStrides;
+  array<Index, NumDims> m_inputStrides;
+  TensorEvaluator<ArgType, Device> m_impl;
+};
+
+// Eval as lvalue
+template<typename Strides, typename ArgType, typename Device>
+struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
+    : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
+{
+  typedef TensorStridingOp<Strides, ArgType> XprType;
+  typedef TensorEvaluator<const XprType, Device> Base;
+  //  typedef typename XprType::Index Index;
+  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  //  typedef DSizes<Index, NumDims> Dimensions;
+
+  enum {
+    IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    PreferBlockAccess = false,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,  // to be implemented
+    RawAccess = false
+  };
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : Base(op, device) { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
+  {
+    return this->m_impl.coeffRef(this->srcCoeff(index));
+  }
+
+  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketReturnType& x)
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize());
+
+    Index inputIndices[] = {0, 0};
+    Index indices[] = {index, index + PacketSize - 1};
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      EIGEN_UNROLL_LOOP
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx0 = indices[0] / this->m_outputStrides[i];
+        const Index idx1 = indices[1] / this->m_outputStrides[i];
+        inputIndices[0] += idx0 * this->m_inputStrides[i];
+        inputIndices[1] += idx1 * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * this->m_inputStrides[0];
+      inputIndices[1] += indices[1] * this->m_inputStrides[0];
+    } else {  // RowMajor
+      EIGEN_UNROLL_LOOP
+      for (int i = 0; i < NumDims - 1; ++i) {
+        const Index idx0 = indices[0] / this->m_outputStrides[i];
+        const Index idx1 = indices[1] / this->m_outputStrides[i];
+        inputIndices[0] += idx0 * this->m_inputStrides[i];
+        inputIndices[1] += idx1 * this->m_inputStrides[i];
+        indices[0] -= idx0 * this->m_outputStrides[i];
+        indices[1] -= idx1 * this->m_outputStrides[i];
+      }
+      inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
+      inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
+    }
+    if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
+      this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
+    }
+    else {
+      EIGEN_ALIGN_MAX Scalar values[PacketSize];
+      internal::pstore<Scalar, PacketReturnType>(values, x);
+      this->m_impl.coeffRef(inputIndices[0]) = values[0];
+      this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1];
+      EIGEN_UNROLL_LOOP
+      for (int i = 1; i < PacketSize-1; ++i) {
+        this->coeffRef(index+i) = values[i];
+      }
+    }
+  }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
new file mode 100644
index 0000000..926ecdd
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h
@@ -0,0 +1,303 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gagan Goel <gagan.nith@gmail.com>
+// Copyright (C) 2017 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
+#define EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
+
+namespace Eigen {
+
+/** \class TensorTrace
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Tensor Trace class.
+  *
+  *
+  */
+
+namespace internal {
+template<typename Dims, typename XprType>
+struct traits<TensorTraceOp<Dims, XprType> > : public traits<XprType>
+{
+  typedef typename XprType::Scalar Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
+  static const int Layout = XprTraits::Layout;
+};
+
+template<typename Dims, typename XprType>
+struct eval<TensorTraceOp<Dims, XprType>, Eigen::Dense>
+{
+  typedef const TensorTraceOp<Dims, XprType>& type;
+};
+
+template<typename Dims, typename XprType>
+struct nested<TensorTraceOp<Dims, XprType>, 1, typename eval<TensorTraceOp<Dims, XprType> >::type>
+{
+  typedef TensorTraceOp<Dims, XprType> type;
+};
+
+} // end namespace internal
+
+
+template<typename Dims, typename XprType>
+class TensorTraceOp : public TensorBase<TensorTraceOp<Dims, XprType> >
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorTraceOp>::Scalar Scalar;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename XprType::CoeffReturnType CoeffReturnType;
+    typedef typename Eigen::internal::nested<TensorTraceOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorTraceOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorTraceOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTraceOp(const XprType& expr, const Dims& dims)
+      : m_xpr(expr), m_dims(dims) {
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Dims& dims() const { return m_dims; }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const Dims m_dims;
+};
+
+
+// Eval as rvalue
+template<typename Dims, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorTraceOp<Dims, ArgType>, Device>
+{
+  typedef TensorTraceOp<Dims, ArgType> XprType;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumReducedDims = internal::array_size<Dims>::value;
+  static const int NumOutputDims = NumInputDims - NumReducedDims;
+  typedef typename XprType::Index Index;
+  typedef DSizes<Index, NumOutputDims> Dimensions;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+    : m_impl(op.expression(), device), m_traceDim(1), m_device(device)
+  {
+
+    EIGEN_STATIC_ASSERT((NumOutputDims >= 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT((NumReducedDims >= 2) || ((NumReducedDims == 0) && (NumInputDims == 0)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    for (int i = 0; i < NumInputDims; ++i) {
+      m_reduced[i] = false;
+    }
+
+    const Dims& op_dims = op.dims();
+    for (int i = 0; i < NumReducedDims; ++i) {
+      eigen_assert(op_dims[i] >= 0);
+      eigen_assert(op_dims[i] < NumInputDims);
+      m_reduced[op_dims[i]] = true;
+    }
+
+    // All the dimensions should be distinct to compute the trace
+    int num_distinct_reduce_dims = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (m_reduced[i]) {
+        ++num_distinct_reduce_dims;
+      }
+    }
+
+    eigen_assert(num_distinct_reduce_dims == NumReducedDims);
+
+    // Compute the dimensions of the result.
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    int output_index = 0;
+    int reduced_index = 0;
+    for (int i = 0; i < NumInputDims; ++i) {
+      if (m_reduced[i]) {
+        m_reducedDims[reduced_index] = input_dims[i];
+        if (reduced_index > 0) {
+          // All the trace dimensions must have the same size
+          eigen_assert(m_reducedDims[0] == m_reducedDims[reduced_index]);
+        }
+        ++reduced_index;
+      }
+      else {
+        m_dimensions[output_index] = input_dims[i];
+        ++output_index;
+      }
+    }
+
+    if (NumReducedDims != 0) {
+      m_traceDim = m_reducedDims[0];
+    }
+
+    // Compute the output strides
+    if (NumOutputDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        m_outputStrides[0] = 1;
+        for (int i = 1; i < NumOutputDims; ++i) {
+          m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
+        }
+      }
+      else {
+        m_outputStrides.back() = 1;
+        for (int i = NumOutputDims - 2; i >= 0; --i) {
+          m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
+        }
+      }
+    }
+
+    // Compute the input strides
+    if (NumInputDims > 0) {
+      array<Index, NumInputDims> input_strides;
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        input_strides[0] = 1;
+        for (int i = 1; i < NumInputDims; ++i) {
+          input_strides[i] = input_strides[i - 1] * input_dims[i - 1];
+        }
+      }
+      else {
+        input_strides.back() = 1;
+        for (int i = NumInputDims - 2; i >= 0; --i) {
+          input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
+        }
+      }
+
+      output_index = 0;
+      reduced_index = 0;
+      for (int i = 0; i < NumInputDims; ++i) {
+        if(m_reduced[i]) {
+          m_reducedStrides[reduced_index] = input_strides[i];
+          ++reduced_index;
+        }
+        else {
+          m_preservedStrides[output_index] = input_strides[i];
+          ++output_index;
+        }
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
+    return m_dimensions;
+  }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    // Initialize the result
+    CoeffReturnType result = internal::cast<int, CoeffReturnType>(0);
+    Index index_stride = 0;
+    for (int i = 0; i < NumReducedDims; ++i) {
+      index_stride += m_reducedStrides[i];
+    }
+
+    // If trace is requested along all dimensions, starting index would be 0
+    Index cur_index = 0;
+    if (NumOutputDims != 0)
+      cur_index = firstInput(index);
+    for (Index i = 0; i < m_traceDim; ++i) {
+        result += m_impl.coeff(cur_index);
+        cur_index += index_stride;
+    }
+
+    return result;
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
+
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    for (int i = 0; i < PacketSize; ++i) {
+        values[i] = coeff(index + i);
+    }
+    PacketReturnType result = internal::ploadt<PacketReturnType, LoadMode>(values);
+    return result;
+  }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+
+ protected:
+  // Given the output index, finds the first index in the input tensor used to compute the trace
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index firstInput(Index index) const {
+    Index startInput = 0;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumOutputDims - 1; i > 0; --i) {
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      startInput += index * m_preservedStrides[0];
+    }
+    else {
+      for (int i = 0; i < NumOutputDims - 1; ++i) {
+        const Index idx = index / m_outputStrides[i];
+        startInput += idx * m_preservedStrides[i];
+        index -= idx * m_outputStrides[i];
+      }
+      startInput += index * m_preservedStrides[NumOutputDims - 1];
+    }
+    return startInput;
+  }
+
+  Dimensions m_dimensions;
+  TensorEvaluator<ArgType, Device> m_impl;
+  // Initialize the size of the trace dimension
+  Index m_traceDim;
+  const Device EIGEN_DEVICE_REF m_device;
+  array<bool, NumInputDims> m_reduced;
+  array<Index, NumReducedDims> m_reducedDims;
+  array<Index, NumOutputDims> m_outputStrides;
+  array<Index, NumReducedDims> m_reducedStrides;
+  array<Index, NumOutputDims> m_preservedStrides;
+};
+
+
+} // End namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_TRACE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
new file mode 100644
index 0000000..4f7fd34
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorTraits.h
@@ -0,0 +1,264 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
+#define EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
+
+namespace Eigen {
+namespace internal {
+
+
+template<typename Scalar, int Options>
+class compute_tensor_flags
+{
+  enum {
+    is_dynamic_size_storage = 1,
+
+    is_aligned =
+    (
+        ((Options&DontAlign)==0) && (
+#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
+            (!is_dynamic_size_storage)
+#else
+            0
+#endif
+            |
+#if EIGEN_MAX_ALIGN_BYTES>0
+            is_dynamic_size_storage
+#else
+            0
+#endif
+      )
+     ),
+    packet_access_bit = packet_traits<Scalar>::Vectorizable && is_aligned ? PacketAccessBit : 0
+  };
+
+  public:
+    enum { ret = packet_access_bit };
+};
+
+
+template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct traits<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
+{
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef IndexType_ Index;
+  static const int NumDimensions = NumIndices_;
+  static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+  enum {
+    Options = Options_,
+    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0 : LvalueBit)
+  };
+  template <typename T> struct MakePointer {
+    typedef T* Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+
+template<typename Scalar_, typename Dimensions, int Options_, typename IndexType_>
+struct traits<TensorFixedSize<Scalar_, Dimensions, Options_, IndexType_> >
+{
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef IndexType_ Index;
+  static const int NumDimensions = array_size<Dimensions>::value;
+  static const int Layout = Options_ & RowMajor ? RowMajor : ColMajor;
+  enum {
+    Options = Options_,
+    Flags = compute_tensor_flags<Scalar_, Options_>::ret | (is_const<Scalar_>::value ? 0: LvalueBit)
+  };
+  template <typename T> struct MakePointer {
+    typedef T* Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+
+template<typename PlainObjectType, int Options_, template <class> class MakePointer_>
+struct traits<TensorMap<PlainObjectType, Options_, MakePointer_> >
+  : public traits<PlainObjectType>
+{
+  typedef traits<PlainObjectType> BaseTraits;
+  typedef typename BaseTraits::Scalar Scalar;
+  typedef typename BaseTraits::StorageKind StorageKind;
+  typedef typename BaseTraits::Index Index;
+  static const int NumDimensions = BaseTraits::NumDimensions;
+  static const int Layout = BaseTraits::Layout;
+  enum {
+    Options = Options_,
+    Flags = BaseTraits::Flags
+  };
+  template <class T> struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
+  typedef typename MakePointer<Scalar>::Type PointerType;
+};
+
+template<typename PlainObjectType>
+struct traits<TensorRef<PlainObjectType> >
+  : public traits<PlainObjectType>
+{
+  typedef traits<PlainObjectType> BaseTraits;
+  typedef typename BaseTraits::Scalar Scalar;
+  typedef typename BaseTraits::StorageKind StorageKind;
+  typedef typename BaseTraits::Index Index;
+  static const int NumDimensions = BaseTraits::NumDimensions;
+  static const int Layout = BaseTraits::Layout;
+  enum {
+    Options = BaseTraits::Options,
+    Flags = BaseTraits::Flags
+  };
+  typedef typename BaseTraits::PointerType PointerType;
+};
+
+
+template<typename _Scalar, int NumIndices_, int Options, typename IndexType_>
+struct eval<Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense>
+{
+  typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template<typename _Scalar, int NumIndices_, int Options, typename IndexType_>
+struct eval<const Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense>
+{
+  typedef const Tensor<_Scalar, NumIndices_, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template<typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>
+{
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template<typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense>
+{
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template<typename PlainObjectType, int Options, template <class> class MakePointer>
+struct eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>
+{
+  typedef const TensorMap<PlainObjectType, Options, MakePointer>EIGEN_DEVICE_REF type;
+};
+
+template<typename PlainObjectType, int Options, template <class> class MakePointer>
+struct eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense>
+{
+  typedef const TensorMap<PlainObjectType, Options, MakePointer>EIGEN_DEVICE_REF type;
+};
+
+template<typename PlainObjectType>
+struct eval<TensorRef<PlainObjectType>, Eigen::Dense>
+{
+  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
+};
+
+template<typename PlainObjectType>
+struct eval<const TensorRef<PlainObjectType>, Eigen::Dense>
+{
+  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
+};
+
+// TODO nested<> does not exist anymore in Eigen/Core, and it thus has to be removed in favor of ref_selector.
+template<typename T, int n=1, typename PlainObject = void> struct nested
+{
+  typedef typename ref_selector<T>::type type;
+};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct nested<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
+{
+  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
+struct nested<const Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
+{
+  typedef const Tensor<Scalar_, NumIndices_, Options_, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct nested<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> >
+{
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+template <typename Scalar_, typename Dimensions, int Options, typename IndexType_>
+struct nested<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_> >
+{
+  typedef const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>EIGEN_DEVICE_REF type;
+};
+
+
+template <typename PlainObjectType>
+struct nested<TensorRef<PlainObjectType> >
+{
+  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
+};
+
+template <typename PlainObjectType>
+struct nested<const TensorRef<PlainObjectType> >
+{
+  typedef const TensorRef<PlainObjectType>EIGEN_DEVICE_REF type;
+};
+
+}  // end namespace internal
+
+// Convolutional layers take in an input tensor of shape (D, R, C, B), or (D, C,
+// R, B), and convolve it with a set of filters, which can also be presented as
+// a tensor (D, K, K, M), where M is the number of filters, K is the filter
+// size, and each 3-dimensional tensor of size (D, K, K) is a filter. For
+// simplicity we assume that we always use square filters (which is usually the
+// case in images), hence the two Ks in the tensor dimension.  It also takes in
+// a few additional parameters:
+// Stride (S): The convolution stride is the offset between locations where we
+//             apply the filters.  A larger stride means that the output will be
+//             spatially smaller.
+// Padding (P): The padding we apply to the input tensor along the R and C
+//              dimensions.  This is usually used to make sure that the spatial
+//              dimensions of the output matches our intention.
+//
+// Two types of padding are often used:
+//   SAME: The pad value is computed so that the output will have size
+//         R/S and C/S.
+//   VALID: no padding is carried out.
+// When we do padding, the padded values at the padded locations are usually
+// zero.
+//
+// The output dimensions for convolution, when given all the parameters above,
+// are as follows:
+// When Padding = SAME: the output size is (B, R', C', M), where
+//   R' = ceil(float(R) / float(S))
+//   C' = ceil(float(C) / float(S))
+// where ceil is the ceiling function.  The input tensor is padded with 0 as
+// needed.  The number of padded rows and columns are computed as:
+//   Pr = ((R' - 1) * S + K - R) / 2
+//   Pc = ((C' - 1) * S + K - C) / 2
+// when the stride is 1, we have the simplified case R'=R, C'=C, Pr=Pc=(K-1)/2.
+// This is where SAME comes from - the output has the same size as the input has.
+// When Padding = VALID: the output size is computed as
+//   R' = ceil(float(R - K + 1) / float(S))
+//   C' = ceil(float(C - K + 1) / float(S))
+// and the number of padded rows and columns are computed in the same way as in
+// the SAME case.
+// When the stride is 1, we have the simplified case R'=R-K+1, C'=C-K+1, Pr=0,
+// Pc=0.
+typedef enum {
+  PADDING_VALID = 1,
+  PADDING_SAME = 2
+} PaddingType;
+
+}  // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_TRAITS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
new file mode 100644
index 0000000..d23f2e4
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorUInt128.h
@@ -0,0 +1,249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
+#define EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
+
+namespace Eigen {
+namespace internal {
+
+
+template <uint64_t n>
+struct static_val {
+  static const uint64_t value = n;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator uint64_t() const { return n; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val() { }
+
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static_val(const T& v) {
+    EIGEN_UNUSED_VARIABLE(v);
+    eigen_assert(v == n);
+  }
+};
+
+
+template <typename HIGH = uint64_t, typename LOW = uint64_t>
+struct TensorUInt128
+{
+  HIGH high;
+  LOW low;
+
+  template<typename OTHER_HIGH, typename OTHER_LOW>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  TensorUInt128(const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) : high(other.high), low(other.low) {
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  template<typename OTHER_HIGH, typename OTHER_LOW>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  TensorUInt128& operator = (const TensorUInt128<OTHER_HIGH, OTHER_LOW>& other) {
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_HIGH) <= sizeof(HIGH), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    EIGEN_STATIC_ASSERT(sizeof(OTHER_LOW) <= sizeof(LOW), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    high = other.high;
+    low = other.low;
+    return *this;
+  }
+
+  template<typename T>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  explicit TensorUInt128(const T& x) : high(0), low(x) {
+    eigen_assert((static_cast<typename conditional<sizeof(T) == 8, uint64_t, uint32_t>::type>(x) <= NumTraits<uint64_t>::highest()));
+    eigen_assert(x >= 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  TensorUInt128(HIGH y, LOW x) : high(y), low(x) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE operator LOW() const {
+    return low;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LOW lower() const {
+    return low;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE HIGH upper() const {
+    return high;
+  }
+};
+
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool operator == (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  return (lhs.high == rhs.high) & (lhs.low == rhs.low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool operator != (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  return (lhs.high != rhs.high) | (lhs.low != rhs.low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool operator >= (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  if (lhs.high != rhs.high) {
+    return lhs.high > rhs.high;
+  }
+  return lhs.low >= rhs.low;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool operator < (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  if (lhs.high != rhs.high) {
+    return lhs.high < rhs.high;
+  }
+  return lhs.low < rhs.low;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+TensorUInt128<uint64_t, uint64_t> operator + (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  TensorUInt128<uint64_t, uint64_t> result(lhs.high + rhs.high, lhs.low + rhs.low);
+  if (result.low < rhs.low) {
+    result.high += 1;
+  }
+  return result;
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+TensorUInt128<uint64_t, uint64_t> operator - (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  TensorUInt128<uint64_t, uint64_t> result(lhs.high - rhs.high, lhs.low - rhs.low);
+  if (result.low > lhs.low) {
+    result.high -= 1;
+  }
+  return result;
+}
+
+
+template <typename HL, typename LL, typename HR, typename LR>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+TensorUInt128<uint64_t, uint64_t> operator * (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  // Split each 128-bit integer into 4 32-bit integers, and then do the
+  // multiplications by hand as follow:
+  //   lhs      a  b  c  d
+  //   rhs      e  f  g  h
+  //           -----------
+  //           ah bh ch dh
+  //           bg cg dg
+  //           cf df
+  //           de
+  // The result is stored in 2 64bit integers, high and low.
+
+  const uint64_t LOW = 0x00000000FFFFFFFFLL;
+  const uint64_t HIGH = 0xFFFFFFFF00000000LL;
+
+  uint64_t d = lhs.low & LOW;
+  uint64_t c = (lhs.low & HIGH) >> 32LL;
+  uint64_t b = lhs.high & LOW;
+  uint64_t a = (lhs.high & HIGH) >> 32LL;
+
+  uint64_t h = rhs.low & LOW;
+  uint64_t g = (rhs.low & HIGH) >> 32LL;
+  uint64_t f = rhs.high & LOW;
+  uint64_t e = (rhs.high & HIGH) >> 32LL;
+
+  // Compute the low 32 bits of low
+  uint64_t acc = d * h;
+  uint64_t low = acc & LOW;
+  //  Compute the high 32 bits of low. Add a carry every time we wrap around
+  acc >>= 32LL;
+  uint64_t carry = 0;
+  uint64_t acc2 = acc + c * h;
+  if (acc2 < acc) {
+    carry++;
+  }
+  acc = acc2 + d * g;
+  if (acc < acc2) {
+    carry++;
+  }
+  low |= (acc << 32LL);
+
+  // Carry forward the high bits of acc to initiate the computation of the
+  // low 32 bits of high
+  acc2 = (acc >> 32LL) | (carry << 32LL);
+  carry = 0;
+
+  acc = acc2 + b * h;
+  if (acc < acc2) {
+    carry++;
+  }
+  acc2 = acc + c * g;
+  if (acc2 < acc) {
+    carry++;
+  }
+  acc = acc2 + d * f;
+  if (acc < acc2) {
+    carry++;
+  }
+  uint64_t high = acc & LOW;
+
+  // Start to compute the high 32 bits of high.
+  acc2 = (acc >> 32LL) | (carry << 32LL);
+
+  acc = acc2 + a * h;
+  acc2 = acc + b * g;
+  acc = acc2 + c * f;
+  acc2 = acc + d * e;
+  high |= (acc2 << 32LL);
+
+  return TensorUInt128<uint64_t, uint64_t>(high, low);
+}
+
+template <typename HL, typename LL, typename HR, typename LR>
+static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+TensorUInt128<uint64_t, uint64_t> operator / (const TensorUInt128<HL, LL>& lhs, const TensorUInt128<HR, LR>& rhs)
+{
+  if (rhs == TensorUInt128<static_val<0>, static_val<1> >(1)) {
+    return TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
+  } else if (lhs < rhs) {
+    return TensorUInt128<uint64_t, uint64_t>(0);
+  } else {
+    // calculate the biggest power of 2 times rhs that's less than or equal to lhs
+    TensorUInt128<uint64_t, uint64_t> power2(1);
+    TensorUInt128<uint64_t, uint64_t> d(rhs);
+    TensorUInt128<uint64_t, uint64_t> tmp(lhs - d);
+    while (lhs >= d) {
+      tmp = tmp - d;
+      d = d + d;
+      power2 = power2 + power2;
+    }
+
+    tmp = TensorUInt128<uint64_t, uint64_t>(lhs.high, lhs.low);
+    TensorUInt128<uint64_t, uint64_t> result(0);
+    while (power2 != TensorUInt128<static_val<0>, static_val<0> >(0)) {
+      if (tmp >= d) {
+        tmp = tmp - d;
+        result = result + power2;
+      }
+      // Shift right
+      power2 = TensorUInt128<uint64_t, uint64_t>(power2.high >> 1, (power2.low >> 1) | (power2.high << 63));
+      d = TensorUInt128<uint64_t, uint64_t>(d.high >> 1, (d.low >> 1) | (d.high << 63));
+    }
+
+    return result;
+  }
+}
+
+
+}  // namespace internal
+}  // namespace Eigen
+
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_UINT128_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
new file mode 100644
index 0000000..0beb9ff
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h
@@ -0,0 +1,629 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
+#define EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
+
+namespace Eigen {
+
+/** \class TensorVolumePatch
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief Patch extraction specialized for processing of volumetric data.
+  * This assumes that the input has a least 4 dimensions ordered as follows:
+  *  - channels
+  *  - planes
+  *  - rows
+  *  - columns
+  *  - (optional) additional dimensions such as time or batch size.
+  * Calling the volume patch code with patch_planes, patch_rows, and patch_cols
+  * is equivalent to calling the regular patch extraction code with parameters
+  * d, patch_planes, patch_rows, patch_cols, and 1 for all the additional
+  * dimensions.
+  */
+namespace internal {
+
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct traits<TensorVolumePatchOp<Planes, Rows, Cols, XprType> > : public traits<XprType>
+{
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef traits<XprType> XprTraits;
+  typedef typename XprTraits::StorageKind StorageKind;
+  typedef typename XprTraits::Index Index;
+  typedef typename XprType::Nested Nested;
+  typedef typename remove_reference<Nested>::type _Nested;
+  static const int NumDimensions = XprTraits::NumDimensions + 1;
+  static const int Layout = XprTraits::Layout;
+  typedef typename XprTraits::PointerType PointerType;
+
+};
+
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Eigen::Dense>
+{
+  typedef const TensorVolumePatchOp<Planes, Rows, Cols, XprType>& type;
+};
+
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+struct nested<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, 1, typename eval<TensorVolumePatchOp<Planes, Rows, Cols, XprType> >::type>
+{
+  typedef TensorVolumePatchOp<Planes, Rows, Cols, XprType> type;
+};
+
+}  // end namespace internal
+
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>
+class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows, Cols, XprType>, ReadOnlyAccessors>
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorVolumePatchOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorVolumePatchOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
+                                                            DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
+                                                            DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
+                                                            DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
+                                                            PaddingType padding_type, Scalar padding_value)
+                                                            : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
+                                                            m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
+                                                            m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
+                                                            m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
+                                                            m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
+                                                            m_padding_type(padding_type), m_padding_value(padding_value) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
+                                                           DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
+                                                           DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
+                                                           DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
+                                                           DenseIndex padding_top_z, DenseIndex padding_bottom_z,
+                                                           DenseIndex padding_top, DenseIndex padding_bottom,
+                                                           DenseIndex padding_left, DenseIndex padding_right,
+                                                           Scalar padding_value)
+                                                           : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
+                                                           m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
+                                                           m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
+                                                           m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
+                                                           m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
+                                                           m_padding_left(padding_left), m_padding_right(padding_right),
+                                                           m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
+
+    EIGEN_DEVICE_FUNC
+    DenseIndex patch_planes() const { return m_patch_planes; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex patch_rows() const { return m_patch_rows; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex patch_cols() const { return m_patch_cols; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex plane_strides() const { return m_plane_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex row_strides() const { return m_row_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex col_strides() const { return m_col_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex in_plane_strides() const { return m_in_plane_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex in_row_strides() const { return m_in_row_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex in_col_strides() const { return m_in_col_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex plane_inflate_strides() const { return m_plane_inflate_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
+    EIGEN_DEVICE_FUNC
+    bool padding_explicit() const { return m_padding_explicit; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_top_z() const { return m_padding_top_z; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_bottom_z() const { return m_padding_bottom_z; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_top() const { return m_padding_top; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_bottom() const { return m_padding_bottom; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_left() const { return m_padding_left; }
+    EIGEN_DEVICE_FUNC
+    DenseIndex padding_right() const { return m_padding_right; }
+    EIGEN_DEVICE_FUNC
+    PaddingType padding_type() const { return m_padding_type; }
+    EIGEN_DEVICE_FUNC
+    Scalar padding_value() const { return m_padding_value; }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename XprType::Nested>::type&
+    expression() const { return m_xpr; }
+
+  protected:
+    typename XprType::Nested m_xpr;
+    const DenseIndex m_patch_planes;
+    const DenseIndex m_patch_rows;
+    const DenseIndex m_patch_cols;
+    const DenseIndex m_plane_strides;
+    const DenseIndex m_row_strides;
+    const DenseIndex m_col_strides;
+    const DenseIndex m_in_plane_strides;
+    const DenseIndex m_in_row_strides;
+    const DenseIndex m_in_col_strides;
+    const DenseIndex m_plane_inflate_strides;
+    const DenseIndex m_row_inflate_strides;
+    const DenseIndex m_col_inflate_strides;
+    const bool m_padding_explicit;
+    const DenseIndex m_padding_top_z;
+    const DenseIndex m_padding_bottom_z;
+    const DenseIndex m_padding_top;
+    const DenseIndex m_padding_bottom;
+    const DenseIndex m_padding_left;
+    const DenseIndex m_padding_right;
+    const PaddingType m_padding_type;
+    const Scalar m_padding_value;
+};
+
+
+// Eval as rvalue
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
+struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, Device>
+{
+  typedef TensorVolumePatchOp<Planes, Rows, Cols, ArgType> XprType;
+  typedef typename XprType::Index Index;
+  static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
+  static const int NumDims = NumInputDims + 1;
+  typedef DSizes<Index, NumDims> Dimensions;
+  typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
+  typedef StorageMemory<CoeffReturnType, Device> Storage;
+  typedef typename Storage::Type EvaluatorPointerType;
+
+  enum {
+    IsAligned = false,
+    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
+    BlockAccess = false,
+    PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
+    Layout = TensorEvaluator<ArgType, Device>::Layout,
+    CoordAccess = false,
+    RawAccess = false
+  };
+
+  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
+  typedef internal::TensorBlockNotImplemented TensorBlock;
+  //===--------------------------------------------------------------------===//
+
+  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) :
+ m_impl(op.expression(), device)
+  {
+    EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+    m_paddingValue = op.padding_value();
+
+    const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
+
+    // Cache a few variables.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_inputDepth = input_dims[0];
+      m_inputPlanes = input_dims[1];
+      m_inputRows = input_dims[2];
+      m_inputCols = input_dims[3];
+    } else {
+      m_inputDepth = input_dims[NumInputDims-1];
+      m_inputPlanes = input_dims[NumInputDims-2];
+      m_inputRows = input_dims[NumInputDims-3];
+      m_inputCols = input_dims[NumInputDims-4];
+    }
+
+    m_plane_strides = op.plane_strides();
+    m_row_strides = op.row_strides();
+    m_col_strides = op.col_strides();
+
+    // Input strides and effective input/patch size
+    m_in_plane_strides = op.in_plane_strides();
+    m_in_row_strides = op.in_row_strides();
+    m_in_col_strides = op.in_col_strides();
+    m_plane_inflate_strides = op.plane_inflate_strides();
+    m_row_inflate_strides = op.row_inflate_strides();
+    m_col_inflate_strides = op.col_inflate_strides();
+
+    // The "effective" spatial size after inflating data with zeros.
+    m_input_planes_eff = (m_inputPlanes - 1) * m_plane_inflate_strides + 1;
+    m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
+    m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
+    m_patch_planes_eff = op.patch_planes() + (op.patch_planes() - 1) * (m_in_plane_strides - 1);
+    m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
+    m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
+
+    if (op.padding_explicit()) {
+      m_outputPlanes = numext::ceil((m_input_planes_eff + op.padding_top_z() + op.padding_bottom_z() - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
+      m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+      m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+      m_planePaddingTop = op.padding_top_z();
+      m_rowPaddingTop = op.padding_top();
+      m_colPaddingLeft = op.padding_left();
+    } else {
+      // Computing padding from the type
+      switch (op.padding_type()) {
+        case PADDING_VALID:
+          m_outputPlanes = numext::ceil((m_input_planes_eff - m_patch_planes_eff + 1.f) / static_cast<float>(m_plane_strides));
+          m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
+          m_planePaddingTop = 0;
+          m_rowPaddingTop = 0;
+          m_colPaddingLeft = 0;
+          break;
+        case PADDING_SAME: {
+          m_outputPlanes = numext::ceil(m_input_planes_eff / static_cast<float>(m_plane_strides));
+          m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
+          m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
+          const Index dz = (m_outputPlanes - 1) * m_plane_strides + m_patch_planes_eff - m_input_planes_eff;
+          const Index dy = (m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff;
+          const Index dx = (m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff;
+          m_planePaddingTop = dz / 2;
+          m_rowPaddingTop = dy / 2;
+          m_colPaddingLeft = dx / 2;
+          break;
+        }
+        default:
+          eigen_assert(false && "unexpected padding");
+      }
+    }
+    eigen_assert(m_outputRows > 0);
+    eigen_assert(m_outputCols > 0);
+    eigen_assert(m_outputPlanes > 0);
+
+    // Dimensions for result of extraction.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      // ColMajor
+      // 0: depth
+      // 1: patch_planes
+      // 2: patch_rows
+      // 3: patch_cols
+      // 4: number of patches
+      // 5 and beyond: anything else (such as batch).
+      m_dimensions[0] = input_dims[0];
+      m_dimensions[1] = op.patch_planes();
+      m_dimensions[2] = op.patch_rows();
+      m_dimensions[3] = op.patch_cols();
+      m_dimensions[4] = m_outputPlanes * m_outputRows * m_outputCols;
+      for (int i = 5; i < NumDims; ++i) {
+        m_dimensions[i] = input_dims[i-1];
+      }
+    } else {
+      // RowMajor
+      // NumDims-1: depth
+      // NumDims-2: patch_planes
+      // NumDims-3: patch_rows
+      // NumDims-4: patch_cols
+      // NumDims-5: number of patches
+      // NumDims-6 and beyond: anything else (such as batch).
+      m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
+      m_dimensions[NumDims-2] = op.patch_planes();
+      m_dimensions[NumDims-3] = op.patch_rows();
+      m_dimensions[NumDims-4] = op.patch_cols();
+      m_dimensions[NumDims-5] = m_outputPlanes * m_outputRows * m_outputCols;
+      for (int i = NumDims-6; i >= 0; --i) {
+        m_dimensions[i] = input_dims[i];
+      }
+    }
+
+    // Strides for the output tensor.
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_rowStride = m_dimensions[1];
+      m_colStride = m_dimensions[2] * m_rowStride;
+      m_patchStride = m_colStride * m_dimensions[3] * m_dimensions[0];
+      m_otherStride = m_patchStride * m_dimensions[4];
+    } else {
+      m_rowStride = m_dimensions[NumDims-2];
+      m_colStride = m_dimensions[NumDims-3] * m_rowStride;
+      m_patchStride = m_colStride * m_dimensions[NumDims-4] * m_dimensions[NumDims-1];
+      m_otherStride = m_patchStride * m_dimensions[NumDims-5];
+    }
+
+    // Strides for navigating through the input tensor.
+    m_planeInputStride = m_inputDepth;
+    m_rowInputStride = m_inputDepth * m_inputPlanes;
+    m_colInputStride = m_inputDepth * m_inputRows * m_inputPlanes;
+    m_otherInputStride = m_inputDepth * m_inputRows * m_inputCols * m_inputPlanes;
+
+    m_outputPlanesRows = m_outputPlanes * m_outputRows;
+
+    // Fast representations of different variables.
+    m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
+
+    m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
+    m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
+    m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
+    m_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
+    m_fastInputColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
+    m_fastInputPlaneStride = internal::TensorIntDivisor<Index>(m_plane_inflate_strides);
+    m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
+    m_fastOutputPlanes = internal::TensorIntDivisor<Index>(m_outputPlanes);
+    m_fastOutputPlanesRows = internal::TensorIntDivisor<Index>(m_outputPlanesRows);
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
+    } else {
+      m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
+
+  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
+    m_impl.evalSubExprsIfNeeded(NULL);
+    return true;
+  }
+
+  EIGEN_STRONG_INLINE void cleanup() {
+    m_impl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
+  {
+    // Patch index corresponding to the passed in index.
+    const Index patchIndex = index / m_fastPatchStride;
+
+    // Spatial offset within the patch. This has to be translated into 3D
+    // coordinates within the patch.
+    const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
+
+    // Batch, etc.
+    const Index otherIndex = (NumDims == 5) ? 0 : index / m_fastOtherStride;
+    const Index patch3DIndex = (NumDims == 5) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
+
+    // Calculate column index in the input original tensor.
+    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
+    const Index colOffset = patchOffset / m_fastColStride;
+    const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
+    const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0);
+    if (inputCol < 0 || inputCol >= m_input_cols_eff ||
+        ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate row index in the original input tensor.
+    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
+    const Index rowOffset = (patchOffset - colOffset * m_colStride) / m_fastRowStride;
+    const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
+    const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0);
+    if (inputRow < 0 || inputRow >= m_input_rows_eff ||
+        ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    // Calculate plane index in the original input tensor.
+    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
+    const Index planeOffset = patchOffset - colOffset * m_colStride - rowOffset * m_rowStride;
+    const Index inputPlane = planeIndex * m_plane_strides + planeOffset * m_in_plane_strides - m_planePaddingTop;
+    const Index origInputPlane = (m_plane_inflate_strides == 1) ? inputPlane : ((inputPlane >= 0) ? (inputPlane / m_fastInputPlaneStride) : 0);
+    if (inputPlane < 0 || inputPlane >= m_input_planes_eff ||
+        ((m_plane_inflate_strides != 1) && (inputPlane != origInputPlane * m_plane_inflate_strides))) {
+      return Scalar(m_paddingValue);
+    }
+
+    const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+    const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+
+    const Index inputIndex = depth +
+        origInputRow * m_rowInputStride +
+        origInputCol * m_colInputStride +
+        origInputPlane * m_planeInputStride +
+        otherIndex * m_otherInputStride;
+
+    return m_impl.coeff(inputIndex);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1 ||
+        m_in_plane_strides != 1 || m_plane_inflate_strides != 1) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index indices[2] = {index, index + PacketSize - 1};
+    const Index patchIndex = indices[0] / m_fastPatchStride;
+    if (patchIndex != indices[1] / m_fastPatchStride) {
+      return packetWithPossibleZero(index);
+    }
+    const Index otherIndex = (NumDims == 5) ? 0 : indices[0] / m_fastOtherStride;
+    eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
+
+    // Find the offset of the element wrt the location of the first element.
+    const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
+                                   (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
+
+    const Index patch3DIndex = (NumDims == 5) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
+    eigen_assert(patch3DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
+
+    const Index colIndex = patch3DIndex / m_fastOutputPlanesRows;
+    const Index colOffsets[2] = {
+      patchOffsets[0] / m_fastColStride,
+      patchOffsets[1] / m_fastColStride};
+
+    // Calculate col indices in the original input tensor.
+    const Index inputCols[2] = {
+      colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft,
+      colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
+    if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputCols[0] != inputCols[1]) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index rowIndex = (patch3DIndex - colIndex * m_outputPlanesRows) / m_fastOutputPlanes;
+    const Index rowOffsets[2] = {
+      (patchOffsets[0] - colOffsets[0] * m_colStride) / m_fastRowStride,
+      (patchOffsets[1] - colOffsets[1] * m_colStride) / m_fastRowStride};
+    eigen_assert(rowOffsets[0] <= rowOffsets[1]);
+    // Calculate col indices in the original input tensor.
+    const Index inputRows[2] = {
+      rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop,
+      rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
+
+    if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputRows[0] != inputRows[1]) {
+      return packetWithPossibleZero(index);
+    }
+
+    const Index planeIndex = (patch3DIndex - m_outputPlanes * (colIndex * m_outputRows + rowIndex));
+    const Index planeOffsets[2] = {
+      patchOffsets[0] - colOffsets[0] * m_colStride - rowOffsets[0] * m_rowStride,
+      patchOffsets[1] - colOffsets[1] * m_colStride - rowOffsets[1] * m_rowStride};
+    eigen_assert(planeOffsets[0] <= planeOffsets[1]);
+    const Index inputPlanes[2] = {
+      planeIndex * m_plane_strides + planeOffsets[0] - m_planePaddingTop,
+      planeIndex * m_plane_strides + planeOffsets[1] - m_planePaddingTop};
+
+    if (inputPlanes[1] < 0 || inputPlanes[0] >= m_inputPlanes) {
+      return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
+    }
+
+    if (inputPlanes[0] >= 0 && inputPlanes[1] < m_inputPlanes) {
+      // no padding
+      const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
+      const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
+      const Index inputIndex = depth +
+          inputRows[0] * m_rowInputStride +
+          inputCols[0] * m_colInputStride +
+          m_planeInputStride * inputPlanes[0] +
+          otherIndex * m_otherInputStride;
+      return m_impl.template packet<Unaligned>(inputIndex);
+    }
+
+    return packetWithPossibleZero(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    const double compute_cost =
+        10 * TensorOpCost::DivCost<Index>() + 21 * TensorOpCost::MulCost<Index>() +
+        8 * TensorOpCost::AddCost<Index>();
+    return TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
+  }
+
+  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
+
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planePaddingTop() const { return m_planePaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputPlanes() const { return m_outputPlanes; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userPlaneStride() const { return m_plane_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInPlaneStride() const { return m_in_plane_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index planeInflateStride() const { return m_plane_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; }
+
+#ifdef EIGEN_USE_SYCL
+  // binding placeholder accessors to a command group handler for SYCL
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
+    m_impl.bind(cgh);
+  }
+#endif
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
+  {
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    EIGEN_UNROLL_LOOP
+    for (int i = 0; i < PacketSize; ++i) {
+      values[i] = coeff(index+i);
+    }
+    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
+    return rslt;
+  }
+
+  Dimensions m_dimensions;
+
+  // Parameters passed to the constructor.
+  Index m_plane_strides;
+  Index m_row_strides;
+  Index m_col_strides;
+
+  Index m_outputPlanes;
+  Index m_outputRows;
+  Index m_outputCols;
+
+  Index m_planePaddingTop;
+  Index m_rowPaddingTop;
+  Index m_colPaddingLeft;
+
+  Index m_in_plane_strides;
+  Index m_in_row_strides;
+  Index m_in_col_strides;
+
+  Index m_plane_inflate_strides;
+  Index m_row_inflate_strides;
+  Index m_col_inflate_strides;
+
+  // Cached input size.
+  Index m_inputDepth;
+  Index m_inputPlanes;
+  Index m_inputRows;
+  Index m_inputCols;
+
+  // Other cached variables.
+  Index m_outputPlanesRows;
+
+  // Effective input/patch post-inflation size.
+  Index m_input_planes_eff;
+  Index m_input_rows_eff;
+  Index m_input_cols_eff;
+  Index m_patch_planes_eff;
+  Index m_patch_rows_eff;
+  Index m_patch_cols_eff;
+
+  // Strides for the output tensor.
+  Index m_otherStride;
+  Index m_patchStride;
+  Index m_rowStride;
+  Index m_colStride;
+
+  // Strides for the input tensor.
+  Index m_planeInputStride;
+  Index m_rowInputStride;
+  Index m_colInputStride;
+  Index m_otherInputStride;
+
+  internal::TensorIntDivisor<Index> m_fastOtherStride;
+  internal::TensorIntDivisor<Index> m_fastPatchStride;
+  internal::TensorIntDivisor<Index> m_fastColStride;
+  internal::TensorIntDivisor<Index> m_fastRowStride;
+  internal::TensorIntDivisor<Index> m_fastInputPlaneStride;
+  internal::TensorIntDivisor<Index> m_fastInputRowStride;
+  internal::TensorIntDivisor<Index> m_fastInputColStride;
+  internal::TensorIntDivisor<Index> m_fastInputColsEff;
+  internal::TensorIntDivisor<Index> m_fastOutputPlanesRows;
+  internal::TensorIntDivisor<Index> m_fastOutputPlanes;
+  internal::TensorIntDivisor<Index> m_fastOutputDepth;
+
+  Scalar m_paddingValue;
+
+  TensorEvaluator<ArgType, Device> m_impl;
+
+
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_VOLUME_PATCH_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
new file mode 100644
index 0000000..bc4f202
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/DynamicSymmetry.h
@@ -0,0 +1,293 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+
+namespace Eigen {
+
+class DynamicSGroup
+{
+  public:
+    inline explicit DynamicSGroup() : m_numIndices(1), m_elements(), m_generators(), m_globalFlags(0) { m_elements.push_back(ge(Generator(0, 0, 0))); }
+    inline DynamicSGroup(const DynamicSGroup& o) : m_numIndices(o.m_numIndices), m_elements(o.m_elements), m_generators(o.m_generators), m_globalFlags(o.m_globalFlags) { }
+    inline DynamicSGroup(DynamicSGroup&& o) : m_numIndices(o.m_numIndices), m_elements(), m_generators(o.m_generators), m_globalFlags(o.m_globalFlags) { std::swap(m_elements, o.m_elements); }
+    inline DynamicSGroup& operator=(const DynamicSGroup& o) { m_numIndices = o.m_numIndices; m_elements = o.m_elements; m_generators = o.m_generators; m_globalFlags = o.m_globalFlags; return *this; }
+    inline DynamicSGroup& operator=(DynamicSGroup&& o) { m_numIndices = o.m_numIndices; std::swap(m_elements, o.m_elements); m_generators = o.m_generators; m_globalFlags = o.m_globalFlags; return *this; }
+
+    void add(int one, int two, int flags = 0);
+
+    template<typename Gen_>
+    inline void add(Gen_) { add(Gen_::One, Gen_::Two, Gen_::Flags); }
+    inline void addSymmetry(int one, int two) { add(one, two, 0); }
+    inline void addAntiSymmetry(int one, int two) { add(one, two, NegationFlag); }
+    inline void addHermiticity(int one, int two) { add(one, two, ConjugationFlag); }
+    inline void addAntiHermiticity(int one, int two) { add(one, two, NegationFlag | ConjugationFlag); }
+
+    template<typename Op, typename RV, typename Index, std::size_t N, typename... Args>
+    inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args) const
+    {
+      eigen_assert(N >= m_numIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
+      for (std::size_t i = 0; i < size(); i++)
+        initial = Op::run(h_permute(i, idx, typename internal::gen_numeric_list<int, N>::type()), m_elements[i].flags, initial, std::forward<Args>(args)...);
+      return initial;
+    }
+
+    template<typename Op, typename RV, typename Index, typename... Args>
+    inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args) const
+    {
+      eigen_assert(idx.size() >= m_numIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
+      for (std::size_t i = 0; i < size(); i++)
+        initial = Op::run(h_permute(i, idx), m_elements[i].flags, initial, std::forward<Args>(args)...);
+      return initial;
+    }
+
+    inline int globalFlags() const { return m_globalFlags; }
+    inline std::size_t size() const { return m_elements.size(); }
+
+    template<typename Tensor_, typename... IndexTypes>
+    inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(Tensor_& tensor, typename Tensor_::Index firstIndex, IndexTypes... otherIndices) const
+    {
+      static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+      return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
+    }
+
+    template<typename Tensor_>
+    inline internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup> operator()(Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const
+    {
+      return internal::tensor_symmetry_value_setter<Tensor_, DynamicSGroup>(tensor, *this, indices);
+    }
+  private:
+    struct GroupElement {
+      std::vector<int> representation;
+      int flags;
+      bool isId() const
+      {
+        for (std::size_t i = 0; i < representation.size(); i++)
+          if (i != (size_t)representation[i])
+            return false;
+        return true;
+      }
+    };
+    struct Generator {
+      int one;
+      int two;
+      int flags;
+      constexpr inline Generator(int one_, int two_, int flags_) : one(one_), two(two_), flags(flags_) {}
+    };
+
+    std::size_t m_numIndices;
+    std::vector<GroupElement> m_elements;
+    std::vector<Generator> m_generators;
+    int m_globalFlags;
+
+    template<typename Index, std::size_t N, int... n>
+    inline std::array<Index, N> h_permute(std::size_t which, const std::array<Index, N>& idx, internal::numeric_list<int, n...>) const
+    {
+      return std::array<Index, N>{{ idx[n >= m_numIndices ? n : m_elements[which].representation[n]]... }};
+    }
+
+    template<typename Index>
+    inline std::vector<Index> h_permute(std::size_t which, std::vector<Index> idx) const
+    {
+      std::vector<Index> result;
+      result.reserve(idx.size());
+      for (auto k : m_elements[which].representation)
+        result.push_back(idx[k]);
+      for (std::size_t i = m_numIndices; i < idx.size(); i++)
+        result.push_back(idx[i]);
+      return result;
+    }
+
+    inline GroupElement ge(Generator const& g) const
+    {
+      GroupElement result;
+      result.representation.reserve(m_numIndices);
+      result.flags = g.flags;
+      for (std::size_t k = 0; k < m_numIndices; k++) {
+        if (k == (std::size_t)g.one)
+          result.representation.push_back(g.two);
+        else if (k == (std::size_t)g.two)
+          result.representation.push_back(g.one);
+        else
+          result.representation.push_back(int(k));
+      }
+      return result;
+    }
+
+    GroupElement mul(GroupElement, GroupElement) const;
+    inline GroupElement mul(Generator g1, GroupElement g2) const
+    {
+      return mul(ge(g1), g2);
+    }
+
+    inline GroupElement mul(GroupElement g1, Generator g2) const
+    {
+      return mul(g1, ge(g2));
+    }
+
+    inline GroupElement mul(Generator g1, Generator g2) const
+    {
+      return mul(ge(g1), ge(g2));
+    }
+
+    inline int findElement(GroupElement e) const
+    {
+      for (auto ee : m_elements) {
+        if (ee.representation == e.representation)
+          return ee.flags ^ e.flags;
+      }
+      return -1;
+    }
+
+    void updateGlobalFlags(int flagDiffOfSameGenerator);
+};
+
+// dynamic symmetry group that auto-adds the template parameters in the constructor
+template<typename... Gen>
+class DynamicSGroupFromTemplateArgs : public DynamicSGroup
+{
+  public:
+    inline DynamicSGroupFromTemplateArgs() : DynamicSGroup()
+    {
+      add_all(internal::type_list<Gen...>());
+    }
+    inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs const& other) : DynamicSGroup(other) { }
+    inline DynamicSGroupFromTemplateArgs(DynamicSGroupFromTemplateArgs&& other) : DynamicSGroup(other) { }
+    inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(const DynamicSGroupFromTemplateArgs<Gen...>& o) { DynamicSGroup::operator=(o); return *this; }
+    inline DynamicSGroupFromTemplateArgs<Gen...>& operator=(DynamicSGroupFromTemplateArgs<Gen...>&& o) { DynamicSGroup::operator=(o); return *this; }
+  
+  private:
+    template<typename Gen1, typename... GenNext>
+    inline void add_all(internal::type_list<Gen1, GenNext...>)
+    {
+      add(Gen1());
+      add_all(internal::type_list<GenNext...>());
+    }
+
+    inline void add_all(internal::type_list<>)
+    {
+    }
+};
+
+inline DynamicSGroup::GroupElement DynamicSGroup::mul(GroupElement g1, GroupElement g2) const
+{
+  eigen_internal_assert(g1.representation.size() == m_numIndices);
+  eigen_internal_assert(g2.representation.size() == m_numIndices);
+
+  GroupElement result;
+  result.representation.reserve(m_numIndices);
+  for (std::size_t i = 0; i < m_numIndices; i++) {
+    int v = g2.representation[g1.representation[i]];
+    eigen_assert(v >= 0);
+    result.representation.push_back(v);
+  }
+  result.flags = g1.flags ^ g2.flags;
+  return result;
+}
+
+inline void DynamicSGroup::add(int one, int two, int flags)
+{
+  eigen_assert(one >= 0);
+  eigen_assert(two >= 0);
+  eigen_assert(one != two);
+
+  if ((std::size_t)one >= m_numIndices || (std::size_t)two >= m_numIndices) {
+    std::size_t newNumIndices = (one > two) ? one : two + 1;
+    for (auto& gelem : m_elements) {
+      gelem.representation.reserve(newNumIndices);
+      for (std::size_t i = m_numIndices; i < newNumIndices; i++)
+        gelem.representation.push_back(i);
+    }
+    m_numIndices = newNumIndices;
+  }
+
+  Generator g{one, two, flags};
+  GroupElement e = ge(g);
+
+  /* special case for first generator */
+  if (m_elements.size() == 1) {
+    while (!e.isId()) {
+      m_elements.push_back(e);
+      e = mul(e, g);
+    }
+
+    if (e.flags > 0)
+      updateGlobalFlags(e.flags);
+
+    // only add in case we didn't have identity
+    if (m_elements.size() > 1)
+      m_generators.push_back(g);
+    return;
+  }
+
+  int p = findElement(e);
+  if (p >= 0) {
+    updateGlobalFlags(p);
+    return;
+  }
+
+  std::size_t coset_order = m_elements.size();
+  m_elements.push_back(e);
+  for (std::size_t i = 1; i < coset_order; i++)
+    m_elements.push_back(mul(m_elements[i], e));
+  m_generators.push_back(g);
+
+  std::size_t coset_rep = coset_order;
+  do {
+    for (auto g : m_generators) {
+      e = mul(m_elements[coset_rep], g);
+      p = findElement(e);
+      if (p < 0) {
+        // element not yet in group
+        m_elements.push_back(e);
+        for (std::size_t i = 1; i < coset_order; i++)
+          m_elements.push_back(mul(m_elements[i], e));
+      } else if (p > 0) {
+        updateGlobalFlags(p);
+      }
+    }
+    coset_rep += coset_order;
+  } while (coset_rep < m_elements.size());
+}
+
+inline void DynamicSGroup::updateGlobalFlags(int flagDiffOfSameGenerator)
+{
+    switch (flagDiffOfSameGenerator) {
+      case 0:
+      default:
+        // nothing happened
+        break;
+      case NegationFlag:
+        // every element is it's own negative => whole tensor is zero
+        m_globalFlags |= GlobalZeroFlag;
+        break;
+      case ConjugationFlag:
+        // every element is it's own conjugate => whole tensor is real
+        m_globalFlags |= GlobalRealFlag;
+        break;
+      case (NegationFlag | ConjugationFlag):
+        // every element is it's own negative conjugate => whole tensor is imaginary
+        m_globalFlags |= GlobalImagFlag;
+        break;
+      /* NOTE:
+       *   since GlobalZeroFlag == GlobalRealFlag | GlobalImagFlag, if one generator
+       *   causes the tensor to be real and the next one to be imaginary, this will
+       *   trivially give the correct result
+       */
+    }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSORSYMMETRY_DYNAMICSYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
new file mode 100644
index 0000000..942293b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/StaticSymmetry.h
@@ -0,0 +1,236 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename list> struct tensor_static_symgroup_permutate;
+
+template<int... nn>
+struct tensor_static_symgroup_permutate<numeric_list<int, nn...>>
+{
+  constexpr static std::size_t N = sizeof...(nn);
+
+  template<typename T>
+  constexpr static inline std::array<T, N> run(const std::array<T, N>& indices)
+  {
+    return {{indices[nn]...}};
+  }
+};
+
+template<typename indices_, int flags_>
+struct tensor_static_symgroup_element
+{
+  typedef indices_ indices;
+  constexpr static int flags = flags_;
+};
+
+template<typename Gen, int N>
+struct tensor_static_symgroup_element_ctor
+{
+  typedef tensor_static_symgroup_element<
+    typename gen_numeric_list_swapped_pair<int, N, Gen::One, Gen::Two>::type,
+    Gen::Flags
+  > type;
+};
+
+template<int N>
+struct tensor_static_symgroup_identity_ctor
+{
+  typedef tensor_static_symgroup_element<
+    typename gen_numeric_list<int, N>::type,
+    0
+  > type;
+};
+
+template<typename iib>
+struct tensor_static_symgroup_multiply_helper
+{
+  template<int... iia>
+  constexpr static inline numeric_list<int, get<iia, iib>::value...> helper(numeric_list<int, iia...>) {
+    return numeric_list<int, get<iia, iib>::value...>();
+  }
+};
+
+template<typename A, typename B>
+struct tensor_static_symgroup_multiply
+{
+  private:
+    typedef typename A::indices iia;
+    typedef typename B::indices iib;
+    constexpr static int ffa = A::flags;
+    constexpr static int ffb = B::flags;
+  
+  public:
+    static_assert(iia::count == iib::count, "Cannot multiply symmetry elements with different number of indices.");
+
+    typedef tensor_static_symgroup_element<
+      decltype(tensor_static_symgroup_multiply_helper<iib>::helper(iia())),
+      ffa ^ ffb
+    > type;
+};
+
+template<typename A, typename B>
+struct tensor_static_symgroup_equality
+{
+    typedef typename A::indices iia;
+    typedef typename B::indices iib;
+    constexpr static int ffa = A::flags;
+    constexpr static int ffb = B::flags;
+    static_assert(iia::count == iib::count, "Cannot compare symmetry elements with different number of indices.");
+
+    constexpr static bool value = is_same<iia, iib>::value;
+
+  private:
+    /* this should be zero if they are identical, or else the tensor
+     * will be forced to be pure real, pure imaginary or even pure zero
+     */
+    constexpr static int flags_cmp_ = ffa ^ ffb;
+
+    /* either they are not equal, then we don't care whether the flags
+     * match, or they are equal, and then we have to check
+     */
+    constexpr static bool is_zero      = value && flags_cmp_ == NegationFlag;
+    constexpr static bool is_real      = value && flags_cmp_ == ConjugationFlag;
+    constexpr static bool is_imag      = value && flags_cmp_ == (NegationFlag | ConjugationFlag);
+
+  public:
+    constexpr static int global_flags = 
+      (is_real ? GlobalRealFlag : 0) |
+      (is_imag ? GlobalImagFlag : 0) |
+      (is_zero ? GlobalZeroFlag : 0);
+};
+
+template<std::size_t NumIndices, typename... Gen>
+struct tensor_static_symgroup
+{
+  typedef StaticSGroup<Gen...> type;
+  constexpr static std::size_t size = type::static_size;
+};
+
+template<typename Index, std::size_t N, int... ii, int... jj>
+constexpr static inline std::array<Index, N> tensor_static_symgroup_index_permute(std::array<Index, N> idx, internal::numeric_list<int, ii...>, internal::numeric_list<int, jj...>)
+{
+  return {{ idx[ii]..., idx[jj]... }};
+}
+
+template<typename Index, int... ii>
+static inline std::vector<Index> tensor_static_symgroup_index_permute(std::vector<Index> idx, internal::numeric_list<int, ii...>)
+{
+  std::vector<Index> result{{ idx[ii]... }};
+  std::size_t target_size = idx.size();
+  for (std::size_t i = result.size(); i < target_size; i++)
+    result.push_back(idx[i]);
+  return result;
+}
+
+template<typename T> struct tensor_static_symgroup_do_apply;
+
+template<typename first, typename... next>
+struct tensor_static_symgroup_do_apply<internal::type_list<first, next...>>
+{
+  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices, typename... Args>
+  static inline RV run(const std::array<Index, NumIndices>& idx, RV initial, Args&&... args)
+  {
+    static_assert(NumIndices >= SGNumIndices, "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    typedef typename internal::gen_numeric_list<int, NumIndices - SGNumIndices, SGNumIndices>::type remaining_indices;
+    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices(), remaining_indices()), first::flags, initial, std::forward<Args>(args)...);
+    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(idx, initial, args...);
+  }
+
+  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
+  static inline RV run(const std::vector<Index>& idx, RV initial, Args&&... args)
+  {
+    eigen_assert(idx.size() >= SGNumIndices && "Can only apply symmetry group to objects that have at least the required amount of indices.");
+    initial = Op::run(tensor_static_symgroup_index_permute(idx, typename first::indices()), first::flags, initial, std::forward<Args>(args)...);
+    return tensor_static_symgroup_do_apply<internal::type_list<next...>>::template run<Op, RV, SGNumIndices>(idx, initial, args...);
+  }
+};
+
+template<EIGEN_TPL_PP_SPEC_HACK_DEF(typename, empty)>
+struct tensor_static_symgroup_do_apply<internal::type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>>
+{
+  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, std::size_t NumIndices, typename... Args>
+  static inline RV run(const std::array<Index, NumIndices>&, RV initial, Args&&...)
+  {
+    // do nothing
+    return initial;
+  }
+
+  template<typename Op, typename RV, std::size_t SGNumIndices, typename Index, typename... Args>
+  static inline RV run(const std::vector<Index>&, RV initial, Args&&...)
+  {
+    // do nothing
+    return initial;
+  }
+};
+
+} // end namespace internal
+
+template<typename... Gen>
+class StaticSGroup
+{
+    constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
+    typedef internal::group_theory::enumerate_group_elements<
+      internal::tensor_static_symgroup_multiply,
+      internal::tensor_static_symgroup_equality,
+      typename internal::tensor_static_symgroup_identity_ctor<NumIndices>::type,
+      internal::type_list<typename internal::tensor_static_symgroup_element_ctor<Gen, NumIndices>::type...>
+    > group_elements;
+    typedef typename group_elements::type ge;
+  public:
+    constexpr inline StaticSGroup() {}
+    constexpr inline StaticSGroup(const StaticSGroup<Gen...>&) {}
+    constexpr inline StaticSGroup(StaticSGroup<Gen...>&&) {}
+
+    template<typename Op, typename RV, typename Index, std::size_t N, typename... Args>
+    static inline RV apply(const std::array<Index, N>& idx, RV initial, Args&&... args)
+    {
+      return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
+    }
+
+    template<typename Op, typename RV, typename Index, typename... Args>
+    static inline RV apply(const std::vector<Index>& idx, RV initial, Args&&... args)
+    {
+      eigen_assert(idx.size() == NumIndices);
+      return internal::tensor_static_symgroup_do_apply<ge>::template run<Op, RV, NumIndices>(idx, initial, args...);
+    }
+
+    constexpr static std::size_t static_size = ge::count;
+
+    constexpr static inline std::size_t size() {
+      return ge::count;
+    }
+    constexpr static inline int globalFlags() { return group_elements::global_flags; }
+
+    template<typename Tensor_, typename... IndexTypes>
+    inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(Tensor_& tensor, typename Tensor_::Index firstIndex, IndexTypes... otherIndices) const
+    {
+      static_assert(sizeof...(otherIndices) + 1 == Tensor_::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
+      return operator()(tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices>{{firstIndex, otherIndices...}});
+    }
+
+    template<typename Tensor_>
+    inline internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>> operator()(Tensor_& tensor, std::array<typename Tensor_::Index, Tensor_::NumIndices> const& indices) const
+    {
+      return internal::tensor_symmetry_value_setter<Tensor_, StaticSGroup<Gen...>>(tensor, *this, indices);
+    }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSORSYMMETRY_STATICSYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
new file mode 100644
index 0000000..879d6cd
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/Symmetry.h
@@ -0,0 +1,338 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+
+namespace Eigen {
+
+enum {
+  NegationFlag           = 0x01,
+  ConjugationFlag        = 0x02
+};
+
+enum {
+  GlobalRealFlag         = 0x01,
+  GlobalImagFlag         = 0x02,
+  GlobalZeroFlag         = 0x03
+};
+
+namespace internal {
+
+template<std::size_t NumIndices, typename... Sym>                   struct tensor_symmetry_pre_analysis;
+template<std::size_t NumIndices, typename... Sym>                   struct tensor_static_symgroup;
+template<bool instantiate, std::size_t NumIndices, typename... Sym> struct tensor_static_symgroup_if;
+template<typename Tensor_> struct tensor_symmetry_calculate_flags;
+template<typename Tensor_> struct tensor_symmetry_assign_value;
+template<typename... Sym> struct tensor_symmetry_num_indices;
+
+} // end namespace internal
+
+template<int One_, int Two_>
+struct Symmetry
+{
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = 0;
+};
+
+template<int One_, int Two_>
+struct AntiSymmetry
+{
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = NegationFlag;
+};
+
+template<int One_, int Two_>
+struct Hermiticity
+{
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = ConjugationFlag;
+};
+
+template<int One_, int Two_>
+struct AntiHermiticity
+{
+  static_assert(One_ != Two_, "Symmetries must cover distinct indices.");
+  constexpr static int One = One_;
+  constexpr static int Two = Two_;
+  constexpr static int Flags = ConjugationFlag | NegationFlag;
+};
+
+/** \class DynamicSGroup
+  * \ingroup TensorSymmetry_Module
+  *
+  * \brief Dynamic symmetry group
+  *
+  * The %DynamicSGroup class represents a symmetry group that need not be known at
+  * compile time. It is useful if one wants to support arbitrary run-time defineable
+  * symmetries for tensors, but it is also instantiated if a symmetry group is defined
+  * at compile time that would be either too large for the compiler to reasonably
+  * generate (using templates to calculate this at compile time is very inefficient)
+  * or that the compiler could generate the group but that it wouldn't make sense to
+  * unroll the loop for setting coefficients anymore.
+  */
+class DynamicSGroup;
+
+/** \internal
+  *
+  * \class DynamicSGroupFromTemplateArgs
+  * \ingroup TensorSymmetry_Module
+  *
+  * \brief Dynamic symmetry group, initialized from template arguments
+  *
+  * This class is a child class of DynamicSGroup. It uses the template arguments
+  * specified to initialize itself.
+  */
+template<typename... Gen>
+class DynamicSGroupFromTemplateArgs;
+
+/** \class StaticSGroup
+  * \ingroup TensorSymmetry_Module
+  *
+  * \brief Static symmetry group
+  *
+  * This class represents a symmetry group that is known and resolved completely
+  * at compile time. Ideally, no run-time penalty is incurred compared to the
+  * manual unrolling of the symmetry.
+  *
+  * <b><i>CAUTION:</i></b>
+  *
+  * Do not use this class directly for large symmetry groups. The compiler
+  * may run into a limit, or segfault or in the very least will take a very,
+  * very, very long time to compile the code. Use the SGroup class instead
+  * if you want a static group. That class contains logic that will
+  * automatically select the DynamicSGroup class instead if the symmetry
+  * group becomes too large. (In that case, unrolling may not even be
+  * beneficial.)
+  */
+template<typename... Gen>
+class StaticSGroup;
+
+/** \class SGroup
+  * \ingroup TensorSymmetry_Module
+  *
+  * \brief Symmetry group, initialized from template arguments
+  *
+  * This class represents a symmetry group whose generators are already
+  * known at compile time. It may or may not be resolved at compile time,
+  * depending on the estimated size of the group.
+  *
+  * \sa StaticSGroup
+  * \sa DynamicSGroup
+  */
+template<typename... Gen>
+class SGroup : public internal::tensor_symmetry_pre_analysis<internal::tensor_symmetry_num_indices<Gen...>::value, Gen...>::root_type
+{
+  public:
+    constexpr static std::size_t NumIndices = internal::tensor_symmetry_num_indices<Gen...>::value;
+    typedef typename internal::tensor_symmetry_pre_analysis<NumIndices, Gen...>::root_type Base;
+
+    // make standard constructors + assignment operators public
+    inline SGroup() : Base() { }
+    inline SGroup(const SGroup<Gen...>& other) : Base(other) { }
+    inline SGroup(SGroup<Gen...>&& other) : Base(other) { }
+    inline SGroup<Gen...>& operator=(const SGroup<Gen...>& other) { Base::operator=(other); return *this; }
+    inline SGroup<Gen...>& operator=(SGroup<Gen...>&& other) { Base::operator=(other); return *this; }
+
+    // all else is defined in the base class
+};
+
+namespace internal {
+
+template<typename... Sym> struct tensor_symmetry_num_indices
+{
+  constexpr static std::size_t value = 1;
+};
+
+template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...>
+{
+private:
+  constexpr static std::size_t One = static_cast<std::size_t>(One_);
+  constexpr static std::size_t Two = static_cast<std::size_t>(Two_);
+  constexpr static std::size_t Three = tensor_symmetry_num_indices<Sym...>::value;
+
+  // don't use std::max, since it's not constexpr until C++14...
+  constexpr static std::size_t maxOneTwoPlusOne = ((One > Two) ? One : Two) + 1;
+public:
+  constexpr static std::size_t value = (maxOneTwoPlusOne > Three) ? maxOneTwoPlusOne : Three;
+};
+
+template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<AntiSymmetry<One_, Two_>, Sym...>
+  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<Hermiticity<One_, Two_>, Sym...>
+  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+template<int One_, int Two_, typename... Sym> struct tensor_symmetry_num_indices<AntiHermiticity<One_, Two_>, Sym...>
+  : public tensor_symmetry_num_indices<Symmetry<One_, Two_>, Sym...> {};
+
+/** \internal
+  *
+  * \class tensor_symmetry_pre_analysis
+  * \ingroup TensorSymmetry_Module
+  *
+  * \brief Pre-select whether to use a static or dynamic symmetry group
+  *
+  * When a symmetry group could in principle be determined at compile time,
+  * this template implements the logic whether to actually do that or whether
+  * to rather defer that to runtime.
+  *
+  * The logic is as follows:
+  * <dl>
+  * <dt><b>No generators (trivial symmetry):</b></dt>
+  * <dd>Use a trivial static group. Ideally, this has no performance impact
+  *     compared to not using symmetry at all. In practice, this might not
+  *     be the case.</dd>
+  * <dt><b>More than 4 generators:</b></dt>
+  * <dd>Calculate the group at run time, it is likely far too large for the
+  *     compiler to be able to properly generate it in a realistic time.</dd>
+  * <dt><b>Up to and including 4 generators:</b></dt>
+  * <dd>Actually enumerate all group elements, but then check how many there
+  *     are. If there are more than 16, it is unlikely that unrolling the
+  *     loop (as is done in the static compile-time case) is sensible, so
+  *     use a dynamic group instead. If there are at most 16 elements, actually
+  *     use that static group. Note that the largest group with 4 generators
+  *     still compiles with reasonable resources.</dd>
+  * </dl>
+  *
+  * Note: Example compile time performance with g++-4.6 on an Intenl Core i5-3470
+  *       with 16 GiB RAM (all generators non-redundant and the subgroups don't
+  *       factorize):
+  *
+  *          # Generators          -O0 -ggdb               -O2
+  *          -------------------------------------------------------------------
+  *          1                 0.5 s  /   250 MiB     0.45s /   230 MiB
+  *          2                 0.5 s  /   260 MiB     0.5 s /   250 MiB
+  *          3                 0.65s  /   310 MiB     0.62s /   310 MiB
+  *          4                 2.2 s  /   860 MiB     1.7 s /   770 MiB
+  *          5               130   s  / 13000 MiB   120   s / 11000 MiB
+  *
+  * It is clear that everything is still very efficient up to 4 generators, then
+  * the memory and CPU requirements become unreasonable. Thus we only instantiate
+  * the template group theory logic if the number of generators supplied is 4 or
+  * lower, otherwise this will be forced to be done during runtime, where the
+  * algorithm is reasonably fast.
+  */
+template<std::size_t NumIndices>
+struct tensor_symmetry_pre_analysis<NumIndices>
+{
+  typedef StaticSGroup<> root_type;
+};
+
+template<std::size_t NumIndices, typename Gen_, typename... Gens_>
+struct tensor_symmetry_pre_analysis<NumIndices, Gen_, Gens_...>
+{
+  constexpr static std::size_t max_static_generators = 4;
+  constexpr static std::size_t max_static_elements = 16;
+  typedef tensor_static_symgroup_if<(sizeof...(Gens_) + 1 <= max_static_generators), NumIndices, Gen_, Gens_...> helper;
+  constexpr static std::size_t possible_size = helper::size;
+
+  typedef typename conditional<
+    possible_size == 0 || possible_size >= max_static_elements,
+    DynamicSGroupFromTemplateArgs<Gen_, Gens_...>,
+    typename helper::type
+  >::type root_type;
+};
+
+template<bool instantiate, std::size_t NumIndices, typename... Gens>
+struct tensor_static_symgroup_if
+{
+  constexpr static std::size_t size = 0;
+  typedef void type;
+};
+
+template<std::size_t NumIndices, typename... Gens>
+struct tensor_static_symgroup_if<true, NumIndices, Gens...> : tensor_static_symgroup<NumIndices, Gens...> {};
+
+template<typename Tensor_>
+struct tensor_symmetry_assign_value
+{
+  typedef typename Tensor_::Index Index;
+  typedef typename Tensor_::Scalar Scalar;
+  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transformation_flags, int dummy, Tensor_& tensor, const Scalar& value_)
+  {
+    Scalar value(value_);
+    if (transformation_flags & ConjugationFlag)
+      value = numext::conj(value);
+    if (transformation_flags & NegationFlag)
+      value = -value;
+    tensor.coeffRef(transformed_indices) = value;
+    return dummy;
+  }
+};
+
+template<typename Tensor_>
+struct tensor_symmetry_calculate_flags
+{
+  typedef typename Tensor_::Index Index;
+  constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+  static inline int run(const std::array<Index, NumIndices>& transformed_indices, int transform_flags, int current_flags, const std::array<Index, NumIndices>& orig_indices)
+  {
+    if (transformed_indices == orig_indices) {
+      if (transform_flags & (ConjugationFlag | NegationFlag))
+        return current_flags | GlobalImagFlag; // anti-hermitian diagonal
+      else if (transform_flags & ConjugationFlag)
+        return current_flags | GlobalRealFlag; // hermitian diagonal
+      else if (transform_flags & NegationFlag)
+        return current_flags | GlobalZeroFlag; // anti-symmetric diagonal
+    }
+    return current_flags;
+  }
+};
+
+template<typename Tensor_, typename Symmetry_, int Flags = 0>
+class tensor_symmetry_value_setter
+{
+  public:
+    typedef typename Tensor_::Index Index;
+    typedef typename Tensor_::Scalar Scalar;
+    constexpr static std::size_t NumIndices = Tensor_::NumIndices;
+
+    inline tensor_symmetry_value_setter(Tensor_& tensor, Symmetry_ const& symmetry, std::array<Index, NumIndices> const& indices)
+      : m_tensor(tensor), m_symmetry(symmetry), m_indices(indices) { }
+
+    inline tensor_symmetry_value_setter<Tensor_, Symmetry_, Flags>& operator=(Scalar const& value)
+    {
+      doAssign(value);
+      return *this;
+    }
+  private:
+    Tensor_& m_tensor;
+    Symmetry_ m_symmetry;
+    std::array<Index, NumIndices> m_indices;
+
+    inline void doAssign(Scalar const& value)
+    {
+      #ifdef EIGEN_TENSOR_SYMMETRY_CHECK_VALUES
+        int value_flags = m_symmetry.template apply<internal::tensor_symmetry_calculate_flags<Tensor_>, int>(m_indices, m_symmetry.globalFlags(), m_indices);
+        if (value_flags & GlobalRealFlag)
+          eigen_assert(numext::imag(value) == 0);
+        if (value_flags & GlobalImagFlag)
+          eigen_assert(numext::real(value) == 0);
+      #endif
+      m_symmetry.template apply<internal::tensor_symmetry_assign_value<Tensor_>, int>(m_indices, 0, m_tensor, value);
+    }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSORSYMMETRY_SYMMETRY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
new file mode 100644
index 0000000..54bf9db
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
@@ -0,0 +1,669 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+#define EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+
+namespace Eigen {
+
+namespace internal {
+
+namespace group_theory {
+
+/** \internal
+  * \file CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h
+  * This file contains C++ templates that implement group theory algorithms.
+  *
+  * The algorithms allow for a compile-time analysis of finite groups.
+  *
+  * Currently only Dimino's algorithm is implemented, which returns a list
+  * of all elements in a group given a set of (possibly redundant) generators.
+  * (One could also do that with the so-called orbital algorithm, but that
+  * is much more expensive and usually has no advantages.)
+  */
+
+/**********************************************************************
+ *                "Ok kid, here is where it gets complicated."
+ *                         - Amelia Pond in the "Doctor Who" episode
+ *                           "The Big Bang"
+ *
+ * Dimino's algorithm
+ * ==================
+ *
+ * The following is Dimino's algorithm in sequential form:
+ *
+ * Input: identity element, list of generators, equality check,
+ *        multiplication operation
+ * Output: list of group elements
+ *
+ * 1. add identity element
+ * 2. remove identities from list of generators
+ * 3. add all powers of first generator that aren't the
+ *    identity element
+ * 4. go through all remaining generators:
+ *        a. if generator is already in the list of elements
+ *                -> do nothing
+ *        b. otherwise
+ *                i.   remember current # of elements
+ *                     (i.e. the size of the current subgroup)
+ *                ii.  add all current elements (which includes
+ *                     the identity) each multiplied from right
+ *                     with the current generator to the group
+ *                iii. add all remaining cosets that are generated
+ *                     by products of the new generator with itself
+ *                     and all other generators seen so far
+ *
+ * In functional form, this is implemented as a long set of recursive
+ * templates that have a complicated relationship.
+ *
+ * The main interface for Dimino's algorithm is the template
+ * enumerate_group_elements. All lists are implemented as variadic
+ * type_list<typename...> and numeric_list<typename = int, int...>
+ * templates.
+ *
+ * 'Calling' templates is usually done via typedefs.
+ *
+ * This algorithm is an extended version of the basic version. The
+ * extension consists in the fact that each group element has a set
+ * of flags associated with it. Multiplication of two group elements
+ * with each other results in a group element whose flags are the
+ * XOR of the flags of the previous elements. Each time the algorithm
+ * notices that a group element it just calculated is already in the
+ * list of current elements, the flags of both will be compared and
+ * added to the so-called 'global flags' of the group.
+ *
+ * The rationale behind this extension is that this allows not only
+ * for the description of symmetries between tensor indices, but
+ * also allows for the description of hermiticity, antisymmetry and
+ * antihermiticity. Negation and conjugation each are specific bit
+ * in the flags value and if two different ways to reach a group
+ * element lead to two different flags, this poses a constraint on
+ * the allowed values of the resulting tensor. For example, if a
+ * group element is reach both with and without the conjugation
+ * flags, it is clear that the resulting tensor has to be real.
+ *
+ * Note that this flag mechanism is quite generic and may have other
+ * uses beyond tensor properties.
+ *
+ * IMPORTANT: 
+ *     This algorithm assumes the group to be finite. If you try to
+ *     run it with a group that's infinite, the algorithm will only
+ *     terminate once you hit a compiler limit (max template depth).
+ *     Also note that trying to use this implementation to create a
+ *     very large group will probably either make you hit the same
+ *     limit, cause the compiler to segfault or at the very least
+ *     take a *really* long time (hours, days, weeks - sic!) to
+ *     compile. It is not recommended to plug in more than 4
+ *     generators, unless they are independent of each other.
+ */
+
+/** \internal
+  *
+  * \class strip_identities
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Cleanse a list of group elements of the identity element
+  *
+  * This template is used to make a first pass through all initial
+  * generators of Dimino's algorithm and remove the identity
+  * elements.
+  *
+  * \sa enumerate_group_elements
+  */
+template<template<typename, typename> class Equality, typename id, typename L> struct strip_identities;
+
+template<
+  template<typename, typename> class Equality,
+  typename id,
+  typename t,
+  typename... ts
+>
+struct strip_identities<Equality, id, type_list<t, ts...>>
+{
+  typedef typename conditional<
+    Equality<id, t>::value,
+    typename strip_identities<Equality, id, type_list<ts...>>::type,
+    typename concat<type_list<t>, typename strip_identities<Equality, id, type_list<ts...>>::type>::type
+  >::type type;
+  constexpr static int global_flags = Equality<id, t>::global_flags | strip_identities<Equality, id, type_list<ts...>>::global_flags;
+};
+
+template<
+  template<typename, typename> class Equality,
+  typename id
+  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, ts)
+>
+struct strip_identities<Equality, id, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(ts)>>
+{
+  typedef type_list<> type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+  *
+  * \class dimino_first_step_elements_helper 
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Recursive template that adds powers of the first generator to the list of group elements
+  *
+  * This template calls itself recursively to add powers of the first
+  * generator to the list of group elements. It stops if it reaches
+  * the identity element again.
+  *
+  * \sa enumerate_group_elements, dimino_first_step_elements
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename g,
+  typename current_element,
+  typename elements,
+  bool dont_add_current_element   // = false
+>
+struct dimino_first_step_elements_helper
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  : // recursive inheritance is too difficult for Doxygen
+  public dimino_first_step_elements_helper<
+    Multiply,
+    Equality,
+    id,
+    g,
+    typename Multiply<current_element, g>::type,
+    typename concat<elements, type_list<current_element>>::type,
+    Equality<typename Multiply<current_element, g>::type, id>::value
+  > {};
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename g,
+  typename current_element,
+  typename elements
+>
+struct dimino_first_step_elements_helper<Multiply, Equality, id, g, current_element, elements, true>
+#endif // EIGEN_PARSED_BY_DOXYGEN
+{
+  typedef elements type;
+  constexpr static int global_flags = Equality<current_element, id>::global_flags;
+};
+
+/** \internal
+  *
+  * \class dimino_first_step_elements
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Add all powers of the first generator to the list of group elements
+  *
+  * This template takes the first non-identity generator and generates the initial
+  * list of elements which consists of all powers of that generator. For a group
+  * with just one generated, it would be enumerated after this.
+  *
+  * \sa enumerate_group_elements
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename generators
+>
+struct dimino_first_step_elements
+{
+  typedef typename get<0, generators>::type first_generator;
+  typedef typename skip<1, generators>::type next_generators;
+  typedef type_list<first_generator> generators_done;
+
+  typedef dimino_first_step_elements_helper<
+    Multiply,
+    Equality,
+    id,
+    first_generator,
+    first_generator,
+    type_list<id>,
+    false
+  > helper;
+  typedef typename helper::type type;
+  constexpr static int global_flags = helper::global_flags;
+};
+
+/** \internal
+  *
+  * \class dimino_get_coset_elements
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Generate all elements of a specific coset
+  *
+  * This template generates all the elements of a specific coset by
+  * multiplying all elements in the given subgroup with the new
+  * coset representative. Note that the first element of the
+  * subgroup is always the identity element, so the first element of
+  * the result of this template is going to be the coset
+  * representative itself.
+  *
+  * Note that this template accepts an additional boolean parameter
+  * that specifies whether to actually generate the coset (true) or
+  * just return an empty list (false).
+  *
+  * \sa enumerate_group_elements, dimino_add_cosets_for_rep
+  */
+template<
+  template<typename, typename> class Multiply,
+  typename sub_group_elements,
+  typename new_coset_rep,
+  bool generate_coset      // = true
+>
+struct dimino_get_coset_elements
+{
+  typedef typename apply_op_from_right<Multiply, new_coset_rep, sub_group_elements>::type type;
+};
+
+template<
+  template<typename, typename> class Multiply,
+  typename sub_group_elements,
+  typename new_coset_rep
+>
+struct dimino_get_coset_elements<Multiply, sub_group_elements, new_coset_rep, false>
+{
+  typedef type_list<> type;
+};
+
+/** \internal
+  *
+  * \class dimino_add_cosets_for_rep
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Recursive template for adding coset spaces
+  *
+  * This template multiplies the coset representative with a generator
+  * from the list of previous generators. If the new element is not in
+  * the group already, it adds the corresponding coset. Finally it
+  * proceeds to call itself with the next generator from the list.
+  *
+  * \sa enumerate_group_elements, dimino_add_all_coset_spaces
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename sub_group_elements,
+  typename elements,
+  typename generators,
+  typename rep_element,
+  int sub_group_size
+>
+struct dimino_add_cosets_for_rep;
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename sub_group_elements,
+  typename elements,
+  typename g,
+  typename... gs,
+  typename rep_element,
+  int sub_group_size
+>
+struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, type_list<g, gs...>, rep_element, sub_group_size>
+{
+  typedef typename Multiply<rep_element, g>::type new_coset_rep;
+  typedef contained_in_list_gf<Equality, new_coset_rep, elements> _cil;
+  constexpr static bool add_coset = !_cil::value;
+
+  typedef typename dimino_get_coset_elements<
+    Multiply,
+    sub_group_elements,
+    new_coset_rep,
+    add_coset
+  >::type coset_elements;
+
+  typedef dimino_add_cosets_for_rep<
+    Multiply,
+    Equality,
+    id,
+    sub_group_elements,
+    typename concat<elements, coset_elements>::type,
+    type_list<gs...>,
+    rep_element,
+    sub_group_size
+  > _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _cil::global_flags | _helper::global_flags;
+
+  /* Note that we don't have to update global flags here, since
+   * we will only add these elements if they are not part of
+   * the group already. But that only happens if the coset rep
+   * is not already in the group, so the check for the coset rep
+   * will catch this.
+   */
+};
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename sub_group_elements,
+  typename elements
+  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty),
+  typename rep_element,
+  int sub_group_size
+>
+struct dimino_add_cosets_for_rep<Multiply, Equality, id, sub_group_elements, elements, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, rep_element, sub_group_size>
+{
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+  *
+  * \class dimino_add_all_coset_spaces
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Recursive template for adding all coset spaces for a new generator
+  *
+  * This template tries to go through the list of generators (with
+  * the help of the dimino_add_cosets_for_rep template) as long as
+  * it still finds elements that are not part of the group and add
+  * the corresponding cosets.
+  *
+  * \sa enumerate_group_elements, dimino_add_cosets_for_rep
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename sub_group_elements,
+  typename elements,
+  typename generators,
+  int sub_group_size,
+  int rep_pos,
+  bool stop_condition        // = false
+>
+struct dimino_add_all_coset_spaces
+{
+  typedef typename get<rep_pos, elements>::type rep_element;
+  typedef dimino_add_cosets_for_rep<
+    Multiply,
+    Equality,
+    id,
+    sub_group_elements,
+    elements,
+    generators,
+    rep_element,
+    sub_group_elements::count
+  > _ac4r;
+  typedef typename _ac4r::type new_elements;
+  
+  constexpr static int new_rep_pos = rep_pos + sub_group_elements::count;
+  constexpr static bool new_stop_condition = new_rep_pos >= new_elements::count;
+
+  typedef dimino_add_all_coset_spaces<
+    Multiply,
+    Equality,
+    id,
+    sub_group_elements,
+    new_elements,
+    generators,
+    sub_group_size,
+    new_rep_pos,
+    new_stop_condition
+  > _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _helper::global_flags | _ac4r::global_flags;
+};
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename sub_group_elements,
+  typename elements,
+  typename generators,
+  int sub_group_size,
+  int rep_pos
+>
+struct dimino_add_all_coset_spaces<Multiply, Equality, id, sub_group_elements, elements, generators, sub_group_size, rep_pos, true>
+{
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+  *
+  * \class dimino_add_generator
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Enlarge the group by adding a new generator.
+  *
+  * It accepts a boolean parameter that determines if the generator is redundant,
+  * i.e. was already seen in the group. In that case, it reduces to a no-op.
+  *
+  * \sa enumerate_group_elements, dimino_add_all_coset_spaces
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename elements,
+  typename generators_done,
+  typename current_generator,
+  bool redundant          // = false
+>
+struct dimino_add_generator
+{
+  /* this template is only called if the generator is not redundant
+   * => all elements of the group multiplied with the new generator
+   *    are going to be new elements of the most trivial coset space
+   */
+  typedef typename apply_op_from_right<Multiply, current_generator, elements>::type multiplied_elements;
+  typedef typename concat<elements, multiplied_elements>::type new_elements;
+
+  constexpr static int rep_pos = elements::count;
+
+  typedef dimino_add_all_coset_spaces<
+    Multiply,
+    Equality,
+    id,
+    elements, // elements of previous subgroup
+    new_elements,
+    typename concat<generators_done, type_list<current_generator>>::type,
+    elements::count, // size of previous subgroup
+    rep_pos,
+    false // don't stop (because rep_pos >= new_elements::count is always false at this point)
+  > _helper;
+  typedef typename _helper::type type;
+  constexpr static int global_flags = _helper::global_flags;
+};
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename elements,
+  typename generators_done,
+  typename current_generator
+>
+struct dimino_add_generator<Multiply, Equality, id, elements, generators_done, current_generator, true>
+{
+  // redundant case
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+  *
+  * \class dimino_add_remaining_generators
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Recursive template that adds all remaining generators to a group
+  *
+  * Loop through the list of generators that remain and successively
+  * add them to the group.
+  *
+  * \sa enumerate_group_elements, dimino_add_generator
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename generators_done,
+  typename remaining_generators,
+  typename elements
+>
+struct dimino_add_remaining_generators
+{
+  typedef typename get<0, remaining_generators>::type first_generator;
+  typedef typename skip<1, remaining_generators>::type next_generators;
+
+  typedef contained_in_list_gf<Equality, first_generator, elements> _cil;
+
+  typedef dimino_add_generator<
+    Multiply,
+    Equality,
+    id,
+    elements,
+    generators_done,
+    first_generator,
+    _cil::value
+  > _helper;
+
+  typedef typename _helper::type new_elements;
+
+  typedef dimino_add_remaining_generators<
+    Multiply,
+    Equality,
+    id,
+    typename concat<generators_done, type_list<first_generator>>::type,
+    next_generators,
+    new_elements
+  > _next_iter;
+
+  typedef typename _next_iter::type type;
+  constexpr static int global_flags =
+    _cil::global_flags |
+    _helper::global_flags |
+    _next_iter::global_flags;
+};
+
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename generators_done,
+  typename elements
+>
+struct dimino_add_remaining_generators<Multiply, Equality, id, generators_done, type_list<>, elements>
+{
+  typedef elements type;
+  constexpr static int global_flags = 0;
+};
+
+/** \internal
+  *
+  * \class enumerate_group_elements_noid
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Helper template that implements group element enumeration
+  *
+  * This is a helper template that implements the actual enumeration
+  * of group elements. This has been split so that the list of
+  * generators can be cleansed of the identity element before
+  * performing the actual operation.
+  *
+  * \sa enumerate_group_elements
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename generators,
+  int initial_global_flags = 0
+>
+struct enumerate_group_elements_noid
+{
+  typedef dimino_first_step_elements<Multiply, Equality, id, generators> first_step;
+  typedef typename first_step::type first_step_elements;
+
+  typedef dimino_add_remaining_generators<
+    Multiply,
+    Equality,
+    id,
+    typename first_step::generators_done,
+    typename first_step::next_generators, // remaining_generators
+    typename first_step::type // first_step elements
+  > _helper;
+
+  typedef typename _helper::type type;
+  constexpr static int global_flags =
+    initial_global_flags |
+    first_step::global_flags |
+    _helper::global_flags;
+};
+
+// in case when no generators are specified
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  int initial_global_flags
+>
+struct enumerate_group_elements_noid<Multiply, Equality, id, type_list<>, initial_global_flags>
+{
+  typedef type_list<id> type;
+  constexpr static int global_flags = initial_global_flags;
+};
+
+/** \internal
+  *
+  * \class enumerate_group_elements
+  * \ingroup CXX11_TensorSymmetry_Module
+  *
+  * \brief Enumerate all elements in a finite group
+  *
+  * This template enumerates all elements in a finite group. It accepts
+  * the following template parameters:
+  *
+  * \tparam Multiply      The multiplication operation that multiplies two group elements
+  *                       with each other.
+  * \tparam Equality      The equality check operation that checks if two group elements
+  *                       are equal to another.
+  * \tparam id            The identity element
+  * \tparam _generators   A list of (possibly redundant) generators of the group
+  */
+template<
+  template<typename, typename> class Multiply,
+  template<typename, typename> class Equality,
+  typename id,
+  typename _generators
+>
+struct enumerate_group_elements
+  : public enumerate_group_elements_noid<
+      Multiply,
+      Equality,
+      id,
+      typename strip_identities<Equality, id, _generators>::type,
+      strip_identities<Equality, id, _generators>::global_flags
+    >
+{
+};
+
+} // end namespace group_theory
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_TENSORSYMMETRY_TEMPLATEGROUPTHEORY_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h
new file mode 100644
index 0000000..e4c59dc
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/Barrier.h
@@ -0,0 +1,67 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Barrier is an object that allows one or more threads to wait until
+// Notify has been called a specified number of times.
+
+#ifndef EIGEN_CXX11_THREADPOOL_BARRIER_H
+#define EIGEN_CXX11_THREADPOOL_BARRIER_H
+
+namespace Eigen {
+
+class Barrier {
+ public:
+  Barrier(unsigned int count) : state_(count << 1), notified_(false) {
+    eigen_plain_assert(((count << 1) >> 1) == count);
+  }
+  ~Barrier() { eigen_plain_assert((state_ >> 1) == 0); }
+
+  void Notify() {
+    unsigned int v = state_.fetch_sub(2, std::memory_order_acq_rel) - 2;
+    if (v != 1) {
+      // Clear the lowest bit (waiter flag) and check that the original state
+      // value was not zero. If it was zero, it means that notify was called
+      // more times than the original count.
+      eigen_plain_assert(((v + 2) & ~1) != 0);
+      return;  // either count has not dropped to 0, or waiter is not waiting
+    }
+    std::unique_lock<std::mutex> l(mu_);
+    eigen_plain_assert(!notified_);
+    notified_ = true;
+    cv_.notify_all();
+  }
+
+  void Wait() {
+    unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel);
+    if ((v >> 1) == 0) return;
+    std::unique_lock<std::mutex> l(mu_);
+    while (!notified_) {
+      cv_.wait(l);
+    }
+  }
+
+ private:
+  std::mutex mu_;
+  std::condition_variable cv_;
+  std::atomic<unsigned int> state_;  // low bit is waiter flag
+  bool notified_;
+};
+
+// Notification is an object that allows a user to to wait for another
+// thread to signal a notification that an event has occurred.
+//
+// Multiple threads can wait on the same Notification object,
+// but only one caller must call Notify() on the object.
+struct Notification : Barrier {
+  Notification() : Barrier(1){};
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_BARRIER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h
new file mode 100644
index 0000000..4549aa0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/EventCount.h
@@ -0,0 +1,249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
+#define EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
+
+namespace Eigen {
+
+// EventCount allows to wait for arbitrary predicates in non-blocking
+// algorithms. Think of condition variable, but wait predicate does not need to
+// be protected by a mutex. Usage:
+// Waiting thread does:
+//
+//   if (predicate)
+//     return act();
+//   EventCount::Waiter& w = waiters[my_index];
+//   ec.Prewait(&w);
+//   if (predicate) {
+//     ec.CancelWait(&w);
+//     return act();
+//   }
+//   ec.CommitWait(&w);
+//
+// Notifying thread does:
+//
+//   predicate = true;
+//   ec.Notify(true);
+//
+// Notify is cheap if there are no waiting threads. Prewait/CommitWait are not
+// cheap, but they are executed only if the preceding predicate check has
+// failed.
+//
+// Algorithm outline:
+// There are two main variables: predicate (managed by user) and state_.
+// Operation closely resembles Dekker mutual algorithm:
+// https://en.wikipedia.org/wiki/Dekker%27s_algorithm
+// Waiting thread sets state_ then checks predicate, Notifying thread sets
+// predicate then checks state_. Due to seq_cst fences in between these
+// operations it is guaranteed than either waiter will see predicate change
+// and won't block, or notifying thread will see state_ change and will unblock
+// the waiter, or both. But it can't happen that both threads don't see each
+// other changes, which would lead to deadlock.
+class EventCount {
+ public:
+  class Waiter;
+
+  EventCount(MaxSizeVector<Waiter>& waiters)
+      : state_(kStackMask), waiters_(waiters) {
+    eigen_plain_assert(waiters.size() < (1 << kWaiterBits) - 1);
+  }
+
+  ~EventCount() {
+    // Ensure there are no waiters.
+    eigen_plain_assert(state_.load() == kStackMask);
+  }
+
+  // Prewait prepares for waiting.
+  // After calling Prewait, the thread must re-check the wait predicate
+  // and then call either CancelWait or CommitWait.
+  void Prewait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state);
+      uint64_t newstate = state + kWaiterInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_seq_cst))
+        return;
+    }
+  }
+
+  // CommitWait commits waiting after Prewait.
+  void CommitWait(Waiter* w) {
+    eigen_plain_assert((w->epoch & ~kEpochMask) == 0);
+    w->state = Waiter::kNotSignaled;
+    const uint64_t me = (w - &waiters_[0]) | w->epoch;
+    uint64_t state = state_.load(std::memory_order_seq_cst);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate;
+      if ((state & kSignalMask) != 0) {
+        // Consume the signal and return immidiately.
+        newstate = state - kWaiterInc - kSignalInc;
+      } else {
+        // Remove this thread from pre-wait counter and add to the waiter stack.
+        newstate = ((state & kWaiterMask) - kWaiterInc) | me;
+        w->next.store(state & (kStackMask | kEpochMask),
+                      std::memory_order_relaxed);
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel)) {
+        if ((state & kSignalMask) == 0) {
+          w->epoch += kEpochInc;
+          Park(w);
+        }
+        return;
+      }
+    }
+  }
+
+  // CancelWait cancels effects of the previous Prewait call.
+  void CancelWait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate = state - kWaiterInc;
+      // We don't know if the thread was also notified or not,
+      // so we should not consume a signal unconditionaly.
+      // Only if number of waiters is equal to number of signals,
+      // we know that the thread was notified and we must take away the signal.
+      if (((state & kWaiterMask) >> kWaiterShift) ==
+          ((state & kSignalMask) >> kSignalShift))
+        newstate -= kSignalInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel))
+        return;
+    }
+  }
+
+  // Notify wakes one or all waiting threads.
+  // Must be called after changing the associated wait predicate.
+  void Notify(bool notifyAll) {
+    std::atomic_thread_fence(std::memory_order_seq_cst);
+    uint64_t state = state_.load(std::memory_order_acquire);
+    for (;;) {
+      CheckState(state);
+      const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+      const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+      // Easy case: no waiters.
+      if ((state & kStackMask) == kStackMask && waiters == signals) return;
+      uint64_t newstate;
+      if (notifyAll) {
+        // Empty wait stack and set signal to number of pre-wait threads.
+        newstate =
+            (state & kWaiterMask) | (waiters << kSignalShift) | kStackMask;
+      } else if (signals < waiters) {
+        // There is a thread in pre-wait state, unblock it.
+        newstate = state + kSignalInc;
+      } else {
+        // Pop a waiter from list and unpark it.
+        Waiter* w = &waiters_[state & kStackMask];
+        uint64_t next = w->next.load(std::memory_order_relaxed);
+        newstate = (state & (kWaiterMask | kSignalMask)) | next;
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel)) {
+        if (!notifyAll && (signals < waiters))
+          return;  // unblocked pre-wait thread
+        if ((state & kStackMask) == kStackMask) return;
+        Waiter* w = &waiters_[state & kStackMask];
+        if (!notifyAll) w->next.store(kStackMask, std::memory_order_relaxed);
+        Unpark(w);
+        return;
+      }
+    }
+  }
+
+  class Waiter {
+    friend class EventCount;
+    // Align to 128 byte boundary to prevent false sharing with other Waiter
+    // objects in the same vector.
+    EIGEN_ALIGN_TO_BOUNDARY(128) std::atomic<uint64_t> next;
+    std::mutex mu;
+    std::condition_variable cv;
+    uint64_t epoch = 0;
+    unsigned state = kNotSignaled;
+    enum {
+      kNotSignaled,
+      kWaiting,
+      kSignaled,
+    };
+  };
+
+ private:
+  // State_ layout:
+  // - low kWaiterBits is a stack of waiters committed wait
+  //   (indexes in waiters_ array are used as stack elements,
+  //   kStackMask means empty stack).
+  // - next kWaiterBits is count of waiters in prewait state.
+  // - next kWaiterBits is count of pending signals.
+  // - remaining bits are ABA counter for the stack.
+  //   (stored in Waiter node and incremented on push).
+  static const uint64_t kWaiterBits = 14;
+  static const uint64_t kStackMask = (1ull << kWaiterBits) - 1;
+  static const uint64_t kWaiterShift = kWaiterBits;
+  static const uint64_t kWaiterMask = ((1ull << kWaiterBits) - 1)
+                                      << kWaiterShift;
+  static const uint64_t kWaiterInc = 1ull << kWaiterShift;
+  static const uint64_t kSignalShift = 2 * kWaiterBits;
+  static const uint64_t kSignalMask = ((1ull << kWaiterBits) - 1)
+                                      << kSignalShift;
+  static const uint64_t kSignalInc = 1ull << kSignalShift;
+  static const uint64_t kEpochShift = 3 * kWaiterBits;
+  static const uint64_t kEpochBits = 64 - kEpochShift;
+  static const uint64_t kEpochMask = ((1ull << kEpochBits) - 1) << kEpochShift;
+  static const uint64_t kEpochInc = 1ull << kEpochShift;
+  std::atomic<uint64_t> state_;
+  MaxSizeVector<Waiter>& waiters_;
+
+  static void CheckState(uint64_t state, bool waiter = false) {
+    static_assert(kEpochBits >= 20, "not enough bits to prevent ABA problem");
+    const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+    const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+    eigen_plain_assert(waiters >= signals);
+    eigen_plain_assert(waiters < (1 << kWaiterBits) - 1);
+    eigen_plain_assert(!waiter || waiters > 0);
+    (void)waiters;
+    (void)signals;
+  }
+
+  void Park(Waiter* w) {
+    std::unique_lock<std::mutex> lock(w->mu);
+    while (w->state != Waiter::kSignaled) {
+      w->state = Waiter::kWaiting;
+      w->cv.wait(lock);
+    }
+  }
+
+  void Unpark(Waiter* w) {
+    for (Waiter* next; w; w = next) {
+      uint64_t wnext = w->next.load(std::memory_order_relaxed) & kStackMask;
+      next = wnext == kStackMask ? nullptr : &waiters_[wnext];
+      unsigned state;
+      {
+        std::unique_lock<std::mutex> lock(w->mu);
+        state = w->state;
+        w->state = Waiter::kSignaled;
+      }
+      // Avoid notifying if it wasn't waiting.
+      if (state == Waiter::kWaiting) w->cv.notify_one();
+    }
+  }
+
+  EventCount(const EventCount&) = delete;
+  void operator=(const EventCount&) = delete;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H_
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
new file mode 100644
index 0000000..23a2b54
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/NonBlockingThreadPool.h
@@ -0,0 +1,486 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+#define EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+
+namespace Eigen {
+
+template <typename Environment>
+class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
+ public:
+  typedef typename Environment::Task Task;
+  typedef RunQueue<Task, 1024> Queue;
+
+  ThreadPoolTempl(int num_threads, Environment env = Environment())
+      : ThreadPoolTempl(num_threads, true, env) {}
+
+  ThreadPoolTempl(int num_threads, bool allow_spinning,
+                  Environment env = Environment())
+      : env_(env),
+        num_threads_(num_threads),
+        allow_spinning_(allow_spinning),
+        thread_data_(num_threads),
+        all_coprimes_(num_threads),
+        waiters_(num_threads),
+        global_steal_partition_(EncodePartition(0, num_threads_)),
+        blocked_(0),
+        spinning_(0),
+        done_(false),
+        cancelled_(false),
+        ec_(waiters_) {
+    waiters_.resize(num_threads_);
+    // Calculate coprimes of all numbers [1, num_threads].
+    // Coprimes are used for random walks over all threads in Steal
+    // and NonEmptyQueueIndex. Iteration is based on the fact that if we take
+    // a random starting thread index t and calculate num_threads - 1 subsequent
+    // indices as (t + coprime) % num_threads, we will cover all threads without
+    // repetitions (effectively getting a presudo-random permutation of thread
+    // indices).
+    eigen_plain_assert(num_threads_ < kMaxThreads);
+    for (int i = 1; i <= num_threads_; ++i) {
+      all_coprimes_.emplace_back(i);
+      ComputeCoprimes(i, &all_coprimes_.back());
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    init_barrier_.reset(new Barrier(num_threads_));
+#endif
+    thread_data_.resize(num_threads_);
+    for (int i = 0; i < num_threads_; i++) {
+      SetStealPartition(i, EncodePartition(0, num_threads_));
+      thread_data_[i].thread.reset(
+          env_.CreateThread([this, i]() { WorkerLoop(i); }));
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    // Wait for workers to initialize per_thread_map_. Otherwise we might race
+    // with them in Schedule or CurrentThreadId.
+    init_barrier_->Wait();
+#endif
+  }
+
+  ~ThreadPoolTempl() {
+    done_ = true;
+
+    // Now if all threads block without work, they will start exiting.
+    // But note that threads can continue to work arbitrary long,
+    // block, submit new work, unblock and otherwise live full life.
+    if (!cancelled_) {
+      ec_.Notify(true);
+    } else {
+      // Since we were cancelled, there might be entries in the queues.
+      // Empty them to prevent their destructor from asserting.
+      for (size_t i = 0; i < thread_data_.size(); i++) {
+        thread_data_[i].queue.Flush();
+      }
+    }
+    // Join threads explicitly (by destroying) to avoid destruction order within
+    // this class.
+    for (size_t i = 0; i < thread_data_.size(); ++i)
+      thread_data_[i].thread.reset();
+  }
+
+  void SetStealPartitions(const std::vector<std::pair<unsigned, unsigned>>& partitions) {
+    eigen_plain_assert(partitions.size() == static_cast<std::size_t>(num_threads_));
+
+    // Pass this information to each thread queue.
+    for (int i = 0; i < num_threads_; i++) {
+      const auto& pair = partitions[i];
+      unsigned start = pair.first, end = pair.second;
+      AssertBounds(start, end);
+      unsigned val = EncodePartition(start, end);
+      SetStealPartition(i, val);
+    }
+  }
+
+  void Schedule(std::function<void()> fn) EIGEN_OVERRIDE {
+    ScheduleWithHint(std::move(fn), 0, num_threads_);
+  }
+
+  void ScheduleWithHint(std::function<void()> fn, int start,
+                        int limit) override {
+    Task t = env_.CreateTask(std::move(fn));
+    PerThread* pt = GetPerThread();
+    if (pt->pool == this) {
+      // Worker thread of this pool, push onto the thread's queue.
+      Queue& q = thread_data_[pt->thread_id].queue;
+      t = q.PushFront(std::move(t));
+    } else {
+      // A free-standing thread (or worker of another pool), push onto a random
+      // queue.
+      eigen_plain_assert(start < limit);
+      eigen_plain_assert(limit <= num_threads_);
+      int num_queues = limit - start;
+      int rnd = Rand(&pt->rand) % num_queues;
+      eigen_plain_assert(start + rnd < limit);
+      Queue& q = thread_data_[start + rnd].queue;
+      t = q.PushBack(std::move(t));
+    }
+    // Note: below we touch this after making w available to worker threads.
+    // Strictly speaking, this can lead to a racy-use-after-free. Consider that
+    // Schedule is called from a thread that is neither main thread nor a worker
+    // thread of this pool. Then, execution of w directly or indirectly
+    // completes overall computations, which in turn leads to destruction of
+    // this. We expect that such scenario is prevented by program, that is,
+    // this is kept alive while any threads can potentially be in Schedule.
+    if (!t.f) {
+      ec_.Notify(false);
+    } else {
+      env_.ExecuteTask(t);  // Push failed, execute directly.
+    }
+  }
+
+  void Cancel() EIGEN_OVERRIDE {
+    cancelled_ = true;
+    done_ = true;
+
+    // Let each thread know it's been cancelled.
+#ifdef EIGEN_THREAD_ENV_SUPPORTS_CANCELLATION
+    for (size_t i = 0; i < thread_data_.size(); i++) {
+      thread_data_[i].thread->OnCancel();
+    }
+#endif
+
+    // Wake up the threads without work to let them exit on their own.
+    ec_.Notify(true);
+  }
+
+  int NumThreads() const EIGEN_FINAL { return num_threads_; }
+
+  int CurrentThreadId() const EIGEN_FINAL {
+    const PerThread* pt = const_cast<ThreadPoolTempl*>(this)->GetPerThread();
+    if (pt->pool == this) {
+      return pt->thread_id;
+    } else {
+      return -1;
+    }
+  }
+
+ private:
+  // Create a single atomic<int> that encodes start and limit information for
+  // each thread.
+  // We expect num_threads_ < 65536, so we can store them in a single
+  // std::atomic<unsigned>.
+  // Exposed publicly as static functions so that external callers can reuse
+  // this encode/decode logic for maintaining their own thread-safe copies of
+  // scheduling and steal domain(s).
+  static const int kMaxPartitionBits = 16;
+  static const int kMaxThreads = 1 << kMaxPartitionBits;
+
+  inline unsigned EncodePartition(unsigned start, unsigned limit) {
+    return (start << kMaxPartitionBits) | limit;
+  }
+
+  inline void DecodePartition(unsigned val, unsigned* start, unsigned* limit) {
+    *limit = val & (kMaxThreads - 1);
+    val >>= kMaxPartitionBits;
+    *start = val;
+  }
+
+  void AssertBounds(int start, int end) {
+    eigen_plain_assert(start >= 0);
+    eigen_plain_assert(start < end);  // non-zero sized partition
+    eigen_plain_assert(end <= num_threads_);
+  }
+
+  inline void SetStealPartition(size_t i, unsigned val) {
+    thread_data_[i].steal_partition.store(val, std::memory_order_relaxed);
+  }
+
+  inline unsigned GetStealPartition(int i) {
+    return thread_data_[i].steal_partition.load(std::memory_order_relaxed);
+  }
+
+  void ComputeCoprimes(int N, MaxSizeVector<unsigned>* coprimes) {
+    for (int i = 1; i <= N; i++) {
+      unsigned a = i;
+      unsigned b = N;
+      // If GCD(a, b) == 1, then a and b are coprimes.
+      while (b != 0) {
+        unsigned tmp = a;
+        a = b;
+        b = tmp % b;
+      }
+      if (a == 1) {
+        coprimes->push_back(i);
+      }
+    }
+  }
+
+  typedef typename Environment::EnvThread Thread;
+
+  struct PerThread {
+    constexpr PerThread() : pool(NULL), rand(0), thread_id(-1) {}
+    ThreadPoolTempl* pool;  // Parent pool, or null for normal threads.
+    uint64_t rand;          // Random generator state.
+    int thread_id;          // Worker thread index in pool.
+#ifndef EIGEN_THREAD_LOCAL
+    // Prevent false sharing.
+    char pad_[128];
+#endif
+  };
+
+  struct ThreadData {
+    constexpr ThreadData() : thread(), steal_partition(0), queue() {}
+    std::unique_ptr<Thread> thread;
+    std::atomic<unsigned> steal_partition;
+    Queue queue;
+  };
+
+  Environment env_;
+  const int num_threads_;
+  const bool allow_spinning_;
+  MaxSizeVector<ThreadData> thread_data_;
+  MaxSizeVector<MaxSizeVector<unsigned>> all_coprimes_;
+  MaxSizeVector<EventCount::Waiter> waiters_;
+  unsigned global_steal_partition_;
+  std::atomic<unsigned> blocked_;
+  std::atomic<bool> spinning_;
+  std::atomic<bool> done_;
+  std::atomic<bool> cancelled_;
+  EventCount ec_;
+#ifndef EIGEN_THREAD_LOCAL
+  std::unique_ptr<Barrier> init_barrier_;
+  std::mutex per_thread_map_mutex_;  // Protects per_thread_map_.
+  std::unordered_map<uint64_t, std::unique_ptr<PerThread>> per_thread_map_;
+#endif
+
+  // Main worker thread loop.
+  void WorkerLoop(int thread_id) {
+#ifndef EIGEN_THREAD_LOCAL
+    std::unique_ptr<PerThread> new_pt(new PerThread());
+    per_thread_map_mutex_.lock();
+    bool insertOK = per_thread_map_.emplace(GlobalThreadIdHash(), std::move(new_pt)).second;
+    eigen_plain_assert(insertOK);
+    EIGEN_UNUSED_VARIABLE(insertOK);
+    per_thread_map_mutex_.unlock();
+    init_barrier_->Notify();
+    init_barrier_->Wait();
+#endif
+    PerThread* pt = GetPerThread();
+    pt->pool = this;
+    pt->rand = GlobalThreadIdHash();
+    pt->thread_id = thread_id;
+    Queue& q = thread_data_[thread_id].queue;
+    EventCount::Waiter* waiter = &waiters_[thread_id];
+    // TODO(dvyukov,rmlarsen): The time spent in NonEmptyQueueIndex() is
+    // proportional to num_threads_ and we assume that new work is scheduled at
+    // a constant rate, so we set spin_count to 5000 / num_threads_. The
+    // constant was picked based on a fair dice roll, tune it.
+    const int spin_count =
+        allow_spinning_ && num_threads_ > 0 ? 5000 / num_threads_ : 0;
+    if (num_threads_ == 1) {
+      // For num_threads_ == 1 there is no point in going through the expensive
+      // steal loop. Moreover, since NonEmptyQueueIndex() calls PopBack() on the
+      // victim queues it might reverse the order in which ops are executed
+      // compared to the order in which they are scheduled, which tends to be
+      // counter-productive for the types of I/O workloads the single thread
+      // pools tend to be used for.
+      while (!cancelled_) {
+        Task t = q.PopFront();
+        for (int i = 0; i < spin_count && !t.f; i++) {
+          if (!cancelled_.load(std::memory_order_relaxed)) {
+            t = q.PopFront();
+          }
+        }
+        if (!t.f) {
+          if (!WaitForWork(waiter, &t)) {
+            return;
+          }
+        }
+        if (t.f) {
+          env_.ExecuteTask(t);
+        }
+      }
+    } else {
+      while (!cancelled_) {
+        Task t = q.PopFront();
+        if (!t.f) {
+          t = LocalSteal();
+          if (!t.f) {
+            t = GlobalSteal();
+            if (!t.f) {
+              // Leave one thread spinning. This reduces latency.
+              if (allow_spinning_ && !spinning_ && !spinning_.exchange(true)) {
+                for (int i = 0; i < spin_count && !t.f; i++) {
+                  if (!cancelled_.load(std::memory_order_relaxed)) {
+                    t = GlobalSteal();
+                  } else {
+                    return;
+                  }
+                }
+                spinning_ = false;
+              }
+              if (!t.f) {
+                if (!WaitForWork(waiter, &t)) {
+                  return;
+                }
+              }
+            }
+          }
+        }
+        if (t.f) {
+          env_.ExecuteTask(t);
+        }
+      }
+    }
+  }
+
+  // Steal tries to steal work from other worker threads in the range [start,
+  // limit) in best-effort manner.
+  Task Steal(unsigned start, unsigned limit) {
+    PerThread* pt = GetPerThread();
+    const size_t size = limit - start;
+    unsigned r = Rand(&pt->rand);
+    // Reduce r into [0, size) range, this utilizes trick from
+    // https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
+    eigen_plain_assert(all_coprimes_[size - 1].size() < (1<<30));
+    unsigned victim = ((uint64_t)r * (uint64_t)size) >> 32;
+    unsigned index = ((uint64_t) all_coprimes_[size - 1].size() * (uint64_t)r) >> 32;
+    unsigned inc = all_coprimes_[size - 1][index];
+
+    for (unsigned i = 0; i < size; i++) {
+      eigen_plain_assert(start + victim < limit);
+      Task t = thread_data_[start + victim].queue.PopBack();
+      if (t.f) {
+        return t;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= size;
+      }
+    }
+    return Task();
+  }
+
+  // Steals work within threads belonging to the partition.
+  Task LocalSteal() {
+    PerThread* pt = GetPerThread();
+    unsigned partition = GetStealPartition(pt->thread_id);
+    // If thread steal partition is the same as global partition, there is no
+    // need to go through the steal loop twice.
+    if (global_steal_partition_ == partition) return Task();
+    unsigned start, limit;
+    DecodePartition(partition, &start, &limit);
+    AssertBounds(start, limit);
+
+    return Steal(start, limit);
+  }
+
+  // Steals work from any other thread in the pool.
+  Task GlobalSteal() {
+    return Steal(0, num_threads_);
+  }
+
+
+  // WaitForWork blocks until new work is available (returns true), or if it is
+  // time to exit (returns false). Can optionally return a task to execute in t
+  // (in such case t.f != nullptr on return).
+  bool WaitForWork(EventCount::Waiter* waiter, Task* t) {
+    eigen_plain_assert(!t->f);
+    // We already did best-effort emptiness check in Steal, so prepare for
+    // blocking.
+    ec_.Prewait();
+    // Now do a reliable emptiness check.
+    int victim = NonEmptyQueueIndex();
+    if (victim != -1) {
+      ec_.CancelWait();
+      if (cancelled_) {
+        return false;
+      } else {
+        *t = thread_data_[victim].queue.PopBack();
+        return true;
+      }
+    }
+    // Number of blocked threads is used as termination condition.
+    // If we are shutting down and all worker threads blocked without work,
+    // that's we are done.
+    blocked_++;
+    // TODO is blocked_ required to be unsigned?
+    if (done_ && blocked_ == static_cast<unsigned>(num_threads_)) {
+      ec_.CancelWait();
+      // Almost done, but need to re-check queues.
+      // Consider that all queues are empty and all worker threads are preempted
+      // right after incrementing blocked_ above. Now a free-standing thread
+      // submits work and calls destructor (which sets done_). If we don't
+      // re-check queues, we will exit leaving the work unexecuted.
+      if (NonEmptyQueueIndex() != -1) {
+        // Note: we must not pop from queues before we decrement blocked_,
+        // otherwise the following scenario is possible. Consider that instead
+        // of checking for emptiness we popped the only element from queues.
+        // Now other worker threads can start exiting, which is bad if the
+        // work item submits other work. So we just check emptiness here,
+        // which ensures that all worker threads exit at the same time.
+        blocked_--;
+        return true;
+      }
+      // Reached stable termination state.
+      ec_.Notify(true);
+      return false;
+    }
+    ec_.CommitWait(waiter);
+    blocked_--;
+    return true;
+  }
+
+  int NonEmptyQueueIndex() {
+    PerThread* pt = GetPerThread();
+    // We intentionally design NonEmptyQueueIndex to steal work from
+    // anywhere in the queue so threads don't block in WaitForWork() forever
+    // when all threads in their partition go to sleep. Steal is still local.
+    const size_t size = thread_data_.size();
+    unsigned r = Rand(&pt->rand);
+    unsigned inc = all_coprimes_[size - 1][r % all_coprimes_[size - 1].size()];
+    unsigned victim = r % size;
+    for (unsigned i = 0; i < size; i++) {
+      if (!thread_data_[victim].queue.Empty()) {
+        return victim;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= size;
+      }
+    }
+    return -1;
+  }
+
+  static EIGEN_STRONG_INLINE uint64_t GlobalThreadIdHash() {
+    return std::hash<std::thread::id>()(std::this_thread::get_id());
+  }
+
+  EIGEN_STRONG_INLINE PerThread* GetPerThread() {
+#ifndef EIGEN_THREAD_LOCAL
+    static PerThread dummy;
+    auto it = per_thread_map_.find(GlobalThreadIdHash());
+    if (it == per_thread_map_.end()) {
+      return &dummy;
+    } else {
+      return it->second.get();
+    }
+#else
+    EIGEN_THREAD_LOCAL PerThread per_thread_;
+    PerThread* pt = &per_thread_;
+    return pt;
+#endif
+  }
+
+  static EIGEN_STRONG_INLINE unsigned Rand(uint64_t* state) {
+    uint64_t current = *state;
+    // Update the internal state
+    *state = current * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
+    // Generate the random output (using the PCG-XSH-RS scheme)
+    return static_cast<unsigned>((current ^ (current >> 22)) >>
+                                 (22 + (current >> 61)));
+  }
+};
+
+typedef ThreadPoolTempl<StlThreadEnvironment> ThreadPool;
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h
new file mode 100644
index 0000000..b572ebc
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/RunQueue.h
@@ -0,0 +1,236 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
+#define EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
+
+namespace Eigen {
+
+// RunQueue is a fixed-size, partially non-blocking deque or Work items.
+// Operations on front of the queue must be done by a single thread (owner),
+// operations on back of the queue can be done by multiple threads concurrently.
+//
+// Algorithm outline:
+// All remote threads operating on the queue back are serialized by a mutex.
+// This ensures that at most two threads access state: owner and one remote
+// thread (Size aside). The algorithm ensures that the occupied region of the
+// underlying array is logically continuous (can wraparound, but no stray
+// occupied elements). Owner operates on one end of this region, remote thread
+// operates on the other end. Synchronization between these threads
+// (potential consumption of the last element and take up of the last empty
+// element) happens by means of state variable in each element. States are:
+// empty, busy (in process of insertion of removal) and ready. Threads claim
+// elements (empty->busy and ready->busy transitions) by means of a CAS
+// operation. The finishing transition (busy->empty and busy->ready) are done
+// with plain store as the element is exclusively owned by the current thread.
+//
+// Note: we could permit only pointers as elements, then we would not need
+// separate state variable as null/non-null pointer value would serve as state,
+// but that would require malloc/free per operation for large, complex values
+// (and this is designed to store std::function<()>).
+template <typename Work, unsigned kSize>
+class RunQueue {
+ public:
+  RunQueue() : front_(0), back_(0) {
+    // require power-of-two for fast masking
+    eigen_plain_assert((kSize & (kSize - 1)) == 0);
+    eigen_plain_assert(kSize > 2);            // why would you do this?
+    eigen_plain_assert(kSize <= (64 << 10));  // leave enough space for counter
+    for (unsigned i = 0; i < kSize; i++)
+      array_[i].state.store(kEmpty, std::memory_order_relaxed);
+  }
+
+  ~RunQueue() { eigen_plain_assert(Size() == 0); }
+
+  // PushFront inserts w at the beginning of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushFront(Work w) {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[front & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return w;
+    front_.store(front + 1 + (kSize << 1), std::memory_order_relaxed);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopFront removes and returns the first element in the queue.
+  // If the queue was empty returns default-constructed Work.
+  Work PopFront() {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(front - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    front = ((front - 1) & kMask2) | (front & ~kMask2);
+    front_.store(front, std::memory_order_relaxed);
+    return w;
+  }
+
+  // PushBack adds w at the end of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushBack(Work w) {
+    std::unique_lock<std::mutex> lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(back - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return w;
+    back = ((back - 1) & kMask2) | (back & ~kMask2);
+    back_.store(back, std::memory_order_relaxed);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopBack removes and returns the last elements in the queue.
+  Work PopBack() {
+    if (Empty()) return Work();
+    std::unique_lock<std::mutex> lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[back & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    back_.store(back + 1 + (kSize << 1), std::memory_order_relaxed);
+    return w;
+  }
+
+  // PopBackHalf removes and returns half last elements in the queue.
+  // Returns number of elements removed.
+  unsigned PopBackHalf(std::vector<Work>* result) {
+    if (Empty()) return 0;
+    std::unique_lock<std::mutex> lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    unsigned size = Size();
+    unsigned mid = back;
+    if (size > 1) mid = back + (size - 1) / 2;
+    unsigned n = 0;
+    unsigned start = 0;
+    for (; static_cast<int>(mid - back) >= 0; mid--) {
+      Elem* e = &array_[mid & kMask];
+      uint8_t s = e->state.load(std::memory_order_relaxed);
+      if (n == 0) {
+        if (s != kReady || !e->state.compare_exchange_strong(
+                               s, kBusy, std::memory_order_acquire))
+          continue;
+        start = mid;
+      } else {
+        // Note: no need to store temporal kBusy, we exclusively own these
+        // elements.
+        eigen_plain_assert(s == kReady);
+      }
+      result->push_back(std::move(e->w));
+      e->state.store(kEmpty, std::memory_order_release);
+      n++;
+    }
+    if (n != 0)
+      back_.store(start + 1 + (kSize << 1), std::memory_order_relaxed);
+    return n;
+  }
+
+  // Size returns current queue size.
+  // Can be called by any thread at any time.
+  unsigned Size() const { return SizeOrNotEmpty<true>(); }
+
+  // Empty tests whether container is empty.
+  // Can be called by any thread at any time.
+  bool Empty() const { return SizeOrNotEmpty<false>() == 0; }
+
+  // Delete all the elements from the queue.
+  void Flush() {
+    while (!Empty()) {
+      PopFront();
+    }
+  }
+
+ private:
+  static const unsigned kMask = kSize - 1;
+  static const unsigned kMask2 = (kSize << 1) - 1;
+  struct Elem {
+    std::atomic<uint8_t> state;
+    Work w;
+  };
+  enum {
+    kEmpty,
+    kBusy,
+    kReady,
+  };
+  std::mutex mutex_;
+  // Low log(kSize) + 1 bits in front_ and back_ contain rolling index of
+  // front/back, respectively. The remaining bits contain modification counters
+  // that are incremented on Push operations. This allows us to (1) distinguish
+  // between empty and full conditions (if we would use log(kSize) bits for
+  // position, these conditions would be indistinguishable); (2) obtain
+  // consistent snapshot of front_/back_ for Size operation using the
+  // modification counters.
+  std::atomic<unsigned> front_;
+  std::atomic<unsigned> back_;
+  Elem array_[kSize];
+
+  // SizeOrNotEmpty returns current queue size; if NeedSizeEstimate is false,
+  // only whether the size is 0 is guaranteed to be correct.
+  // Can be called by any thread at any time.
+  template<bool NeedSizeEstimate>
+  unsigned SizeOrNotEmpty() const {
+    // Emptiness plays critical role in thread pool blocking. So we go to great
+    // effort to not produce false positives (claim non-empty queue as empty).
+    unsigned front = front_.load(std::memory_order_acquire);
+    for (;;) {
+      // Capture a consistent snapshot of front/tail.
+      unsigned back = back_.load(std::memory_order_acquire);
+      unsigned front1 = front_.load(std::memory_order_relaxed);
+      if (front != front1) {
+        front = front1;
+        std::atomic_thread_fence(std::memory_order_acquire);
+        continue;
+      }
+      if (NeedSizeEstimate) {
+        return CalculateSize(front, back);
+      } else {
+        // This value will be 0 if the queue is empty, and undefined otherwise.
+        unsigned maybe_zero = ((front ^ back) & kMask2);
+        // Queue size estimate must agree with maybe zero check on the queue
+        // empty/non-empty state.
+        eigen_assert((CalculateSize(front, back) == 0) == (maybe_zero == 0));
+        return maybe_zero;
+      }
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE
+  unsigned CalculateSize(unsigned front, unsigned back) const {
+    int size = (front & kMask2) - (back & kMask2);
+    // Fix overflow.
+    if (size < 0) size += 2 * kSize;
+    // Order of modification in push/pop is crafted to make the queue look
+    // larger than it is during concurrent modifications. E.g. push can
+    // increment size before the corresponding pop has decremented it.
+    // So the computed size can be up to kSize + 1, fix it.
+    if (size > static_cast<int>(kSize)) size = kSize;
+    return static_cast<unsigned>(size);
+  }
+
+  RunQueue(const RunQueue&) = delete;
+  void operator=(const RunQueue&) = delete;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_RUNQUEUE_H_
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h
new file mode 100644
index 0000000..a05685f
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadCancel.h
@@ -0,0 +1,23 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+
+// Try to come up with a portable way to cancel a thread
+#if EIGEN_OS_GNULINUX
+  #define EIGEN_THREAD_CANCEL(t)                  \
+    pthread_cancel(t.native_handle());
+  #define EIGEN_SUPPORTS_THREAD_CANCELLATION 1
+#else
+#define EIGEN_THREAD_CANCEL(t)
+#endif
+
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h
new file mode 100644
index 0000000..d94a064
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadEnvironment.h
@@ -0,0 +1,40 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+
+namespace Eigen {
+
+struct StlThreadEnvironment {
+  struct Task {
+    std::function<void()> f;
+  };
+
+  // EnvThread constructor must start the thread,
+  // destructor must join the thread.
+  class EnvThread {
+   public:
+    EnvThread(std::function<void()> f) : thr_(std::move(f)) {}
+    ~EnvThread() { thr_.join(); }
+    // This function is called when the threadpool is cancelled.
+    void OnCancel() { }
+
+   private:
+    std::thread thr_;
+  };
+
+  EnvThread* CreateThread(std::function<void()> f) { return new EnvThread(std::move(f)); }
+  Task CreateTask(std::function<void()> f) { return Task{std::move(f)}; }
+  void ExecuteTask(const Task& t) { t.f(); }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h
new file mode 100644
index 0000000..4e68474
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadLocal.h
@@ -0,0 +1,301 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+
+#ifdef EIGEN_AVOID_THREAD_LOCAL
+
+#ifdef EIGEN_THREAD_LOCAL
+#undef EIGEN_THREAD_LOCAL
+#endif
+
+#else
+
+#if EIGEN_MAX_CPP_VER >= 11 &&                         \
+    ((EIGEN_COMP_GNUC && EIGEN_GNUC_AT_LEAST(4, 8)) || \
+     __has_feature(cxx_thread_local)                || \
+     (EIGEN_COMP_MSVC >= 1900) )
+#define EIGEN_THREAD_LOCAL static thread_local
+#endif
+
+// Disable TLS for Apple and Android builds with older toolchains.
+#if defined(__APPLE__)
+// Included for TARGET_OS_IPHONE, __IPHONE_OS_VERSION_MIN_REQUIRED,
+// __IPHONE_8_0.
+#include <Availability.h>
+#include <TargetConditionals.h>
+#endif
+// Checks whether C++11's `thread_local` storage duration specifier is
+// supported.
+#if defined(__apple_build_version__) &&     \
+    ((__apple_build_version__ < 8000042) || \
+     (TARGET_OS_IPHONE && __IPHONE_OS_VERSION_MIN_REQUIRED < __IPHONE_9_0))
+// Notes: Xcode's clang did not support `thread_local` until version
+// 8, and even then not for all iOS < 9.0.
+#undef EIGEN_THREAD_LOCAL
+
+#elif defined(__ANDROID__) && EIGEN_COMP_CLANG
+// There are platforms for which TLS should not be used even though the compiler
+// makes it seem like it's supported (Android NDK < r12b for example).
+// This is primarily because of linker problems and toolchain misconfiguration:
+// TLS isn't supported until NDK r12b per
+// https://developer.android.com/ndk/downloads/revision_history.html
+// Since NDK r16, `__NDK_MAJOR__` and `__NDK_MINOR__` are defined in
+// <android/ndk-version.h>. For NDK < r16, users should define these macros,
+// e.g. `-D__NDK_MAJOR__=11 -D__NKD_MINOR__=0` for NDK r11.
+#if __has_include(<android/ndk-version.h>)
+#include <android/ndk-version.h>
+#endif  // __has_include(<android/ndk-version.h>)
+#if defined(__ANDROID__) && defined(__clang__) && defined(__NDK_MAJOR__) && \
+    defined(__NDK_MINOR__) &&                                               \
+    ((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
+#undef EIGEN_THREAD_LOCAL
+#endif
+#endif  // defined(__ANDROID__) && defined(__clang__)
+
+#endif  // EIGEN_AVOID_THREAD_LOCAL
+
+namespace Eigen {
+
+namespace internal {
+template <typename T>
+struct ThreadLocalNoOpInitialize {
+  void operator()(T&) const {}
+};
+
+template <typename T>
+struct ThreadLocalNoOpRelease {
+  void operator()(T&) const {}
+};
+
+}  // namespace internal
+
+// Thread local container for elements of type T, that does not use thread local
+// storage. As long as the number of unique threads accessing this storage
+// is smaller than `capacity_`, it is lock-free and wait-free. Otherwise it will
+// use a mutex for synchronization.
+//
+// Type `T` has to be default constructible, and by default each thread will get
+// a default constructed value. It is possible to specify custom `initialize`
+// callable, that will be called lazily from each thread accessing this object,
+// and will be passed a default initialized object of type `T`. Also it's
+// possible to pass a custom `release` callable, that will be invoked before
+// calling ~T().
+//
+// Example:
+//
+//   struct Counter {
+//     int value = 0;
+//   }
+//
+//   Eigen::ThreadLocal<Counter> counter(10);
+//
+//   // Each thread will have access to it's own counter object.
+//   Counter& cnt = counter.local();
+//   cnt++;
+//
+// WARNING: Eigen::ThreadLocal uses the OS-specific value returned by
+// std::this_thread::get_id() to identify threads. This value is not guaranteed
+// to be unique except for the life of the thread. A newly created thread may
+// get an OS-specific ID equal to that of an already destroyed thread.
+//
+// Somewhat similar to TBB thread local storage, with similar restrictions:
+// https://www.threadingbuildingblocks.org/docs/help/reference/thread_local_storage/enumerable_thread_specific_cls.html
+//
+template <typename T,
+          typename Initialize = internal::ThreadLocalNoOpInitialize<T>,
+          typename Release = internal::ThreadLocalNoOpRelease<T>>
+class ThreadLocal {
+  // We preallocate default constructed elements in MaxSizedVector.
+  static_assert(std::is_default_constructible<T>::value,
+                "ThreadLocal data type must be default constructible");
+
+ public:
+  explicit ThreadLocal(int capacity)
+      : ThreadLocal(capacity, internal::ThreadLocalNoOpInitialize<T>(),
+                    internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize)
+      : ThreadLocal(capacity, std::move(initialize),
+                    internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize, Release release)
+      : initialize_(std::move(initialize)),
+        release_(std::move(release)),
+        capacity_(capacity),
+        data_(capacity_),
+        ptr_(capacity_),
+        filled_records_(0) {
+    eigen_assert(capacity_ >= 0);
+    data_.resize(capacity_);
+    for (int i = 0; i < capacity_; ++i) {
+      ptr_.emplace_back(nullptr);
+    }
+  }
+
+  T& local() {
+    std::thread::id this_thread = std::this_thread::get_id();
+    if (capacity_ == 0) return SpilledLocal(this_thread);
+
+    std::size_t h = std::hash<std::thread::id>()(this_thread);
+    const int start_idx = h % capacity_;
+
+    // NOTE: From the definition of `std::this_thread::get_id()` it is
+    // guaranteed that we never can have concurrent insertions with the same key
+    // to our hash-map like data structure. If we didn't find an element during
+    // the initial traversal, it's guaranteed that no one else could have
+    // inserted it while we are in this function. This allows to massively
+    // simplify out lock-free insert-only hash map.
+
+    // Check if we already have an element for `this_thread`.
+    int idx = start_idx;
+    while (ptr_[idx].load() != nullptr) {
+      ThreadIdAndValue& record = *(ptr_[idx].load());
+      if (record.thread_id == this_thread) return record.value;
+
+      idx += 1;
+      if (idx >= capacity_) idx -= capacity_;
+      if (idx == start_idx) break;
+    }
+
+    // If we are here, it means that we found an insertion point in lookup
+    // table at `idx`, or we did a full traversal and table is full.
+
+    // If lock-free storage is full, fallback on mutex.
+    if (filled_records_.load() >= capacity_) return SpilledLocal(this_thread);
+
+    // We double check that we still have space to insert an element into a lock
+    // free storage. If old value in `filled_records_` is larger than the
+    // records capacity, it means that some other thread added an element while
+    // we were traversing lookup table.
+    int insertion_index =
+        filled_records_.fetch_add(1, std::memory_order_relaxed);
+    if (insertion_index >= capacity_) return SpilledLocal(this_thread);
+
+    // At this point it's guaranteed that we can access to
+    // data_[insertion_index_] without a data race.
+    data_[insertion_index].thread_id = this_thread;
+    initialize_(data_[insertion_index].value);
+
+    // That's the pointer we'll put into the lookup table.
+    ThreadIdAndValue* inserted = &data_[insertion_index];
+
+    // We'll use nullptr pointer to ThreadIdAndValue in a compare-and-swap loop.
+    ThreadIdAndValue* empty = nullptr;
+
+    // Now we have to find an insertion point into the lookup table. We start
+    // from the `idx` that was identified as an insertion point above, it's
+    // guaranteed that we will have an empty record somewhere in a lookup table
+    // (because we created a record in the `data_`).
+    const int insertion_idx = idx;
+
+    do {
+      // Always start search from the original insertion candidate.
+      idx = insertion_idx;
+      while (ptr_[idx].load() != nullptr) {
+        idx += 1;
+        if (idx >= capacity_) idx -= capacity_;
+        // If we did a full loop, it means that we don't have any free entries
+        // in the lookup table, and this means that something is terribly wrong.
+        eigen_assert(idx != insertion_idx);
+      }
+      // Atomic CAS of the pointer guarantees that any other thread, that will
+      // follow this pointer will see all the mutations in the `data_`.
+    } while (!ptr_[idx].compare_exchange_weak(empty, inserted));
+
+    return inserted->value;
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  void ForEach(std::function<void(std::thread::id, T&)> f) {
+    // Reading directly from `data_` is unsafe, because only CAS to the
+    // record in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      f(record->thread_id, record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    std::unique_lock<std::mutex> lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      f(kv.first, kv.second);
+    }
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  ~ThreadLocal() {
+    // Reading directly from `data_` is unsafe, because only CAS to the record
+    // in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      release_(record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    std::unique_lock<std::mutex> lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      release_(kv.second);
+    }
+  }
+
+ private:
+  struct ThreadIdAndValue {
+    std::thread::id thread_id;
+    T value;
+  };
+
+  // Use unordered map guarded by a mutex when lock free storage is full.
+  T& SpilledLocal(std::thread::id this_thread) {
+    std::unique_lock<std::mutex> lock(mu_);
+
+    auto it = per_thread_map_.find(this_thread);
+    if (it == per_thread_map_.end()) {
+      auto result = per_thread_map_.emplace(this_thread, T());
+      eigen_assert(result.second);
+      initialize_((*result.first).second);
+      return (*result.first).second;
+    } else {
+      return it->second;
+    }
+  }
+
+  Initialize initialize_;
+  Release release_;
+  const int capacity_;
+
+  // Storage that backs lock-free lookup table `ptr_`. Records stored in this
+  // storage contiguously starting from index 0.
+  MaxSizeVector<ThreadIdAndValue> data_;
+
+  // Atomic pointers to the data stored in `data_`. Used as a lookup table for
+  // linear probing hash map (https://en.wikipedia.org/wiki/Linear_probing).
+  MaxSizeVector<std::atomic<ThreadIdAndValue*>> ptr_;
+
+  // Number of records stored in the `data_`.
+  std::atomic<int> filled_records_;
+
+  // We fallback on per thread map if lock-free storage is full. In practice
+  // this should never happen, if `capacity_` is a reasonable estimate of the
+  // number of threads running in a system.
+  std::mutex mu_;  // Protects per_thread_map_.
+  std::unordered_map<std::thread::id, T> per_thread_map_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h
new file mode 100644
index 0000000..25030dc
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadPoolInterface.h
@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+
+namespace Eigen {
+
+// This defines an interface that ThreadPoolDevice can take to use
+// custom thread pools underneath.
+class ThreadPoolInterface {
+ public:
+  // Submits a closure to be run by a thread in the pool.
+  virtual void Schedule(std::function<void()> fn) = 0;
+
+  // Submits a closure to be run by threads in the range [start, end) in the
+  // pool.
+  virtual void ScheduleWithHint(std::function<void()> fn, int /*start*/,
+                                int /*end*/) {
+    // Just defer to Schedule in case sub-classes aren't interested in
+    // overriding this functionality.
+    Schedule(fn);
+  }
+
+  // If implemented, stop processing the closures that have been enqueued.
+  // Currently running closures may still be processed.
+  // If not implemented, does nothing.
+  virtual void Cancel() {}
+
+  // Returns the number of threads in the pool.
+  virtual int NumThreads() const = 0;
+
+  // Returns a logical thread index between 0 and NumThreads() - 1 if called
+  // from one of the threads in the pool. Returns -1 otherwise.
+  virtual int CurrentThreadId() const = 0;
+
+  virtual ~ThreadPoolInterface() {}
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h
new file mode 100644
index 0000000..a859c7b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/ThreadPool/ThreadYield.h
@@ -0,0 +1,20 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+
+// Try to come up with a portable way to yield
+#if EIGEN_COMP_GNUC && EIGEN_GNUC_AT_MOST(4, 7)
+#define EIGEN_THREAD_YIELD() sched_yield()
+#else
+#define EIGEN_THREAD_YIELD() std::this_thread::yield()
+#endif
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
new file mode 100644
index 0000000..149ceaf
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Meta.h
@@ -0,0 +1,537 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11META_H
+#define EIGEN_CXX11META_H
+
+#include <vector>
+#include "EmulateArray.h"
+
+#include "CXX11Workarounds.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+  * \file CXX11/util/CXX11Meta.h
+  * This file contains generic metaprogramming classes which are not specifically related to Eigen.
+  * This file expands upon Core/util/Meta.h and adds support for C++11 specific features.
+  */
+
+template<typename... tt>
+struct type_list { constexpr static int count = sizeof...(tt); };
+
+template<typename t, typename... tt>
+struct type_list<t, tt...> { constexpr static int count = sizeof...(tt) + 1; typedef t first_type; };
+
+template<typename T, T... nn>
+struct numeric_list { constexpr static std::size_t count = sizeof...(nn); };
+
+template<typename T, T n, T... nn>
+struct numeric_list<T, n, nn...> { static const std::size_t count = sizeof...(nn) + 1; const static T first_value = n; };
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+/* numeric list constructors
+ *
+ * equivalencies:
+ *     constructor                                              result
+ *     typename gen_numeric_list<int, 5>::type                  numeric_list<int, 0,1,2,3,4>
+ *     typename gen_numeric_list_reversed<int, 5>::type         numeric_list<int, 4,3,2,1,0>
+ *     typename gen_numeric_list_swapped_pair<int, 5,1,2>::type numeric_list<int, 0,2,1,3,4>
+ *     typename gen_numeric_list_repeated<int, 0, 5>::type      numeric_list<int, 0,0,0,0,0>
+ */
+
+template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list                     : gen_numeric_list<T, n-1, start, start + n-1, ii...> {};
+template<typename T, T start, T... ii>                    struct gen_numeric_list<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list_reversed                     : gen_numeric_list_reversed<T, n-1, start, ii..., start + n-1> {};
+template<typename T, T start, T... ii>                    struct gen_numeric_list_reversed<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T a, T b, T start = 0, T... ii> struct gen_numeric_list_swapped_pair                           : gen_numeric_list_swapped_pair<T, n-1, a, b, start, (start + n-1) == a ? b : ((start + n-1) == b ? a : (start + n-1)), ii...> {};
+template<typename T, T a, T b, T start, T... ii>                    struct gen_numeric_list_swapped_pair<T, 0, a, b, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T V, T... nn> struct gen_numeric_list_repeated                 : gen_numeric_list_repeated<T, n-1, V, V, nn...> {};
+template<typename T, T V, T... nn>                struct gen_numeric_list_repeated<T, 0, V, nn...> { typedef numeric_list<T, nn...> type; };
+
+/* list manipulation: concatenate */
+
+template<class a, class b> struct concat;
+
+template<typename... as, typename... bs> struct concat<type_list<as...>,       type_list<bs...>>        { typedef type_list<as..., bs...> type; };
+template<typename T, T... as, T... bs>   struct concat<numeric_list<T, as...>, numeric_list<T, bs...> > { typedef numeric_list<T, as..., bs...> type; };
+
+template<typename... p> struct mconcat;
+template<typename a>                             struct mconcat<a>           { typedef a type; };
+template<typename a, typename b>                 struct mconcat<a, b>        : concat<a, b> {};
+template<typename a, typename b, typename... cs> struct mconcat<a, b, cs...> : concat<a, typename mconcat<b, cs...>::type> {};
+
+/* list manipulation: extract slices */
+
+template<int n, typename x> struct take;
+template<int n, typename a, typename... as> struct take<n, type_list<a, as...>> : concat<type_list<a>, typename take<n-1, type_list<as...>>::type> {};
+template<int n>                             struct take<n, type_list<>>         { typedef type_list<> type; };
+template<typename a, typename... as>        struct take<0, type_list<a, as...>> { typedef type_list<> type; };
+template<>                                  struct take<0, type_list<>>         { typedef type_list<> type; };
+
+template<typename T, int n, T a, T... as> struct take<n, numeric_list<T, a, as...>> : concat<numeric_list<T, a>, typename take<n-1, numeric_list<T, as...>>::type> {};
+template<typename T, int n>               struct take<n, numeric_list<T>>           { typedef numeric_list<T> type; };
+template<typename T, T a, T... as>        struct take<0, numeric_list<T, a, as...>> { typedef numeric_list<T> type; };
+template<typename T>                      struct take<0, numeric_list<T>>           { typedef numeric_list<T> type; };
+
+template<typename T, int n, T... ii>      struct h_skip_helper_numeric;
+template<typename T, int n, T i, T... ii> struct h_skip_helper_numeric<T, n, i, ii...> : h_skip_helper_numeric<T, n-1, ii...> {};
+template<typename T, T i, T... ii>        struct h_skip_helper_numeric<T, 0, i, ii...> { typedef numeric_list<T, i, ii...> type; };
+template<typename T, int n>               struct h_skip_helper_numeric<T, n>           { typedef numeric_list<T> type; };
+template<typename T>                      struct h_skip_helper_numeric<T, 0>           { typedef numeric_list<T> type; };
+
+template<int n, typename... tt>             struct h_skip_helper_type;
+template<int n, typename t, typename... tt> struct h_skip_helper_type<n, t, tt...> : h_skip_helper_type<n-1, tt...> {};
+template<typename t, typename... tt>        struct h_skip_helper_type<0, t, tt...> { typedef type_list<t, tt...> type; };
+template<int n>                             struct h_skip_helper_type<n>           { typedef type_list<> type; };
+template<>                                  struct h_skip_helper_type<0>           { typedef type_list<> type; };
+#endif //not EIGEN_PARSED_BY_DOXYGEN
+
+template<int n>
+struct h_skip {
+  template<typename T, T... ii>
+  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_numeric<T, n, ii...>::type helper(numeric_list<T, ii...>) { return typename h_skip_helper_numeric<T, n, ii...>::type(); }
+  template<typename... tt>
+  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_type<n, tt...>::type helper(type_list<tt...>) { return typename h_skip_helper_type<n, tt...>::type(); }
+};
+
+template<int n, typename a> struct skip { typedef decltype(h_skip<n>::helper(a())) type; };
+
+template<int start, int count, typename a> struct slice : take<count, typename skip<start, a>::type> {};
+
+/* list manipulation: retrieve single element from list */
+
+template<int n, typename x> struct get;
+
+template<int n, typename a, typename... as>               struct get<n, type_list<a, as...>>   : get<n-1, type_list<as...>> {};
+template<typename a, typename... as>                      struct get<0, type_list<a, as...>>   { typedef a type; };
+
+template<typename T, int n, T a, T... as>                        struct get<n, numeric_list<T, a, as...>>   : get<n-1, numeric_list<T, as...>> {};
+template<typename T, T a, T... as>                               struct get<0, numeric_list<T, a, as...>>   { constexpr static T value = a; };
+
+template<std::size_t n, typename T, T a, T... as> constexpr T       array_get(const numeric_list<T, a, as...>&) {
+   return get<(int)n, numeric_list<T, a, as...>>::value;
+}
+
+/* always get type, regardless of dummy; good for parameter pack expansion */
+
+template<typename T, T dummy, typename t> struct id_numeric  { typedef t type; };
+template<typename dummy, typename t>      struct id_type     { typedef t type; };
+
+/* equality checking, flagged version */
+
+template<typename a, typename b> struct is_same_gf : is_same<a, b> { constexpr static int global_flags = 0; };
+
+/* apply_op to list */
+
+template<
+  bool from_left, // false
+  template<typename, typename> class op,
+  typename additional_param,
+  typename... values
+>
+struct h_apply_op_helper                                        { typedef type_list<typename op<values, additional_param>::type...> type; };
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename... values
+>
+struct h_apply_op_helper<true, op, additional_param, values...> { typedef type_list<typename op<additional_param, values>::type...> type; };
+
+template<
+  bool from_left,
+  template<typename, typename> class op,
+  typename additional_param
+>
+struct h_apply_op
+{
+  template<typename... values>
+  constexpr static typename h_apply_op_helper<from_left, op, additional_param, values...>::type helper(type_list<values...>)
+  { return typename h_apply_op_helper<from_left, op, additional_param, values...>::type(); }
+};
+
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename a
+>
+struct apply_op_from_left { typedef decltype(h_apply_op<true, op, additional_param>::helper(a())) type; };
+
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename a
+>
+struct apply_op_from_right { typedef decltype(h_apply_op<false, op, additional_param>::helper(a())) type; };
+
+/* see if an element is in a list */
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  bool last_check_positive = false
+>
+struct contained_in_list;
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list
+>
+struct contained_in_list<test, check_against, h_list, true>
+{
+  constexpr static bool value = true;
+};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename a,
+  typename... as
+>
+struct contained_in_list<test, check_against, type_list<a, as...>, false> : contained_in_list<test, check_against, type_list<as...>, test<check_against, a>::value> {};
+
+template<
+  template<typename, typename> class test,
+  typename check_against
+  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty)
+>
+struct contained_in_list<test, check_against, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, false> { constexpr static bool value = false; };
+
+/* see if an element is in a list and check for global flags */
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  int default_flags = 0,
+  bool last_check_positive = false,
+  int last_check_flags = default_flags
+>
+struct contained_in_list_gf;
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, h_list, default_flags, true, last_check_flags>
+{
+  constexpr static bool value = true;
+  constexpr static int global_flags = last_check_flags;
+};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename a,
+  typename... as,
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, type_list<a, as...>, default_flags, false, last_check_flags> : contained_in_list_gf<test, check_against, type_list<as...>, default_flags, test<check_against, a>::value, test<check_against, a>::global_flags> {};
+
+template<
+  template<typename, typename> class test,
+  typename check_against
+  EIGEN_TPL_PP_SPEC_HACK_DEFC(typename, empty),
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, type_list<EIGEN_TPL_PP_SPEC_HACK_USE(empty)>, default_flags, false, last_check_flags> { constexpr static bool value = false; constexpr static int global_flags = default_flags; };
+
+/* generic reductions */
+
+template<
+  typename Reducer,
+  typename... Ts
+> struct reduce;
+
+template<
+  typename Reducer
+> struct reduce<Reducer>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE int run() { return Reducer::Identity; }
+};
+
+template<
+  typename Reducer,
+  typename A
+> struct reduce<Reducer, A>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE A run(A a) { return a; }
+};
+
+template<
+  typename Reducer,
+  typename A,
+  typename... Ts
+> struct reduce<Reducer, A, Ts...>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, Ts... ts) -> decltype(Reducer::run(a, reduce<Reducer, Ts...>::run(ts...))) {
+    return Reducer::run(a, reduce<Reducer, Ts...>::run(ts...));
+  }
+};
+
+/* generic binary operations */
+
+struct sum_op           {
+  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a + b)   { return a + b;   }
+  static constexpr int Identity = 0;
+};
+struct product_op       {
+  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a * b)   { return a * b;   }
+  static constexpr int Identity = 1;
+};
+
+struct logical_and_op   { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a && b)  { return a && b;  } };
+struct logical_or_op    { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a || b)  { return a || b;  } };
+
+struct equal_op         { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a == b)  { return a == b;  } };
+struct not_equal_op     { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a != b)  { return a != b;  } };
+struct lesser_op        { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a < b)   { return a < b;   } };
+struct lesser_equal_op  { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a <= b)  { return a <= b;  } };
+struct greater_op       { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a > b)   { return a > b;   } };
+struct greater_equal_op { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a >= b)  { return a >= b;  } };
+
+/* generic unary operations */
+
+struct not_op                { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(!a)      { return !a;      } };
+struct negation_op           { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(-a)      { return -a;      } };
+struct greater_equal_zero_op { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(a >= 0)  { return a >= 0;  } };
+
+
+/* reductions for lists */
+
+// using auto -> return value spec makes ICC 13.0 and 13.1 crash here, so we have to hack it
+// together in front... (13.0 doesn't work with array_prod/array_reduce/... anyway, but 13.1
+// does...
+template<typename... Ts>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE decltype(reduce<product_op, Ts...>::run((*((Ts*)0))...)) arg_prod(Ts... ts)
+{
+  return reduce<product_op, Ts...>::run(ts...);
+}
+
+template<typename... Ts>
+constexpr EIGEN_STRONG_INLINE decltype(reduce<sum_op, Ts...>::run((*((Ts*)0))...)) arg_sum(Ts... ts)
+{
+  return reduce<sum_op, Ts...>::run(ts...);
+}
+
+/* reverse arrays */
+
+template<typename Array, int... n>
+constexpr EIGEN_STRONG_INLINE Array h_array_reverse(Array arr, numeric_list<int, n...>)
+{
+  return {{array_get<sizeof...(n) - n - 1>(arr)...}};
+}
+
+template<typename T, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<T, N> array_reverse(array<T, N> arr)
+{
+  return h_array_reverse(arr, typename gen_numeric_list<int, N>::type());
+}
+
+
+/* generic array reductions */
+
+// can't reuse standard reduce() interface above because Intel's Compiler
+// *really* doesn't like it, so we just reimplement the stuff
+// (start from N - 1 and work down to 0 because specialization for
+// n == N - 1 also doesn't work in Intel's compiler, so it goes into
+// an infinite loop)
+template<typename Reducer, typename T, std::size_t N, std::size_t n = N - 1>
+struct h_array_reduce {
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(array<T, N> arr, T identity) -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr)))
+  {
+    return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr));
+  }
+};
+
+template<typename Reducer, typename T, std::size_t N>
+struct h_array_reduce<Reducer, T, N, 0>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, N>& arr, T)
+  {
+    return array_get<0>(arr);
+  }
+};
+
+template<typename Reducer, typename T>
+struct h_array_reduce<Reducer, T, 0>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, 0>&, T identity)
+  {
+    return identity;
+  }
+};
+
+template<typename Reducer, typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_reduce(const array<T, N>& arr, T identity) -> decltype(h_array_reduce<Reducer, T, N>::run(arr, identity))
+{
+  return h_array_reduce<Reducer, T, N>::run(arr, identity);
+}
+
+/* standard array reductions */
+
+template<typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_sum(const array<T, N>& arr) -> decltype(array_reduce<sum_op, T, N>(arr, static_cast<T>(0)))
+{
+  return array_reduce<sum_op, T, N>(arr, static_cast<T>(0));
+}
+
+template<typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_prod(const array<T, N>& arr) -> decltype(array_reduce<product_op, T, N>(arr, static_cast<T>(1)))
+{
+  return array_reduce<product_op, T, N>(arr, static_cast<T>(1));
+}
+
+template<typename t>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
+  eigen_assert(a.size() > 0);
+  t prod = 1;
+  for (size_t i = 0; i < a.size(); ++i) { prod *= a[i]; }
+  return prod;
+}
+
+/* zip an array */
+
+template<typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> h_array_zip(array<A, N> a, array<B, N> b, numeric_list<int, n...>)
+{
+  return array<decltype(Op::run(A(), B())),N>{{ Op::run(array_get<n>(a), array_get<n>(b))... }};
+}
+
+template<typename Op, typename A, typename B, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> array_zip(array<A, N> a, array<B, N> b)
+{
+  return h_array_zip<Op>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* zip an array and reduce the result */
+
+template<typename Reducer, typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE auto h_array_zip_and_reduce(array<A, N> a, array<B, N> b, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...))
+{
+  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...);
+}
+
+template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
+constexpr EIGEN_STRONG_INLINE auto array_zip_and_reduce(array<A, N> a, array<B, N> b) -> decltype(h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type()))
+{
+  return h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array */
+
+template<typename Op, typename A, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> h_array_apply(array<A, N> a, numeric_list<int, n...>)
+{
+  return array<decltype(Op::run(A())),N>{{ Op::run(array_get<n>(a))... }};
+}
+
+template<typename Op, typename A, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> array_apply(array<A, N> a)
+{
+  return h_array_apply<Op>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array and reduce */
+
+template<typename Reducer, typename Op, typename A, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE auto h_array_apply_and_reduce(array<A, N> arr, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...))
+{
+  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...);
+}
+
+template<typename Reducer, typename Op, typename A, std::size_t N>
+constexpr EIGEN_STRONG_INLINE auto array_apply_and_reduce(array<A, N> a) -> decltype(h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type()))
+{
+  return h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* repeat a value n times (and make an array out of it
+ * usage:
+ *   array<int, 16> = repeat<16>(42);
+ */
+
+template<int n>
+struct h_repeat
+{
+  template<typename t, int... ii>
+  constexpr static EIGEN_STRONG_INLINE array<t, n> run(t v, numeric_list<int, ii...>)
+  {
+    return {{ typename id_numeric<int, ii, t>::type(v)... }};
+  }
+};
+
+template<int n, typename t>
+constexpr array<t, n> repeat(t v) { return h_repeat<n>::run(v, typename gen_numeric_list<int, n>::type()); }
+
+/* instantiate a class by a C-style array */
+template<class InstType, typename ArrType, std::size_t N, bool Reverse, typename... Ps>
+struct h_instantiate_by_c_array;
+
+template<class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, false, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, Ps..., ArrType>::run(arr + 1, args..., arr[0]);
+  }
+};
+
+template<class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, true, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, ArrType, Ps...>::run(arr + 1, arr[0], args...);
+  }
+};
+
+template<class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, false, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template<class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, true, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template<class InstType, typename ArrType, std::size_t N, bool Reverse = false>
+InstType instantiate_by_c_array(ArrType* arr)
+{
+  return h_instantiate_by_c_array<InstType, ArrType, N, Reverse>::run(arr);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11META_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
new file mode 100644
index 0000000..056736c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/CXX11Workarounds.h
@@ -0,0 +1,88 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11WORKAROUNDS_H
+#define EIGEN_CXX11WORKAROUNDS_H
+
+/* COMPATIBILITY CHECKS
+ * (so users of compilers that are too old get some realistic error messages)
+ */
+#if defined(__INTEL_COMPILER) && (__INTEL_COMPILER < 1310)
+#error Intel Compiler only supports required C++ features since version 13.1.
+// note that most stuff in principle works with 13.0 but when combining
+// some features, at some point 13.0 will just fail with an internal assertion
+#elif defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER) && (__GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 6))
+// G++ < 4.6 by default will continue processing the source files - even if we use #error to make
+// it error out. For this reason, we use the pragma to make sure G++ aborts at the first error
+// it sees. Unfortunately, that is still not our #error directive, but at least the output is
+// short enough the user has a chance to see that the compiler version is not sufficient for
+// the funky template mojo we use.
+#pragma GCC diagnostic error "-Wfatal-errors"
+#error GNU C++ Compiler (g++) only supports required C++ features since version 4.6.
+#endif
+
+/* Check that the compiler at least claims to support C++11. It might not be sufficient
+ * because the compiler may not implement it correctly, but at least we'll know.
+ * On the other hand, visual studio still doesn't claim to support C++11 although it's
+ * compliant enugh for our purpose.
+ */
+#if (EIGEN_COMP_CXXVER < 11)
+#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#pragma GCC diagnostic error "-Wfatal-errors"
+#endif
+#error This library needs at least a C++11 compliant compiler. If you use g++/clang, please enable the -std=c++11 compiler flag. (-std=c++0x on older versions.)
+#endif
+
+namespace Eigen {
+
+namespace internal {
+
+/* std::get is only constexpr in C++14, not yet in C++11
+ */
+
+
+template<std::size_t I_, class T> constexpr inline T&       array_get(std::vector<T>&       a) { return a[I_]; }
+template<std::size_t I_, class T> constexpr inline T&&      array_get(std::vector<T>&&      a) { return a[I_]; }
+template<std::size_t I_, class T> constexpr inline T const& array_get(std::vector<T> const& a) { return a[I_]; }
+
+/* Suppose you have a template of the form
+ * template<typename T> struct X;
+ * And you want to specialize it in such a way:
+ *    template<typename S1, typename... SN> struct X<Foo<S1, SN...>> { ::: };
+ *    template<>                            struct X<Foo<>>          { ::: };
+ * This will work in Intel's compiler 13.0, but only to some extent in g++ 4.6, since
+ * g++ can only match templates called with parameter packs if the number of template
+ * arguments is not a fixed size (so inside the first specialization, referencing
+ * X<Foo<Sn...>> will fail in g++). On the other hand, g++ will accept the following:
+ *    template<typename S...> struct X<Foo<S...>> { ::: }:
+ * as an additional (!) specialization, which will then only match the empty case.
+ * But Intel's compiler 13.0 won't accept that, it will only accept the empty syntax,
+ * so we have to create a workaround for this.
+ */
+#if defined(__GNUC__) && !defined(__INTEL_COMPILER)
+#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)    mt... n
+#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n)   , EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_USE(n)        n...
+#define EIGEN_TPL_PP_SPEC_HACK_USEC(n)       , n...
+#else
+#define EIGEN_TPL_PP_SPEC_HACK_DEF(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_DEFC(mt, n)
+#define EIGEN_TPL_PP_SPEC_HACK_USE(n)
+#define EIGEN_TPL_PP_SPEC_HACK_USEC(n)
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11WORKAROUNDS_H
+
+/*
+ * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
+ */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h
new file mode 100644
index 0000000..834b20b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/EmulateArray.h
@@ -0,0 +1,261 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EMULATE_ARRAY_H
+#define EIGEN_EMULATE_ARRAY_H
+
+
+
+// The array class is only available starting with cxx11. Emulate our own here
+// if needed. Beware, msvc still doesn't advertise itself as a c++11 compiler!
+// Moreover, CUDA doesn't support the STL containers, so we use our own instead.
+#if (__cplusplus <= 199711L && EIGEN_COMP_MSVC < 1900) || defined(EIGEN_GPUCC) || defined(EIGEN_AVOID_STL_ARRAY)
+
+namespace Eigen {
+template <typename T, size_t n> class array {
+ public:
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& operator[] (size_t index) { eigen_internal_assert(index < size()); return values[index]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { eigen_internal_assert(index < size()); return values[index]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& at(size_t index) { eigen_assert(index < size()); return values[index]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& at(size_t index) const { eigen_assert(index < size()); return values[index]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& front() { return values[0]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& back() { return values[n-1]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& back() const { return values[n-1]; }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  static std::size_t size() { return n; }
+
+  T values[n];
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array() { }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v) {
+    EIGEN_STATIC_ASSERT(n==1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2) {
+    EIGEN_STATIC_ASSERT(n==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3) {
+    EIGEN_STATIC_ASSERT(n==3, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3,
+                            const T& v4) {
+    EIGEN_STATIC_ASSERT(n==4, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5) {
+    EIGEN_STATIC_ASSERT(n==5, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5, const T& v6) {
+    EIGEN_STATIC_ASSERT(n==6, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5, const T& v6, const T& v7) {
+    EIGEN_STATIC_ASSERT(n==7, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(
+      const T& v1, const T& v2, const T& v3, const T& v4,
+      const T& v5, const T& v6, const T& v7, const T& v8) {
+    EIGEN_STATIC_ASSERT(n==8, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+    values[7] = v8;
+  }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(std::initializer_list<T> l) {
+    eigen_assert(l.size() == n);
+    internal::smart_copy(l.begin(), l.end(), values);
+  }
+#endif
+};
+
+
+// Specialize array for zero size
+template <typename T> class array<T, 0> {
+ public:
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& operator[] (size_t) {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& operator[] (size_t) const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& front() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& front() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& back() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& back() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array() : dummy() { }
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  EIGEN_DEVICE_FUNC array(std::initializer_list<T> l) : dummy() {
+    EIGEN_UNUSED_VARIABLE(l);
+    eigen_assert(l.size() == 0);
+  }
+#endif
+
+ private:
+  T dummy;
+};
+
+// Comparison operator
+// Todo: implement !=, <, <=, >,  and >=
+template<class T, std::size_t N>
+EIGEN_DEVICE_FUNC bool operator==(const array<T,N>& lhs, const array<T,N>& rhs) {
+  for (std::size_t i = 0; i < N; ++i) {
+    if (lhs[i] != rhs[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+
+
+namespace internal {
+template<std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(array<T,N>& a) {
+  return a[I_];
+}
+template<std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const array<T,N>& a) {
+  return a[I_];
+}
+
+template<class T, std::size_t N> struct array_size<array<T,N> > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<array<T,N>& > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<const array<T,N> > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<const array<T,N>& > {
+  enum { value = N };
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#else
+
+// The compiler supports c++11, and we're not targeting cuda: use std::array as Eigen::array
+#include <array>
+namespace Eigen {
+
+template <typename T, std::size_t N> using array = std::array<T, N>;
+
+namespace internal {
+/* std::get is only constexpr in C++14, not yet in C++11
+ *     - libstdc++ from version 4.7 onwards has it nevertheless,
+ *                                          so use that
+ *     - libstdc++ older versions: use _M_instance directly
+ *     - libc++ all versions so far: use __elems_ directly
+ *     - all other libs: use std::get to be portable, but
+ *                       this may not be constexpr
+ */
+#if defined(__GLIBCXX__) && __GLIBCXX__ < 20120322
+#define STD_GET_ARR_HACK             a._M_instance[I_]
+#elif defined(_LIBCPP_VERSION)
+#define STD_GET_ARR_HACK             a.__elems_[I_]
+#else
+#define STD_GET_ARR_HACK             std::template get<I_, T, N>(a)
+#endif
+
+template<std::size_t I_, class T, std::size_t N> constexpr inline T&       array_get(std::array<T,N>&       a) { return (T&)       STD_GET_ARR_HACK; }
+template<std::size_t I_, class T, std::size_t N> constexpr inline T&&      array_get(std::array<T,N>&&      a) { return (T&&)      STD_GET_ARR_HACK; }
+template<std::size_t I_, class T, std::size_t N> constexpr inline T const& array_get(std::array<T,N> const& a) { return (T const&) STD_GET_ARR_HACK; }
+
+#undef STD_GET_ARR_HACK
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif
+
+#endif  // EIGEN_EMULATE_ARRAY_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
new file mode 100644
index 0000000..277ab14
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/CXX11/src/util/MaxSizeVector.h
@@ -0,0 +1,158 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FIXEDSIZEVECTOR_H
+#define EIGEN_FIXEDSIZEVECTOR_H
+
+namespace Eigen {
+
+/** \class MaxSizeVector
+  * \ingroup Core
+  *
+  * \brief The MaxSizeVector class.
+  *
+  * The %MaxSizeVector provides a subset of std::vector functionality.
+  *
+  * The goal is to provide basic std::vector operations when using
+  * std::vector is not an option (e.g. on GPU or when compiling using
+  * FMA/AVX, as this can cause either compilation failures or illegal
+  * instruction failures).
+  *
+  * Beware: The constructors are not API compatible with these of
+  * std::vector.
+  */
+template <typename T>
+class MaxSizeVector {
+  static const size_t alignment = EIGEN_PLAIN_ENUM_MAX(EIGEN_ALIGNOF(T), sizeof(void*));
+ public:
+  // Construct a new MaxSizeVector, reserve n elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit MaxSizeVector(size_t n)
+      : reserve_(n), size_(0),
+        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
+  }
+
+  // Construct a new MaxSizeVector, reserve and resize to n.
+  // Copy the init value to all elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  MaxSizeVector(size_t n, const T& init)
+      : reserve_(n), size_(n),
+        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
+    size_t i = 0;
+    EIGEN_TRY
+    {
+      for(; i < size_; ++i) { new (&data_[i]) T(init); }
+    }
+    EIGEN_CATCH(...)
+    {
+      // Construction failed, destruct in reverse order:
+      for(; (i+1) > 0; --i) { data_[i-1].~T(); }
+      internal::handmade_aligned_free(data_);
+      EIGEN_THROW;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ~MaxSizeVector() {
+    for (size_t i = size_; i > 0; --i) {
+      data_[i-1].~T();
+    }
+    internal::handmade_aligned_free(data_);
+  }
+
+  void resize(size_t n) {
+    eigen_assert(n <= reserve_);
+    for (; size_ < n; ++size_) {
+      new (&data_[size_]) T;
+    }
+    for (; size_ > n; --size_) {
+      data_[size_-1].~T();
+    }
+    eigen_assert(size_ == n);
+  }
+
+  // Append new elements (up to reserved size).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void push_back(const T& t) {
+    eigen_assert(size_ < reserve_);
+    new (&data_[size_++]) T(t);
+  }
+
+  // For C++03 compatibility this only takes one argument
+  template<class X>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void emplace_back(const X& x) {
+    eigen_assert(size_ < reserve_);
+    new (&data_[size_++]) T(x);
+  }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T& operator[] (size_t i) const {
+    eigen_assert(i < size_);
+    return data_[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T& operator[] (size_t i) {
+    eigen_assert(i < size_);
+    return data_[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T& back() {
+    eigen_assert(size_ > 0);
+    return data_[size_ - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T& back() const {
+    eigen_assert(size_ > 0);
+    return data_[size_ - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void pop_back() {
+    eigen_assert(size_ > 0);
+    data_[--size_].~T();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  size_t size() const { return size_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  bool empty() const { return size_ == 0; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* data() { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* data() const { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* begin() { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* end() { return data_ + size_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* begin() const { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* end() const { return data_ + size_; }
+
+ private:
+  size_t reserve_;
+  size_t size_;
+  T* data_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_FIXEDSIZEVECTOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/EulerAngles b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/EulerAngles
new file mode 100644
index 0000000..f8f1c5d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/EulerAngles
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLES_MODULE_H
+#define EIGEN_EULERANGLES_MODULE_H
+
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Geometry"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+  * \defgroup EulerAngles_Module EulerAngles module
+  * \brief This module provides generic euler angles rotation.
+  *
+  * Euler angles are a way to represent 3D rotation.
+  *
+  * In order to use this module in your code, include this header:
+  * \code
+  * #include <unsupported/Eigen/EulerAngles>
+  * \endcode
+  *
+  * See \ref EulerAngles for more information.
+  *
+  */
+
+}
+
+#include "src/EulerAngles/EulerSystem.h"
+#include "src/EulerAngles/EulerAngles.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_EULERANGLES_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/FFT b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/FFT
new file mode 100644
index 0000000..c8c311a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/FFT
@@ -0,0 +1,419 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. 
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FFT_H
+#define EIGEN_FFT_H
+
+#include <complex>
+#include <vector>
+#include <map>
+#include "../../Eigen/Core"
+
+
+/**
+  * \defgroup FFT_Module Fast Fourier Transform module
+  *
+  * \code
+  * #include <unsupported/Eigen/FFT>
+  * \endcode
+  *
+  * This module provides Fast Fourier transformation, with a configurable backend
+  * implementation.
+  *
+  * The default implementation is based on kissfft. It is a small, free, and
+  * reasonably efficient default.
+  *
+  * There are currently two implementation backend:
+  *
+  * - fftw (http://www.fftw.org) : faster, GPL -- incompatible with Eigen in LGPL form, bigger code size.
+  * - MKL (http://en.wikipedia.org/wiki/Math_Kernel_Library) : fastest, commercial -- may be incompatible with Eigen in GPL form.
+  *
+  * \section FFTDesign Design
+  *
+  * The following design decisions were made concerning scaling and
+  * half-spectrum for real FFT.
+  *
+  * The intent is to facilitate generic programming and ease migrating code
+  * from  Matlab/octave.
+  * We think the default behavior of Eigen/FFT should favor correctness and
+  * generality over speed. Of course, the caller should be able to "opt-out" from this
+  * behavior and get the speed increase if they want it.
+  *
+  * 1) %Scaling:
+  * Other libraries (FFTW,IMKL,KISSFFT)  do not perform scaling, so there
+  * is a constant gain incurred after the forward&inverse transforms , so 
+  * IFFT(FFT(x)) = Kx;  this is done to avoid a vector-by-value multiply.  
+  * The downside is that algorithms that worked correctly in Matlab/octave 
+  * don't behave the same way once implemented in C++.
+  *
+  * How Eigen/FFT differs: invertible scaling is performed so IFFT( FFT(x) ) = x. 
+  *
+  * 2) Real FFT half-spectrum
+  * Other libraries use only half the frequency spectrum (plus one extra 
+  * sample for the Nyquist bin) for a real FFT, the other half is the 
+  * conjugate-symmetric of the first half.  This saves them a copy and some 
+  * memory.  The downside is the caller needs to have special logic for the 
+  * number of bins in complex vs real.
+  *
+  * How Eigen/FFT differs: The full spectrum is returned from the forward 
+  * transform.  This facilitates generic template programming by obviating 
+  * separate specializations for real vs complex.  On the inverse
+  * transform, only half the spectrum is actually used if the output type is real.
+  */
+ 
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#ifdef EIGEN_FFTW_DEFAULT
+// FFTW: faster, GPL -- incompatible with Eigen in LGPL form, bigger code size
+#  include <fftw3.h>
+#  include "src/FFT/ei_fftw_impl.h"
+   namespace Eigen {
+     //template <typename T> typedef struct internal::fftw_impl  default_fft_impl; this does not work
+     template <typename T> struct default_fft_impl : public internal::fftw_impl<T> {};
+   }
+#elif defined EIGEN_MKL_DEFAULT
+// TODO 
+// intel Math Kernel Library: fastest, commercial -- may be incompatible with Eigen in GPL form
+#  include "src/FFT/ei_imklfft_impl.h"
+   namespace Eigen {
+     template <typename T> struct default_fft_impl : public internal::imklfft_impl {};
+   }
+#else
+// internal::kissfft_impl:  small, free, reasonably efficient default, derived from kissfft
+//
+# include "src/FFT/ei_kissfft_impl.h"
+  namespace Eigen {
+     template <typename T> 
+       struct default_fft_impl : public internal::kissfft_impl<T> {};
+  }
+#endif
+
+namespace Eigen {
+
+ 
+// 
+template<typename T_SrcMat,typename T_FftIfc> struct fft_fwd_proxy;
+template<typename T_SrcMat,typename T_FftIfc> struct fft_inv_proxy;
+
+namespace internal {
+template<typename T_SrcMat,typename T_FftIfc>
+struct traits< fft_fwd_proxy<T_SrcMat,T_FftIfc> >
+{
+  typedef typename T_SrcMat::PlainObject ReturnType;
+};
+template<typename T_SrcMat,typename T_FftIfc>
+struct traits< fft_inv_proxy<T_SrcMat,T_FftIfc> >
+{
+  typedef typename T_SrcMat::PlainObject ReturnType;
+};
+}
+
+template<typename T_SrcMat,typename T_FftIfc> 
+struct fft_fwd_proxy
+ : public ReturnByValue<fft_fwd_proxy<T_SrcMat,T_FftIfc> >
+{
+  typedef DenseIndex Index;
+
+  fft_fwd_proxy(const T_SrcMat& src,T_FftIfc & fft, Index nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {}
+
+  template<typename T_DestMat> void evalTo(T_DestMat& dst) const;
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+protected:
+  const T_SrcMat & m_src;
+  T_FftIfc & m_ifc;
+  Index m_nfft;
+};
+
+template<typename T_SrcMat,typename T_FftIfc> 
+struct fft_inv_proxy
+ : public ReturnByValue<fft_inv_proxy<T_SrcMat,T_FftIfc> >
+{
+  typedef DenseIndex Index;
+
+  fft_inv_proxy(const T_SrcMat& src,T_FftIfc & fft, Index nfft) : m_src(src),m_ifc(fft), m_nfft(nfft) {}
+
+  template<typename T_DestMat> void evalTo(T_DestMat& dst) const;
+
+  Index rows() const { return m_src.rows(); }
+  Index cols() const { return m_src.cols(); }
+protected:
+  const T_SrcMat & m_src;
+  T_FftIfc & m_ifc;
+  Index m_nfft;
+};
+
+
+template <typename T_Scalar,
+         typename T_Impl=default_fft_impl<T_Scalar> >
+class FFT
+{
+  public:
+    typedef T_Impl impl_type;
+    typedef DenseIndex Index;
+    typedef typename impl_type::Scalar Scalar;
+    typedef typename impl_type::Complex Complex;
+
+    enum Flag {
+      Default=0, // goof proof
+      Unscaled=1,
+      HalfSpectrum=2,
+      // SomeOtherSpeedOptimization=4
+      Speedy=32767
+    };
+
+    FFT( const impl_type & impl=impl_type() , Flag flags=Default ) :m_impl(impl),m_flag(flags) { }
+
+    inline
+    bool HasFlag(Flag f) const { return (m_flag & (int)f) == f;}
+
+    inline
+    void SetFlag(Flag f) { m_flag |= (int)f;}
+
+    inline
+    void ClearFlag(Flag f) { m_flag &= (~(int)f);}
+
+    inline
+    void fwd( Complex * dst, const Scalar * src, Index nfft)
+    {
+        m_impl.fwd(dst,src,static_cast<int>(nfft));
+        if ( HasFlag(HalfSpectrum) == false)
+          ReflectSpectrum(dst,nfft);
+    }
+
+    inline
+    void fwd( Complex * dst, const Complex * src, Index nfft)
+    {
+        m_impl.fwd(dst,src,static_cast<int>(nfft));
+    }
+
+    /*
+    inline 
+    void fwd2(Complex * dst, const Complex * src, int n0,int n1)
+    {
+      m_impl.fwd2(dst,src,n0,n1);
+    }
+    */
+
+    template <typename _Input>
+    inline
+    void fwd( std::vector<Complex> & dst, const std::vector<_Input> & src) 
+    {
+      if ( NumTraits<_Input>::IsComplex == 0 && HasFlag(HalfSpectrum) )
+        dst.resize( (src.size()>>1)+1); // half the bins + Nyquist bin
+      else
+        dst.resize(src.size());
+      fwd(&dst[0],&src[0],src.size());
+    }
+
+    template<typename InputDerived, typename ComplexDerived>
+    inline
+    void fwd( MatrixBase<ComplexDerived> & dst, const MatrixBase<InputDerived> & src, Index nfft=-1)
+    {
+      typedef typename ComplexDerived::Scalar dst_type;
+      typedef typename InputDerived::Scalar src_type;
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(InputDerived)
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
+      EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,InputDerived) // size at compile-time
+      EIGEN_STATIC_ASSERT((internal::is_same<dst_type, Complex>::value),
+            YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+      EIGEN_STATIC_ASSERT(int(InputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit,
+            THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
+
+      if (nfft<1)
+        nfft = src.size();
+
+      if ( NumTraits< src_type >::IsComplex == 0 && HasFlag(HalfSpectrum) )
+        dst.derived().resize( (nfft>>1)+1);
+      else
+        dst.derived().resize(nfft);
+
+      if ( src.innerStride() != 1 || src.size() < nfft ) {
+        Matrix<src_type,1,Dynamic> tmp;
+        if (src.size()<nfft) {
+          tmp.setZero(nfft);
+          tmp.block(0,0,src.size(),1 ) = src;
+        }else{
+          tmp = src;
+        }
+        fwd( &dst[0],&tmp[0],nfft );
+      }else{
+        fwd( &dst[0],&src[0],nfft );
+      }
+    }
+ 
+    template<typename InputDerived>
+    inline
+    fft_fwd_proxy< MatrixBase<InputDerived>, FFT<T_Scalar,T_Impl> >
+    fwd( const MatrixBase<InputDerived> & src, Index nfft=-1)
+    {
+      return fft_fwd_proxy< MatrixBase<InputDerived> ,FFT<T_Scalar,T_Impl> >( src, *this,nfft );
+    }
+
+    template<typename InputDerived>
+    inline
+    fft_inv_proxy< MatrixBase<InputDerived>, FFT<T_Scalar,T_Impl> >
+    inv( const MatrixBase<InputDerived> & src, Index nfft=-1)
+    {
+      return  fft_inv_proxy< MatrixBase<InputDerived> ,FFT<T_Scalar,T_Impl> >( src, *this,nfft );
+    }
+
+    inline
+    void inv( Complex * dst, const Complex * src, Index nfft)
+    {
+      m_impl.inv( dst,src,static_cast<int>(nfft) );
+      if ( HasFlag( Unscaled ) == false)
+        scale(dst,Scalar(1./nfft),nfft); // scale the time series
+    }
+
+    inline
+    void inv( Scalar * dst, const Complex * src, Index nfft)
+    {
+      m_impl.inv( dst,src,static_cast<int>(nfft) );
+      if ( HasFlag( Unscaled ) == false)
+        scale(dst,Scalar(1./nfft),nfft); // scale the time series
+    }
+
+    template<typename OutputDerived, typename ComplexDerived>
+    inline
+    void inv( MatrixBase<OutputDerived> & dst, const MatrixBase<ComplexDerived> & src, Index nfft=-1)
+    {
+      typedef typename ComplexDerived::Scalar src_type;
+      typedef typename ComplexDerived::RealScalar real_type;
+      typedef typename OutputDerived::Scalar dst_type;
+      const bool realfft= (NumTraits<dst_type>::IsComplex == 0);
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived)
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(ComplexDerived)
+      EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(ComplexDerived,OutputDerived) // size at compile-time
+      EIGEN_STATIC_ASSERT((internal::is_same<src_type, Complex>::value),
+            YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+      EIGEN_STATIC_ASSERT(int(OutputDerived::Flags)&int(ComplexDerived::Flags)&DirectAccessBit,
+            THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES)
+
+      if (nfft<1) { //automatic FFT size determination
+        if ( realfft && HasFlag(HalfSpectrum) ) 
+          nfft = 2*(src.size()-1); //assume even fft size
+        else
+          nfft = src.size();
+      }
+      dst.derived().resize( nfft );
+
+      // check for nfft that does not fit the input data size
+      Index resize_input= ( realfft && HasFlag(HalfSpectrum) )
+        ? ( (nfft/2+1) - src.size() )
+        : ( nfft - src.size() );
+
+      if ( src.innerStride() != 1 || resize_input ) {
+        // if the vector is strided, then we need to copy it to a packed temporary
+        Matrix<src_type,1,Dynamic> tmp;
+        if ( resize_input ) {
+          size_t ncopy = (std::min)(src.size(),src.size() + resize_input);
+          tmp.setZero(src.size() + resize_input);
+          if ( realfft && HasFlag(HalfSpectrum) ) {
+            // pad at the Nyquist bin
+            tmp.head(ncopy) = src.head(ncopy);
+            tmp(ncopy-1) = real(tmp(ncopy-1)); // enforce real-only Nyquist bin
+          }else{
+            size_t nhead,ntail;
+            nhead = 1+ncopy/2-1; // range  [0:pi)
+            ntail = ncopy/2-1;   // range (-pi:0)
+            tmp.head(nhead) = src.head(nhead);
+            tmp.tail(ntail) = src.tail(ntail);
+            if (resize_input<0) { //shrinking -- create the Nyquist bin as the average of the two bins that fold into it
+              tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*real_type(.5);
+            }else{ // expanding -- split the old Nyquist bin into two halves
+              tmp(nhead) = src(nhead) * real_type(.5);
+              tmp(tmp.size()-nhead) = tmp(nhead);
+            }
+          }
+        }else{
+          tmp = src;
+        }
+        inv( &dst[0],&tmp[0], nfft);
+      }else{
+        inv( &dst[0],&src[0], nfft);
+      }
+    }
+
+    template <typename _Output>
+    inline
+    void inv( std::vector<_Output> & dst, const std::vector<Complex> & src,Index nfft=-1)
+    {
+      if (nfft<1)
+        nfft = ( NumTraits<_Output>::IsComplex == 0 && HasFlag(HalfSpectrum) ) ? 2*(src.size()-1) : src.size();
+      dst.resize( nfft );
+      inv( &dst[0],&src[0],nfft);
+    }
+
+
+    /*
+    // TODO: multi-dimensional FFTs
+    inline 
+    void inv2(Complex * dst, const Complex * src, int n0,int n1)
+    {
+      m_impl.inv2(dst,src,n0,n1);
+      if ( HasFlag( Unscaled ) == false)
+          scale(dst,1./(n0*n1),n0*n1);
+    }
+  */
+
+    inline
+    impl_type & impl() {return m_impl;}
+  private:
+
+    template <typename T_Data>
+    inline
+    void scale(T_Data * x,Scalar s,Index nx)
+    {
+#if 1
+      for (int k=0;k<nx;++k)
+        *x++ *= s;
+#else
+      if ( ((ptrdiff_t)x) & 15 )
+        Matrix<T_Data, Dynamic, 1>::Map(x,nx) *= s;
+      else
+        Matrix<T_Data, Dynamic, 1>::MapAligned(x,nx) *= s;
+         //Matrix<T_Data, Dynamic, Dynamic>::Map(x,nx) * s;
+#endif  
+    }
+
+    inline
+    void ReflectSpectrum(Complex * freq, Index nfft)
+    {
+      // create the implicit right-half spectrum (conjugate-mirror of the left-half)
+      Index nhbins=(nfft>>1)+1;
+      for (Index k=nhbins;k < nfft; ++k )
+        freq[k] = conj(freq[nfft-k]);
+    }
+
+    impl_type m_impl;
+    int m_flag;
+};
+
+template<typename T_SrcMat,typename T_FftIfc> 
+template<typename T_DestMat> inline 
+void fft_fwd_proxy<T_SrcMat,T_FftIfc>::evalTo(T_DestMat& dst) const
+{
+    m_ifc.fwd( dst, m_src, m_nfft);
+}
+
+template<typename T_SrcMat,typename T_FftIfc> 
+template<typename T_DestMat> inline 
+void fft_inv_proxy<T_SrcMat,T_FftIfc>::evalTo(T_DestMat& dst) const
+{
+    m_ifc.inv( dst, m_src, m_nfft);
+}
+
+}
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/IterativeSolvers b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/IterativeSolvers
new file mode 100644
index 0000000..a3f58d6
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/IterativeSolvers
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVE_SOLVERS_MODULE_H
+#define EIGEN_ITERATIVE_SOLVERS_MODULE_H
+
+#include "../../Eigen/Sparse"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/Householder"
+
+
+/**
+  * \defgroup IterativeLinearSolvers_Module Iterative solvers module
+  * This module aims to provide various iterative linear and non linear solver algorithms.
+  * It currently provides:
+  *  - a constrained conjugate gradient
+  *  - a Householder GMRES implementation
+  *  - an IDR(s) implementation
+  *  - a DGMRES implementation
+  *  - a MINRES implementation
+  *
+  * \code
+  * #include <unsupported/Eigen/IterativeSolvers>
+  * \endcode
+  */
+
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#ifndef EIGEN_MPL2_ONLY
+#include "src/IterativeSolvers/IterationController.h"
+#include "src/IterativeSolvers/ConstrainedConjGrad.h"
+#endif
+
+#include "src/IterativeSolvers/IncompleteLU.h"
+#include "src/IterativeSolvers/GMRES.h"
+#include "src/IterativeSolvers/DGMRES.h"
+//#include "src/IterativeSolvers/SSORPreconditioner.h"
+#include "src/IterativeSolvers/MINRES.h"
+#include "src/IterativeSolvers/IDRS.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+
+#endif // EIGEN_ITERATIVE_SOLVERS_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/KroneckerProduct b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/KroneckerProduct
new file mode 100644
index 0000000..5f5afb8
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/KroneckerProduct
@@ -0,0 +1,36 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KRONECKER_PRODUCT_MODULE_H
+#define EIGEN_KRONECKER_PRODUCT_MODULE_H
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include "../../Eigen/src/SparseCore/SparseUtil.h"
+
+namespace Eigen {
+
+/**
+  * \defgroup KroneckerProduct_Module KroneckerProduct module
+  *
+  * This module contains an experimental Kronecker product implementation.
+  *
+  * \code
+  * #include <Eigen/KroneckerProduct>
+  * \endcode
+  */
+
+} // namespace Eigen
+
+#include "src/KroneckerProduct/KroneckerTensorProduct.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_KRONECKER_PRODUCT_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/LevenbergMarquardt b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/LevenbergMarquardt
new file mode 100644
index 0000000..1090505
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/LevenbergMarquardt
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT_MODULE
+#define EIGEN_LEVENBERGMARQUARDT_MODULE
+
+// #include <vector>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/QR"
+#include "NumericalDiff"
+
+#include "../../Eigen/SparseQR"
+
+/**
+  * \defgroup LevenbergMarquardt_Module Levenberg-Marquardt module
+  *
+  * \code
+  * #include </Eigen/LevenbergMarquardt>
+  * \endcode
+  *
+  * 
+  */
+
+#include "../../Eigen/SparseCore"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+#include "src/LevenbergMarquardt/LMqrsolv.h"
+#include "src/LevenbergMarquardt/LMcovar.h"
+#include "src/LevenbergMarquardt/LMpar.h"
+
+#endif
+
+#include "src/LevenbergMarquardt/LevenbergMarquardt.h"
+#include "src/LevenbergMarquardt/LMonestep.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_LEVENBERGMARQUARDT_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MPRealSupport b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MPRealSupport
new file mode 100644
index 0000000..c4ea4ec
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MPRealSupport
@@ -0,0 +1,213 @@
+// This file is part of a joint effort between Eigen, a lightweight C++ template library
+// for linear algebra, and MPFR C++, a C++ interface to MPFR library (http://www.holoborodko.com/pavel/)
+//
+// Copyright (C) 2010-2012 Pavel Holoborodko <pavel@holoborodko.com>
+// Copyright (C) 2010 Konstantin Holoborodko <konstantin@holoborodko.com>
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MPREALSUPPORT_MODULE_H
+#define EIGEN_MPREALSUPPORT_MODULE_H
+
+#include "../../Eigen/Core"
+#include <mpreal.h>
+
+namespace Eigen {
+  
+/**
+  * \defgroup MPRealSupport_Module MPFRC++ Support module
+  * \code
+  * #include <Eigen/MPRealSupport>
+  * \endcode
+  *
+  * This module provides support for multi precision floating point numbers
+  * via the <a href="http://www.holoborodko.com/pavel/mpfr">MPFR C++</a>
+  * library which itself is built upon <a href="http://www.mpfr.org/">MPFR</a>/<a href="http://gmplib.org/">GMP</a>.
+  *
+  * \warning MPFR C++ is licensed under the GPL.
+  *
+  * You can find a copy of MPFR C++ that is known to be compatible in the unsupported/test/mpreal folder.
+  *
+  * Here is an example:
+  *
+\code
+#include <iostream>
+#include <Eigen/MPRealSupport>
+#include <Eigen/LU>
+using namespace mpfr;
+using namespace Eigen;
+int main()
+{
+  // set precision to 256 bits (double has only 53 bits)
+  mpreal::set_default_prec(256);
+  // Declare matrix and vector types with multi-precision scalar type
+  typedef Matrix<mpreal,Dynamic,Dynamic>  MatrixXmp;
+  typedef Matrix<mpreal,Dynamic,1>        VectorXmp;
+
+  MatrixXmp A = MatrixXmp::Random(100,100);
+  VectorXmp b = VectorXmp::Random(100);
+
+  // Solve Ax=b using LU
+  VectorXmp x = A.lu().solve(b);
+  std::cout << "relative error: " << (A*x - b).norm() / b.norm() << std::endl;
+  return 0;
+}
+\endcode
+  *
+  */
+	
+  template<> struct NumTraits<mpfr::mpreal>
+    : GenericNumTraits<mpfr::mpreal>
+  {
+    enum {
+      IsInteger = 0,
+      IsSigned = 1,
+      IsComplex = 0,
+      RequireInitialization = 1,
+      ReadCost = HugeCost,
+      AddCost  = HugeCost,
+      MulCost  = HugeCost
+    };
+
+    typedef mpfr::mpreal Real;
+    typedef mpfr::mpreal NonInteger;
+    
+    static inline Real highest  (long Precision = mpfr::mpreal::get_default_prec()) { return  mpfr::maxval(Precision); }
+    static inline Real lowest   (long Precision = mpfr::mpreal::get_default_prec()) { return -mpfr::maxval(Precision); }
+
+    // Constants
+    static inline Real Pi      (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_pi(Precision);        }
+    static inline Real Euler   (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_euler(Precision);     }
+    static inline Real Log2    (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_log2(Precision);      }
+    static inline Real Catalan (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::const_catalan(Precision);   }
+
+    static inline Real epsilon (long Precision = mpfr::mpreal::get_default_prec())  { return mpfr::machine_epsilon(Precision); }
+    static inline Real epsilon (const Real& x)                                      { return mpfr::machine_epsilon(x); }
+
+#ifdef MPREAL_HAVE_DYNAMIC_STD_NUMERIC_LIMITS
+    static inline int digits10 (long Precision = mpfr::mpreal::get_default_prec())  { return std::numeric_limits<Real>::digits10(Precision); }
+    static inline int digits10 (const Real& x)                                      { return std::numeric_limits<Real>::digits10(x); }
+    
+    static inline int digits ()               { return std::numeric_limits<Real>::digits(); }
+    static inline int digits (const Real& x)  { return std::numeric_limits<Real>::digits(x); }
+#endif
+
+    static inline Real dummy_precision()
+    {
+      mpfr_prec_t weak_prec = ((mpfr::mpreal::get_default_prec()-1) * 90) / 100;
+      return mpfr::machine_epsilon(weak_prec);
+    }
+  };
+
+  namespace internal {
+
+  template<> inline mpfr::mpreal random<mpfr::mpreal>()
+  {
+    return mpfr::random();
+  }
+
+  template<> inline mpfr::mpreal random<mpfr::mpreal>(const mpfr::mpreal& a, const mpfr::mpreal& b)
+  {
+    return a + (b-a) * random<mpfr::mpreal>();
+  }
+
+  inline bool isMuchSmallerThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return mpfr::abs(a) <= mpfr::abs(b) * eps;
+  }
+
+  inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return mpfr::isEqualFuzzy(a,b,eps);
+  }
+
+  inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& eps)
+  {
+    return a <= b || mpfr::isEqualFuzzy(a,b,eps);
+  }
+
+  template<> inline long double cast<mpfr::mpreal,long double>(const mpfr::mpreal& x)
+  { return x.toLDouble(); }
+
+  template<> inline double cast<mpfr::mpreal,double>(const mpfr::mpreal& x)
+  { return x.toDouble(); }
+
+  template<> inline long cast<mpfr::mpreal,long>(const mpfr::mpreal& x)
+  { return x.toLong(); }
+
+  template<> inline int cast<mpfr::mpreal,int>(const mpfr::mpreal& x)
+  { return int(x.toLong()); }
+
+  // Specialize GEBP kernel and traits for mpreal (no need for peeling, nor complicated stuff)
+  // This also permits to directly call mpfr's routines and avoid many temporaries produced by mpreal
+    template<>
+    class gebp_traits<mpfr::mpreal, mpfr::mpreal, false, false>
+    {
+    public:
+      typedef mpfr::mpreal ResScalar;
+      enum {
+        Vectorizable = false,
+        LhsPacketSize = 1,
+        RhsPacketSize = 1,
+        ResPacketSize = 1,
+        NumberOfRegisters = 1,
+        nr = 1,
+        mr = 1,
+        LhsProgress = 1,
+        RhsProgress = 1
+      };
+      typedef ResScalar LhsPacket;
+      typedef ResScalar RhsPacket;
+      typedef ResScalar ResPacket;
+      typedef LhsPacket LhsPacket4Packing;
+      
+    };
+
+
+
+    template<typename Index, typename DataMapper, bool ConjugateLhs, bool ConjugateRhs>
+    struct gebp_kernel<mpfr::mpreal,mpfr::mpreal,Index,DataMapper,1,1,ConjugateLhs,ConjugateRhs>
+    {
+      typedef mpfr::mpreal mpreal;
+
+      EIGEN_DONT_INLINE
+      void operator()(const DataMapper& res, const mpreal* blockA, const mpreal* blockB, 
+                      Index rows, Index depth, Index cols, const mpreal& alpha,
+                      Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0)
+      {
+        if(rows==0 || cols==0 || depth==0)
+          return;
+
+        mpreal  acc1(0,mpfr_get_prec(blockA[0].mpfr_srcptr())),
+                tmp (0,mpfr_get_prec(blockA[0].mpfr_srcptr()));
+
+        if(strideA==-1) strideA = depth;
+        if(strideB==-1) strideB = depth;
+
+        for(Index i=0; i<rows; ++i)
+        {
+          for(Index j=0; j<cols; ++j)
+          {
+            const mpreal *A = blockA + i*strideA + offsetA;
+            const mpreal *B = blockB + j*strideB + offsetB;
+            
+            acc1 = 0;
+            for(Index k=0; k<depth; k++)
+            {
+              mpfr_mul(tmp.mpfr_ptr(), A[k].mpfr_srcptr(), B[k].mpfr_srcptr(), mpreal::get_default_rnd());
+              mpfr_add(acc1.mpfr_ptr(), acc1.mpfr_ptr(), tmp.mpfr_ptr(),  mpreal::get_default_rnd());
+            }
+            
+            mpfr_mul(acc1.mpfr_ptr(), acc1.mpfr_srcptr(), alpha.mpfr_srcptr(), mpreal::get_default_rnd());
+            mpfr_add(res(i,j).mpfr_ptr(), res(i,j).mpfr_srcptr(), acc1.mpfr_srcptr(),  mpreal::get_default_rnd());
+          }
+        }
+      }
+    };
+  } // end namespace internal
+}
+
+#endif // EIGEN_MPREALSUPPORT_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MatrixFunctions b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MatrixFunctions
new file mode 100644
index 0000000..20c23d1
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MatrixFunctions
@@ -0,0 +1,504 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_FUNCTIONS
+#define EIGEN_MATRIX_FUNCTIONS
+
+#include <cfloat>
+#include <list>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/LU"
+#include "../../Eigen/Eigenvalues"
+
+/**
+  * \defgroup MatrixFunctions_Module Matrix functions module
+  * \brief This module aims to provide various methods for the computation of
+  * matrix functions. 
+  *
+  * To use this module, add 
+  * \code
+  * #include <unsupported/Eigen/MatrixFunctions>
+  * \endcode
+  * at the start of your source file.
+  *
+  * This module defines the following MatrixBase methods.
+  *  - \ref matrixbase_cos "MatrixBase::cos()", for computing the matrix cosine
+  *  - \ref matrixbase_cosh "MatrixBase::cosh()", for computing the matrix hyperbolic cosine
+  *  - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential
+  *  - \ref matrixbase_log "MatrixBase::log()", for computing the matrix logarithm
+  *  - \ref matrixbase_pow "MatrixBase::pow()", for computing the matrix power
+  *  - \ref matrixbase_matrixfunction "MatrixBase::matrixFunction()", for computing general matrix functions
+  *  - \ref matrixbase_sin "MatrixBase::sin()", for computing the matrix sine
+  *  - \ref matrixbase_sinh "MatrixBase::sinh()", for computing the matrix hyperbolic sine
+  *  - \ref matrixbase_sqrt "MatrixBase::sqrt()", for computing the matrix square root
+  *
+  * These methods are the main entry points to this module. 
+  *
+  * %Matrix functions are defined as follows.  Suppose that \f$ f \f$
+  * is an entire function (that is, a function on the complex plane
+  * that is everywhere complex differentiable).  Then its Taylor
+  * series
+  * \f[ f(0) + f'(0) x + \frac{f''(0)}{2} x^2 + \frac{f'''(0)}{3!} x^3 + \cdots \f]
+  * converges to \f$ f(x) \f$. In this case, we can define the matrix
+  * function by the same series:
+  * \f[ f(M) = f(0) + f'(0) M + \frac{f''(0)}{2} M^2 + \frac{f'''(0)}{3!} M^3 + \cdots \f]
+  *
+  */
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include "src/MatrixFunctions/MatrixExponential.h"
+#include "src/MatrixFunctions/MatrixFunction.h"
+#include "src/MatrixFunctions/MatrixSquareRoot.h"
+#include "src/MatrixFunctions/MatrixLogarithm.h"
+#include "src/MatrixFunctions/MatrixPower.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+
+/** 
+\page matrixbaseextra_page
+\ingroup MatrixFunctions_Module
+
+\section matrixbaseextra MatrixBase methods defined in the MatrixFunctions module
+
+The remainder of the page documents the following MatrixBase methods
+which are defined in the MatrixFunctions module.
+
+
+
+\subsection matrixbase_cos MatrixBase::cos()
+
+Compute the matrix cosine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \cos(M) \f$.
+
+This function computes the matrix cosine. Use ArrayBase::cos() for computing the entry-wise cosine.
+
+The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos().
+
+\sa \ref matrixbase_sin "sin()" for an example.
+
+
+
+\subsection matrixbase_cosh MatrixBase::cosh()
+
+Compute the matrix hyberbolic cosine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \cosh(M) \f$
+
+This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cosh().
+
+\sa \ref matrixbase_sinh "sinh()" for an example.
+
+
+
+\subsection matrixbase_exp MatrixBase::exp()
+
+Compute the matrix exponential.
+
+\code
+const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
+\endcode
+
+\param[in]  M  matrix whose exponential is to be computed.
+\returns    expression representing the matrix exponential of \p M.
+
+The matrix exponential of \f$ M \f$ is defined by
+\f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f]
+The matrix exponential can be used to solve linear ordinary
+differential equations: the solution of \f$ y' = My \f$ with the
+initial condition \f$ y(0) = y_0 \f$ is given by
+\f$ y(t) = \exp(M) y_0 \f$.
+
+The matrix exponential is different from applying the exp function to all the entries in the matrix.
+Use ArrayBase::exp() if you want to do the latter.
+
+The cost of the computation is approximately \f$ 20 n^3 \f$ for
+matrices of size \f$ n \f$. The number 20 depends weakly on the
+norm of the matrix.
+
+The matrix exponential is computed using the scaling-and-squaring
+method combined with Pad&eacute; approximation. The matrix is first
+rescaled, then the exponential of the reduced matrix is computed
+approximant, and then the rescaling is undone by repeated
+squaring. The degree of the Pad&eacute; approximant is chosen such
+that the approximation error is less than the round-off
+error. However, errors may accumulate during the squaring phase.
+
+Details of the algorithm can be found in: Nicholas J. Higham, "The
+scaling and squaring method for the matrix exponential revisited,"
+<em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179&ndash;1193,
+2005.
+
+Example: The following program checks that
+\f[ \exp \left[ \begin{array}{ccc}
+      0 & \frac14\pi & 0 \\
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis.
+
+\include MatrixExponential.cpp
+Output: \verbinclude MatrixExponential.out
+
+\note \p M has to be a matrix of \c float, \c double, `long double`
+\c complex<float>, \c complex<double>, or `complex<long double>` .
+
+
+\subsection matrixbase_log MatrixBase::log()
+
+Compute the matrix logarithm.
+
+\code
+const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const
+\endcode
+
+\param[in]  M  invertible matrix whose logarithm is to be computed.
+\returns    expression representing the matrix logarithm root of \p M.
+
+The matrix logarithm of \f$ M \f$ is a matrix \f$ X \f$ such that 
+\f$ \exp(X) = M \f$ where exp denotes the matrix exponential. As for
+the scalar logarithm, the equation \f$ \exp(X) = M \f$ may have
+multiple solutions; this function returns a matrix whose eigenvalues
+have imaginary part in the interval \f$ (-\pi,\pi] \f$.
+
+The matrix logarithm is different from applying the log function to all the entries in the matrix.
+Use ArrayBase::log() if you want to do the latter.
+
+In the real case, the matrix \f$ M \f$ should be invertible and
+it should have no eigenvalues which are real and negative (pairs of
+complex conjugate eigenvalues are allowed). In the complex case, it
+only needs to be invertible.
+
+This function computes the matrix logarithm using the Schur-Parlett
+algorithm as implemented by MatrixBase::matrixFunction(). The
+logarithm of an atomic block is computed by MatrixLogarithmAtomic,
+which uses direct computation for 1-by-1 and 2-by-2 blocks and an
+inverse scaling-and-squaring algorithm for bigger blocks, with the
+square roots computed by MatrixBase::sqrt().
+
+Details of the algorithm can be found in Section 11.6.2 of:
+Nicholas J. Higham,
+<em>Functions of Matrices: Theory and Computation</em>,
+SIAM 2008. ISBN 978-0-898716-46-7.
+
+Example: The following program checks that
+\f[ \log \left[ \begin{array}{ccc} 
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      0 & \frac14\pi & 0 \\ 
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0 
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis. This is the inverse of the example used in the
+documentation of \ref matrixbase_exp "exp()".
+
+\include MatrixLogarithm.cpp
+Output: \verbinclude MatrixLogarithm.out
+
+\note \p M has to be a matrix of \c float, \c double, `long
+double`, \c complex<float>, \c complex<double>, or `complex<long double>`.
+
+\sa MatrixBase::exp(), MatrixBase::matrixFunction(), 
+    class MatrixLogarithmAtomic, MatrixBase::sqrt().
+
+
+\subsection matrixbase_pow MatrixBase::pow()
+
+Compute the matrix raised to arbitrary real power.
+
+\code
+const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) const
+\endcode
+
+\param[in]  M  base of the matrix power, should be a square matrix.
+\param[in]  p  exponent of the matrix power.
+
+The matrix power \f$ M^p \f$ is defined as \f$ \exp(p \log(M)) \f$,
+where exp denotes the matrix exponential, and log denotes the matrix
+logarithm. This is different from raising all the entries in the matrix
+to the p-th power. Use ArrayBase::pow() if you want to do the latter.
+
+If \p p is complex, the scalar type of \p M should be the type of \p
+p . \f$ M^p \f$ simply evaluates into \f$ \exp(p \log(M)) \f$.
+Therefore, the matrix \f$ M \f$ should meet the conditions to be an
+argument of matrix logarithm.
+
+If \p p is real, it is casted into the real scalar type of \p M. Then
+this function computes the matrix power using the Schur-Pad&eacute;
+algorithm as implemented by class MatrixPower. The exponent is split
+into integral part and fractional part, where the fractional part is
+in the interval \f$ (-1, 1) \f$. The main diagonal and the first
+super-diagonal is directly computed.
+
+If \p M is singular with a semisimple zero eigenvalue and \p p is
+positive, the Schur factor \f$ T \f$ is reordered with Givens
+rotations, i.e.
+
+\f[ T = \left[ \begin{array}{cc}
+      T_1 & T_2 \\
+      0   & 0
+    \end{array} \right] \f]
+
+where \f$ T_1 \f$ is invertible. Then \f$ T^p \f$ is given by
+
+\f[ T^p = \left[ \begin{array}{cc}
+      T_1^p & T_1^{-1} T_1^p T_2 \\
+      0     & 0
+    \end{array}. \right] \f]
+
+\warning Fractional power of a matrix with a non-semisimple zero
+eigenvalue is not well-defined. We introduce an assertion failure
+against inaccurate result, e.g. \code
+#include <unsupported/Eigen/MatrixFunctions>
+#include <iostream>
+
+int main()
+{
+  Eigen::Matrix4d A;
+  A << 0, 0, 2, 3,
+       0, 0, 4, 5,
+       0, 0, 6, 7,
+       0, 0, 8, 9;
+  std::cout << A.pow(0.37) << std::endl;
+  
+  // The 1 makes eigenvalue 0 non-semisimple.
+  A.coeffRef(0, 1) = 1;
+
+  // This fails if EIGEN_NO_DEBUG is undefined.
+  std::cout << A.pow(0.37) << std::endl;
+
+  return 0;
+}
+\endcode
+
+Details of the algorithm can be found in: Nicholas J. Higham and
+Lijing Lin, "A Schur-Pad&eacute; algorithm for fractional powers of a
+matrix," <em>SIAM J. %Matrix Anal. Applic.</em>,
+<b>32(3)</b>:1056&ndash;1078, 2011.
+
+Example: The following program checks that
+\f[ \left[ \begin{array}{ccc}
+      \cos1 & -\sin1 & 0 \\
+      \sin1 & \cos1 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]^{\frac14\pi} = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to \f$ \frac14\pi \f$ rotations of 1 radian around
+the z-axis.
+
+\include MatrixPower.cpp
+Output: \verbinclude MatrixPower.out
+
+MatrixBase::pow() is user-friendly. However, there are some
+circumstances under which you should use class MatrixPower directly.
+MatrixPower can save the result of Schur decomposition, so it's
+better for computing various powers for the same matrix.
+
+Example:
+\include MatrixPower_optimal.cpp
+Output: \verbinclude MatrixPower_optimal.out
+
+\note \p M has to be a matrix of \c float, \c double, `long
+double`, \c complex<float>, \c complex<double>, or
+\c complex<long double> .
+
+\sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower.
+
+
+\subsection matrixbase_matrixfunction MatrixBase::matrixFunction()
+
+Compute a matrix function.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const
+\endcode
+
+\param[in]  M  argument of matrix function, should be a square matrix.
+\param[in]  f  an entire function; \c f(x,n) should compute the n-th
+derivative of f at x.
+\returns  expression representing \p f applied to \p M.
+
+Suppose that \p M is a matrix whose entries have type \c Scalar. 
+Then, the second argument, \p f, should be a function with prototype
+\code 
+ComplexScalar f(ComplexScalar, int) 
+\endcode
+where \c ComplexScalar = \c std::complex<Scalar> if \c Scalar is
+real (e.g., \c float or \c double) and \c ComplexScalar =
+\c Scalar if \c Scalar is complex. The return value of \c f(x,n)
+should be \f$ f^{(n)}(x) \f$, the n-th derivative of f at x.
+
+This routine uses the algorithm described in:
+Philip Davies and Nicholas J. Higham, 
+"A Schur-Parlett algorithm for computing matrix functions", 
+<em>SIAM J. %Matrix Anal. Applic.</em>, <b>25</b>:464&ndash;485, 2003.
+
+The actual work is done by the MatrixFunction class.
+
+Example: The following program checks that
+\f[ \exp \left[ \begin{array}{ccc} 
+      0 & \frac14\pi & 0 \\ 
+      -\frac14\pi & 0 & 0 \\
+      0 & 0 & 0 
+    \end{array} \right] = \left[ \begin{array}{ccc}
+      \frac12\sqrt2 & -\frac12\sqrt2 & 0 \\
+      \frac12\sqrt2 & \frac12\sqrt2 & 0 \\
+      0 & 0 & 1
+    \end{array} \right]. \f]
+This corresponds to a rotation of \f$ \frac14\pi \f$ radians around
+the z-axis. This is the same example as used in the documentation
+of \ref matrixbase_exp "exp()".
+
+\include MatrixFunction.cpp
+Output: \verbinclude MatrixFunction.out
+
+Note that the function \c expfn is defined for complex numbers 
+\c x, even though the matrix \c A is over the reals. Instead of
+\c expfn, we could also have used StdStemFunctions::exp:
+\code
+A.matrixFunction(StdStemFunctions<std::complex<double> >::exp, &B);
+\endcode
+
+
+
+\subsection matrixbase_sin MatrixBase::sin()
+
+Compute the matrix sine.
+
+\code
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \sin(M) \f$.
+
+This function computes the matrix sine. Use ArrayBase::sin() for computing the entry-wise sine.
+
+The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin().
+
+Example: \include MatrixSine.cpp
+Output: \verbinclude MatrixSine.out
+
+
+
+\subsection matrixbase_sinh MatrixBase::sinh()
+
+Compute the matrix hyperbolic sine.
+
+\code
+MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
+\endcode
+
+\param[in]  M  a square matrix.
+\returns  expression representing \f$ \sinh(M) \f$
+
+This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sinh().
+
+Example: \include MatrixSinh.cpp
+Output: \verbinclude MatrixSinh.out
+
+
+\subsection matrixbase_sqrt MatrixBase::sqrt()
+
+Compute the matrix square root.
+
+\code
+const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
+\endcode
+
+\param[in]  M  invertible matrix whose square root is to be computed.
+\returns    expression representing the matrix square root of \p M.
+
+The matrix square root of \f$ M \f$ is the matrix \f$ M^{1/2} \f$
+whose square is the original matrix; so if \f$ S = M^{1/2} \f$ then
+\f$ S^2 = M \f$. This is different from taking the square root of all
+the entries in the matrix; use ArrayBase::sqrt() if you want to do the
+latter.
+
+In the <b>real case</b>, the matrix \f$ M \f$ should be invertible and
+it should have no eigenvalues which are real and negative (pairs of
+complex conjugate eigenvalues are allowed). In that case, the matrix
+has a square root which is also real, and this is the square root
+computed by this function. 
+
+The matrix square root is computed by first reducing the matrix to
+quasi-triangular form with the real Schur decomposition. The square
+root of the quasi-triangular matrix can then be computed directly. The
+cost is approximately \f$ 25 n^3 \f$ real flops for the real Schur
+decomposition and \f$ 3\frac13 n^3 \f$ real flops for the remainder
+(though the computation time in practice is likely more than this
+indicates).
+
+Details of the algorithm can be found in: Nicholas J. Highan,
+"Computing real square roots of a real matrix", <em>Linear Algebra
+Appl.</em>, 88/89:405&ndash;430, 1987.
+
+If the matrix is <b>positive-definite symmetric</b>, then the square
+root is also positive-definite symmetric. In this case, it is best to
+use SelfAdjointEigenSolver::operatorSqrt() to compute it.
+
+In the <b>complex case</b>, the matrix \f$ M \f$ should be invertible;
+this is a restriction of the algorithm. The square root computed by
+this algorithm is the one whose eigenvalues have an argument in the
+interval \f$ (-\frac12\pi, \frac12\pi] \f$. This is the usual branch
+cut.
+
+The computation is the same as in the real case, except that the
+complex Schur decomposition is used to reduce the matrix to a
+triangular matrix. The theoretical cost is the same. Details are in:
+&Aring;ke Bj&ouml;rck and Sven Hammarling, "A Schur method for the
+square root of a matrix", <em>Linear Algebra Appl.</em>,
+52/53:127&ndash;140, 1983.
+
+Example: The following program checks that the square root of
+\f[ \left[ \begin{array}{cc} 
+              \cos(\frac13\pi) & -\sin(\frac13\pi) \\
+              \sin(\frac13\pi) & \cos(\frac13\pi)
+    \end{array} \right], \f]
+corresponding to a rotation over 60 degrees, is a rotation over 30 degrees:
+\f[ \left[ \begin{array}{cc} 
+              \cos(\frac16\pi) & -\sin(\frac16\pi) \\
+              \sin(\frac16\pi) & \cos(\frac16\pi)
+    \end{array} \right]. \f]
+
+\include MatrixSquareRoot.cpp
+Output: \verbinclude MatrixSquareRoot.out
+
+\sa class RealSchur, class ComplexSchur, class MatrixSquareRoot,
+    SelfAdjointEigenSolver::operatorSqrt().
+
+*/
+
+#endif // EIGEN_MATRIX_FUNCTIONS
+
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MoreVectorization b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MoreVectorization
new file mode 100644
index 0000000..7662b47
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/MoreVectorization
@@ -0,0 +1,24 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MOREVECTORIZATION_MODULE_H
+#define EIGEN_MOREVECTORIZATION_MODULE_H
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+  * \defgroup MoreVectorization More vectorization module
+  */
+
+}
+
+#include "src/MoreVectorization/MathFunctions.h"
+
+#endif // EIGEN_MOREVECTORIZATION_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NonLinearOptimization b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NonLinearOptimization
new file mode 100644
index 0000000..961f192
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NonLinearOptimization
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE
+#define EIGEN_NONLINEAROPTIMIZATION_MODULE
+
+#include <vector>
+
+#include "../../Eigen/Core"
+#include "../../Eigen/Jacobi"
+#include "../../Eigen/QR"
+#include "NumericalDiff"
+
+/**
+  * \defgroup NonLinearOptimization_Module Non linear optimization module
+  *
+  * \code
+  * #include <unsupported/Eigen/NonLinearOptimization>
+  * \endcode
+  *
+  * This module provides implementation of two important algorithms in non linear
+  * optimization. In both cases, we consider a system of non linear functions. Of
+  * course, this should work, and even work very well if those functions are
+  * actually linear. But if this is so, you should probably better use other
+  * methods more fitted to this special case.
+  *
+  * One algorithm allows to find a least-squares solution of such a system
+  * (Levenberg-Marquardt algorithm) and the second one is used to find 
+  * a zero for the system (Powell hybrid "dogleg" method).
+  *
+  * This code is a port of minpack (http://en.wikipedia.org/wiki/MINPACK).
+  * Minpack is a very famous, old, robust and well renowned package, written in
+  * fortran. Those implementations have been carefully tuned, tested, and used
+  * for several decades.
+  *
+  * The original fortran code was automatically translated using f2c (http://en.wikipedia.org/wiki/F2c) in C,
+  * then c++, and then cleaned by several different authors.
+  * The last one of those cleanings being our starting point : 
+  * http://devernay.free.fr/hacks/cminpack.html
+  * 
+  * Finally, we ported this code to Eigen, creating classes and API
+  * coherent with Eigen. When possible, we switched to Eigen
+  * implementation, such as most linear algebra (vectors, matrices, stable norms).
+  *
+  * Doing so, we were very careful to check the tests we setup at the very
+  * beginning, which ensure that the same results are found.
+  *
+  * \section Tests Tests
+  * 
+  * The tests are placed in the file unsupported/test/NonLinear.cpp.
+  * 
+  * There are two kinds of tests : those that come from examples bundled with cminpack.
+  * They guaranty we get the same results as the original algorithms (value for 'x',
+  * for the number of evaluations of the function, and for the number of evaluations
+  * of the Jacobian if ever).
+  * 
+  * Other tests were added by myself at the very beginning of the 
+  * process and check the results for Levenberg-Marquardt using the reference data 
+  * on http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml. Since then i've 
+  * carefully checked that the same results were obtained when modifying the
+  * code. Please note that we do not always get the exact same decimals as they do,
+  * but this is ok : they use 128bits float, and we do the tests using the C type 'double',
+  * which is 64 bits on most platforms (x86 and amd64, at least).
+  * I've performed those tests on several other implementations of Levenberg-Marquardt, and
+  * (c)minpack performs VERY well compared to those, both in accuracy and speed.
+  * 
+  * The documentation for running the tests is on the wiki
+  * http://eigen.tuxfamily.org/index.php?title=Tests
+  * 
+  * \section API API: overview of methods
+  * 
+  * Both algorithms needs a functor computing the Jacobian. It can be computed by
+  * hand, using auto-differentiation (see \ref AutoDiff_Module), or using numerical
+  * differences (see \ref NumericalDiff_Module). For instance:
+  *\code
+  * MyFunc func;
+  * NumericalDiff<MyFunc> func_with_num_diff(func);
+  * LevenbergMarquardt<NumericalDiff<MyFunc> > lm(func_with_num_diff);
+  * \endcode
+  * For HybridNonLinearSolver, the method solveNumericalDiff() does the above wrapping for
+  * you.
+  * 
+  * The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and 
+  * HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original 
+  * minpack package that you probably should NOT use until you are porting a code that
+  * was previously using minpack. They just define a 'simple' API with default values 
+  * for some parameters.
+  * 
+  * All algorithms are provided using two APIs :
+  *     - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants : 
+  * this way the caller have control over the steps
+  *     - one where the user just calls a method (optimize() or solve()) which will 
+  * handle the loop: init + loop until a stop condition is met. Those are provided for
+  *  convenience.
+  * 
+  * As an example, the method LevenbergMarquardt::minimize() is 
+  * implemented as follow: 
+  * \code
+  * Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType  &x, const int mode)
+  * {
+  *     Status status = minimizeInit(x, mode);
+  *     do {
+  *         status = minimizeOneStep(x, mode);
+  *     } while (status==Running);
+  *     return status;
+  * }
+  * \endcode
+  * 
+  * \section examples Examples
+  * 
+  * The easiest way to understand how to use this module is by looking at the many examples in the file
+  * unsupported/test/NonLinearOptimization.cpp.
+  */
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+#include "src/NonLinearOptimization/qrsolv.h"
+#include "src/NonLinearOptimization/r1updt.h"
+#include "src/NonLinearOptimization/r1mpyq.h"
+#include "src/NonLinearOptimization/rwupdt.h"
+#include "src/NonLinearOptimization/fdjac1.h"
+#include "src/NonLinearOptimization/lmpar.h"
+#include "src/NonLinearOptimization/dogleg.h"
+#include "src/NonLinearOptimization/covar.h"
+
+#include "src/NonLinearOptimization/chkder.h"
+
+#endif
+
+#include "src/NonLinearOptimization/HybridNonLinearSolver.h"
+#include "src/NonLinearOptimization/LevenbergMarquardt.h"
+
+
+#endif // EIGEN_NONLINEAROPTIMIZATION_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NumericalDiff b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NumericalDiff
new file mode 100644
index 0000000..0668f96
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/NumericalDiff
@@ -0,0 +1,56 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMERICALDIFF_MODULE
+#define EIGEN_NUMERICALDIFF_MODULE
+
+#include "../../Eigen/Core"
+
+namespace Eigen {
+
+/**
+  * \defgroup NumericalDiff_Module Numerical differentiation module
+  *
+  * \code
+  * #include <unsupported/Eigen/NumericalDiff>
+  * \endcode
+  *
+  * See http://en.wikipedia.org/wiki/Numerical_differentiation
+  *
+  * Warning : this should NOT be confused with automatic differentiation, which
+  * is a different method and has its own module in Eigen : \ref
+  * AutoDiff_Module.
+  *
+  * Currently only "Forward" and "Central" schemes are implemented. Those
+  * are basic methods, and there exist some more elaborated way of
+  * computing such approximates. They are implemented using both
+  * proprietary and free software, and usually requires linking to an
+  * external library. It is very easy for you to write a functor
+  * using such software, and the purpose is quite orthogonal to what we
+  * want to achieve with Eigen.
+  *
+  * This is why we will not provide wrappers for every great numerical
+  * differentiation software that exist, but should rather stick with those
+  * basic ones, that still are useful for testing.
+  *
+  * Also, the \ref NonLinearOptimization_Module needs this in order to
+  * provide full features compatibility with the original (c)minpack
+  * package.
+  *
+  */
+}
+
+//@{
+
+#include "src/NumericalDiff/NumericalDiff.h"
+
+//@}
+
+
+#endif // EIGEN_NUMERICALDIFF_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/OpenGLSupport b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/OpenGLSupport
new file mode 100644
index 0000000..f8c2130
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/OpenGLSupport
@@ -0,0 +1,322 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_OPENGL_MODULE
+#define EIGEN_OPENGL_MODULE
+
+#include "../../Eigen/Geometry"
+
+#if defined(__APPLE_CC__)
+  #include <OpenGL/gl.h>
+#else
+  #include <GL/gl.h>
+#endif
+
+namespace Eigen {
+
+/**
+  * \defgroup OpenGLSUpport_Module OpenGL Support module
+  *
+  * This module provides wrapper functions for a couple of OpenGL functions
+  * which simplify the way to pass Eigen's object to openGL.
+  * Here is an example:
+  * 
+  * \code
+  * // You need to add path_to_eigen/unsupported to your include path.
+  * #include <Eigen/OpenGLSupport>
+  * // ...
+  * Vector3f x, y;
+  * Matrix3f rot;
+  * 
+  * glVertex(y + x * rot);
+  * 
+  * Quaternion q;
+  * glRotate(q);
+  * 
+  * // ...
+  * \endcode
+  *
+  */
+//@{
+
+#define EIGEN_GL_FUNC_DECLARATION(FUNC)                                                                             \
+namespace internal {                                                                                                \
+  template< typename XprType,                                                                                       \
+            typename Scalar = typename XprType::Scalar,                                                             \
+            int Rows = XprType::RowsAtCompileTime,                                                                  \
+            int Cols = XprType::ColsAtCompileTime,                                                                  \
+            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags&LinearAccessBit)                         \
+                              && bool(XprType::Flags&DirectAccessBit)                                               \
+                              && (XprType::IsVectorAtCompileTime || (XprType::Flags&RowMajorBit)==0)>               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl);                                                                      \
+                                                                                                                    \
+  template<typename XprType, typename Scalar, int Rows, int Cols>                                                   \
+  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType,Scalar,Rows,Cols,false> {                                     \
+    inline static void run(const XprType& p) {                                                                      \
+      EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<typename plain_matrix_type_column_major<XprType>::type>::run(p); }       \
+  };                                                                                                                \
+}                                                                                                                   \
+                                                                                                                    \
+template<typename Derived> inline void FUNC(const Eigen::DenseBase<Derived>& p) {                                   \
+  EIGEN_CAT(EIGEN_CAT(internal::gl_,FUNC),_impl)<Derived>::run(p.derived());                                        \
+}
+
+
+#define EIGEN_GL_FUNC_SPECIALIZATION_MAT(FUNC,SCALAR,ROWS,COLS,SUFFIX)                                              \
+namespace internal {                                                                                                \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, ROWS, COLS, true> {      \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
+  };                                                                                                                \
+}
+
+  
+#define EIGEN_GL_FUNC_SPECIALIZATION_VEC(FUNC,SCALAR,SIZE,SUFFIX)                                                   \
+namespace internal {                                                                                                \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, SIZE, 1, true> {         \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
+  };                                                                                                                \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, 1, SIZE, true> {         \
+    inline static void run(const XprType& p) { FUNC##SUFFIX(p.data()); }                                            \
+  };                                                                                                                \
+}
+
+  
+EIGEN_GL_FUNC_DECLARATION       (glVertex)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    2,2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  2,2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  2,2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 2,2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    3,3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  3,3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 3,3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,int,    4,4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,short,  4,4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,float,  4,4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glVertex,double, 4,4dv)
+
+EIGEN_GL_FUNC_DECLARATION       (glTexCoord)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    2,2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  2,2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  2,2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 2,2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    3,3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  3,3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 3,3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,int,    4,4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,short,  4,4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,float,  4,4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTexCoord,double, 4,4dv)
+
+EIGEN_GL_FUNC_DECLARATION       (glColor)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    2,2iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  2,2sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  2,2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 2,2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    3,3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  3,3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 3,3dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,int,    4,4iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,short,  4,4sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,float,  4,4fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glColor,double, 4,4dv)
+
+EIGEN_GL_FUNC_DECLARATION       (glNormal)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,int,    3,3iv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,short,  3,3sv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glNormal,double, 3,3dv)
+
+inline void glScale2fv(const float*  v) { glScalef(v[0], v[1], 1.f);  }
+inline void glScale2dv(const double* v) { glScaled(v[0], v[1], 1.0);  }
+inline void glScale3fv(const float*  v) { glScalef(v[0], v[1], v[2]); }
+inline void glScale3dv(const double* v) { glScaled(v[0], v[1], v[2]); }
+
+EIGEN_GL_FUNC_DECLARATION       (glScale)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,float,  2,2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,double, 2,2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glScale,double, 3,3dv)
+
+template<typename Scalar> void glScale(const UniformScaling<Scalar>& s)  { glScale(Matrix<Scalar,3,1>::Constant(s.factor())); }
+
+inline void glTranslate2fv(const float*  v) { glTranslatef(v[0], v[1], 0.f);  }
+inline void glTranslate2dv(const double* v) { glTranslated(v[0], v[1], 0.0);  }
+inline void glTranslate3fv(const float*  v) { glTranslatef(v[0], v[1], v[2]); }
+inline void glTranslate3dv(const double* v) { glTranslated(v[0], v[1], v[2]); }
+
+EIGEN_GL_FUNC_DECLARATION       (glTranslate)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,float,  2,2fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,double, 2,2dv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,float,  3,3fv)
+EIGEN_GL_FUNC_SPECIALIZATION_VEC(glTranslate,double, 3,3dv)
+
+template<typename Scalar> void glTranslate(const Translation<Scalar,2>& t)  { glTranslate(t.vector()); }
+template<typename Scalar> void glTranslate(const Translation<Scalar,3>& t)  { glTranslate(t.vector()); }
+
+EIGEN_GL_FUNC_DECLARATION       (glMultMatrix)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix,float,  4,4,f)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glMultMatrix,double, 4,4,d)
+
+template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,Affine>& t)        { glMultMatrix(t.matrix()); }
+template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,Projective>& t)    { glMultMatrix(t.matrix()); }
+template<typename Scalar> void glMultMatrix(const Transform<Scalar,3,AffineCompact>& t) { glMultMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
+
+EIGEN_GL_FUNC_DECLARATION       (glLoadMatrix)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix,float,  4,4,f)
+EIGEN_GL_FUNC_SPECIALIZATION_MAT(glLoadMatrix,double, 4,4,d)
+
+template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Affine>& t)        { glLoadMatrix(t.matrix()); }
+template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,Projective>& t)    { glLoadMatrix(t.matrix()); }
+template<typename Scalar> void glLoadMatrix(const Transform<Scalar,3,AffineCompact>& t) { glLoadMatrix(Transform<Scalar,3,Affine>(t).matrix()); }
+
+inline void glRotate(const Rotation2D<float>& rot)
+{
+  glRotatef(rot.angle()*180.f/float(EIGEN_PI), 0.f, 0.f, 1.f);
+}
+inline void glRotate(const Rotation2D<double>& rot)
+{
+  glRotated(rot.angle()*180.0/double(EIGEN_PI), 0.0, 0.0, 1.0);
+}
+
+template<typename Derived> void glRotate(const RotationBase<Derived,3>& rot)
+{  
+  Transform<typename Derived::Scalar,3,Projective> tr(rot);
+  glMultMatrix(tr.matrix());
+}
+
+#define EIGEN_GL_MAKE_CONST_const const
+#define EIGEN_GL_MAKE_CONST__ 
+#define EIGEN_GL_EVAL(X) X
+
+#define EIGEN_GL_FUNC1_DECLARATION(FUNC,ARG1,CONST)                                                                             \
+namespace internal {                                                                                                            \
+  template< typename XprType,                                                                                                   \
+            typename Scalar = typename XprType::Scalar,                                                                         \
+            int Rows = XprType::RowsAtCompileTime,                                                                              \
+            int Cols = XprType::ColsAtCompileTime,                                                                              \
+            bool IsGLCompatible = bool(internal::evaluator<XprType>::Flags&LinearAccessBit)                                     \
+                              && bool(XprType::Flags&DirectAccessBit)                                                           \
+                              && (XprType::IsVectorAtCompileTime || (XprType::Flags&RowMajorBit)==0)>                           \
+  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl);                                                                                  \
+                                                                                                                                \
+  template<typename XprType, typename Scalar, int Rows, int Cols>                                                               \
+  struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType,Scalar,Rows,Cols,false> {                                                 \
+    inline static void run(ARG1 a,EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) {                                      \
+      EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<typename plain_matrix_type_column_major<XprType>::type>::run(a,p); }                 \
+  };                                                                                                                            \
+}                                                                                                                               \
+                                                                                                                                \
+template<typename Derived> inline void FUNC(ARG1 a,EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) Eigen::DenseBase<Derived>& p) {   \
+  EIGEN_CAT(EIGEN_CAT(internal::gl_,FUNC),_impl)<Derived>::run(a,p.derived());                                                  \
+}
+
+
+#define EIGEN_GL_FUNC1_SPECIALIZATION_MAT(FUNC,ARG1,CONST,SCALAR,ROWS,COLS,SUFFIX)                                              \
+namespace internal {                                                                                                            \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, ROWS, COLS, true> {                  \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
+  }; \
+}
+
+  
+#define EIGEN_GL_FUNC1_SPECIALIZATION_VEC(FUNC,ARG1,CONST,SCALAR,SIZE,SUFFIX)                                                   \
+namespace internal {                                                                                                            \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, SIZE, 1, true> {                     \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
+  };                                                                                                                            \
+  template< typename XprType> struct EIGEN_CAT(EIGEN_CAT(gl_,FUNC),_impl)<XprType, SCALAR, 1, SIZE, true> {                     \
+    inline static void run(ARG1 a, EIGEN_GL_EVAL(EIGEN_GL_MAKE_CONST_##CONST) XprType& p) { FUNC##SUFFIX(a,p.data()); }         \
+  };                                                                                                                            \
+}
+
+EIGEN_GL_FUNC1_DECLARATION       (glGet,GLenum,_)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,float,  4,4,Floatv)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glGet,GLenum,_,double, 4,4,Doublev)
+
+// glUniform API
+
+#ifdef GL_VERSION_2_0
+
+inline void glUniform2fv_ei  (GLint loc, const float* v)         { glUniform2fv(loc,1,v); }
+inline void glUniform2iv_ei  (GLint loc, const int* v)           { glUniform2iv(loc,1,v); }
+
+inline void glUniform3fv_ei  (GLint loc, const float* v)         { glUniform3fv(loc,1,v); }
+inline void glUniform3iv_ei  (GLint loc, const int* v)           { glUniform3iv(loc,1,v); }
+
+inline void glUniform4fv_ei  (GLint loc, const float* v)         { glUniform4fv(loc,1,v); }
+inline void glUniform4iv_ei  (GLint loc, const int* v)           { glUniform4iv(loc,1,v); }
+
+inline void glUniformMatrix2fv_ei  (GLint loc, const float* v)         { glUniformMatrix2fv(loc,1,false,v); }
+inline void glUniformMatrix3fv_ei  (GLint loc, const float* v)         { glUniformMatrix3fv(loc,1,false,v); }
+inline void glUniformMatrix4fv_ei  (GLint loc, const float* v)         { glUniformMatrix4fv(loc,1,false,v); }
+
+
+EIGEN_GL_FUNC1_DECLARATION       (glUniform,GLint,const)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        2,2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          2,2iv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        3,3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          3,3iv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,float,        4,4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,int,          4,4iv_ei)
+
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,2,Matrix2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,3,Matrix3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,4,Matrix4fv_ei)
+
+#endif
+
+#ifdef GL_VERSION_2_1
+
+inline void glUniformMatrix2x3fv_ei(GLint loc, const float* v)         { glUniformMatrix2x3fv(loc,1,false,v); }
+inline void glUniformMatrix3x2fv_ei(GLint loc, const float* v)         { glUniformMatrix3x2fv(loc,1,false,v); }
+inline void glUniformMatrix2x4fv_ei(GLint loc, const float* v)         { glUniformMatrix2x4fv(loc,1,false,v); }
+inline void glUniformMatrix4x2fv_ei(GLint loc, const float* v)         { glUniformMatrix4x2fv(loc,1,false,v); }
+inline void glUniformMatrix3x4fv_ei(GLint loc, const float* v)         { glUniformMatrix3x4fv(loc,1,false,v); }
+inline void glUniformMatrix4x3fv_ei(GLint loc, const float* v)         { glUniformMatrix4x3fv(loc,1,false,v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,3,Matrix2x3fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,2,Matrix3x2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        2,4,Matrix2x4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,2,Matrix4x2fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        3,4,Matrix3x4fv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_MAT(glUniform,GLint,const,float,        4,3,Matrix4x3fv_ei)
+
+#endif
+
+#ifdef GL_VERSION_3_0
+
+inline void glUniform2uiv_ei (GLint loc, const unsigned int* v)  { glUniform2uiv(loc,1,v); }
+inline void glUniform3uiv_ei (GLint loc, const unsigned int* v)  { glUniform3uiv(loc,1,v); }
+inline void glUniform4uiv_ei (GLint loc, const unsigned int* v)  { glUniform4uiv(loc,1,v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 2,2uiv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 3,3uiv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,unsigned int, 4,4uiv_ei)
+
+#endif
+
+#ifdef GL_ARB_gpu_shader_fp64
+inline void glUniform2dv_ei  (GLint loc, const double* v)        { glUniform2dv(loc,1,v); }
+inline void glUniform3dv_ei  (GLint loc, const double* v)        { glUniform3dv(loc,1,v); }
+inline void glUniform4dv_ei  (GLint loc, const double* v)        { glUniform4dv(loc,1,v); }
+
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       2,2dv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       3,3dv_ei)
+EIGEN_GL_FUNC1_SPECIALIZATION_VEC(glUniform,GLint,const,double,       4,4dv_ei)
+#endif
+
+
+//@}
+
+}
+
+#endif // EIGEN_OPENGL_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Polynomials b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Polynomials
new file mode 100644
index 0000000..32ce2a2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Polynomials
@@ -0,0 +1,137 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIALS_MODULE_H
+#define EIGEN_POLYNOMIALS_MODULE_H
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/Eigenvalues"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+// Note that EIGEN_HIDE_HEAVY_CODE has to be defined per module
+#if (defined EIGEN_EXTERN_INSTANTIATIONS) && (EIGEN_EXTERN_INSTANTIATIONS>=2)
+  #ifndef EIGEN_HIDE_HEAVY_CODE
+  #define EIGEN_HIDE_HEAVY_CODE
+  #endif
+#elif defined EIGEN_HIDE_HEAVY_CODE
+  #undef EIGEN_HIDE_HEAVY_CODE
+#endif
+
+/**
+  * \defgroup Polynomials_Module Polynomials module
+  * \brief This module provides a QR based polynomial solver.
+	*
+  * To use this module, add
+  * \code
+  * #include <unsupported/Eigen/Polynomials>
+  * \endcode
+	* at the start of your source file.
+  */
+
+#include "src/Polynomials/PolynomialUtils.h"
+#include "src/Polynomials/Companion.h"
+#include "src/Polynomials/PolynomialSolver.h"
+
+/**
+	\page polynomials Polynomials defines functions for dealing with polynomials
+	and a QR based polynomial solver.
+	\ingroup Polynomials_Module
+
+	The remainder of the page documents first the functions for evaluating, computing
+	polynomials, computing estimates about polynomials and next the QR based polynomial
+	solver.
+
+	\section polynomialUtils convenient functions to deal with polynomials
+	\subsection roots_to_monicPolynomial
+	The function
+	\code
+	void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly )
+	\endcode
+	computes the coefficients \f$ a_i \f$ of
+
+	\f$ p(x) = a_0 + a_{1}x + ... + a_{n-1}x^{n-1} + x^n \f$
+
+	where \f$ p \f$ is known through its roots i.e. \f$ p(x) = (x-r_1)(x-r_2)...(x-r_n) \f$.
+
+	\subsection poly_eval
+	The function
+	\code
+	T poly_eval( const Polynomials& poly, const T& x )
+	\endcode
+	evaluates a polynomial at a given point using stabilized H&ouml;rner method.
+
+	The following code: first computes the coefficients in the monomial basis of the monic polynomial that has the provided roots;
+	then, it evaluates the computed polynomial, using a stabilized H&ouml;rner method.
+
+	\include PolynomialUtils1.cpp
+  Output: \verbinclude PolynomialUtils1.out
+
+	\subsection Cauchy bounds
+	The function
+	\code
+	Real cauchy_max_bound( const Polynomial& poly )
+	\endcode
+	provides a maximum bound (the Cauchy one: \f$C(p)\f$) for the absolute value of a root of the given polynomial i.e.
+	\f$ \forall r_i \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$,
+	\f$ |r_i| \le C(p) = \sum_{k=0}^{d} \left | \frac{a_k}{a_d} \right | \f$
+	The leading coefficient \f$ p \f$: should be non zero \f$a_d \neq 0\f$.
+
+
+	The function
+	\code
+	Real cauchy_min_bound( const Polynomial& poly )
+	\endcode
+	provides a minimum bound (the Cauchy one: \f$c(p)\f$) for the absolute value of a non zero root of the given polynomial i.e.
+	\f$ \forall r_i \neq 0 \f$ root of \f$ p(x) = \sum_{k=0}^d a_k x^k \f$,
+	\f$ |r_i| \ge c(p) = \left( \sum_{k=0}^{d} \left | \frac{a_k}{a_0} \right | \right)^{-1} \f$
+
+
+
+
+	\section QR polynomial solver class
+	Computes the complex roots of a polynomial by computing the eigenvalues of the associated companion matrix with the QR algorithm.
+	
+	The roots of \f$ p(x) = a_0 + a_1 x + a_2 x^2 + a_{3} x^3 + x^4 \f$ are the eigenvalues of
+	\f$
+	\left [
+	\begin{array}{cccc}
+	0 & 0 &  0 & a_0 \\
+	1 & 0 &  0 & a_1 \\
+	0 & 1 &  0 & a_2 \\
+	0 & 0 &  1 & a_3
+	\end{array} \right ]
+	\f$
+
+	However, the QR algorithm is not guaranteed to converge when there are several eigenvalues with same modulus.
+
+	Therefore the current polynomial solver is guaranteed to provide a correct result only when the complex roots \f$r_1,r_2,...,r_d\f$ have distinct moduli i.e.
+	
+	\f$ \forall i,j \in [1;d],~ \| r_i \| \neq \| r_j \| \f$.
+
+	With 32bit (float) floating types this problem shows up frequently.
+  However, almost always, correct accuracy is reached even in these cases for 64bit
+  (double) floating types and small polynomial degree (<20).
+
+	\include PolynomialSolver1.cpp
+	
+	In the above example:
+	
+	-# a simple use of the polynomial solver is shown;
+	-# the accuracy problem with the QR algorithm is presented: a polynomial with almost conjugate roots is provided to the solver.
+	Those roots have almost same module therefore the QR algorithm failed to converge: the accuracy
+	of the last root is bad;
+	-# a simple way to circumvent the problem is shown: use doubles instead of floats.
+
+  Output: \verbinclude PolynomialSolver1.out
+*/
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_POLYNOMIALS_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Skyline b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Skyline
new file mode 100644
index 0000000..ebdf143
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Skyline
@@ -0,0 +1,39 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINE_MODULE_H
+#define EIGEN_SKYLINE_MODULE_H
+
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+
+/**
+ *  \defgroup Skyline_Module Skyline module
+ *
+ *
+ *
+ *
+ */
+
+#include "src/Skyline/SkylineUtil.h"
+#include "src/Skyline/SkylineMatrixBase.h"
+#include "src/Skyline/SkylineStorage.h"
+#include "src/Skyline/SkylineMatrix.h"
+#include "src/Skyline/SkylineInplaceLU.h"
+#include "src/Skyline/SkylineProduct.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SKYLINE_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SparseExtra b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SparseExtra
new file mode 100644
index 0000000..ba5cbd6
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SparseExtra
@@ -0,0 +1,54 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_EXTRA_MODULE_H
+#define EIGEN_SPARSE_EXTRA_MODULE_H
+
+#include "../../Eigen/Sparse"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+#include <fstream>
+#include <sstream>
+
+#ifdef EIGEN_GOOGLEHASH_SUPPORT
+  #include <google/dense_hash_map>
+  #include <google/sparse_hash_map>
+#endif
+
+/**
+  * \defgroup SparseExtra_Module SparseExtra module
+  *
+  * This module contains some experimental features extending the sparse module.
+  *
+  * \code
+  * #include <Eigen/SparseExtra>
+  * \endcode
+  */
+
+
+#include "src/SparseExtra/DynamicSparseMatrix.h"
+#include "src/SparseExtra/BlockOfDynamicSparseMatrix.h"
+#include "src/SparseExtra/RandomSetter.h"
+
+#include "src/SparseExtra/MarketIO.h"
+
+#if !defined(_WIN32)
+#include <dirent.h>
+#include "src/SparseExtra/MatrixMarketIterator.h"
+#endif
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSE_EXTRA_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SpecialFunctions b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SpecialFunctions
new file mode 100644
index 0000000..f6a2460
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/SpecialFunctions
@@ -0,0 +1,103 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <g.gael@free.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_MODULE
+#define EIGEN_SPECIALFUNCTIONS_MODULE
+
+#include <math.h>
+
+#include "../../Eigen/Core"
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+namespace Eigen {
+
+/**
+  * \defgroup SpecialFunctions_Module Special math functions module
+  *
+  * This module features additional coefficient-wise math functions available
+  * within the numext:: namespace for the scalar version, and as method and/or free
+  * functions of Array. Those include:
+  *
+  * - erf
+  * - erfc
+  * - lgamma
+  * - igamma
+  * - igamma_der_a
+  * - gamma_sample_der_alpha
+  * - igammac
+  * - digamma
+  * - ndtri
+  * - polygamma
+  * - zeta
+  * - betainc
+  *
+  * Bessel Functions
+  * - bessel_i0
+  * - bessel_i0e
+  * - bessel_i1
+  * - bessel_i1e
+  * - bessel_j0
+  * - bessel_j1
+  * - bessel_k0
+  * - bessel_k0e
+  * - bessel_k1
+  * - bessel_k1e
+  * - bessel_y0
+  * - bessel_y1
+  *
+  * \code
+  * #include <unsupported/Eigen/SpecialFunctions>
+  * \endcode
+  */
+//@{
+
+}
+
+#include "src/SpecialFunctions/BesselFunctionsImpl.h"
+#include "src/SpecialFunctions/BesselFunctionsBFloat16.h"
+#include "src/SpecialFunctions/BesselFunctionsHalf.h"
+#include "src/SpecialFunctions/BesselFunctionsPacketMath.h"
+#include "src/SpecialFunctions/BesselFunctionsFunctors.h"
+#include "src/SpecialFunctions/BesselFunctionsArrayAPI.h"
+#include "src/SpecialFunctions/SpecialFunctionsImpl.h"
+#if defined(EIGEN_HIPCC)
+#include "src/SpecialFunctions/HipVectorCompatibility.h"
+#endif
+#include "src/SpecialFunctions/SpecialFunctionsBFloat16.h"
+#include "src/SpecialFunctions/SpecialFunctionsHalf.h"
+#include "src/SpecialFunctions/SpecialFunctionsPacketMath.h"
+#include "src/SpecialFunctions/SpecialFunctionsFunctors.h"
+#include "src/SpecialFunctions/SpecialFunctionsArrayAPI.h"
+
+#if defined EIGEN_VECTORIZE_AVX512
+  #include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
+  #include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
+  #include "src/SpecialFunctions/arch/AVX512/BesselFunctions.h"
+  #include "src/SpecialFunctions/arch/AVX512/SpecialFunctions.h"
+#elif defined EIGEN_VECTORIZE_AVX
+  #include "src/SpecialFunctions/arch/AVX/BesselFunctions.h"
+  #include "src/SpecialFunctions/arch/AVX/SpecialFunctions.h"
+#elif defined EIGEN_VECTORIZE_NEON
+  #include "src/SpecialFunctions/arch/NEON/BesselFunctions.h"
+  #include "src/SpecialFunctions/arch/NEON/SpecialFunctions.h"
+#endif
+
+#if defined EIGEN_VECTORIZE_GPU
+  #include "src/SpecialFunctions/arch/GPU/SpecialFunctions.h"
+#endif
+
+namespace Eigen {
+//@}
+}
+
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPECIALFUNCTIONS_MODULE
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Splines b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Splines
new file mode 100644
index 0000000..2ca5813
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/Splines
@@ -0,0 +1,35 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINES_MODULE_H
+#define EIGEN_SPLINES_MODULE_H
+
+namespace Eigen 
+{
+/**
+  * \defgroup Splines_Module Spline and spline fitting module
+  *
+  * This module provides a simple multi-dimensional spline class while
+  * offering most basic functionality to fit a spline to point sets.
+  *
+  * \code
+  * #include <unsupported/Eigen/Splines>
+  * \endcode
+  */
+}
+
+#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
+
+#include "src/Splines/SplineFwd.h"
+#include "src/Splines/Spline.h"
+#include "src/Splines/SplineFitting.h"
+
+#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPLINES_MODULE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
new file mode 100644
index 0000000..33b6c39
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
@@ -0,0 +1,108 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_JACOBIAN_H
+#define EIGEN_AUTODIFF_JACOBIAN_H
+
+namespace Eigen
+{
+
+template<typename Functor> class AutoDiffJacobian : public Functor
+{
+public:
+  AutoDiffJacobian() : Functor() {}
+  AutoDiffJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  template<typename... T>
+  AutoDiffJacobian(const T& ...Values) : Functor(Values...) {}
+#else
+  template<typename T0>
+  AutoDiffJacobian(const T0& a0) : Functor(a0) {}
+  template<typename T0, typename T1>
+  AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
+  template<typename T0, typename T1, typename T2>
+  AutoDiffJacobian(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2) {}
+#endif
+
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename ValueType::Scalar Scalar;
+
+  enum {
+    InputsAtCompileTime = InputType::RowsAtCompileTime,
+    ValuesAtCompileTime = ValueType::RowsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
+  typedef typename JacobianType::Index Index;
+
+  typedef Matrix<Scalar, InputsAtCompileTime, 1> DerivativeType;
+  typedef AutoDiffScalar<DerivativeType> ActiveScalar;
+
+  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+  // Some compilers don't accept variadic parameters after a default parameter,
+  // i.e., we can't just write _jac=0 but we need to overload operator():
+  EIGEN_STRONG_INLINE
+  void operator() (const InputType& x, ValueType* v) const
+  {
+      this->operator()(x, v, 0);
+  }
+  template<typename... ParamsType>
+  void operator() (const InputType& x, ValueType* v, JacobianType* _jac,
+                   const ParamsType&... Params) const
+#else
+  void operator() (const InputType& x, ValueType* v, JacobianType* _jac=0) const
+#endif
+  {
+    eigen_assert(v!=0);
+
+    if (!_jac)
+    {
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+      Functor::operator()(x, v, Params...);
+#else
+      Functor::operator()(x, v);
+#endif
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+
+    if(InputsAtCompileTime==Dynamic)
+      for (Index j=0; j<jac.rows(); j++)
+        av[j].derivatives().resize(x.rows());
+
+    for (Index i=0; i<jac.cols(); i++)
+      ax[i].derivatives() = DerivativeType::Unit(x.rows(),i);
+
+#if EIGEN_HAS_VARIADIC_TEMPLATES
+    Functor::operator()(ax, &av, Params...);
+#else
+    Functor::operator()(ax, &av);
+#endif
+
+    for (Index i=0; i<jac.rows(); i++)
+    {
+      (*v)[i] = av[i].value();
+      jac.row(i) = av[i].derivatives();
+    }
+  }
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_JACOBIAN_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
new file mode 100644
index 0000000..0f166e3
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h
@@ -0,0 +1,730 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_SCALAR_H
+#define EIGEN_AUTODIFF_SCALAR_H
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename A, typename B>
+struct make_coherent_impl {
+  static void run(A&, B&) {}
+};
+
+// resize a to match b is a.size()==0, and conversely.
+template<typename A, typename B>
+void make_coherent(const A& a, const B&b)
+{
+  make_coherent_impl<A,B>::run(a.const_cast_derived(), b.const_cast_derived());
+}
+
+template<typename DerivativeType, bool Enable> struct auto_diff_special_op;
+
+} // end namespace internal
+
+template<typename DerivativeType> class AutoDiffScalar;
+
+template<typename NewDerType>
+inline AutoDiffScalar<NewDerType> MakeAutoDiffScalar(const typename NewDerType::Scalar& value, const NewDerType &der) {
+  return AutoDiffScalar<NewDerType>(value,der);
+}
+
+/** \class AutoDiffScalar
+  * \brief A scalar type replacement with automatic differentiation capability
+  *
+  * \param DerivativeType the vector type used to store/represent the derivatives. The base scalar type
+  *                 as well as the number of derivatives to compute are determined from this type.
+  *                 Typical choices include, e.g., \c Vector4f for 4 derivatives, or \c VectorXf
+  *                 if the number of derivatives is not known at compile time, and/or, the number
+  *                 of derivatives is large.
+  *                 Note that DerivativeType can also be a reference (e.g., \c VectorXf&) to wrap a
+  *                 existing vector into an AutoDiffScalar.
+  *                 Finally, DerivativeType can also be any Eigen compatible expression.
+  *
+  * This class represents a scalar value while tracking its respective derivatives using Eigen's expression
+  * template mechanism.
+  *
+  * It supports the following list of global math function:
+  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
+  *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
+  *  - internal::conj, internal::real, internal::imag, numext::abs2.
+  *
+  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+  * in that case, the expression template mechanism only occurs at the top Matrix level,
+  * while derivatives are computed right away.
+  *
+  */
+
+template<typename DerivativeType>
+class AutoDiffScalar
+  : public internal::auto_diff_special_op
+            <DerivativeType, !internal::is_same<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar,
+                                          typename NumTraits<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar>::Real>::value>
+{
+  public:
+    typedef internal::auto_diff_special_op
+            <DerivativeType, !internal::is_same<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar,
+                       typename NumTraits<typename internal::traits<typename internal::remove_all<DerivativeType>::type>::Scalar>::Real>::value> Base;
+    typedef typename internal::remove_all<DerivativeType>::type DerType;
+    typedef typename internal::traits<DerType>::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real Real;
+
+    using Base::operator+;
+    using Base::operator*;
+
+    /** Default constructor without any initialization. */
+    AutoDiffScalar() {}
+
+    /** Constructs an active scalar from its \a value,
+        and initializes the \a nbDer derivatives such that it corresponds to the \a derNumber -th variable */
+    AutoDiffScalar(const Scalar& value, int nbDer, int derNumber)
+      : m_value(value), m_derivatives(DerType::Zero(nbDer))
+    {
+      m_derivatives.coeffRef(derNumber) = Scalar(1);
+    }
+
+    /** Conversion from a scalar constant to an active scalar.
+      * The derivatives are set to zero. */
+    /*explicit*/ AutoDiffScalar(const Real& value)
+      : m_value(value)
+    {
+      if(m_derivatives.size()>0)
+        m_derivatives.setZero();
+    }
+
+    /** Constructs an active scalar from its \a value and derivatives \a der */
+    AutoDiffScalar(const Scalar& value, const DerType& der)
+      : m_value(value), m_derivatives(der)
+    {}
+
+    template<typename OtherDerType>
+    AutoDiffScalar(const AutoDiffScalar<OtherDerType>& other
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    , typename internal::enable_if<
+            internal::is_same<Scalar, typename internal::traits<typename internal::remove_all<OtherDerType>::type>::Scalar>::value
+        &&  internal::is_convertible<OtherDerType,DerType>::value , void*>::type = 0
+#endif
+    )
+      : m_value(other.value()), m_derivatives(other.derivatives())
+    {}
+
+    friend  std::ostream & operator << (std::ostream & s, const AutoDiffScalar& a)
+    {
+      return s << a.value();
+    }
+
+    AutoDiffScalar(const AutoDiffScalar& other)
+      : m_value(other.value()), m_derivatives(other.derivatives())
+    {}
+
+    template<typename OtherDerType>
+    inline AutoDiffScalar& operator=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      m_value = other.value();
+      m_derivatives = other.derivatives();
+      return *this;
+    }
+
+    inline AutoDiffScalar& operator=(const AutoDiffScalar& other)
+    {
+      m_value = other.value();
+      m_derivatives = other.derivatives();
+      return *this;
+    }
+
+    inline AutoDiffScalar& operator=(const Scalar& other)
+    {
+      m_value = other;
+      if(m_derivatives.size()>0)
+        m_derivatives.setZero();
+      return *this;
+    }
+
+//     inline operator const Scalar& () const { return m_value; }
+//     inline operator Scalar& () { return m_value; }
+
+    inline const Scalar& value() const { return m_value; }
+    inline Scalar& value() { return m_value; }
+
+    inline const DerType& derivatives() const { return m_derivatives; }
+    inline DerType& derivatives() { return m_derivatives; }
+
+    inline bool operator< (const Scalar& other) const  { return m_value <  other; }
+    inline bool operator<=(const Scalar& other) const  { return m_value <= other; }
+    inline bool operator> (const Scalar& other) const  { return m_value >  other; }
+    inline bool operator>=(const Scalar& other) const  { return m_value >= other; }
+    inline bool operator==(const Scalar& other) const  { return m_value == other; }
+    inline bool operator!=(const Scalar& other) const  { return m_value != other; }
+
+    friend inline bool operator< (const Scalar& a, const AutoDiffScalar& b) { return a <  b.value(); }
+    friend inline bool operator<=(const Scalar& a, const AutoDiffScalar& b) { return a <= b.value(); }
+    friend inline bool operator> (const Scalar& a, const AutoDiffScalar& b) { return a >  b.value(); }
+    friend inline bool operator>=(const Scalar& a, const AutoDiffScalar& b) { return a >= b.value(); }
+    friend inline bool operator==(const Scalar& a, const AutoDiffScalar& b) { return a == b.value(); }
+    friend inline bool operator!=(const Scalar& a, const AutoDiffScalar& b) { return a != b.value(); }
+
+    template<typename OtherDerType> inline bool operator< (const AutoDiffScalar<OtherDerType>& b) const  { return m_value <  b.value(); }
+    template<typename OtherDerType> inline bool operator<=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value <= b.value(); }
+    template<typename OtherDerType> inline bool operator> (const AutoDiffScalar<OtherDerType>& b) const  { return m_value >  b.value(); }
+    template<typename OtherDerType> inline bool operator>=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value >= b.value(); }
+    template<typename OtherDerType> inline bool operator==(const AutoDiffScalar<OtherDerType>& b) const  { return m_value == b.value(); }
+    template<typename OtherDerType> inline bool operator!=(const AutoDiffScalar<OtherDerType>& b) const  { return m_value != b.value(); }
+
+    inline const AutoDiffScalar<DerType&> operator+(const Scalar& other) const
+    {
+      return AutoDiffScalar<DerType&>(m_value + other, m_derivatives);
+    }
+
+    friend inline const AutoDiffScalar<DerType&> operator+(const Scalar& a, const AutoDiffScalar& b)
+    {
+      return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
+    }
+
+//     inline const AutoDiffScalar<DerType&> operator+(const Real& other) const
+//     {
+//       return AutoDiffScalar<DerType&>(m_value + other, m_derivatives);
+//     }
+
+//     friend inline const AutoDiffScalar<DerType&> operator+(const Real& a, const AutoDiffScalar& b)
+//     {
+//       return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
+//     }
+
+    inline AutoDiffScalar& operator+=(const Scalar& other)
+    {
+      value() += other;
+      return *this;
+    }
+
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,const DerType,const typename internal::remove_all<OtherDerType>::type> >
+    operator+(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      internal::make_coherent(m_derivatives, other.derivatives());
+      return AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,const DerType,const typename internal::remove_all<OtherDerType>::type> >(
+        m_value + other.value(),
+        m_derivatives + other.derivatives());
+    }
+
+    template<typename OtherDerType>
+    inline AutoDiffScalar&
+    operator+=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      (*this) = (*this) + other;
+      return *this;
+    }
+
+    inline const AutoDiffScalar<DerType&> operator-(const Scalar& b) const
+    {
+      return AutoDiffScalar<DerType&>(m_value - b, m_derivatives);
+    }
+
+    friend inline const AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
+    operator-(const Scalar& a, const AutoDiffScalar& b)
+    {
+      return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
+            (a - b.value(), -b.derivatives());
+    }
+
+    inline AutoDiffScalar& operator-=(const Scalar& other)
+    {
+      value() -= other;
+      return *this;
+    }
+
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_difference_op<Scalar>, const DerType,const typename internal::remove_all<OtherDerType>::type> >
+    operator-(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      internal::make_coherent(m_derivatives, other.derivatives());
+      return AutoDiffScalar<CwiseBinaryOp<internal::scalar_difference_op<Scalar>, const DerType,const typename internal::remove_all<OtherDerType>::type> >(
+        m_value - other.value(),
+        m_derivatives - other.derivatives());
+    }
+
+    template<typename OtherDerType>
+    inline AutoDiffScalar&
+    operator-=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      *this = *this - other;
+      return *this;
+    }
+
+    inline const AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >
+    operator-() const
+    {
+      return AutoDiffScalar<CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const DerType> >(
+        -m_value,
+        -m_derivatives);
+    }
+
+    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
+    operator*(const Scalar& other) const
+    {
+      return MakeAutoDiffScalar(m_value * other, m_derivatives * other);
+    }
+
+    friend inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
+    operator*(const Scalar& other, const AutoDiffScalar& a)
+    {
+      return MakeAutoDiffScalar(a.value() * other, a.derivatives() * other);
+    }
+
+//     inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
+//     operator*(const Real& other) const
+//     {
+//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
+//         m_value * other,
+//         (m_derivatives * other));
+//     }
+//
+//     friend inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
+//     operator*(const Real& other, const AutoDiffScalar& a)
+//     {
+//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
+//         a.value() * other,
+//         a.derivatives() * other);
+//     }
+
+    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
+    operator/(const Scalar& other) const
+    {
+      return MakeAutoDiffScalar(m_value / other, (m_derivatives * (Scalar(1)/other)));
+    }
+
+    friend inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) >
+    operator/(const Scalar& other, const AutoDiffScalar& a)
+    {
+      return MakeAutoDiffScalar(other / a.value(), a.derivatives() * (Scalar(-other) / (a.value()*a.value())));
+    }
+
+//     inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
+//     operator/(const Real& other) const
+//     {
+//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
+//         m_value / other,
+//         (m_derivatives * (Real(1)/other)));
+//     }
+//
+//     friend inline const AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >
+//     operator/(const Real& other, const AutoDiffScalar& a)
+//     {
+//       return AutoDiffScalar<typename CwiseUnaryOp<internal::scalar_multiple_op<Real>, DerType>::Type >(
+//         other / a.value(),
+//         a.derivatives() * (-Real(1)/other));
+//     }
+
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(
+        CwiseBinaryOp<internal::scalar_difference_op<Scalar> EIGEN_COMMA
+          const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product) EIGEN_COMMA
+          const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<OtherDerType>::type,Scalar,product) >,Scalar,product) >
+    operator/(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      internal::make_coherent(m_derivatives, other.derivatives());
+      return MakeAutoDiffScalar(
+        m_value / other.value(),
+          ((m_derivatives * other.value()) - (other.derivatives() * m_value))
+        * (Scalar(1)/(other.value()*other.value())));
+    }
+
+    template<typename OtherDerType>
+    inline const AutoDiffScalar<CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
+        const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DerType,Scalar,product),
+        const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<OtherDerType>::type,Scalar,product) > >
+    operator*(const AutoDiffScalar<OtherDerType>& other) const
+    {
+      internal::make_coherent(m_derivatives, other.derivatives());
+      return MakeAutoDiffScalar(
+        m_value * other.value(),
+        (m_derivatives * other.value()) + (other.derivatives() * m_value));
+    }
+
+    inline AutoDiffScalar& operator*=(const Scalar& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+
+    template<typename OtherDerType>
+    inline AutoDiffScalar& operator*=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+
+    inline AutoDiffScalar& operator/=(const Scalar& other)
+    {
+      *this = *this / other;
+      return *this;
+    }
+
+    template<typename OtherDerType>
+    inline AutoDiffScalar& operator/=(const AutoDiffScalar<OtherDerType>& other)
+    {
+      *this = *this / other;
+      return *this;
+    }
+
+  protected:
+    Scalar m_value;
+    DerType m_derivatives;
+
+};
+
+namespace internal {
+
+template<typename DerivativeType>
+struct auto_diff_special_op<DerivativeType, true>
+//   : auto_diff_scalar_op<DerivativeType, typename NumTraits<Scalar>::Real,
+//                            is_same<Scalar,typename NumTraits<Scalar>::Real>::value>
+{
+  typedef typename remove_all<DerivativeType>::type DerType;
+  typedef typename traits<DerType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+//   typedef auto_diff_scalar_op<DerivativeType, typename NumTraits<Scalar>::Real,
+//                            is_same<Scalar,typename NumTraits<Scalar>::Real>::value> Base;
+
+//   using Base::operator+;
+//   using Base::operator+=;
+//   using Base::operator-;
+//   using Base::operator-=;
+//   using Base::operator*;
+//   using Base::operator*=;
+
+  const AutoDiffScalar<DerivativeType>& derived() const { return *static_cast<const AutoDiffScalar<DerivativeType>*>(this); }
+  AutoDiffScalar<DerivativeType>& derived() { return *static_cast<AutoDiffScalar<DerivativeType>*>(this); }
+
+
+  inline const AutoDiffScalar<DerType&> operator+(const Real& other) const
+  {
+    return AutoDiffScalar<DerType&>(derived().value() + other, derived().derivatives());
+  }
+
+  friend inline const AutoDiffScalar<DerType&> operator+(const Real& a, const AutoDiffScalar<DerivativeType>& b)
+  {
+    return AutoDiffScalar<DerType&>(a + b.value(), b.derivatives());
+  }
+
+  inline AutoDiffScalar<DerivativeType>& operator+=(const Real& other)
+  {
+    derived().value() += other;
+    return derived();
+  }
+
+
+  inline const AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar,Real> >, DerType>::Type >
+  operator*(const Real& other) const
+  {
+    return AutoDiffScalar<typename CwiseUnaryOp<bind2nd_op<scalar_product_op<Scalar,Real> >, DerType>::Type >(
+      derived().value() * other,
+      derived().derivatives() * other);
+  }
+
+  friend inline const AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real,Scalar> >, DerType>::Type >
+  operator*(const Real& other, const AutoDiffScalar<DerivativeType>& a)
+  {
+    return AutoDiffScalar<typename CwiseUnaryOp<bind1st_op<scalar_product_op<Real,Scalar> >, DerType>::Type >(
+      a.value() * other,
+      a.derivatives() * other);
+  }
+
+  inline AutoDiffScalar<DerivativeType>& operator*=(const Scalar& other)
+  {
+    *this = *this * other;
+    return derived();
+  }
+};
+
+template<typename DerivativeType>
+struct auto_diff_special_op<DerivativeType, false>
+{
+  void operator*() const;
+  void operator-() const;
+  void operator+() const;
+};
+
+template<typename BinOp, typename A, typename B, typename RefType>
+void make_coherent_expression(CwiseBinaryOp<BinOp,A,B> xpr, const RefType &ref)
+{
+  make_coherent(xpr.const_cast_derived().lhs(), ref);
+  make_coherent(xpr.const_cast_derived().rhs(), ref);
+}
+
+template<typename UnaryOp, typename A, typename RefType>
+void make_coherent_expression(const CwiseUnaryOp<UnaryOp,A> &xpr, const RefType &ref)
+{
+  make_coherent(xpr.nestedExpression().const_cast_derived(), ref);
+}
+
+// needed for compilation only
+template<typename UnaryOp, typename A, typename RefType>
+void make_coherent_expression(const CwiseNullaryOp<UnaryOp,A> &, const RefType &)
+{}
+
+template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols, typename B>
+struct make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>, B> {
+  typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
+  static void run(A& a, B& b) {
+    if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
+    {
+      a.resize(b.size());
+      a.setZero();
+    }
+    else if (B::SizeAtCompileTime==Dynamic && a.size()!=0 && b.size()==0)
+    {
+      make_coherent_expression(b,a);
+    }
+  }
+};
+
+template<typename A, typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
+struct make_coherent_impl<A, Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
+  typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
+  static void run(A& a, B& b) {
+    if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
+    {
+      b.resize(a.size());
+      b.setZero();
+    }
+    else if (A::SizeAtCompileTime==Dynamic && b.size()!=0 && a.size()==0)
+    {
+      make_coherent_expression(a,b);
+    }
+  }
+};
+
+template<typename A_Scalar, int A_Rows, int A_Cols, int A_Options, int A_MaxRows, int A_MaxCols,
+         typename B_Scalar, int B_Rows, int B_Cols, int B_Options, int B_MaxRows, int B_MaxCols>
+struct make_coherent_impl<Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols>,
+                          Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> > {
+  typedef Matrix<A_Scalar, A_Rows, A_Cols, A_Options, A_MaxRows, A_MaxCols> A;
+  typedef Matrix<B_Scalar, B_Rows, B_Cols, B_Options, B_MaxRows, B_MaxCols> B;
+  static void run(A& a, B& b) {
+    if((A_Rows==Dynamic || A_Cols==Dynamic) && (a.size()==0))
+    {
+      a.resize(b.size());
+      a.setZero();
+    }
+    else if((B_Rows==Dynamic || B_Cols==Dynamic) && (b.size()==0))
+    {
+      b.resize(a.size());
+      b.setZero();
+    }
+  }
+};
+
+} // end namespace internal
+
+template<typename DerType, typename BinOp>
+struct ScalarBinaryOpTraits<AutoDiffScalar<DerType>,typename DerType::Scalar,BinOp>
+{
+  typedef AutoDiffScalar<DerType> ReturnType;
+};
+
+template<typename DerType, typename BinOp>
+struct ScalarBinaryOpTraits<typename DerType::Scalar,AutoDiffScalar<DerType>, BinOp>
+{
+  typedef AutoDiffScalar<DerType> ReturnType;
+};
+
+
+// The following is an attempt to let Eigen's known about expression template, but that's more tricky!
+
+// template<typename DerType, typename BinOp>
+// struct ScalarBinaryOpTraits<AutoDiffScalar<DerType>,AutoDiffScalar<DerType>, BinOp>
+// {
+//   enum { Defined = 1 };
+//   typedef AutoDiffScalar<typename DerType::PlainObject> ReturnType;
+// };
+//
+// template<typename DerType1,typename DerType2, typename BinOp>
+// struct ScalarBinaryOpTraits<AutoDiffScalar<DerType1>,AutoDiffScalar<DerType2>, BinOp>
+// {
+//   enum { Defined = 1 };//internal::is_same<typename DerType1::Scalar,typename DerType2::Scalar>::value };
+//   typedef AutoDiffScalar<typename DerType1::PlainObject> ReturnType;
+// };
+
+#define EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(FUNC,CODE) \
+  template<typename DerType> \
+  inline const Eigen::AutoDiffScalar< \
+  EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename Eigen::internal::remove_all<DerType>::type, typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar, product) > \
+  FUNC(const Eigen::AutoDiffScalar<DerType>& x) { \
+    using namespace Eigen; \
+    typedef typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar Scalar; \
+    EIGEN_UNUSED_VARIABLE(sizeof(Scalar)); \
+    CODE; \
+  }
+
+template<typename DerType>
+struct CleanedUpDerType {
+  typedef AutoDiffScalar<typename Eigen::internal::remove_all<DerType>::type::PlainObject> type;
+};
+
+template<typename DerType>
+inline const AutoDiffScalar<DerType>& conj(const AutoDiffScalar<DerType>& x)  { return x; }
+template<typename DerType>
+inline const AutoDiffScalar<DerType>& real(const AutoDiffScalar<DerType>& x)  { return x; }
+template<typename DerType>
+inline typename DerType::Scalar imag(const AutoDiffScalar<DerType>&)    { return 0.; }
+template<typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type (min)(const AutoDiffScalar<DerType>& x, const T& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x <= y ? ADS(x) : ADS(y));
+}
+template<typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type (max)(const AutoDiffScalar<DerType>& x, const T& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x >= y ? ADS(x) : ADS(y));
+}
+template<typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type (min)(const T& x, const AutoDiffScalar<DerType>& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x < y ? ADS(x) : ADS(y));
+}
+template<typename DerType, typename T>
+inline typename CleanedUpDerType<DerType>::type (max)(const T& x, const AutoDiffScalar<DerType>& y) {
+  typedef typename CleanedUpDerType<DerType>::type ADS;
+  return (x > y ? ADS(x) : ADS(y));
+}
+template<typename DerType>
+inline typename CleanedUpDerType<DerType>::type (min)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
+  return (x.value() < y.value() ? x : y);
+}
+template<typename DerType>
+inline typename CleanedUpDerType<DerType>::type (max)(const AutoDiffScalar<DerType>& x, const AutoDiffScalar<DerType>& y) {
+  return (x.value() >= y.value() ? x : y);
+}
+
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs,
+  using std::abs;
+  return Eigen::MakeAutoDiffScalar(abs(x.value()), x.derivatives() * (x.value()<0 ? -1 : 1) );)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs2,
+  using numext::abs2;
+  return Eigen::MakeAutoDiffScalar(abs2(x.value()), x.derivatives() * (Scalar(2)*x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sqrt,
+  using std::sqrt;
+  Scalar sqrtx = sqrt(x.value());
+  return Eigen::MakeAutoDiffScalar(sqrtx,x.derivatives() * (Scalar(0.5) / sqrtx));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cos,
+  using std::cos;
+  using std::sin;
+  return Eigen::MakeAutoDiffScalar(cos(x.value()), x.derivatives() * (-sin(x.value())));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sin,
+  using std::sin;
+  using std::cos;
+  return Eigen::MakeAutoDiffScalar(sin(x.value()),x.derivatives() * cos(x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(exp,
+  using std::exp;
+  Scalar expx = exp(x.value());
+  return Eigen::MakeAutoDiffScalar(expx,x.derivatives() * expx);)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(log,
+  using std::log;
+  return Eigen::MakeAutoDiffScalar(log(x.value()),x.derivatives() * (Scalar(1)/x.value()));)
+
+template<typename DerType>
+inline const Eigen::AutoDiffScalar<
+EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename internal::remove_all<DerType>::type,typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar,product) >
+pow(const Eigen::AutoDiffScalar<DerType> &x, const typename internal::traits<typename internal::remove_all<DerType>::type>::Scalar &y)
+{
+  using namespace Eigen;
+  using std::pow;
+  return Eigen::MakeAutoDiffScalar(pow(x.value(),y), x.derivatives() * (y * pow(x.value(),y-1)));
+}
+
+
+template<typename DerTypeA,typename DerTypeB>
+inline const AutoDiffScalar<Matrix<typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar,Dynamic,1> >
+atan2(const AutoDiffScalar<DerTypeA>& a, const AutoDiffScalar<DerTypeB>& b)
+{
+  using std::atan2;
+  typedef typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar Scalar;
+  typedef AutoDiffScalar<Matrix<Scalar,Dynamic,1> > PlainADS;
+  PlainADS ret;
+  ret.value() = atan2(a.value(), b.value());
+  
+  Scalar squared_hypot = a.value() * a.value() + b.value() * b.value();
+  
+  // if (squared_hypot==0) the derivation is undefined and the following results in a NaN:
+  ret.derivatives() = (a.derivatives() * b.value() - a.value() * b.derivatives()) / squared_hypot;
+
+  return ret;
+}
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tan,
+  using std::tan;
+  using std::cos;
+  return Eigen::MakeAutoDiffScalar(tan(x.value()),x.derivatives() * (Scalar(1)/numext::abs2(cos(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(asin,
+  using std::sqrt;
+  using std::asin;
+  return Eigen::MakeAutoDiffScalar(asin(x.value()),x.derivatives() * (Scalar(1)/sqrt(1-numext::abs2(x.value()))));)
+  
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(acos,
+  using std::sqrt;
+  using std::acos;
+  return Eigen::MakeAutoDiffScalar(acos(x.value()),x.derivatives() * (Scalar(-1)/sqrt(1-numext::abs2(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tanh,
+  using std::cosh;
+  using std::tanh;
+  return Eigen::MakeAutoDiffScalar(tanh(x.value()),x.derivatives() * (Scalar(1)/numext::abs2(cosh(x.value()))));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(sinh,
+  using std::sinh;
+  using std::cosh;
+  return Eigen::MakeAutoDiffScalar(sinh(x.value()),x.derivatives() * cosh(x.value()));)
+
+EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cosh,
+  using std::sinh;
+  using std::cosh;
+  return Eigen::MakeAutoDiffScalar(cosh(x.value()),x.derivatives() * sinh(x.value()));)
+
+#undef EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY
+
+template<typename DerType> struct NumTraits<AutoDiffScalar<DerType> >
+  : NumTraits< typename NumTraits<typename internal::remove_all<DerType>::type::Scalar>::Real >
+{
+  typedef typename internal::remove_all<DerType>::type DerTypeCleaned;
+  typedef AutoDiffScalar<Matrix<typename NumTraits<typename DerTypeCleaned::Scalar>::Real,DerTypeCleaned::RowsAtCompileTime,DerTypeCleaned::ColsAtCompileTime,
+                                0, DerTypeCleaned::MaxRowsAtCompileTime, DerTypeCleaned::MaxColsAtCompileTime> > Real;
+  typedef AutoDiffScalar<DerType> NonInteger;
+  typedef AutoDiffScalar<DerType> Nested;
+  typedef typename NumTraits<typename DerTypeCleaned::Scalar>::Literal Literal;
+  enum{
+    RequireInitialization = 1
+  };
+};
+
+}
+
+namespace std {
+
+template <typename T>
+class numeric_limits<Eigen::AutoDiffScalar<T> >
+  : public numeric_limits<typename T::Scalar> {};
+
+template <typename T>
+class numeric_limits<Eigen::AutoDiffScalar<T&> >
+  : public numeric_limits<typename T::Scalar> {};
+
+}  // namespace std
+
+#endif // EIGEN_AUTODIFF_SCALAR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
new file mode 100644
index 0000000..8c2d048
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h
@@ -0,0 +1,220 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_VECTOR_H
+#define EIGEN_AUTODIFF_VECTOR_H
+
+namespace Eigen {
+
+/* \class AutoDiffScalar
+  * \brief A scalar type replacement with automatic differentation capability
+  *
+  * \param DerType the vector type used to store/represent the derivatives (e.g. Vector3f)
+  *
+  * This class represents a scalar value while tracking its respective derivatives.
+  *
+  * It supports the following list of global math function:
+  *  - std::abs, std::sqrt, std::pow, std::exp, std::log, std::sin, std::cos,
+  *  - internal::abs, internal::sqrt, numext::pow, internal::exp, internal::log, internal::sin, internal::cos,
+  *  - internal::conj, internal::real, internal::imag, numext::abs2.
+  *
+  * AutoDiffScalar can be used as the scalar type of an Eigen::Matrix object. However,
+  * in that case, the expression template mechanism only occurs at the top Matrix level,
+  * while derivatives are computed right away.
+  *
+  */
+template<typename ValueType, typename JacobianType>
+class AutoDiffVector
+{
+  public:
+    //typedef typename internal::traits<ValueType>::Scalar Scalar;
+    typedef typename internal::traits<ValueType>::Scalar BaseScalar;
+    typedef AutoDiffScalar<Matrix<BaseScalar,JacobianType::RowsAtCompileTime,1> > ActiveScalar;
+    typedef ActiveScalar Scalar;
+    typedef AutoDiffScalar<typename JacobianType::ColXpr> CoeffType;
+    typedef typename JacobianType::Index Index;
+
+    inline AutoDiffVector() {}
+
+    inline AutoDiffVector(const ValueType& values)
+      : m_values(values)
+    {
+      m_jacobian.setZero();
+    }
+
+
+    CoeffType operator[] (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+    const CoeffType operator[] (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+    CoeffType operator() (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+    const CoeffType operator() (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+    CoeffType coeffRef(Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); }
+    const CoeffType coeffRef(Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); }
+
+    Index size() const { return m_values.size(); }
+
+    // FIXME here we could return an expression of the sum
+    Scalar sum() const { /*std::cerr << "sum \n\n";*/ /*std::cerr << m_jacobian.rowwise().sum() << "\n\n";*/ return Scalar(m_values.sum(), m_jacobian.rowwise().sum()); }
+
+
+    inline AutoDiffVector(const ValueType& values, const JacobianType& jac)
+      : m_values(values), m_jacobian(jac)
+    {}
+
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
+      : m_values(other.values()), m_jacobian(other.jacobian())
+    {}
+
+    inline AutoDiffVector(const AutoDiffVector& other)
+      : m_values(other.values()), m_jacobian(other.jacobian())
+    {}
+
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector& operator=(const AutoDiffVector<OtherValueType, OtherJacobianType>& other)
+    {
+      m_values = other.values();
+      m_jacobian = other.jacobian();
+      return *this;
+    }
+
+    inline AutoDiffVector& operator=(const AutoDiffVector& other)
+    {
+      m_values = other.values();
+      m_jacobian = other.jacobian();
+      return *this;
+    }
+
+    inline const ValueType& values() const { return m_values; }
+    inline ValueType& values() { return m_values; }
+
+    inline const JacobianType& jacobian() const { return m_jacobian; }
+    inline JacobianType& jacobian() { return m_jacobian; }
+
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline const AutoDiffVector<
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,ValueType,OtherValueType>::Type,
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,JacobianType,OtherJacobianType>::Type >
+    operator+(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
+    {
+      return AutoDiffVector<
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,ValueType,OtherValueType>::Type,
+      typename MakeCwiseBinaryOp<internal::scalar_sum_op<BaseScalar>,JacobianType,OtherJacobianType>::Type >(
+        m_values + other.values(),
+        m_jacobian + other.jacobian());
+    }
+
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector&
+    operator+=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
+    {
+      m_values += other.values();
+      m_jacobian += other.jacobian();
+      return *this;
+    }
+
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline const AutoDiffVector<
+      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,ValueType,OtherValueType>::Type,
+      typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,JacobianType,OtherJacobianType>::Type >
+    operator-(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
+    {
+      return AutoDiffVector<
+        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,ValueType,OtherValueType>::Type,
+        typename MakeCwiseBinaryOp<internal::scalar_difference_op<Scalar>,JacobianType,OtherJacobianType>::Type >(
+          m_values - other.values(),
+          m_jacobian - other.jacobian());
+    }
+
+    template<typename OtherValueType, typename OtherJacobianType>
+    inline AutoDiffVector&
+    operator-=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
+    {
+      m_values -= other.values();
+      m_jacobian -= other.jacobian();
+      return *this;
+    }
+
+    inline const AutoDiffVector<
+      typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
+      typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type >
+    operator-() const
+    {
+      return AutoDiffVector<
+        typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, ValueType>::Type,
+        typename MakeCwiseUnaryOp<internal::scalar_opposite_op<Scalar>, JacobianType>::Type >(
+          -m_values,
+          -m_jacobian);
+    }
+
+    inline const AutoDiffVector<
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type>
+    operator*(const BaseScalar& other) const
+    {
+      return AutoDiffVector<
+        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >(
+          m_values * other,
+          m_jacobian * other);
+    }
+
+    friend inline const AutoDiffVector<
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+      typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >
+    operator*(const Scalar& other, const AutoDiffVector& v)
+    {
+      return AutoDiffVector<
+        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, ValueType>::Type,
+        typename MakeCwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>::Type >(
+          v.values() * other,
+          v.jacobian() * other);
+    }
+
+//     template<typename OtherValueType,typename OtherJacobianType>
+//     inline const AutoDiffVector<
+//       CwiseBinaryOp<internal::scalar_multiple_op<Scalar>, ValueType, OtherValueType>
+//       CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
+//         CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>,
+//         CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, OtherJacobianType> > >
+//     operator*(const AutoDiffVector<OtherValueType,OtherJacobianType>& other) const
+//     {
+//       return AutoDiffVector<
+//         CwiseBinaryOp<internal::scalar_multiple_op<Scalar>, ValueType, OtherValueType>
+//         CwiseBinaryOp<internal::scalar_sum_op<Scalar>,
+//           CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, JacobianType>,
+//           CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, OtherJacobianType> > >(
+//             m_values.cwise() * other.values(),
+//             (m_jacobian * other.values()) + (m_values * other.jacobian()));
+//     }
+
+    inline AutoDiffVector& operator*=(const Scalar& other)
+    {
+      m_values *= other;
+      m_jacobian *= other;
+      return *this;
+    }
+
+    template<typename OtherValueType,typename OtherJacobianType>
+    inline AutoDiffVector& operator*=(const AutoDiffVector<OtherValueType,OtherJacobianType>& other)
+    {
+      *this = *this * other;
+      return *this;
+    }
+
+  protected:
+    ValueType m_values;
+    JacobianType m_jacobian;
+
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_VECTOR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
new file mode 100644
index 0000000..994c8af
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/BVAlgorithms.h
@@ -0,0 +1,293 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BVALGORITHMS_H
+#define EIGEN_BVALGORITHMS_H
+
+namespace Eigen { 
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename BVH, typename Intersector>
+bool intersect_helper(const BVH &tree, Intersector &intersector, typename BVH::Index root)
+{
+  typedef typename BVH::Index Index;
+  typedef typename BVH::VolumeIterator VolIter;
+  typedef typename BVH::ObjectIterator ObjIter;
+
+  VolIter vBegin = VolIter(), vEnd = VolIter();
+  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
+
+  std::vector<Index> todo(1, root);
+
+  while(!todo.empty()) {
+    tree.getChildren(todo.back(), vBegin, vEnd, oBegin, oEnd);
+    todo.pop_back();
+
+    for(; vBegin != vEnd; ++vBegin) //go through child volumes
+      if(intersector.intersectVolume(tree.getVolume(*vBegin)))
+        todo.push_back(*vBegin);
+
+    for(; oBegin != oEnd; ++oBegin) //go through child objects
+      if(intersector.intersectObject(*oBegin))
+        return true; //intersector said to stop query
+  }
+  return false;
+}
+#endif //not EIGEN_PARSED_BY_DOXYGEN
+
+template<typename Volume1, typename Object1, typename Object2, typename Intersector>
+struct intersector_helper1
+{
+  intersector_helper1(const Object2 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
+  bool intersectVolume(const Volume1 &vol) { return intersector.intersectVolumeObject(vol, stored); }
+  bool intersectObject(const Object1 &obj) { return intersector.intersectObjectObject(obj, stored); }
+  Object2 stored;
+  Intersector &intersector;
+private:
+  intersector_helper1& operator=(const intersector_helper1&);
+};
+
+template<typename Volume2, typename Object2, typename Object1, typename Intersector>
+struct intersector_helper2
+{
+  intersector_helper2(const Object1 &inStored, Intersector &in) : stored(inStored), intersector(in) {}
+  bool intersectVolume(const Volume2 &vol) { return intersector.intersectObjectVolume(stored, vol); }
+  bool intersectObject(const Object2 &obj) { return intersector.intersectObjectObject(stored, obj); }
+  Object1 stored;
+  Intersector &intersector;
+private:
+  intersector_helper2& operator=(const intersector_helper2&);
+};
+
+} // end namespace internal
+
+/**  Given a BVH, runs the query encapsulated by \a intersector.
+  *  The Intersector type must provide the following members: \code
+     bool intersectVolume(const BVH::Volume &volume) //returns true if volume intersects the query
+     bool intersectObject(const BVH::Object &object) //returns true if the search should terminate immediately
+  \endcode
+  */
+template<typename BVH, typename Intersector>
+void BVIntersect(const BVH &tree, Intersector &intersector)
+{
+  internal::intersect_helper(tree, intersector, tree.getRootIndex());
+}
+
+/**  Given two BVH's, runs the query on their Cartesian product encapsulated by \a intersector.
+  *  The Intersector type must provide the following members: \code
+     bool intersectVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2) //returns true if product of volumes intersects the query
+     bool intersectVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2) //returns true if the volume-object product intersects the query
+     bool intersectObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2) //returns true if the volume-object product intersects the query
+     bool intersectObjectObject(const BVH1::Object &o1, const BVH2::Object &o2) //returns true if the search should terminate immediately
+  \endcode
+  */
+template<typename BVH1, typename BVH2, typename Intersector>
+void BVIntersect(const BVH1 &tree1, const BVH2 &tree2, Intersector &intersector) //TODO: tandem descent when it makes sense
+{
+  typedef typename BVH1::Index Index1;
+  typedef typename BVH2::Index Index2;
+  typedef internal::intersector_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object, Intersector> Helper1;
+  typedef internal::intersector_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object, Intersector> Helper2;
+  typedef typename BVH1::VolumeIterator VolIter1;
+  typedef typename BVH1::ObjectIterator ObjIter1;
+  typedef typename BVH2::VolumeIterator VolIter2;
+  typedef typename BVH2::ObjectIterator ObjIter2;
+
+  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
+  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
+  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
+  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
+
+  std::vector<std::pair<Index1, Index2> > todo(1, std::make_pair(tree1.getRootIndex(), tree2.getRootIndex()));
+
+  while(!todo.empty()) {
+    tree1.getChildren(todo.back().first, vBegin1, vEnd1, oBegin1, oEnd1);
+    tree2.getChildren(todo.back().second, vBegin2, vEnd2, oBegin2, oEnd2);
+    todo.pop_back();
+
+    for(; vBegin1 != vEnd1; ++vBegin1) { //go through child volumes of first tree
+      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
+      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
+        if(intersector.intersectVolumeVolume(vol1, tree2.getVolume(*vCur2)))
+          todo.push_back(std::make_pair(*vBegin1, *vCur2));
+      }
+
+      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
+        Helper1 helper(*oCur2, intersector);
+        if(internal::intersect_helper(tree1, helper, *vBegin1))
+          return; //intersector said to stop query
+      }
+    }
+
+    for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
+      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
+        Helper2 helper(*oBegin1, intersector);
+        if(internal::intersect_helper(tree2, helper, *vCur2))
+          return; //intersector said to stop query
+      }
+
+      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
+        if(intersector.intersectObjectObject(*oBegin1, *oCur2))
+          return; //intersector said to stop query
+      }
+    }
+  }
+}
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename BVH, typename Minimizer>
+typename Minimizer::Scalar minimize_helper(const BVH &tree, Minimizer &minimizer, typename BVH::Index root, typename Minimizer::Scalar minimum)
+{
+  typedef typename Minimizer::Scalar Scalar;
+  typedef typename BVH::Index Index;
+  typedef std::pair<Scalar, Index> QueueElement; //first element is priority
+  typedef typename BVH::VolumeIterator VolIter;
+  typedef typename BVH::ObjectIterator ObjIter;
+
+  VolIter vBegin = VolIter(), vEnd = VolIter();
+  ObjIter oBegin = ObjIter(), oEnd = ObjIter();
+  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
+
+  todo.push(std::make_pair(Scalar(), root));
+
+  while(!todo.empty()) {
+    tree.getChildren(todo.top().second, vBegin, vEnd, oBegin, oEnd);
+    todo.pop();
+
+    for(; oBegin != oEnd; ++oBegin) //go through child objects
+      minimum = (std::min)(minimum, minimizer.minimumOnObject(*oBegin));
+
+    for(; vBegin != vEnd; ++vBegin) { //go through child volumes
+      Scalar val = minimizer.minimumOnVolume(tree.getVolume(*vBegin));
+      if(val < minimum)
+        todo.push(std::make_pair(val, *vBegin));
+    }
+  }
+
+  return minimum;
+}
+#endif //not EIGEN_PARSED_BY_DOXYGEN
+
+
+template<typename Volume1, typename Object1, typename Object2, typename Minimizer>
+struct minimizer_helper1
+{
+  typedef typename Minimizer::Scalar Scalar;
+  minimizer_helper1(const Object2 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
+  Scalar minimumOnVolume(const Volume1 &vol) { return minimizer.minimumOnVolumeObject(vol, stored); }
+  Scalar minimumOnObject(const Object1 &obj) { return minimizer.minimumOnObjectObject(obj, stored); }
+  Object2 stored;
+  Minimizer &minimizer;
+private:
+  minimizer_helper1& operator=(const minimizer_helper1&);
+};
+
+template<typename Volume2, typename Object2, typename Object1, typename Minimizer>
+struct minimizer_helper2
+{
+  typedef typename Minimizer::Scalar Scalar;
+  minimizer_helper2(const Object1 &inStored, Minimizer &m) : stored(inStored), minimizer(m) {}
+  Scalar minimumOnVolume(const Volume2 &vol) { return minimizer.minimumOnObjectVolume(stored, vol); }
+  Scalar minimumOnObject(const Object2 &obj) { return minimizer.minimumOnObjectObject(stored, obj); }
+  Object1 stored;
+  Minimizer &minimizer;
+private:
+  minimizer_helper2& operator=(const minimizer_helper2&);
+};
+
+} // end namespace internal
+
+/**  Given a BVH, runs the query encapsulated by \a minimizer.
+  *  \returns the minimum value.
+  *  The Minimizer type must provide the following members: \code
+     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
+     Scalar minimumOnVolume(const BVH::Volume &volume)
+     Scalar minimumOnObject(const BVH::Object &object)
+  \endcode
+  */
+template<typename BVH, typename Minimizer>
+typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer)
+{
+  return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), (std::numeric_limits<typename Minimizer::Scalar>::max)());
+}
+
+/**  Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer.
+  *  \returns the minimum value.
+  *  The Minimizer type must provide the following members: \code
+     typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
+     Scalar minimumOnVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2)
+     Scalar minimumOnVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2)
+     Scalar minimumOnObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2)
+     Scalar minimumOnObjectObject(const BVH1::Object &o1, const BVH2::Object &o2)
+  \endcode
+  */
+template<typename BVH1, typename BVH2, typename Minimizer>
+typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Minimizer &minimizer)
+{
+  typedef typename Minimizer::Scalar Scalar;
+  typedef typename BVH1::Index Index1;
+  typedef typename BVH2::Index Index2;
+  typedef internal::minimizer_helper1<typename BVH1::Volume, typename BVH1::Object, typename BVH2::Object, Minimizer> Helper1;
+  typedef internal::minimizer_helper2<typename BVH2::Volume, typename BVH2::Object, typename BVH1::Object, Minimizer> Helper2;
+  typedef std::pair<Scalar, std::pair<Index1, Index2> > QueueElement; //first element is priority
+  typedef typename BVH1::VolumeIterator VolIter1;
+  typedef typename BVH1::ObjectIterator ObjIter1;
+  typedef typename BVH2::VolumeIterator VolIter2;
+  typedef typename BVH2::ObjectIterator ObjIter2;
+
+  VolIter1 vBegin1 = VolIter1(), vEnd1 = VolIter1();
+  ObjIter1 oBegin1 = ObjIter1(), oEnd1 = ObjIter1();
+  VolIter2 vBegin2 = VolIter2(), vEnd2 = VolIter2(), vCur2 = VolIter2();
+  ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2();
+  std::priority_queue<QueueElement, std::vector<QueueElement>, std::greater<QueueElement> > todo; //smallest is at the top
+
+  Scalar minimum = (std::numeric_limits<Scalar>::max)();
+  todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex())));
+
+  while(!todo.empty()) {
+    tree1.getChildren(todo.top().second.first, vBegin1, vEnd1, oBegin1, oEnd1);
+    tree2.getChildren(todo.top().second.second, vBegin2, vEnd2, oBegin2, oEnd2);
+    todo.pop();
+
+    for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
+      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
+        minimum = (std::min)(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
+      }
+
+      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
+        Helper2 helper(*oBegin1, minimizer);
+        minimum = (std::min)(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
+      }
+    }
+
+    for(; vBegin1 != vEnd1; ++vBegin1) { //go through child volumes of first tree
+      const typename BVH1::Volume &vol1 = tree1.getVolume(*vBegin1);
+
+      for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
+        Helper1 helper(*oCur2, minimizer);
+        minimum = (std::min)(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
+      }
+
+      for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
+        Scalar val = minimizer.minimumOnVolumeVolume(vol1, tree2.getVolume(*vCur2));
+        if(val < minimum)
+          todo.push(std::make_pair(val, std::make_pair(*vBegin1, *vCur2)));
+      }
+    }
+  }
+  return minimum;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_BVALGORITHMS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
new file mode 100644
index 0000000..2d5b76a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/BVH/KdBVH.h
@@ -0,0 +1,223 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Ilya Baran <ibaran@mit.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef KDBVH_H_INCLUDED
+#define KDBVH_H_INCLUDED
+
+namespace Eigen { 
+
+namespace internal {
+
+//internal pair class for the BVH--used instead of std::pair because of alignment
+template<typename Scalar, int Dim>
+struct vector_int_pair
+{
+EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar, Dim)
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+
+  vector_int_pair(const VectorType &v, int i) : first(v), second(i) {}
+
+  VectorType first;
+  int second;
+};
+
+//these templates help the tree initializer get the bounding boxes either from a provided
+//iterator range or using bounding_box in a unified way
+template<typename ObjectList, typename VolumeList, typename BoxIter>
+struct get_boxes_helper {
+  void operator()(const ObjectList &objects, BoxIter boxBegin, BoxIter boxEnd, VolumeList &outBoxes)
+  {
+    outBoxes.insert(outBoxes.end(), boxBegin, boxEnd);
+    eigen_assert(outBoxes.size() == objects.size());
+    EIGEN_ONLY_USED_FOR_DEBUG(objects);
+  }
+};
+
+template<typename ObjectList, typename VolumeList>
+struct get_boxes_helper<ObjectList, VolumeList, int> {
+  void operator()(const ObjectList &objects, int, int, VolumeList &outBoxes)
+  {
+    outBoxes.reserve(objects.size());
+    for(int i = 0; i < (int)objects.size(); ++i)
+      outBoxes.push_back(bounding_box(objects[i]));
+  }
+};
+
+} // end namespace internal
+
+
+/** \class KdBVH
+ *  \brief A simple bounding volume hierarchy based on AlignedBox
+ *
+ *  \param _Scalar The underlying scalar type of the bounding boxes
+ *  \param _Dim The dimension of the space in which the hierarchy lives
+ *  \param _Object The object type that lives in the hierarchy.  It must have value semantics.  Either bounding_box(_Object) must
+ *                 be defined and return an AlignedBox<_Scalar, _Dim> or bounding boxes must be provided to the tree initializer.
+ *
+ *  This class provides a simple (as opposed to optimized) implementation of a bounding volume hierarchy analogous to a Kd-tree.
+ *  Given a sequence of objects, it computes their bounding boxes, constructs a Kd-tree of their centers
+ *  and builds a BVH with the structure of that Kd-tree.  When the elements of the tree are too expensive to be copied around,
+ *  it is useful for _Object to be a pointer.
+ */
+template<typename _Scalar, int _Dim, typename _Object> class KdBVH
+{
+public:
+  enum { Dim = _Dim };
+  typedef _Object Object;
+  typedef std::vector<Object, aligned_allocator<Object> > ObjectList;
+  typedef _Scalar Scalar;
+  typedef AlignedBox<Scalar, Dim> Volume;
+  typedef std::vector<Volume, aligned_allocator<Volume> > VolumeList;
+  typedef int Index;
+  typedef const int *VolumeIterator; //the iterators are just pointers into the tree's vectors
+  typedef const Object *ObjectIterator;
+
+  KdBVH() {}
+
+  /** Given an iterator range over \a Object references, constructs the BVH.  Requires that bounding_box(Object) return a Volume. */
+  template<typename Iter> KdBVH(Iter begin, Iter end) { init(begin, end, 0, 0); } //int is recognized by init as not being an iterator type
+
+  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes, constructs the BVH */
+  template<typename OIter, typename BIter> KdBVH(OIter begin, OIter end, BIter boxBegin, BIter boxEnd) { init(begin, end, boxBegin, boxEnd); }
+
+  /** Given an iterator range over \a Object references, constructs the BVH, overwriting whatever is in there currently.
+    * Requires that bounding_box(Object) return a Volume. */
+  template<typename Iter> void init(Iter begin, Iter end) { init(begin, end, 0, 0); }
+
+  /** Given an iterator range over \a Object references and an iterator range over their bounding boxes,
+    * constructs the BVH, overwriting whatever is in there currently. */
+  template<typename OIter, typename BIter> void init(OIter begin, OIter end, BIter boxBegin, BIter boxEnd)
+  {
+    objects.clear();
+    boxes.clear();
+    children.clear();
+
+    objects.insert(objects.end(), begin, end);
+    int n = static_cast<int>(objects.size());
+
+    if(n < 2)
+      return; //if we have at most one object, we don't need any internal nodes
+
+    VolumeList objBoxes;
+    VIPairList objCenters;
+
+    //compute the bounding boxes depending on BIter type
+    internal::get_boxes_helper<ObjectList, VolumeList, BIter>()(objects, boxBegin, boxEnd, objBoxes);
+
+    objCenters.reserve(n);
+    boxes.reserve(n - 1);
+    children.reserve(2 * n - 2);
+
+    for(int i = 0; i < n; ++i)
+      objCenters.push_back(VIPair(objBoxes[i].center(), i));
+
+    build(objCenters, 0, n, objBoxes, 0); //the recursive part of the algorithm
+
+    ObjectList tmp(n);
+    tmp.swap(objects);
+    for(int i = 0; i < n; ++i)
+      objects[i] = tmp[objCenters[i].second];
+  }
+
+  /** \returns the index of the root of the hierarchy */
+  inline Index getRootIndex() const { return (int)boxes.size() - 1; }
+
+  /** Given an \a index of a node, on exit, \a outVBegin and \a outVEnd range over the indices of the volume children of the node
+    * and \a outOBegin and \a outOEnd range over the object children of the node */
+  EIGEN_STRONG_INLINE void getChildren(Index index, VolumeIterator &outVBegin, VolumeIterator &outVEnd,
+                                       ObjectIterator &outOBegin, ObjectIterator &outOEnd) const
+  { //inlining this function should open lots of optimization opportunities to the compiler
+    if(index < 0) {
+      outVBegin = outVEnd;
+      if(!objects.empty())
+        outOBegin = &(objects[0]);
+      outOEnd = outOBegin + objects.size(); //output all objects--necessary when the tree has only one object
+      return;
+    }
+
+    int numBoxes = static_cast<int>(boxes.size());
+
+    int idx = index * 2;
+    if(children[idx + 1] < numBoxes) { //second index is always bigger
+      outVBegin = &(children[idx]);
+      outVEnd = outVBegin + 2;
+      outOBegin = outOEnd;
+    }
+    else if(children[idx] >= numBoxes) { //if both children are objects
+      outVBegin = outVEnd;
+      outOBegin = &(objects[children[idx] - numBoxes]);
+      outOEnd = outOBegin + 2;
+    } else { //if the first child is a volume and the second is an object
+      outVBegin = &(children[idx]);
+      outVEnd = outVBegin + 1;
+      outOBegin = &(objects[children[idx + 1] - numBoxes]);
+      outOEnd = outOBegin + 1;
+    }
+  }
+
+  /** \returns the bounding box of the node at \a index */
+  inline const Volume &getVolume(Index index) const
+  {
+    return boxes[index];
+  }
+
+private:
+  typedef internal::vector_int_pair<Scalar, Dim> VIPair;
+  typedef std::vector<VIPair, aligned_allocator<VIPair> > VIPairList;
+  typedef Matrix<Scalar, Dim, 1> VectorType;
+  struct VectorComparator //compares vectors, or more specifically, VIPairs along a particular dimension
+  {
+    VectorComparator(int inDim) : dim(inDim) {}
+    inline bool operator()(const VIPair &v1, const VIPair &v2) const { return v1.first[dim] < v2.first[dim]; }
+    int dim;
+  };
+
+  //Build the part of the tree between objects[from] and objects[to] (not including objects[to]).
+  //This routine partitions the objCenters in [from, to) along the dimension dim, recursively constructs
+  //the two halves, and adds their parent node.  TODO: a cache-friendlier layout
+  void build(VIPairList &objCenters, int from, int to, const VolumeList &objBoxes, int dim)
+  {
+    eigen_assert(to - from > 1);
+    if(to - from == 2) {
+      boxes.push_back(objBoxes[objCenters[from].second].merged(objBoxes[objCenters[from + 1].second]));
+      children.push_back(from + (int)objects.size() - 1); //there are objects.size() - 1 tree nodes
+      children.push_back(from + (int)objects.size());
+    }
+    else if(to - from == 3) {
+      int mid = from + 2;
+      std::nth_element(objCenters.begin() + from, objCenters.begin() + mid,
+                        objCenters.begin() + to, VectorComparator(dim)); //partition
+      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
+      int idx1 = (int)boxes.size() - 1;
+      boxes.push_back(boxes[idx1].merged(objBoxes[objCenters[mid].second]));
+      children.push_back(idx1);
+      children.push_back(mid + (int)objects.size() - 1);
+    }
+    else {
+      int mid = from + (to - from) / 2;
+      nth_element(objCenters.begin() + from, objCenters.begin() + mid,
+                  objCenters.begin() + to, VectorComparator(dim)); //partition
+      build(objCenters, from, mid, objBoxes, (dim + 1) % Dim);
+      int idx1 = (int)boxes.size() - 1;
+      build(objCenters, mid, to, objBoxes, (dim + 1) % Dim);
+      int idx2 = (int)boxes.size() - 1;
+      boxes.push_back(boxes[idx1].merged(boxes[idx2]));
+      children.push_back(idx1);
+      children.push_back(idx2);
+    }
+  }
+
+  std::vector<int> children; //children of x are children[2x] and children[2x+1], indices bigger than boxes.size() index into objects.
+  VolumeList boxes;
+  ObjectList objects;
+};
+
+} // end namespace Eigen
+
+#endif //KDBVH_H_INCLUDED
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
new file mode 100644
index 0000000..0fbd847
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
@@ -0,0 +1,790 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 David Harmon <dharmon@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
+#define EIGEN_ARPACKGENERALIZEDSELFADJOINTEIGENSOLVER_H
+
+#include "../../../../Eigen/Dense"
+
+namespace Eigen { 
+
+namespace internal {
+  template<typename Scalar, typename RealScalar> struct arpack_wrapper;
+  template<typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD> struct OP;
+}
+
+
+
+template<typename MatrixType, typename MatrixSolver=SimplicialLLT<MatrixType>, bool BisSPD=false>
+class ArpackGeneralizedSelfAdjointEigenSolver
+{
+public:
+  //typedef typename MatrixSolver::MatrixType MatrixType;
+
+  /** \brief Scalar type for matrices of type \p MatrixType. */
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+
+  /** \brief Real scalar type for \p MatrixType.
+   *
+   * This is just \c Scalar if #Scalar is real (e.g., \c float or
+   * \c Scalar), and the type of the real part of \c Scalar if #Scalar is
+   * complex.
+   */
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+   *
+   * This is a column vector with entries of type #RealScalar.
+   * The length of the vector is the size of \p nbrEigenvalues.
+   */
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
+
+  /** \brief Default constructor.
+   *
+   * The default constructor is for cases in which the user intends to
+   * perform decompositions via compute().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver()
+   : m_eivec(),
+     m_eivalues(),
+     m_isInitialized(false),
+     m_eigenvectorsOk(false),
+     m_nbrConverged(0),
+     m_nbrIterations(0)
+  { }
+
+  /** \brief Constructor; computes generalized eigenvalues of given matrix with respect to another matrix.
+   *
+   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
+   *    computed. By default, the upper triangular part is used, but can be changed
+   *    through the template parameter.
+   * \param[in] B Self-adjoint matrix for the generalized eigenvalue problem.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * This constructor calls compute(const MatrixType&, const MatrixType&, Index, string, int, RealScalar)
+   * to compute the eigenvalues of the matrix \p A with respect to \p B. The eigenvectors are computed if
+   * \p options equals #ComputeEigenvectors.
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType& A, const MatrixType& B,
+                                          Index nbrEigenvalues, std::string eigs_sigma="LM",
+                               int options=ComputeEigenvectors, RealScalar tol=0.0)
+    : m_eivec(),
+      m_eivalues(),
+      m_isInitialized(false),
+      m_eigenvectorsOk(false),
+      m_nbrConverged(0),
+      m_nbrIterations(0)
+  {
+    compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
+  }
+
+  /** \brief Constructor; computes eigenvalues of given matrix.
+   *
+   * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
+   *    computed. By default, the upper triangular part is used, but can be changed
+   *    through the template parameter.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * This constructor calls compute(const MatrixType&, Index, string, int, RealScalar)
+   * to compute the eigenvalues of the matrix \p A. The eigenvectors are computed if
+   * \p options equals #ComputeEigenvectors.
+   *
+   */
+
+  ArpackGeneralizedSelfAdjointEigenSolver(const MatrixType& A,
+                                          Index nbrEigenvalues, std::string eigs_sigma="LM",
+                               int options=ComputeEigenvectors, RealScalar tol=0.0)
+    : m_eivec(),
+      m_eivalues(),
+      m_isInitialized(false),
+      m_eigenvectorsOk(false),
+      m_nbrConverged(0),
+      m_nbrIterations(0)
+  {
+    compute(A, nbrEigenvalues, eigs_sigma, options, tol);
+  }
+
+
+  /** \brief Computes generalized eigenvalues / eigenvectors of given matrix using the external ARPACK library.
+   *
+   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
+   * \param[in]  B  Selfadjoint matrix for generalized eigenvalues.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * \returns    Reference to \c *this
+   *
+   * This function computes the generalized eigenvalues of \p A with respect to \p B using ARPACK.  The eigenvalues()
+   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+   * then the eigenvectors are also computed and can be retrieved by
+   * calling eigenvectors().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver& compute(const MatrixType& A, const MatrixType& B,
+                                                   Index nbrEigenvalues, std::string eigs_sigma="LM",
+                                        int options=ComputeEigenvectors, RealScalar tol=0.0);
+  
+  /** \brief Computes eigenvalues / eigenvectors of given matrix using the external ARPACK library.
+   *
+   * \param[in]  A  Selfadjoint matrix whose eigendecomposition is to be computed.
+   * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
+   *    Must be less than the size of the input matrix, or an error is returned.
+   * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
+   *    respective meanings to find the largest magnitude , smallest magnitude,
+   *    largest algebraic, or smallest algebraic eigenvalues. Alternatively, this
+   *    value can contain floating point value in string form, in which case the
+   *    eigenvalues closest to this value will be found.
+   * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+   * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
+   *    means machine precision.
+   *
+   * \returns    Reference to \c *this
+   *
+   * This function computes the eigenvalues of \p A using ARPACK.  The eigenvalues()
+   * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+   * then the eigenvectors are also computed and can be retrieved by
+   * calling eigenvectors().
+   *
+   */
+  ArpackGeneralizedSelfAdjointEigenSolver& compute(const MatrixType& A,
+                                                   Index nbrEigenvalues, std::string eigs_sigma="LM",
+                                        int options=ComputeEigenvectors, RealScalar tol=0.0);
+
+
+  /** \brief Returns the eigenvectors of given matrix.
+   *
+   * \returns  A const reference to the matrix whose columns are the eigenvectors.
+   *
+   * \pre The eigenvectors have been computed before.
+   *
+   * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+   * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+   * eigenvectors are normalized to have (Euclidean) norm equal to one. If
+   * this object was used to solve the eigenproblem for the selfadjoint
+   * matrix \f$ A \f$, then the matrix returned by this function is the
+   * matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
+   * For the generalized eigenproblem, the matrix returned is the solution \f$ A V = D B V \f$
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
+   *
+   * \sa eigenvalues()
+   */
+  const Matrix<Scalar, Dynamic, Dynamic>& eigenvectors() const
+  {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec;
+  }
+
+  /** \brief Returns the eigenvalues of given matrix.
+   *
+   * \returns A const reference to the column vector containing the eigenvalues.
+   *
+   * \pre The eigenvalues have been computed before.
+   *
+   * The eigenvalues are repeated according to their algebraic multiplicity,
+   * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+   * are sorted in increasing order.
+   *
+   * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
+   *
+   * \sa eigenvectors(), MatrixBase::eigenvalues()
+   */
+  const Matrix<Scalar, Dynamic, 1>& eigenvalues() const
+  {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    return m_eivalues;
+  }
+
+  /** \brief Computes the positive-definite square root of the matrix.
+   *
+   * \returns the positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * The square root of a positive-definite matrix \f$ A \f$ is the
+   * positive-definite matrix whose square equals \f$ A \f$. This function
+   * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
+   * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
+   *
+   * \sa operatorInverseSqrt(),
+   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
+   */
+  Matrix<Scalar, Dynamic, Dynamic> operatorSqrt() const
+  {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Computes the inverse square root of the matrix.
+   *
+   * \returns the inverse positive-definite square root of the matrix
+   *
+   * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+   * have been computed before.
+   *
+   * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
+   * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
+   * cheaper than first computing the square root with operatorSqrt() and
+   * then its inverse with MatrixBase::inverse().
+   *
+   * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
+   * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
+   *
+   * \sa operatorSqrt(), MatrixBase::inverse(),
+   *     \ref MatrixFunctions_Module "MatrixFunctions Module"
+   */
+  Matrix<Scalar, Dynamic, Dynamic> operatorInverseSqrt() const
+  {
+    eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+    eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+    return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+  }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+   */
+  ComputationInfo info() const
+  {
+    eigen_assert(m_isInitialized && "ArpackGeneralizedSelfAdjointEigenSolver is not initialized.");
+    return m_info;
+  }
+
+  size_t getNbrConvergedEigenValues() const
+  { return m_nbrConverged; }
+
+  size_t getNbrIterations() const
+  { return m_nbrIterations; }
+
+protected:
+  Matrix<Scalar, Dynamic, Dynamic> m_eivec;
+  Matrix<Scalar, Dynamic, 1> m_eivalues;
+  ComputationInfo m_info;
+  bool m_isInitialized;
+  bool m_eigenvectorsOk;
+
+  size_t m_nbrConverged;
+  size_t m_nbrIterations;
+};
+
+
+
+
+
+template<typename MatrixType, typename MatrixSolver, bool BisSPD>
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&
+    ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>
+::compute(const MatrixType& A, Index nbrEigenvalues,
+          std::string eigs_sigma, int options, RealScalar tol)
+{
+    MatrixType B(0,0);
+    compute(A, B, nbrEigenvalues, eigs_sigma, options, tol);
+    
+    return *this;
+}
+
+
+template<typename MatrixType, typename MatrixSolver, bool BisSPD>
+ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>&
+    ArpackGeneralizedSelfAdjointEigenSolver<MatrixType, MatrixSolver, BisSPD>
+::compute(const MatrixType& A, const MatrixType& B, Index nbrEigenvalues,
+          std::string eigs_sigma, int options, RealScalar tol)
+{
+  eigen_assert(A.cols() == A.rows());
+  eigen_assert(B.cols() == B.rows());
+  eigen_assert(B.rows() == 0 || A.cols() == B.rows());
+  eigen_assert((options &~ (EigVecMask | GenEigMask)) == 0
+            && (options & EigVecMask) != EigVecMask
+            && "invalid option parameter");
+
+  bool isBempty = (B.rows() == 0) || (B.cols() == 0);
+
+  // For clarity, all parameters match their ARPACK name
+  //
+  // Always 0 on the first call
+  //
+  int ido = 0;
+
+  int n = (int)A.cols();
+
+  // User options: "LA", "SA", "SM", "LM", "BE"
+  //
+  char whch[3] = "LM";
+    
+  // Specifies the shift if iparam[6] = { 3, 4, 5 }, not used if iparam[6] = { 1, 2 }
+  //
+  RealScalar sigma = 0.0;
+
+  if (eigs_sigma.length() >= 2 && isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1]))
+  {
+      eigs_sigma[0] = toupper(eigs_sigma[0]);
+      eigs_sigma[1] = toupper(eigs_sigma[1]);
+
+      // In the following special case we're going to invert the problem, since solving
+      // for larger magnitude is much much faster
+      // i.e., if 'SM' is specified, we're going to really use 'LM', the default
+      //
+      if (eigs_sigma.substr(0,2) != "SM")
+      {
+          whch[0] = eigs_sigma[0];
+          whch[1] = eigs_sigma[1];
+      }
+  }
+  else
+  {
+      eigen_assert(false && "Specifying clustered eigenvalues is not yet supported!");
+
+      // If it's not scalar values, then the user may be explicitly
+      // specifying the sigma value to cluster the evs around
+      //
+      sigma = atof(eigs_sigma.c_str());
+
+      // If atof fails, it returns 0.0, which is a fine default
+      //
+  }
+
+  // "I" means normal eigenvalue problem, "G" means generalized
+  //
+  char bmat[2] = "I";
+  if (eigs_sigma.substr(0,2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])) || (!isBempty && !BisSPD))
+      bmat[0] = 'G';
+
+  // Now we determine the mode to use
+  //
+  int mode = (bmat[0] == 'G') + 1;
+  if (eigs_sigma.substr(0,2) == "SM" || !(isalpha(eigs_sigma[0]) && isalpha(eigs_sigma[1])))
+  {
+      // We're going to use shift-and-invert mode, and basically find
+      // the largest eigenvalues of the inverse operator
+      //
+      mode = 3;
+  }
+
+  // The user-specified number of eigenvalues/vectors to compute
+  //
+  int nev = (int)nbrEigenvalues;
+
+  // Allocate space for ARPACK to store the residual
+  //
+  Scalar *resid = new Scalar[n];
+
+  // Number of Lanczos vectors, must satisfy nev < ncv <= n
+  // Note that this indicates that nev != n, and we cannot compute
+  // all eigenvalues of a mtrix
+  //
+  int ncv = std::min(std::max(2*nev, 20), n);
+
+  // The working n x ncv matrix, also store the final eigenvectors (if computed)
+  //
+  Scalar *v = new Scalar[n*ncv];
+  int ldv = n;
+
+  // Working space
+  //
+  Scalar *workd = new Scalar[3*n];
+  int lworkl = ncv*ncv+8*ncv; // Must be at least this length
+  Scalar *workl = new Scalar[lworkl];
+
+  int *iparam= new int[11];
+  iparam[0] = 1; // 1 means we let ARPACK perform the shifts, 0 means we'd have to do it
+  iparam[2] = std::max(300, (int)std::ceil(2*n/std::max(ncv,1)));
+  iparam[6] = mode; // The mode, 1 is standard ev problem, 2 for generalized ev, 3 for shift-and-invert
+
+  // Used during reverse communicate to notify where arrays start
+  //
+  int *ipntr = new int[11]; 
+
+  // Error codes are returned in here, initial value of 0 indicates a random initial
+  // residual vector is used, any other values means resid contains the initial residual
+  // vector, possibly from a previous run
+  //
+  int info = 0;
+
+  Scalar scale = 1.0;
+  //if (!isBempty)
+  //{
+  //Scalar scale = B.norm() / std::sqrt(n);
+  //scale = std::pow(2, std::floor(std::log(scale+1)));
+  ////M /= scale;
+  //for (size_t i=0; i<(size_t)B.outerSize(); i++)
+  //    for (typename MatrixType::InnerIterator it(B, i); it; ++it)
+  //        it.valueRef() /= scale;
+  //}
+
+  MatrixSolver OP;
+  if (mode == 1 || mode == 2)
+  {
+      if (!isBempty)
+          OP.compute(B);
+  }
+  else if (mode == 3)
+  {
+      if (sigma == 0.0)
+      {
+          OP.compute(A);
+      }
+      else
+      {
+          // Note: We will never enter here because sigma must be 0.0
+          //
+          if (isBempty)
+          {
+            MatrixType AminusSigmaB(A);
+            for (Index i=0; i<A.rows(); ++i)
+                AminusSigmaB.coeffRef(i,i) -= sigma;
+            
+            OP.compute(AminusSigmaB);
+          }
+          else
+          {
+              MatrixType AminusSigmaB = A - sigma * B;
+              OP.compute(AminusSigmaB);
+          }
+      }
+  }
+ 
+  if (!(mode == 1 && isBempty) && !(mode == 2 && isBempty) && OP.info() != Success)
+      std::cout << "Error factoring matrix" << std::endl;
+
+  do
+  {
+    internal::arpack_wrapper<Scalar, RealScalar>::saupd(&ido, bmat, &n, whch, &nev, &tol, resid, 
+                                                        &ncv, v, &ldv, iparam, ipntr, workd, workl,
+                                                        &lworkl, &info);
+
+    if (ido == -1 || ido == 1)
+    {
+      Scalar *in  = workd + ipntr[0] - 1;
+      Scalar *out = workd + ipntr[1] - 1;
+
+      if (ido == 1 && mode != 2)
+      {
+          Scalar *out2 = workd + ipntr[2] - 1;
+          if (isBempty || mode == 1)
+            Matrix<Scalar, Dynamic, 1>::Map(out2, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
+          else
+            Matrix<Scalar, Dynamic, 1>::Map(out2, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+          
+          in = workd + ipntr[2] - 1;
+      }
+
+      if (mode == 1)
+      {
+        if (isBempty)
+        {
+          // OP = A
+          //
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+        }
+        else
+        {
+          // OP = L^{-1}AL^{-T}
+          //
+          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::applyOP(OP, A, n, in, out);
+        }
+      }
+      else if (mode == 2)
+      {
+        if (ido == 1)
+          Matrix<Scalar, Dynamic, 1>::Map(in, n)  = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+        
+        // OP = B^{-1} A
+        //
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+      }
+      else if (mode == 3)
+      {
+        // OP = (A-\sigmaB)B (\sigma could be 0, and B could be I)
+        // The B * in is already computed and stored at in if ido == 1
+        //
+        if (ido == 1 || isBempty)
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+        else
+          Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(B * Matrix<Scalar, Dynamic, 1>::Map(in, n));
+      }
+    }
+    else if (ido == 2)
+    {
+      Scalar *in  = workd + ipntr[0] - 1;
+      Scalar *out = workd + ipntr[1] - 1;
+
+      if (isBempty || mode == 1)
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
+      else
+        Matrix<Scalar, Dynamic, 1>::Map(out, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
+    }
+  } while (ido != 99);
+
+  if (info == 1)
+    m_info = NoConvergence;
+  else if (info == 3)
+    m_info = NumericalIssue;
+  else if (info < 0)
+    m_info = InvalidInput;
+  else if (info != 0)
+    eigen_assert(false && "Unknown ARPACK return value!");
+  else
+  {
+    // Do we compute eigenvectors or not?
+    //
+    int rvec = (options & ComputeEigenvectors) == ComputeEigenvectors;
+
+    // "A" means "All", use "S" to choose specific eigenvalues (not yet supported in ARPACK))
+    //
+    char howmny[2] = "A"; 
+
+    // if howmny == "S", specifies the eigenvalues to compute (not implemented in ARPACK)
+    //
+    int *select = new int[ncv];
+
+    // Final eigenvalues
+    //
+    m_eivalues.resize(nev, 1);
+
+    internal::arpack_wrapper<Scalar, RealScalar>::seupd(&rvec, howmny, select, m_eivalues.data(), v, &ldv,
+                                                        &sigma, bmat, &n, whch, &nev, &tol, resid, &ncv,
+                                                        v, &ldv, iparam, ipntr, workd, workl, &lworkl, &info);
+
+    if (info == -14)
+      m_info = NoConvergence;
+    else if (info != 0)
+      m_info = InvalidInput;
+    else
+    {
+      if (rvec)
+      {
+        m_eivec.resize(A.rows(), nev);
+        for (int i=0; i<nev; i++)
+          for (int j=0; j<n; j++)
+            m_eivec(j,i) = v[i*n+j] / scale;
+      
+        if (mode == 1 && !isBempty && BisSPD)
+          internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::project(OP, n, nev, m_eivec.data());
+
+        m_eigenvectorsOk = true;
+      }
+
+      m_nbrIterations = iparam[2];
+      m_nbrConverged  = iparam[4];
+
+      m_info = Success;
+    }
+
+    delete[] select;
+  }
+
+  delete[] v;
+  delete[] iparam;
+  delete[] ipntr;
+  delete[] workd;
+  delete[] workl;
+  delete[] resid;
+
+  m_isInitialized = true;
+
+  return *this;
+}
+
+
+// Single precision
+//
+extern "C" void ssaupd_(int *ido, char *bmat, int *n, char *which,
+    int *nev, float *tol, float *resid, int *ncv,
+    float *v, int *ldv, int *iparam, int *ipntr,
+    float *workd, float *workl, int *lworkl,
+    int *info);
+
+extern "C" void sseupd_(int *rvec, char *All, int *select, float *d,
+    float *z, int *ldz, float *sigma, 
+    char *bmat, int *n, char *which, int *nev,
+    float *tol, float *resid, int *ncv, float *v,
+    int *ldv, int *iparam, int *ipntr, float *workd,
+    float *workl, int *lworkl, int *ierr);
+
+// Double precision
+//
+extern "C" void dsaupd_(int *ido, char *bmat, int *n, char *which,
+    int *nev, double *tol, double *resid, int *ncv,
+    double *v, int *ldv, int *iparam, int *ipntr,
+    double *workd, double *workl, int *lworkl,
+    int *info);
+
+extern "C" void dseupd_(int *rvec, char *All, int *select, double *d,
+    double *z, int *ldz, double *sigma, 
+    char *bmat, int *n, char *which, int *nev,
+    double *tol, double *resid, int *ncv, double *v,
+    int *ldv, int *iparam, int *ipntr, double *workd,
+    double *workl, int *lworkl, int *ierr);
+
+
+namespace internal {
+
+template<typename Scalar, typename RealScalar> struct arpack_wrapper
+{
+  static inline void saupd(int *ido, char *bmat, int *n, char *which,
+      int *nev, RealScalar *tol, Scalar *resid, int *ncv,
+      Scalar *v, int *ldv, int *iparam, int *ipntr,
+      Scalar *workd, Scalar *workl, int *lworkl, int *info)
+  { 
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, Scalar *d,
+      Scalar *z, int *ldz, RealScalar *sigma,
+      char *bmat, int *n, char *which, int *nev,
+      RealScalar *tol, Scalar *resid, int *ncv, Scalar *v,
+      int *ldv, int *iparam, int *ipntr, Scalar *workd,
+      Scalar *workl, int *lworkl, int *ierr)
+  {
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  }
+};
+
+template <> struct arpack_wrapper<float, float>
+{
+  static inline void saupd(int *ido, char *bmat, int *n, char *which,
+      int *nev, float *tol, float *resid, int *ncv,
+      float *v, int *ldv, int *iparam, int *ipntr,
+      float *workd, float *workl, int *lworkl, int *info)
+  {
+    ssaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, float *d,
+      float *z, int *ldz, float *sigma,
+      char *bmat, int *n, char *which, int *nev,
+      float *tol, float *resid, int *ncv, float *v,
+      int *ldv, int *iparam, int *ipntr, float *workd,
+      float *workl, int *lworkl, int *ierr)
+  {
+    sseupd_(rvec, All, select, d, z, ldz, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,
+        workd, workl, lworkl, ierr);
+  }
+};
+
+template <> struct arpack_wrapper<double, double>
+{
+  static inline void saupd(int *ido, char *bmat, int *n, char *which,
+      int *nev, double *tol, double *resid, int *ncv,
+      double *v, int *ldv, int *iparam, int *ipntr,
+      double *workd, double *workl, int *lworkl, int *info)
+  {
+    dsaupd_(ido, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr, workd, workl, lworkl, info);
+  }
+
+  static inline void seupd(int *rvec, char *All, int *select, double *d,
+      double *z, int *ldz, double *sigma,
+      char *bmat, int *n, char *which, int *nev,
+      double *tol, double *resid, int *ncv, double *v,
+      int *ldv, int *iparam, int *ipntr, double *workd,
+      double *workl, int *lworkl, int *ierr)
+  {
+    dseupd_(rvec, All, select, d, v, ldv, sigma, bmat, n, which, nev, tol, resid, ncv, v, ldv, iparam, ipntr,
+        workd, workl, lworkl, ierr);
+  }
+};
+
+
+template<typename MatrixSolver, typename MatrixType, typename Scalar, bool BisSPD>
+struct OP
+{
+    static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out);
+    static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs);
+};
+
+template<typename MatrixSolver, typename MatrixType, typename Scalar>
+struct OP<MatrixSolver, MatrixType, Scalar, true>
+{
+  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
+{
+    // OP = L^{-1} A L^{-T}  (B = LL^T)
+    //
+    // First solve L^T out = in
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationPinv() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+
+    // Then compute out = A out
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+
+    // Then solve L out = out
+    //
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationP() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
+    Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixL().solve(Matrix<Scalar, Dynamic, 1>::Map(out, n));
+}
+
+  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs)
+{
+    // Solve L^T out = in
+    //
+    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.matrixU().solve(Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k));
+    Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.permutationPinv() * Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k);
+}
+
+};
+
+template<typename MatrixSolver, typename MatrixType, typename Scalar>
+struct OP<MatrixSolver, MatrixType, Scalar, false>
+{
+  static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
+{
+    eigen_assert(false && "Should never be in here...");
+}
+
+  static inline void project(MatrixSolver &OP, int n, int k, Scalar *vecs)
+{
+    eigen_assert(false && "Should never be in here...");
+}
+
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ARPACKSELFADJOINTEIGENSOLVER_H
+
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
new file mode 100644
index 0000000..e43cdb7
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerAngles.h
@@ -0,0 +1,355 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLESCLASS_H// TODO: Fix previous "EIGEN_EULERANGLES_H" definition?
+#define EIGEN_EULERANGLESCLASS_H
+
+namespace Eigen
+{
+  /** \class EulerAngles
+    *
+    * \ingroup EulerAngles_Module
+    *
+    * \brief Represents a rotation in a 3 dimensional space as three Euler angles.
+    *
+    * Euler rotation is a set of three rotation of three angles over three fixed axes, defined by the EulerSystem given as a template parameter.
+    * 
+    * Here is how intrinsic Euler angles works:
+    *  - first, rotate the axes system over the alpha axis in angle alpha
+    *  - then, rotate the axes system over the beta axis(which was rotated in the first stage) in angle beta
+    *  - then, rotate the axes system over the gamma axis(which was rotated in the two stages above) in angle gamma
+    *
+    * \note This class support only intrinsic Euler angles for simplicity,
+    *  see EulerSystem how to easily overcome this for extrinsic systems.
+    *
+    * ### Rotation representation and conversions ###
+    *
+    * It has been proved(see Wikipedia link below) that every rotation can be represented
+    *  by Euler angles, but there is no single representation (e.g. unlike rotation matrices).
+    * Therefore, you can convert from Eigen rotation and to them
+    *  (including rotation matrices, which is not called "rotations" by Eigen design).
+    *
+    * Euler angles usually used for:
+    *  - convenient human representation of rotation, especially in interactive GUI.
+    *  - gimbal systems and robotics
+    *  - efficient encoding(i.e. 3 floats only) of rotation for network protocols.
+    *
+    * However, Euler angles are slow comparing to quaternion or matrices,
+    *  because their unnatural math definition, although it's simple for human.
+    * To overcome this, this class provide easy movement from the math friendly representation
+    *  to the human friendly representation, and vise-versa.
+    *
+    * All the user need to do is a safe simple C++ type conversion,
+    *  and this class take care for the math.
+    * Additionally, some axes related computation is done in compile time.
+    *
+    * #### Euler angles ranges in conversions ####
+    * Rotations representation as EulerAngles are not single (unlike matrices),
+    *  and even have infinite EulerAngles representations.<BR>
+    * For example, add or subtract 2*PI from either angle of EulerAngles
+    *  and you'll get the same rotation.
+    * This is the general reason for infinite representation,
+    *  but it's not the only general reason for not having a single representation.
+    *
+    * When converting rotation to EulerAngles, this class convert it to specific ranges
+    * When converting some rotation to EulerAngles, the rules for ranges are as follow:
+    * - If the rotation we converting from is an EulerAngles
+    *  (even when it represented as RotationBase explicitly), angles ranges are __undefined__.
+    * - otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
+    *   As for Beta angle:
+    *    - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+    *    - otherwise:
+    *      - If the beta axis is positive, the beta angle will be in the range [0, PI]
+    *      - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+    *
+    * \sa EulerAngles(const MatrixBase<Derived>&)
+    * \sa EulerAngles(const RotationBase<Derived, 3>&)
+    *
+    * ### Convenient user typedefs ###
+    *
+    * Convenient typedefs for EulerAngles exist for float and double scalar,
+    *  in a form of EulerAngles{A}{B}{C}{scalar},
+    *  e.g. \ref EulerAnglesXYZd, \ref EulerAnglesZYZf.
+    *
+    * Only for positive axes{+x,+y,+z} Euler systems are have convenient typedef.
+    * If you need negative axes{-x,-y,-z}, it is recommended to create you own typedef with
+    *  a word that represent what you need.
+    *
+    * ### Example ###
+    *
+    * \include EulerAngles.cpp
+    * Output: \verbinclude EulerAngles.out
+    *
+    * ### Additional reading ###
+    *
+    * If you're want to get more idea about how Euler system work in Eigen see EulerSystem.
+    *
+    * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
+    *
+    * \tparam _Scalar the scalar type, i.e. the type of the angles.
+    *
+    * \tparam _System the EulerSystem to use, which represents the axes of rotation.
+    */
+  template <typename _Scalar, class _System>
+  class EulerAngles : public RotationBase<EulerAngles<_Scalar, _System>, 3>
+  {
+    public:
+      typedef RotationBase<EulerAngles<_Scalar, _System>, 3> Base;
+      
+      /** the scalar type of the angles */
+      typedef _Scalar Scalar;
+      typedef typename NumTraits<Scalar>::Real RealScalar;
+      
+      /** the EulerSystem to use, which represents the axes of rotation. */
+      typedef _System System;
+    
+      typedef Matrix<Scalar,3,3> Matrix3; /*!< the equivalent rotation matrix type */
+      typedef Matrix<Scalar,3,1> Vector3; /*!< the equivalent 3 dimension vector type */
+      typedef Quaternion<Scalar> QuaternionType; /*!< the equivalent quaternion type */
+      typedef AngleAxis<Scalar> AngleAxisType; /*!< the equivalent angle-axis type */
+      
+      /** \returns the axis vector of the first (alpha) rotation */
+      static Vector3 AlphaAxisVector() {
+        const Vector3& u = Vector3::Unit(System::AlphaAxisAbs - 1);
+        return System::IsAlphaOpposite ? -u : u;
+      }
+      
+      /** \returns the axis vector of the second (beta) rotation */
+      static Vector3 BetaAxisVector() {
+        const Vector3& u = Vector3::Unit(System::BetaAxisAbs - 1);
+        return System::IsBetaOpposite ? -u : u;
+      }
+      
+      /** \returns the axis vector of the third (gamma) rotation */
+      static Vector3 GammaAxisVector() {
+        const Vector3& u = Vector3::Unit(System::GammaAxisAbs - 1);
+        return System::IsGammaOpposite ? -u : u;
+      }
+
+    private:
+      Vector3 m_angles;
+
+    public:
+      /** Default constructor without initialization. */
+      EulerAngles() {}
+      /** Constructs and initialize an EulerAngles (\p alpha, \p beta, \p gamma). */
+      EulerAngles(const Scalar& alpha, const Scalar& beta, const Scalar& gamma) :
+        m_angles(alpha, beta, gamma) {}
+      
+      // TODO: Test this constructor
+      /** Constructs and initialize an EulerAngles from the array data {alpha, beta, gamma} */
+      explicit EulerAngles(const Scalar* data) : m_angles(data) {}
+      
+      /** Constructs and initializes an EulerAngles from either:
+        *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
+        *  - a 3D vector expression representing Euler angles.
+        *
+        * \note If \p other is a 3x3 rotation matrix, the angles range rules will be as follow:<BR>
+        *  Alpha and gamma angles will be in the range [-PI, PI].<BR>
+        *  As for Beta angle:
+        *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+        *   - otherwise:
+        *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
+        *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+       */
+      template<typename Derived>
+      explicit EulerAngles(const MatrixBase<Derived>& other) { *this = other; }
+      
+      /** Constructs and initialize Euler angles from a rotation \p rot.
+        *
+        * \note If \p rot is an EulerAngles (even when it represented as RotationBase explicitly),
+        *  angles ranges are __undefined__.
+        *  Otherwise, alpha and gamma angles will be in the range [-PI, PI].<BR>
+        *  As for Beta angle:
+        *   - If the system is Tait-Bryan, the beta angle will be in the range [-PI/2, PI/2].
+        *   - otherwise:
+        *     - If the beta axis is positive, the beta angle will be in the range [0, PI]
+        *     - If the beta axis is negative, the beta angle will be in the range [-PI, 0]
+      */
+      template<typename Derived>
+      EulerAngles(const RotationBase<Derived, 3>& rot) { System::CalcEulerAngles(*this, rot.toRotationMatrix()); }
+      
+      /*EulerAngles(const QuaternionType& q)
+      {
+        // TODO: Implement it in a faster way for quaternions
+        // According to http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToEuler/
+        //  we can compute only the needed matrix cells and then convert to euler angles. (see ZYX example below)
+        // Currently we compute all matrix cells from quaternion.
+
+        // Special case only for ZYX
+        //Scalar y2 = q.y() * q.y();
+        //m_angles[0] = std::atan2(2*(q.w()*q.z() + q.x()*q.y()), (1 - 2*(y2 + q.z()*q.z())));
+        //m_angles[1] = std::asin( 2*(q.w()*q.y() - q.z()*q.x()));
+        //m_angles[2] = std::atan2(2*(q.w()*q.x() + q.y()*q.z()), (1 - 2*(q.x()*q.x() + y2)));
+      }*/
+
+      /** \returns The angle values stored in a vector (alpha, beta, gamma). */
+      const Vector3& angles() const { return m_angles; }
+      /** \returns A read-write reference to the angle values stored in a vector (alpha, beta, gamma). */
+      Vector3& angles() { return m_angles; }
+
+      /** \returns The value of the first angle. */
+      Scalar alpha() const { return m_angles[0]; }
+      /** \returns A read-write reference to the angle of the first angle. */
+      Scalar& alpha() { return m_angles[0]; }
+
+      /** \returns The value of the second angle. */
+      Scalar beta() const { return m_angles[1]; }
+      /** \returns A read-write reference to the angle of the second angle. */
+      Scalar& beta() { return m_angles[1]; }
+
+      /** \returns The value of the third angle. */
+      Scalar gamma() const { return m_angles[2]; }
+      /** \returns A read-write reference to the angle of the third angle. */
+      Scalar& gamma() { return m_angles[2]; }
+
+      /** \returns The Euler angles rotation inverse (which is as same as the negative),
+        *  (-alpha, -beta, -gamma).
+      */
+      EulerAngles inverse() const
+      {
+        EulerAngles res;
+        res.m_angles = -m_angles;
+        return res;
+      }
+
+      /** \returns The Euler angles rotation negative (which is as same as the inverse),
+        *  (-alpha, -beta, -gamma).
+      */
+      EulerAngles operator -() const
+      {
+        return inverse();
+      }
+      
+      /** Set \c *this from either:
+        *  - a 3x3 rotation matrix expression(i.e. pure orthogonal matrix with determinant of +1),
+        *  - a 3D vector expression representing Euler angles.
+        *
+        * See EulerAngles(const MatrixBase<Derived, 3>&) for more information about
+        *  angles ranges output.
+      */
+      template<class Derived>
+      EulerAngles& operator=(const MatrixBase<Derived>& other)
+      {
+        EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename Derived::Scalar>::value),
+         YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+        
+        internal::eulerangles_assign_impl<System, Derived>::run(*this, other.derived());
+        return *this;
+      }
+
+      // TODO: Assign and construct from another EulerAngles (with different system)
+      
+      /** Set \c *this from a rotation.
+        *
+        * See EulerAngles(const RotationBase<Derived, 3>&) for more information about
+        *  angles ranges output.
+      */
+      template<typename Derived>
+      EulerAngles& operator=(const RotationBase<Derived, 3>& rot) {
+        System::CalcEulerAngles(*this, rot.toRotationMatrix());
+        return *this;
+      }
+      
+      /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+        * determined by \a prec.
+        *
+        * \sa MatrixBase::isApprox() */
+      bool isApprox(const EulerAngles& other,
+        const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
+      { return angles().isApprox(other.angles(), prec); }
+
+      /** \returns an equivalent 3x3 rotation matrix. */
+      Matrix3 toRotationMatrix() const
+      {
+        // TODO: Calc it faster
+        return static_cast<QuaternionType>(*this).toRotationMatrix();
+      }
+
+      /** Convert the Euler angles to quaternion. */
+      operator QuaternionType() const
+      {
+        return
+          AngleAxisType(alpha(), AlphaAxisVector()) *
+          AngleAxisType(beta(), BetaAxisVector())   *
+          AngleAxisType(gamma(), GammaAxisVector());
+      }
+      
+      friend std::ostream& operator<<(std::ostream& s, const EulerAngles<Scalar, System>& eulerAngles)
+      {
+        s << eulerAngles.angles().transpose();
+        return s;
+      }
+      
+      /** \returns \c *this with scalar type casted to \a NewScalarType */
+      template <typename NewScalarType>
+      EulerAngles<NewScalarType, System> cast() const
+      {
+        EulerAngles<NewScalarType, System> e;
+        e.angles() = angles().template cast<NewScalarType>();
+        return e;
+      }
+  };
+
+#define EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(AXES, SCALAR_TYPE, SCALAR_POSTFIX) \
+  /** \ingroup EulerAngles_Module */ \
+  typedef EulerAngles<SCALAR_TYPE, EulerSystem##AXES> EulerAngles##AXES##SCALAR_POSTFIX;
+
+#define EIGEN_EULER_ANGLES_TYPEDEFS(SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XYX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(XZX, SCALAR_TYPE, SCALAR_POSTFIX) \
+ \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YZY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(YXY, SCALAR_TYPE, SCALAR_POSTFIX) \
+ \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXY, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZXZ, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYX, SCALAR_TYPE, SCALAR_POSTFIX) \
+  EIGEN_EULER_ANGLES_SINGLE_TYPEDEF(ZYZ, SCALAR_TYPE, SCALAR_POSTFIX)
+
+EIGEN_EULER_ANGLES_TYPEDEFS(float, f)
+EIGEN_EULER_ANGLES_TYPEDEFS(double, d)
+
+  namespace internal
+  {
+    template<typename _Scalar, class _System>
+    struct traits<EulerAngles<_Scalar, _System> >
+    {
+      typedef _Scalar Scalar;
+    };
+    
+    // set from a rotation matrix
+    template<class System, class Other>
+    struct eulerangles_assign_impl<System,Other,3,3>
+    {
+      typedef typename Other::Scalar Scalar;
+      static void run(EulerAngles<Scalar, System>& e, const Other& m)
+      {
+        System::CalcEulerAngles(e, m);
+      }
+    };
+    
+    // set from a vector of Euler angles
+    template<class System, class Other>
+    struct eulerangles_assign_impl<System,Other,3,1>
+    {
+      typedef typename Other::Scalar Scalar;
+      static void run(EulerAngles<Scalar, System>& e, const Other& vec)
+      {
+        e.angles() = vec;
+      }
+    };
+  }
+}
+
+#endif // EIGEN_EULERANGLESCLASS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
new file mode 100644
index 0000000..2a833b0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/EulerAngles/EulerSystem.h
@@ -0,0 +1,305 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Tal Hadad <tal_hd@hotmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERSYSTEM_H
+#define EIGEN_EULERSYSTEM_H
+
+namespace Eigen
+{
+  // Forward declarations
+  template <typename _Scalar, class _System>
+  class EulerAngles;
+  
+  namespace internal
+  {
+    // TODO: Add this trait to the Eigen internal API?
+    template <int Num, bool IsPositive = (Num > 0)>
+    struct Abs
+    {
+      enum { value = Num };
+    };
+  
+    template <int Num>
+    struct Abs<Num, false>
+    {
+      enum { value = -Num };
+    };
+
+    template <int Axis>
+    struct IsValidAxis
+    {
+      enum { value = Axis != 0 && Abs<Axis>::value <= 3 };
+    };
+    
+    template<typename System,
+            typename Other,
+            int OtherRows=Other::RowsAtCompileTime,
+            int OtherCols=Other::ColsAtCompileTime>
+    struct eulerangles_assign_impl;
+  }
+  
+  #define EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(COND,MSG) typedef char static_assertion_##MSG[(COND)?1:-1]
+  
+  /** \brief Representation of a fixed signed rotation axis for EulerSystem.
+    *
+    * \ingroup EulerAngles_Module
+    *
+    * Values here represent:
+    *  - The axis of the rotation: X, Y or Z.
+    *  - The sign (i.e. direction of the rotation along the axis): positive(+) or negative(-)
+    *
+    * Therefore, this could express all the axes {+X,+Y,+Z,-X,-Y,-Z}
+    *
+    * For positive axis, use +EULER_{axis}, and for negative axis use -EULER_{axis}.
+    */
+  enum EulerAxis
+  {
+    EULER_X = 1, /*!< the X axis */
+    EULER_Y = 2, /*!< the Y axis */
+    EULER_Z = 3  /*!< the Z axis */
+  };
+  
+  /** \class EulerSystem
+    *
+    * \ingroup EulerAngles_Module
+    *
+    * \brief Represents a fixed Euler rotation system.
+    *
+    * This meta-class goal is to represent the Euler system in compilation time, for EulerAngles.
+    *
+    * You can use this class to get two things:
+    *  - Build an Euler system, and then pass it as a template parameter to EulerAngles.
+    *  - Query some compile time data about an Euler system. (e.g. Whether it's Tait-Bryan)
+    *
+    * Euler rotation is a set of three rotation on fixed axes. (see \ref EulerAngles)
+    * This meta-class store constantly those signed axes. (see \ref EulerAxis)
+    *
+    * ### Types of Euler systems ###
+    *
+    * All and only valid 3 dimension Euler rotation over standard
+    *  signed axes{+X,+Y,+Z,-X,-Y,-Z} are supported:
+    *  - all axes X, Y, Z in each valid order (see below what order is valid)
+    *  - rotation over the axis is supported both over the positive and negative directions.
+    *  - both Tait-Bryan and proper/classic Euler angles (i.e. the opposite).
+    *
+    * Since EulerSystem support both positive and negative directions,
+    *  you may call this rotation distinction in other names:
+    *  - _right handed_ or _left handed_
+    *  - _counterclockwise_ or _clockwise_
+    *
+    * Notice all axed combination are valid, and would trigger a static assertion.
+    * Same unsigned axes can't be neighbors, e.g. {X,X,Y} is invalid.
+    * This yield two and only two classes:
+    *  - _Tait-Bryan_ - all unsigned axes are distinct, e.g. {X,Y,Z}
+    *  - _proper/classic Euler angles_ - The first and the third unsigned axes is equal,
+    *     and the second is different, e.g. {X,Y,X}
+    *
+    * ### Intrinsic vs extrinsic Euler systems ###
+    *
+    * Only intrinsic Euler systems are supported for simplicity.
+    *  If you want to use extrinsic Euler systems,
+    *   just use the equal intrinsic opposite order for axes and angles.
+    *  I.e axes (A,B,C) becomes (C,B,A), and angles (a,b,c) becomes (c,b,a).
+    *
+    * ### Convenient user typedefs ###
+    *
+    * Convenient typedefs for EulerSystem exist (only for positive axes Euler systems),
+    *  in a form of EulerSystem{A}{B}{C}, e.g. \ref EulerSystemXYZ.
+    *
+    * ### Additional reading ###
+    *
+    * More information about Euler angles: https://en.wikipedia.org/wiki/Euler_angles
+    *
+    * \tparam _AlphaAxis the first fixed EulerAxis
+    *
+    * \tparam _BetaAxis the second fixed EulerAxis
+    *
+    * \tparam _GammaAxis the third fixed EulerAxis
+    */
+  template <int _AlphaAxis, int _BetaAxis, int _GammaAxis>
+  class EulerSystem
+  {
+    public:
+    // It's defined this way and not as enum, because I think
+    //  that enum is not guerantee to support negative numbers
+    
+    /** The first rotation axis */
+    static const int AlphaAxis = _AlphaAxis;
+    
+    /** The second rotation axis */
+    static const int BetaAxis = _BetaAxis;
+    
+    /** The third rotation axis */
+    static const int GammaAxis = _GammaAxis;
+
+    enum
+    {
+      AlphaAxisAbs = internal::Abs<AlphaAxis>::value, /*!< the first rotation axis unsigned */
+      BetaAxisAbs = internal::Abs<BetaAxis>::value, /*!< the second rotation axis unsigned */
+      GammaAxisAbs = internal::Abs<GammaAxis>::value, /*!< the third rotation axis unsigned */
+      
+      IsAlphaOpposite = (AlphaAxis < 0) ? 1 : 0, /*!< whether alpha axis is negative */
+      IsBetaOpposite = (BetaAxis < 0) ? 1 : 0, /*!< whether beta axis is negative */
+      IsGammaOpposite = (GammaAxis < 0) ? 1 : 0, /*!< whether gamma axis is negative */
+
+      // Parity is even if alpha axis X is followed by beta axis Y, or Y is followed
+      // by Z, or Z is followed by X; otherwise it is odd.
+      IsOdd = ((AlphaAxisAbs)%3 == (BetaAxisAbs - 1)%3) ? 0 : 1, /*!< whether the Euler system is odd */
+      IsEven = IsOdd ? 0 : 1, /*!< whether the Euler system is even */
+
+      IsTaitBryan = ((unsigned)AlphaAxisAbs != (unsigned)GammaAxisAbs) ? 1 : 0 /*!< whether the Euler system is Tait-Bryan */
+    };
+    
+    private:
+    
+    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<AlphaAxis>::value,
+      ALPHA_AXIS_IS_INVALID);
+      
+    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<BetaAxis>::value,
+      BETA_AXIS_IS_INVALID);
+      
+    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT(internal::IsValidAxis<GammaAxis>::value,
+      GAMMA_AXIS_IS_INVALID);
+      
+    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)AlphaAxisAbs != (unsigned)BetaAxisAbs,
+      ALPHA_AXIS_CANT_BE_EQUAL_TO_BETA_AXIS);
+      
+    EIGEN_EULER_ANGLES_CLASS_STATIC_ASSERT((unsigned)BetaAxisAbs != (unsigned)GammaAxisAbs,
+      BETA_AXIS_CANT_BE_EQUAL_TO_GAMMA_AXIS);
+
+    static const int
+      // I, J, K are the pivot indexes permutation for the rotation matrix, that match this Euler system. 
+      // They are used in this class converters.
+      // They are always different from each other, and their possible values are: 0, 1, or 2.
+      I_ = AlphaAxisAbs - 1,
+      J_ = (AlphaAxisAbs - 1 + 1 + IsOdd)%3,
+      K_ = (AlphaAxisAbs - 1 + 2 - IsOdd)%3
+    ;
+    
+    // TODO: Get @mat parameter in form that avoids double evaluation.
+    template <typename Derived>
+    static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar, 3, 1>& res, const MatrixBase<Derived>& mat, internal::true_type /*isTaitBryan*/)
+    {
+      using std::atan2;
+      using std::sqrt;
+      
+      typedef typename Derived::Scalar Scalar;
+
+      const Scalar plusMinus = IsEven? 1 : -1;
+      const Scalar minusPlus = IsOdd?  1 : -1;
+
+      const Scalar Rsum = sqrt((mat(I_,I_) * mat(I_,I_) + mat(I_,J_) * mat(I_,J_) + mat(J_,K_) * mat(J_,K_) + mat(K_,K_) * mat(K_,K_))/2);
+      res[1] = atan2(plusMinus * mat(I_,K_), Rsum);
+
+      // There is a singularity when cos(beta) == 0
+      if(Rsum > 4 * NumTraits<Scalar>::epsilon()) {// cos(beta) != 0
+        res[0] = atan2(minusPlus * mat(J_, K_), mat(K_, K_));
+        res[2] = atan2(minusPlus * mat(I_, J_), mat(I_, I_));
+      }
+      else if(plusMinus * mat(I_, K_) > 0) {// cos(beta) == 0 and sin(beta) == 1
+        Scalar spos = mat(J_, I_) + plusMinus * mat(K_, J_); // 2*sin(alpha + plusMinus * gamma
+        Scalar cpos = mat(J_, J_) + minusPlus * mat(K_, I_); // 2*cos(alpha + plusMinus * gamma)
+        Scalar alphaPlusMinusGamma = atan2(spos, cpos);
+        res[0] = alphaPlusMinusGamma;
+        res[2] = 0;
+      }
+      else {// cos(beta) == 0 and sin(beta) == -1
+        Scalar sneg = plusMinus * (mat(K_, J_) + minusPlus * mat(J_, I_)); // 2*sin(alpha + minusPlus*gamma)
+        Scalar cneg = mat(J_, J_) + plusMinus * mat(K_, I_);               // 2*cos(alpha + minusPlus*gamma)
+        Scalar alphaMinusPlusBeta = atan2(sneg, cneg);
+        res[0] = alphaMinusPlusBeta;
+        res[2] = 0;
+      }
+    }
+
+    template <typename Derived>
+    static void CalcEulerAngles_imp(Matrix<typename MatrixBase<Derived>::Scalar,3,1>& res,
+                                    const MatrixBase<Derived>& mat, internal::false_type /*isTaitBryan*/)
+    {
+      using std::atan2;
+      using std::sqrt;
+
+      typedef typename Derived::Scalar Scalar;
+
+      const Scalar plusMinus = IsEven? 1 : -1;
+      const Scalar minusPlus = IsOdd?  1 : -1;
+
+      const Scalar Rsum = sqrt((mat(I_, J_) * mat(I_, J_) + mat(I_, K_) * mat(I_, K_) + mat(J_, I_) * mat(J_, I_) + mat(K_, I_) * mat(K_, I_)) / 2);
+
+      res[1] = atan2(Rsum, mat(I_, I_));
+
+      // There is a singularity when sin(beta) == 0
+      if(Rsum > 4 * NumTraits<Scalar>::epsilon()) {// sin(beta) != 0
+        res[0] = atan2(mat(J_, I_), minusPlus * mat(K_, I_));
+        res[2] = atan2(mat(I_, J_), plusMinus * mat(I_, K_));
+      }
+      else if(mat(I_, I_) > 0) {// sin(beta) == 0 and cos(beta) == 1
+        Scalar spos = plusMinus * mat(K_, J_) + minusPlus * mat(J_, K_); // 2*sin(alpha + gamma)
+        Scalar cpos = mat(J_, J_) + mat(K_, K_);                         // 2*cos(alpha + gamma)
+        res[0] = atan2(spos, cpos);
+        res[2] = 0;
+      }
+      else {// sin(beta) == 0 and cos(beta) == -1
+        Scalar sneg = plusMinus * mat(K_, J_) + plusMinus * mat(J_, K_); // 2*sin(alpha - gamma)
+        Scalar cneg = mat(J_, J_) - mat(K_, K_);                         // 2*cos(alpha - gamma)
+        res[0] = atan2(sneg, cneg);
+        res[2] = 0;
+      }
+    }
+    
+    template<typename Scalar>
+    static void CalcEulerAngles(
+      EulerAngles<Scalar, EulerSystem>& res,
+      const typename EulerAngles<Scalar, EulerSystem>::Matrix3& mat)
+    {
+      CalcEulerAngles_imp(
+        res.angles(), mat,
+        typename internal::conditional<IsTaitBryan, internal::true_type, internal::false_type>::type());
+
+      if (IsAlphaOpposite)
+        res.alpha() = -res.alpha();
+        
+      if (IsBetaOpposite)
+        res.beta() = -res.beta();
+        
+      if (IsGammaOpposite)
+        res.gamma() = -res.gamma();
+    }
+    
+    template <typename _Scalar, class _System>
+    friend class Eigen::EulerAngles;
+    
+    template<typename System,
+            typename Other,
+            int OtherRows,
+            int OtherCols>
+    friend struct internal::eulerangles_assign_impl;
+  };
+
+#define EIGEN_EULER_SYSTEM_TYPEDEF(A, B, C) \
+  /** \ingroup EulerAngles_Module */ \
+  typedef EulerSystem<EULER_##A, EULER_##B, EULER_##C> EulerSystem##A##B##C;
+  
+  EIGEN_EULER_SYSTEM_TYPEDEF(X,Y,Z)
+  EIGEN_EULER_SYSTEM_TYPEDEF(X,Y,X)
+  EIGEN_EULER_SYSTEM_TYPEDEF(X,Z,Y)
+  EIGEN_EULER_SYSTEM_TYPEDEF(X,Z,X)
+  
+  EIGEN_EULER_SYSTEM_TYPEDEF(Y,Z,X)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Y,Z,Y)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Y,X,Z)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Y,X,Y)
+  
+  EIGEN_EULER_SYSTEM_TYPEDEF(Z,X,Y)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Z,X,Z)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Z,Y,X)
+  EIGEN_EULER_SYSTEM_TYPEDEF(Z,Y,Z)
+}
+
+#endif // EIGEN_EULERSYSTEM_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
new file mode 100644
index 0000000..1c2cd24
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_fftw_impl.h
@@ -0,0 +1,261 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. 
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+namespace Eigen { 
+
+namespace internal {
+
+  // FFTW uses non-const arguments
+  // so we must use ugly const_cast calls for all the args it uses
+  //
+  // This should be safe as long as 
+  // 1. we use FFTW_ESTIMATE for all our planning
+  //       see the FFTW docs section 4.3.2 "Planner Flags"
+  // 2. fftw_complex is compatible with std::complex
+  //    This assumes std::complex<T> layout is array of size 2 with real,imag
+  template <typename T> 
+  inline 
+  T * fftw_cast(const T* p)
+  { 
+      return const_cast<T*>( p); 
+  }
+
+  inline 
+  fftw_complex * fftw_cast( const std::complex<double> * p)
+  {
+      return const_cast<fftw_complex*>( reinterpret_cast<const fftw_complex*>(p) ); 
+  }
+
+  inline 
+  fftwf_complex * fftw_cast( const std::complex<float> * p)
+  { 
+      return const_cast<fftwf_complex*>( reinterpret_cast<const fftwf_complex*>(p) ); 
+  }
+
+  inline 
+  fftwl_complex * fftw_cast( const std::complex<long double> * p)
+  { 
+      return const_cast<fftwl_complex*>( reinterpret_cast<const fftwl_complex*>(p) ); 
+  }
+
+  template <typename T> 
+  struct fftw_plan {};
+
+  template <> 
+  struct fftw_plan<float>
+  {
+      typedef float scalar_type;
+      typedef fftwf_complex complex_type;
+      fftwf_plan m_plan;
+      fftw_plan() :m_plan(NULL) {}
+      ~fftw_plan() {if (m_plan) fftwf_destroy_plan(m_plan);}
+
+      inline
+      void fwd(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwf_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void inv(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwf_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void fwd(complex_type * dst,scalar_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwf_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft_r2c( m_plan,src,dst);
+      }
+      inline
+      void inv(scalar_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL)
+              m_plan = fftwf_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft_c2r( m_plan, src,dst);
+      }
+
+      inline 
+      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftwf_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft( m_plan, src,dst);
+      }
+      inline 
+      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftwf_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwf_execute_dft( m_plan, src,dst);
+      }
+
+  };
+  template <> 
+  struct fftw_plan<double>
+  {
+      typedef double scalar_type;
+      typedef fftw_complex complex_type;
+      ::fftw_plan m_plan;
+      fftw_plan() :m_plan(NULL) {}
+      ~fftw_plan() {if (m_plan) fftw_destroy_plan(m_plan);}
+
+      inline
+      void fwd(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftw_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void inv(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftw_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void fwd(complex_type * dst,scalar_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftw_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft_r2c( m_plan,src,dst);
+      }
+      inline
+      void inv(scalar_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL)
+              m_plan = fftw_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft_c2r( m_plan, src,dst);
+      }
+      inline 
+      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftw_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft( m_plan, src,dst);
+      }
+      inline 
+      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftw_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftw_execute_dft( m_plan, src,dst);
+      }
+  };
+  template <> 
+  struct fftw_plan<long double>
+  {
+      typedef long double scalar_type;
+      typedef fftwl_complex complex_type;
+      fftwl_plan m_plan;
+      fftw_plan() :m_plan(NULL) {}
+      ~fftw_plan() {if (m_plan) fftwl_destroy_plan(m_plan);}
+
+      inline
+      void fwd(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwl_plan_dft_1d(nfft,src,dst, FFTW_FORWARD, FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void inv(complex_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwl_plan_dft_1d(nfft,src,dst, FFTW_BACKWARD , FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft( m_plan, src,dst);
+      }
+      inline
+      void fwd(complex_type * dst,scalar_type * src,int nfft) {
+          if (m_plan==NULL) m_plan = fftwl_plan_dft_r2c_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft_r2c( m_plan,src,dst);
+      }
+      inline
+      void inv(scalar_type * dst,complex_type * src,int nfft) {
+          if (m_plan==NULL)
+              m_plan = fftwl_plan_dft_c2r_1d(nfft,src,dst,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft_c2r( m_plan, src,dst);
+      }
+      inline 
+      void fwd2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftwl_plan_dft_2d(n0,n1,src,dst,FFTW_FORWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft( m_plan, src,dst);
+      }
+      inline 
+      void inv2( complex_type * dst,complex_type * src,int n0,int n1) {
+          if (m_plan==NULL) m_plan = fftwl_plan_dft_2d(n0,n1,src,dst,FFTW_BACKWARD,FFTW_ESTIMATE|FFTW_PRESERVE_INPUT);
+          fftwl_execute_dft( m_plan, src,dst);
+      }
+  };
+
+  template <typename _Scalar>
+  struct fftw_impl
+  {
+      typedef _Scalar Scalar;
+      typedef std::complex<Scalar> Complex;
+
+      inline
+      void clear() 
+      {
+        m_plans.clear();
+      }
+
+      // complex-to-complex forward FFT
+      inline
+      void fwd( Complex * dst,const Complex *src,int nfft)
+      {
+        get_plan(nfft,false,dst,src).fwd(fftw_cast(dst), fftw_cast(src),nfft );
+      }
+
+      // real-to-complex forward FFT
+      inline
+      void fwd( Complex * dst,const Scalar * src,int nfft) 
+      {
+          get_plan(nfft,false,dst,src).fwd(fftw_cast(dst), fftw_cast(src) ,nfft);
+      }
+
+      // 2-d complex-to-complex
+      inline
+      void fwd2(Complex * dst, const Complex * src, int n0,int n1)
+      {
+          get_plan(n0,n1,false,dst,src).fwd2(fftw_cast(dst), fftw_cast(src) ,n0,n1);
+      }
+
+      // inverse complex-to-complex
+      inline
+      void inv(Complex * dst,const Complex  *src,int nfft)
+      {
+        get_plan(nfft,true,dst,src).inv(fftw_cast(dst), fftw_cast(src),nfft );
+      }
+
+      // half-complex to scalar
+      inline
+      void inv( Scalar * dst,const Complex * src,int nfft) 
+      {
+        get_plan(nfft,true,dst,src).inv(fftw_cast(dst), fftw_cast(src),nfft );
+      }
+
+      // 2-d complex-to-complex
+      inline
+      void inv2(Complex * dst, const Complex * src, int n0,int n1)
+      {
+        get_plan(n0,n1,true,dst,src).inv2(fftw_cast(dst), fftw_cast(src) ,n0,n1);
+      }
+
+
+  protected:
+      typedef fftw_plan<Scalar> PlanData;
+
+      typedef Eigen::numext::int64_t int64_t;
+
+      typedef std::map<int64_t,PlanData> PlanMap;
+
+      PlanMap m_plans;
+
+      inline
+      PlanData & get_plan(int nfft,bool inverse,void * dst,const void * src)
+      {
+          bool inplace = (dst==src);
+          bool aligned = ( (reinterpret_cast<size_t>(src)&15) | (reinterpret_cast<size_t>(dst)&15) ) == 0;
+          int64_t key = ( (nfft<<3 ) | (inverse<<2) | (inplace<<1) | aligned ) << 1;
+          return m_plans[key];
+      }
+
+      inline
+      PlanData & get_plan(int n0,int n1,bool inverse,void * dst,const void * src)
+      {
+          bool inplace = (dst==src);
+          bool aligned = ( (reinterpret_cast<size_t>(src)&15) | (reinterpret_cast<size_t>(dst)&15) ) == 0;
+          int64_t key = ( ( (((int64_t)n0) << 30)|(n1<<3 ) | (inverse<<2) | (inplace<<1) | aligned ) << 1 ) + 1;
+          return m_plans[key];
+      }
+  };
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
new file mode 100644
index 0000000..430953a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/FFT/ei_kissfft_impl.h
@@ -0,0 +1,449 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Mark Borgerding mark a borgerding net
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+namespace Eigen { 
+
+namespace internal {
+
+  // This FFT implementation was derived from kissfft http:sourceforge.net/projects/kissfft
+  // Copyright 2003-2009 Mark Borgerding
+
+template <typename _Scalar>
+struct kiss_cpx_fft
+{
+  typedef _Scalar Scalar;
+  typedef std::complex<Scalar> Complex;
+  std::vector<Complex> m_twiddles;
+  std::vector<int> m_stageRadix;
+  std::vector<int> m_stageRemainder;
+  std::vector<Complex> m_scratchBuf;
+  bool m_inverse;
+
+  inline void make_twiddles(int nfft, bool inverse)
+  {
+    using numext::sin;
+    using numext::cos;
+    m_inverse = inverse;
+    m_twiddles.resize(nfft);
+    double phinc =  0.25 * double(EIGEN_PI) / nfft;
+    Scalar flip = inverse ? Scalar(1) : Scalar(-1);
+    m_twiddles[0] = Complex(Scalar(1), Scalar(0));
+    if ((nfft&1)==0)
+      m_twiddles[nfft/2] = Complex(Scalar(-1), Scalar(0));
+    int i=1;
+    for (;i*8<nfft;++i)
+    {
+      Scalar c = Scalar(cos(i*8*phinc));
+      Scalar s = Scalar(sin(i*8*phinc));
+      m_twiddles[i] = Complex(c, s*flip);
+      m_twiddles[nfft-i] = Complex(c, -s*flip);
+    }
+    for (;i*4<nfft;++i)
+    {
+      Scalar c = Scalar(cos((2*nfft-8*i)*phinc));
+      Scalar s = Scalar(sin((2*nfft-8*i)*phinc));
+      m_twiddles[i] = Complex(s, c*flip);
+      m_twiddles[nfft-i] = Complex(s, -c*flip);
+    }
+    for (;i*8<3*nfft;++i)
+    {
+      Scalar c = Scalar(cos((8*i-2*nfft)*phinc));
+      Scalar s = Scalar(sin((8*i-2*nfft)*phinc));
+      m_twiddles[i] = Complex(-s, c*flip);
+      m_twiddles[nfft-i] = Complex(-s, -c*flip);
+    }
+    for (;i*2<nfft;++i)
+    {
+      Scalar c = Scalar(cos((4*nfft-8*i)*phinc));
+      Scalar s = Scalar(sin((4*nfft-8*i)*phinc));
+      m_twiddles[i] = Complex(-c, s*flip);
+      m_twiddles[nfft-i] = Complex(-c, -s*flip);
+    }
+  }
+
+  void factorize(int nfft)
+  {
+    //start factoring out 4's, then 2's, then 3,5,7,9,...
+    int n= nfft;
+    int p=4;
+    do {
+      while (n % p) {
+        switch (p) {
+          case 4: p = 2; break;
+          case 2: p = 3; break;
+          default: p += 2; break;
+        }
+        if (p*p>n)
+          p=n;// impossible to have a factor > sqrt(n)
+      }
+      n /= p;
+      m_stageRadix.push_back(p);
+      m_stageRemainder.push_back(n);
+      if ( p > 5 )
+        m_scratchBuf.resize(p); // scratchbuf will be needed in bfly_generic
+    }while(n>1);
+  }
+
+  template <typename _Src>
+    inline
+    void work( int stage,Complex * xout, const _Src * xin, size_t fstride,size_t in_stride)
+    {
+      int p = m_stageRadix[stage];
+      int m = m_stageRemainder[stage];
+      Complex * Fout_beg = xout;
+      Complex * Fout_end = xout + p*m;
+
+      if (m>1) {
+        do{
+          // recursive call:
+          // DFT of size m*p performed by doing
+          // p instances of smaller DFTs of size m, 
+          // each one takes a decimated version of the input
+          work(stage+1, xout , xin, fstride*p,in_stride);
+          xin += fstride*in_stride;
+        }while( (xout += m) != Fout_end );
+      }else{
+        do{
+          *xout = *xin;
+          xin += fstride*in_stride;
+        }while(++xout != Fout_end );
+      }
+      xout=Fout_beg;
+
+      // recombine the p smaller DFTs 
+      switch (p) {
+        case 2: bfly2(xout,fstride,m); break;
+        case 3: bfly3(xout,fstride,m); break;
+        case 4: bfly4(xout,fstride,m); break;
+        case 5: bfly5(xout,fstride,m); break;
+        default: bfly_generic(xout,fstride,m,p); break;
+      }
+    }
+
+  inline
+    void bfly2( Complex * Fout, const size_t fstride, int m)
+    {
+      for (int k=0;k<m;++k) {
+        Complex t = Fout[m+k] * m_twiddles[k*fstride];
+        Fout[m+k] = Fout[k] - t;
+        Fout[k] += t;
+      }
+    }
+
+  inline
+    void bfly4( Complex * Fout, const size_t fstride, const size_t m)
+    {
+      Complex scratch[6];
+      int negative_if_inverse = m_inverse * -2 +1;
+      for (size_t k=0;k<m;++k) {
+        scratch[0] = Fout[k+m] * m_twiddles[k*fstride];
+        scratch[1] = Fout[k+2*m] * m_twiddles[k*fstride*2];
+        scratch[2] = Fout[k+3*m] * m_twiddles[k*fstride*3];
+        scratch[5] = Fout[k] - scratch[1];
+
+        Fout[k] += scratch[1];
+        scratch[3] = scratch[0] + scratch[2];
+        scratch[4] = scratch[0] - scratch[2];
+        scratch[4] = Complex( scratch[4].imag()*negative_if_inverse , -scratch[4].real()* negative_if_inverse );
+
+        Fout[k+2*m]  = Fout[k] - scratch[3];
+        Fout[k] += scratch[3];
+        Fout[k+m] = scratch[5] + scratch[4];
+        Fout[k+3*m] = scratch[5] - scratch[4];
+      }
+    }
+
+  inline
+    void bfly3( Complex * Fout, const size_t fstride, const size_t m)
+    {
+      size_t k=m;
+      const size_t m2 = 2*m;
+      Complex *tw1,*tw2;
+      Complex scratch[5];
+      Complex epi3;
+      epi3 = m_twiddles[fstride*m];
+
+      tw1=tw2=&m_twiddles[0];
+
+      do{
+        scratch[1]=Fout[m] * *tw1;
+        scratch[2]=Fout[m2] * *tw2;
+
+        scratch[3]=scratch[1]+scratch[2];
+        scratch[0]=scratch[1]-scratch[2];
+        tw1 += fstride;
+        tw2 += fstride*2;
+        Fout[m] = Complex( Fout->real() - Scalar(.5)*scratch[3].real() , Fout->imag() - Scalar(.5)*scratch[3].imag() );
+        scratch[0] *= epi3.imag();
+        *Fout += scratch[3];
+        Fout[m2] = Complex(  Fout[m].real() + scratch[0].imag() , Fout[m].imag() - scratch[0].real() );
+        Fout[m] += Complex( -scratch[0].imag(),scratch[0].real() );
+        ++Fout;
+      }while(--k);
+    }
+
+  inline
+    void bfly5( Complex * Fout, const size_t fstride, const size_t m)
+    {
+      Complex *Fout0,*Fout1,*Fout2,*Fout3,*Fout4;
+      size_t u;
+      Complex scratch[13];
+      Complex * twiddles = &m_twiddles[0];
+      Complex *tw;
+      Complex ya,yb;
+      ya = twiddles[fstride*m];
+      yb = twiddles[fstride*2*m];
+
+      Fout0=Fout;
+      Fout1=Fout0+m;
+      Fout2=Fout0+2*m;
+      Fout3=Fout0+3*m;
+      Fout4=Fout0+4*m;
+
+      tw=twiddles;
+      for ( u=0; u<m; ++u ) {
+        scratch[0] = *Fout0;
+
+        scratch[1]  = *Fout1 * tw[u*fstride];
+        scratch[2]  = *Fout2 * tw[2*u*fstride];
+        scratch[3]  = *Fout3 * tw[3*u*fstride];
+        scratch[4]  = *Fout4 * tw[4*u*fstride];
+
+        scratch[7] = scratch[1] + scratch[4];
+        scratch[10] = scratch[1] - scratch[4];
+        scratch[8] = scratch[2] + scratch[3];
+        scratch[9] = scratch[2] - scratch[3];
+
+        *Fout0 +=  scratch[7];
+        *Fout0 +=  scratch[8];
+
+        scratch[5] = scratch[0] + Complex(
+            (scratch[7].real()*ya.real() ) + (scratch[8].real() *yb.real() ),
+            (scratch[7].imag()*ya.real()) + (scratch[8].imag()*yb.real())
+            );
+
+        scratch[6] = Complex(
+            (scratch[10].imag()*ya.imag()) + (scratch[9].imag()*yb.imag()),
+            -(scratch[10].real()*ya.imag()) - (scratch[9].real()*yb.imag())
+            );
+
+        *Fout1 = scratch[5] - scratch[6];
+        *Fout4 = scratch[5] + scratch[6];
+
+        scratch[11] = scratch[0] +
+          Complex(
+              (scratch[7].real()*yb.real()) + (scratch[8].real()*ya.real()),
+              (scratch[7].imag()*yb.real()) + (scratch[8].imag()*ya.real())
+              );
+
+        scratch[12] = Complex(
+            -(scratch[10].imag()*yb.imag()) + (scratch[9].imag()*ya.imag()),
+            (scratch[10].real()*yb.imag()) - (scratch[9].real()*ya.imag())
+            );
+
+        *Fout2=scratch[11]+scratch[12];
+        *Fout3=scratch[11]-scratch[12];
+
+        ++Fout0;++Fout1;++Fout2;++Fout3;++Fout4;
+      }
+    }
+
+  /* perform the butterfly for one stage of a mixed radix FFT */
+  inline
+    void bfly_generic(
+        Complex * Fout,
+        const size_t fstride,
+        int m,
+        int p
+        )
+    {
+      int u,k,q1,q;
+      Complex * twiddles = &m_twiddles[0];
+      Complex t;
+      int Norig = static_cast<int>(m_twiddles.size());
+      Complex * scratchbuf = &m_scratchBuf[0];
+
+      for ( u=0; u<m; ++u ) {
+        k=u;
+        for ( q1=0 ; q1<p ; ++q1 ) {
+          scratchbuf[q1] = Fout[ k  ];
+          k += m;
+        }
+
+        k=u;
+        for ( q1=0 ; q1<p ; ++q1 ) {
+          int twidx=0;
+          Fout[ k ] = scratchbuf[0];
+          for (q=1;q<p;++q ) {
+            twidx += static_cast<int>(fstride) * k;
+            if (twidx>=Norig) twidx-=Norig;
+            t=scratchbuf[q] * twiddles[twidx];
+            Fout[ k ] += t;
+          }
+          k += m;
+        }
+      }
+    }
+};
+
+template <typename _Scalar>
+struct kissfft_impl
+{
+  typedef _Scalar Scalar;
+  typedef std::complex<Scalar> Complex;
+
+  void clear() 
+  {
+    m_plans.clear();
+    m_realTwiddles.clear();
+  }
+
+  inline
+    void fwd( Complex * dst,const Complex *src,int nfft)
+    {
+      get_plan(nfft,false).work(0, dst, src, 1,1);
+    }
+
+  inline
+    void fwd2( Complex * dst,const Complex *src,int n0,int n1)
+    {
+        EIGEN_UNUSED_VARIABLE(dst);
+        EIGEN_UNUSED_VARIABLE(src);
+        EIGEN_UNUSED_VARIABLE(n0);
+        EIGEN_UNUSED_VARIABLE(n1);
+    }
+
+  inline
+    void inv2( Complex * dst,const Complex *src,int n0,int n1)
+    {
+        EIGEN_UNUSED_VARIABLE(dst);
+        EIGEN_UNUSED_VARIABLE(src);
+        EIGEN_UNUSED_VARIABLE(n0);
+        EIGEN_UNUSED_VARIABLE(n1);
+    }
+
+  // real-to-complex forward FFT
+  // perform two FFTs of src even and src odd
+  // then twiddle to recombine them into the half-spectrum format
+  // then fill in the conjugate symmetric half
+  inline
+    void fwd( Complex * dst,const Scalar * src,int nfft) 
+    {
+      if ( nfft&3  ) {
+        // use generic mode for odd
+        m_tmpBuf1.resize(nfft);
+        get_plan(nfft,false).work(0, &m_tmpBuf1[0], src, 1,1);
+        std::copy(m_tmpBuf1.begin(),m_tmpBuf1.begin()+(nfft>>1)+1,dst );
+      }else{
+        int ncfft = nfft>>1;
+        int ncfft2 = nfft>>2;
+        Complex * rtw = real_twiddles(ncfft2);
+
+        // use optimized mode for even real
+        fwd( dst, reinterpret_cast<const Complex*> (src), ncfft);
+        Complex dc(dst[0].real() +  dst[0].imag());
+        Complex nyquist(dst[0].real() -  dst[0].imag());
+        int k;
+        for ( k=1;k <= ncfft2 ; ++k ) {
+          Complex fpk = dst[k];
+          Complex fpnk = conj(dst[ncfft-k]);
+          Complex f1k = fpk + fpnk;
+          Complex f2k = fpk - fpnk;
+          Complex tw= f2k * rtw[k-1];
+          dst[k] =  (f1k + tw) * Scalar(.5);
+          dst[ncfft-k] =  conj(f1k -tw)*Scalar(.5);
+        }
+        dst[0] = dc;
+        dst[ncfft] = nyquist;
+      }
+    }
+
+  // inverse complex-to-complex
+  inline
+    void inv(Complex * dst,const Complex  *src,int nfft)
+    {
+      get_plan(nfft,true).work(0, dst, src, 1,1);
+    }
+
+  // half-complex to scalar
+  inline
+    void inv( Scalar * dst,const Complex * src,int nfft) 
+    {
+      if (nfft&3) {
+        m_tmpBuf1.resize(nfft);
+        m_tmpBuf2.resize(nfft);
+        std::copy(src,src+(nfft>>1)+1,m_tmpBuf1.begin() );
+        for (int k=1;k<(nfft>>1)+1;++k)
+          m_tmpBuf1[nfft-k] = conj(m_tmpBuf1[k]);
+        inv(&m_tmpBuf2[0],&m_tmpBuf1[0],nfft);
+        for (int k=0;k<nfft;++k)
+          dst[k] = m_tmpBuf2[k].real();
+      }else{
+        // optimized version for multiple of 4
+        int ncfft = nfft>>1;
+        int ncfft2 = nfft>>2;
+        Complex * rtw = real_twiddles(ncfft2);
+        m_tmpBuf1.resize(ncfft);
+        m_tmpBuf1[0] = Complex( src[0].real() + src[ncfft].real(), src[0].real() - src[ncfft].real() );
+        for (int k = 1; k <= ncfft / 2; ++k) {
+          Complex fk = src[k];
+          Complex fnkc = conj(src[ncfft-k]);
+          Complex fek = fk + fnkc;
+          Complex tmp = fk - fnkc;
+          Complex fok = tmp * conj(rtw[k-1]);
+          m_tmpBuf1[k] = fek + fok;
+          m_tmpBuf1[ncfft-k] = conj(fek - fok);
+        }
+        get_plan(ncfft,true).work(0, reinterpret_cast<Complex*>(dst), &m_tmpBuf1[0], 1,1);
+      }
+    }
+
+  protected:
+  typedef kiss_cpx_fft<Scalar> PlanData;
+  typedef std::map<int,PlanData> PlanMap;
+
+  PlanMap m_plans;
+  std::map<int, std::vector<Complex> > m_realTwiddles;
+  std::vector<Complex> m_tmpBuf1;
+  std::vector<Complex> m_tmpBuf2;
+
+  inline
+    int PlanKey(int nfft, bool isinverse) const { return (nfft<<1) | int(isinverse); }
+
+  inline
+    PlanData & get_plan(int nfft, bool inverse)
+    {
+      // TODO look for PlanKey(nfft, ! inverse) and conjugate the twiddles
+      PlanData & pd = m_plans[ PlanKey(nfft,inverse) ];
+      if ( pd.m_twiddles.size() == 0 ) {
+        pd.make_twiddles(nfft,inverse);
+        pd.factorize(nfft);
+      }
+      return pd;
+    }
+
+  inline
+    Complex * real_twiddles(int ncfft2)
+    {
+      using std::acos;
+      std::vector<Complex> & twidref = m_realTwiddles[ncfft2];// creates new if not there
+      if ( (int)twidref.size() != ncfft2 ) {
+        twidref.resize(ncfft2);
+        int ncfft= ncfft2<<1;
+        Scalar pi =  acos( Scalar(-1) );
+        for (int k=1;k<=ncfft2;++k) 
+          twidref[k-1] = exp( Complex(0,-pi * (Scalar(k) / ncfft + Scalar(.5)) ) );
+      }
+      return &twidref[0];
+    }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
new file mode 100644
index 0000000..e7d70f3
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
@@ -0,0 +1,187 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+
+/* NOTE The functions of this file have been adapted from the GMM++ library */
+
+//========================================================================
+//
+// Copyright (C) 2002-2007 Yves Renard
+//
+// This file is a part of GETFEM++
+//
+// Getfem++ is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 of the License.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU Lesser General Public License for more details.
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301,
+// USA.
+//
+//========================================================================
+
+#include "../../../../Eigen/src/Core/util/NonMPL2.h"
+
+#ifndef EIGEN_CONSTRAINEDCG_H
+#define EIGEN_CONSTRAINEDCG_H
+
+#include "../../../../Eigen/Core"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \ingroup IterativeLinearSolvers_Module
+  * Compute the pseudo inverse of the non-square matrix C such that
+  * \f$ CINV = (C * C^T)^{-1} * C \f$ based on a conjugate gradient method.
+  *
+  * This function is internally used by constrained_cg.
+  */
+template <typename CMatrix, typename CINVMatrix>
+void pseudo_inverse(const CMatrix &C, CINVMatrix &CINV)
+{
+  // optimisable : copie de la ligne, precalcul de C * trans(C).
+  typedef typename CMatrix::Scalar Scalar;
+  typedef typename CMatrix::Index Index;
+  // FIXME use sparse vectors ?
+  typedef Matrix<Scalar,Dynamic,1> TmpVec;
+
+  Index rows = C.rows(), cols = C.cols();
+
+  TmpVec d(rows), e(rows), l(cols), p(rows), q(rows), r(rows);
+  Scalar rho, rho_1, alpha;
+  d.setZero();
+
+  typedef Triplet<double> T;
+  std::vector<T> tripletList;
+    
+  for (Index i = 0; i < rows; ++i)
+  {
+    d[i] = 1.0;
+    rho = 1.0;
+    e.setZero();
+    r = d;
+    p = d;
+
+    while (rho >= 1e-38)
+    { /* conjugate gradient to compute e             */
+      /* which is the i-th row of inv(C * trans(C))  */
+      l = C.transpose() * p;
+      q = C * l;
+      alpha = rho / p.dot(q);
+      e +=  alpha * p;
+      r += -alpha * q;
+      rho_1 = rho;
+      rho = r.dot(r);
+      p = (rho/rho_1) * p + r;
+    }
+
+    l = C.transpose() * e; // l is the i-th row of CINV
+    // FIXME add a generic "prune/filter" expression for both dense and sparse object to sparse
+    for (Index j=0; j<l.size(); ++j)
+      if (l[j]<1e-15)
+	tripletList.push_back(T(i,j,l(j)));
+
+	
+    d[i] = 0.0;
+  }
+  CINV.setFromTriplets(tripletList.begin(), tripletList.end());
+}
+
+
+
+/** \ingroup IterativeLinearSolvers_Module
+  * Constrained conjugate gradient
+  *
+  * Computes the minimum of \f$ 1/2((Ax).x) - bx \f$ under the constraint \f$ Cx \le f \f$
+  */
+template<typename TMatrix, typename CMatrix,
+         typename VectorX, typename VectorB, typename VectorF>
+void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
+                       const VectorB& b, const VectorF& f, IterationController &iter)
+{
+  using std::sqrt;
+  typedef typename TMatrix::Scalar Scalar;
+  typedef typename TMatrix::Index Index;
+  typedef Matrix<Scalar,Dynamic,1>  TmpVec;
+
+  Scalar rho = 1.0, rho_1, lambda, gamma;
+  Index xSize = x.size();
+  TmpVec  p(xSize), q(xSize), q2(xSize),
+          r(xSize), old_z(xSize), z(xSize),
+          memox(xSize);
+  std::vector<bool> satured(C.rows());
+  p.setZero();
+  iter.setRhsNorm(sqrt(b.dot(b))); // gael vect_sp(PS, b, b)
+  if (iter.rhsNorm() == 0.0) iter.setRhsNorm(1.0);
+
+  SparseMatrix<Scalar,RowMajor> CINV(C.rows(), C.cols());
+  pseudo_inverse(C, CINV);
+
+  while(true)
+  {
+    // computation of residual
+    old_z = z;
+    memox = x;
+    r = b;
+    r += A * -x;
+    z = r;
+    bool transition = false;
+    for (Index i = 0; i < C.rows(); ++i)
+    {
+      Scalar al = C.row(i).dot(x) - f.coeff(i);
+      if (al >= -1.0E-15)
+      {
+        if (!satured[i])
+        {
+          satured[i] = true;
+          transition = true;
+        }
+        Scalar bb = CINV.row(i).dot(z);
+        if (bb > 0.0)
+          // FIXME: we should allow that: z += -bb * C.row(i);
+          for (typename CMatrix::InnerIterator it(C,i); it; ++it)
+            z.coeffRef(it.index()) -= bb*it.value();
+      }
+      else
+        satured[i] = false;
+    }
+
+    // descent direction
+    rho_1 = rho;
+    rho = r.dot(z);
+
+    if (iter.finished(rho)) break;
+    if (transition || iter.first()) gamma = 0.0;
+    else gamma = (std::max)(0.0, (rho - old_z.dot(z)) / rho_1);
+    p = z + gamma*p;
+
+    ++iter;
+    // one dimensionnal optimization
+    q = A * p;
+    lambda = rho / q.dot(p);
+    for (Index i = 0; i < C.rows(); ++i)
+    {
+      if (!satured[i])
+      {
+        Scalar bb = C.row(i).dot(p) - f[i];
+        if (bb > 0.0)
+          lambda = (std::min)(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
+      }
+    }
+    x += lambda * p;
+    memox -= x;
+  }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONSTRAINEDCG_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
new file mode 100644
index 0000000..5ae011b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/DGMRES.h
@@ -0,0 +1,511 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DGMRES_H
+#define EIGEN_DGMRES_H
+
+#include "../../../../Eigen/Eigenvalues"
+
+namespace Eigen { 
+  
+template< typename _MatrixType,
+          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+class DGMRES;
+
+namespace internal {
+
+template< typename _MatrixType, typename _Preconditioner>
+struct traits<DGMRES<_MatrixType,_Preconditioner> >
+{
+  typedef _MatrixType MatrixType;
+  typedef _Preconditioner Preconditioner;
+};
+
+/** \brief Computes a permutation vector to have a sorted sequence
+  * \param vec The vector to reorder.
+  * \param perm gives the sorted sequence on output. Must be initialized with 0..n-1
+  * \param ncut Put  the ncut smallest elements at the end of the vector
+  * WARNING This is an expensive sort, so should be used only 
+  * for small size vectors
+  * TODO Use modified QuickSplit or std::nth_element to get the smallest values 
+  */
+template <typename VectorType, typename IndexType>
+void sortWithPermutation (VectorType& vec, IndexType& perm, typename IndexType::Scalar& ncut)
+{
+  eigen_assert(vec.size() == perm.size());
+  bool flag; 
+  for (Index k  = 0; k < ncut; k++)
+  {
+    flag = false;
+    for (Index j = 0; j < vec.size()-1; j++)
+    {
+      if ( vec(perm(j)) < vec(perm(j+1)) )
+      {
+        std::swap(perm(j),perm(j+1)); 
+        flag = true;
+      }
+      if (!flag) break; // The vector is in sorted order
+    }
+  }
+}
+
+}
+/**
+ * \ingroup IterativeLinearSolvers_Module
+ * \brief A Restarted GMRES with deflation.
+ * This class implements a modification of the GMRES solver for
+ * sparse linear systems. The basis is built with modified 
+ * Gram-Schmidt. At each restart, a few approximated eigenvectors
+ * corresponding to the smallest eigenvalues are used to build a
+ * preconditioner for the next cycle. This preconditioner 
+ * for deflation can be combined with any other preconditioner, 
+ * the IncompleteLUT for instance. The preconditioner is applied 
+ * at right of the matrix and the combination is multiplicative.
+ * 
+ * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+ * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+ * Typical usage :
+ * \code
+ * SparseMatrix<double> A;
+ * VectorXd x, b; 
+ * //Fill A and b ...
+ * DGMRES<SparseMatrix<double> > solver;
+ * solver.set_restart(30); // Set restarting value
+ * solver.setEigenv(1); // Set the number of eigenvalues to deflate
+ * solver.compute(A);
+ * x = solver.solve(b);
+ * \endcode
+ * 
+ * DGMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+ *
+ * References :
+ * [1] D. NUENTSA WAKAM and F. PACULL, Memory Efficient Hybrid
+ *  Algebraic Solvers for Linear Systems Arising from Compressible
+ *  Flows, Computers and Fluids, In Press,
+ *  https://doi.org/10.1016/j.compfluid.2012.03.023   
+ * [2] K. Burrage and J. Erhel, On the performance of various 
+ * adaptive preconditioned GMRES strategies, 5(1998), 101-121.
+ * [3] J. Erhel, K. Burrage and B. Pohl, Restarted GMRES 
+ *  preconditioned by deflation,J. Computational and Applied
+ *  Mathematics, 69(1996), 303-318. 
+
+ * 
+ */
+template< typename _MatrixType, typename _Preconditioner>
+class DGMRES : public IterativeSolverBase<DGMRES<_MatrixType,_Preconditioner> >
+{
+    typedef IterativeSolverBase<DGMRES> Base;
+    using Base::matrix;
+    using Base::m_error;
+    using Base::m_iterations;
+    using Base::m_info;
+    using Base::m_isInitialized;
+    using Base::m_tolerance; 
+  public:
+    using Base::_solve_impl;
+    using Base::_solve_with_guess_impl;
+    typedef _MatrixType MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef _Preconditioner Preconditioner;
+    typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; 
+    typedef Matrix<RealScalar,Dynamic,Dynamic> DenseRealMatrix; 
+    typedef Matrix<Scalar,Dynamic,1> DenseVector;
+    typedef Matrix<RealScalar,Dynamic,1> DenseRealVector; 
+    typedef Matrix<std::complex<RealScalar>, Dynamic, 1> ComplexVector;
+ 
+    
+  /** Default constructor. */
+  DGMRES() : Base(),m_restart(30),m_neig(0),m_r(0),m_maxNeig(5),m_isDeflAllocated(false),m_isDeflInitialized(false) {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit DGMRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_restart(30),m_neig(0),m_r(0),m_maxNeig(5),m_isDeflAllocated(false),m_isDeflInitialized(false) {}
+
+  ~DGMRES() {}
+  
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+  {
+    EIGEN_STATIC_ASSERT(Rhs::ColsAtCompileTime==1 || Dest::ColsAtCompileTime==1, YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX);
+    
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    
+    dgmres(matrix(), b, x, Base::m_preconditioner);
+  }
+
+  /** 
+   * Get the restart value
+    */
+  Index restart() { return m_restart; }
+  
+  /** 
+   * Set the restart value (default is 30)  
+   */
+  void set_restart(const Index restart) { m_restart=restart; }
+  
+  /** 
+   * Set the number of eigenvalues to deflate at each restart 
+   */
+  void setEigenv(const Index neig) 
+  {
+    m_neig = neig;
+    if (neig+1 > m_maxNeig) m_maxNeig = neig+1; // To allow for complex conjugates
+  }
+  
+  /** 
+   * Get the size of the deflation subspace size
+   */ 
+  Index deflSize() {return m_r; }
+  
+  /**
+   * Set the maximum size of the deflation subspace
+   */
+  void setMaxEigenv(const Index maxNeig) { m_maxNeig = maxNeig; }
+  
+  protected:
+    // DGMRES algorithm 
+    template<typename Rhs, typename Dest>
+    void dgmres(const MatrixType& mat,const Rhs& rhs, Dest& x, const Preconditioner& precond) const;
+    // Perform one cycle of GMRES
+    template<typename Dest>
+    Index dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x, DenseVector& r0, RealScalar& beta, const RealScalar& normRhs, Index& nbIts) const; 
+    // Compute data to use for deflation 
+    Index dgmresComputeDeflationData(const MatrixType& mat, const Preconditioner& precond, const Index& it, StorageIndex& neig) const;
+    // Apply deflation to a vector
+    template<typename RhsType, typename DestType>
+    Index dgmresApplyDeflation(const RhsType& In, DestType& Out) const; 
+    ComplexVector schurValues(const ComplexSchur<DenseMatrix>& schurofH) const;
+    ComplexVector schurValues(const RealSchur<DenseMatrix>& schurofH) const;
+    // Init data for deflation
+    void dgmresInitDeflation(Index& rows) const; 
+    mutable DenseMatrix m_V; // Krylov basis vectors
+    mutable DenseMatrix m_H; // Hessenberg matrix 
+    mutable DenseMatrix m_Hes; // Initial hessenberg matrix without Givens rotations applied
+    mutable Index m_restart; // Maximum size of the Krylov subspace
+    mutable DenseMatrix m_U; // Vectors that form the basis of the invariant subspace 
+    mutable DenseMatrix m_MU; // matrix operator applied to m_U (for next cycles)
+    mutable DenseMatrix m_T; /* T=U^T*M^{-1}*A*U */
+    mutable PartialPivLU<DenseMatrix> m_luT; // LU factorization of m_T
+    mutable StorageIndex m_neig; //Number of eigenvalues to extract at each restart
+    mutable Index m_r; // Current number of deflated eigenvalues, size of m_U
+    mutable Index m_maxNeig; // Maximum number of eigenvalues to deflate
+    mutable RealScalar m_lambdaN; //Modulus of the largest eigenvalue of A
+    mutable bool m_isDeflAllocated;
+    mutable bool m_isDeflInitialized;
+    
+    //Adaptive strategy 
+    mutable RealScalar m_smv; // Smaller multiple of the remaining number of steps allowed
+    mutable bool m_force; // Force the use of deflation at each restart
+    
+}; 
+/** 
+ * \brief Perform several cycles of restarted GMRES with modified Gram Schmidt, 
+ * 
+ * A right preconditioner is used combined with deflation.
+ * 
+ */
+template< typename _MatrixType, typename _Preconditioner>
+template<typename Rhs, typename Dest>
+void DGMRES<_MatrixType, _Preconditioner>::dgmres(const MatrixType& mat,const Rhs& rhs, Dest& x,
+              const Preconditioner& precond) const
+{
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  RealScalar normRhs = rhs.norm();
+  if(normRhs <= considerAsZero) 
+  {
+    x.setZero();
+    m_error = 0;
+    return;
+  }
+
+  //Initialization
+  m_isDeflInitialized = false;
+  Index n = mat.rows(); 
+  DenseVector r0(n); 
+  Index nbIts = 0; 
+  m_H.resize(m_restart+1, m_restart);
+  m_Hes.resize(m_restart, m_restart);
+  m_V.resize(n,m_restart+1);
+  //Initial residual vector and initial norm
+  if(x.squaredNorm()==0) 
+    x = precond.solve(rhs);
+  r0 = rhs - mat * x; 
+  RealScalar beta = r0.norm(); 
+  
+  m_error = beta/normRhs; 
+  if(m_error < m_tolerance)
+    m_info = Success; 
+  else
+    m_info = NoConvergence;
+  
+  // Iterative process
+  while (nbIts < m_iterations && m_info == NoConvergence)
+  {
+    dgmresCycle(mat, precond, x, r0, beta, normRhs, nbIts); 
+    
+    // Compute the new residual vector for the restart 
+    if (nbIts < m_iterations && m_info == NoConvergence) {
+      r0 = rhs - mat * x;
+      beta = r0.norm();
+    }
+  }
+} 
+
+/**
+ * \brief Perform one restart cycle of DGMRES
+ * \param mat The coefficient matrix
+ * \param precond The preconditioner
+ * \param x the new approximated solution
+ * \param r0 The initial residual vector
+ * \param beta The norm of the residual computed so far
+ * \param normRhs The norm of the right hand side vector
+ * \param nbIts The number of iterations
+ */
+template< typename _MatrixType, typename _Preconditioner>
+template<typename Dest>
+Index DGMRES<_MatrixType, _Preconditioner>::dgmresCycle(const MatrixType& mat, const Preconditioner& precond, Dest& x, DenseVector& r0, RealScalar& beta, const RealScalar& normRhs, Index& nbIts) const
+{
+  //Initialization 
+  DenseVector g(m_restart+1); // Right hand side of the least square problem
+  g.setZero();  
+  g(0) = Scalar(beta); 
+  m_V.col(0) = r0/beta; 
+  m_info = NoConvergence; 
+  std::vector<JacobiRotation<Scalar> >gr(m_restart); // Givens rotations
+  Index it = 0; // Number of inner iterations 
+  Index n = mat.rows();
+  DenseVector tv1(n), tv2(n);  //Temporary vectors
+  while (m_info == NoConvergence && it < m_restart && nbIts < m_iterations)
+  {    
+    // Apply preconditioner(s) at right
+    if (m_isDeflInitialized )
+    {
+      dgmresApplyDeflation(m_V.col(it), tv1); // Deflation
+      tv2 = precond.solve(tv1); 
+    }
+    else
+    {
+      tv2 = precond.solve(m_V.col(it)); // User's selected preconditioner
+    }
+    tv1 = mat * tv2; 
+   
+    // Orthogonalize it with the previous basis in the basis using modified Gram-Schmidt
+    Scalar coef; 
+    for (Index i = 0; i <= it; ++i)
+    { 
+      coef = tv1.dot(m_V.col(i));
+      tv1 = tv1 - coef * m_V.col(i); 
+      m_H(i,it) = coef; 
+      m_Hes(i,it) = coef; 
+    }
+    // Normalize the vector 
+    coef = tv1.norm(); 
+    m_V.col(it+1) = tv1/coef;
+    m_H(it+1, it) = coef;
+//     m_Hes(it+1,it) = coef; 
+    
+    // FIXME Check for happy breakdown 
+    
+    // Update Hessenberg matrix with Givens rotations
+    for (Index i = 1; i <= it; ++i) 
+    {
+      m_H.col(it).applyOnTheLeft(i-1,i,gr[i-1].adjoint());
+    }
+    // Compute the new plane rotation 
+    gr[it].makeGivens(m_H(it, it), m_H(it+1,it)); 
+    // Apply the new rotation
+    m_H.col(it).applyOnTheLeft(it,it+1,gr[it].adjoint());
+    g.applyOnTheLeft(it,it+1, gr[it].adjoint()); 
+    
+    beta = std::abs(g(it+1));
+    m_error = beta/normRhs; 
+    // std::cerr << nbIts << " Relative Residual Norm " << m_error << std::endl;
+    it++; nbIts++; 
+    
+    if (m_error < m_tolerance)
+    {
+      // The method has converged
+      m_info = Success;
+      break;
+    }
+  }
+  
+  // Compute the new coefficients by solving the least square problem
+//   it++;
+  //FIXME  Check first if the matrix is singular ... zero diagonal
+  DenseVector nrs(m_restart); 
+  nrs = m_H.topLeftCorner(it,it).template triangularView<Upper>().solve(g.head(it)); 
+  
+  // Form the new solution
+  if (m_isDeflInitialized)
+  {
+    tv1 = m_V.leftCols(it) * nrs; 
+    dgmresApplyDeflation(tv1, tv2); 
+    x = x + precond.solve(tv2);
+  }
+  else
+    x = x + precond.solve(m_V.leftCols(it) * nrs); 
+  
+  // Go for a new cycle and compute data for deflation
+  if(nbIts < m_iterations && m_info == NoConvergence && m_neig > 0 && (m_r+m_neig) < m_maxNeig)
+    dgmresComputeDeflationData(mat, precond, it, m_neig); 
+  return 0; 
+  
+}
+
+
+template< typename _MatrixType, typename _Preconditioner>
+void DGMRES<_MatrixType, _Preconditioner>::dgmresInitDeflation(Index& rows) const
+{
+  m_U.resize(rows, m_maxNeig);
+  m_MU.resize(rows, m_maxNeig); 
+  m_T.resize(m_maxNeig, m_maxNeig);
+  m_lambdaN = 0.0; 
+  m_isDeflAllocated = true; 
+}
+
+template< typename _MatrixType, typename _Preconditioner>
+inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_MatrixType, _Preconditioner>::schurValues(const ComplexSchur<DenseMatrix>& schurofH) const
+{
+  return schurofH.matrixT().diagonal();
+}
+
+template< typename _MatrixType, typename _Preconditioner>
+inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_MatrixType, _Preconditioner>::schurValues(const RealSchur<DenseMatrix>& schurofH) const
+{
+  const DenseMatrix& T = schurofH.matrixT();
+  Index it = T.rows();
+  ComplexVector eig(it);
+  Index j = 0;
+  while (j < it-1)
+  {
+    if (T(j+1,j) ==Scalar(0))
+    {
+      eig(j) = std::complex<RealScalar>(T(j,j),RealScalar(0)); 
+      j++; 
+    }
+    else
+    {
+      eig(j) = std::complex<RealScalar>(T(j,j),T(j+1,j)); 
+      eig(j+1) = std::complex<RealScalar>(T(j,j+1),T(j+1,j+1));
+      j++;
+    }
+  }
+  if (j < it-1) eig(j) = std::complex<RealScalar>(T(j,j),RealScalar(0));
+  return eig;
+}
+
+template< typename _MatrixType, typename _Preconditioner>
+Index DGMRES<_MatrixType, _Preconditioner>::dgmresComputeDeflationData(const MatrixType& mat, const Preconditioner& precond, const Index& it, StorageIndex& neig) const
+{
+  // First, find the Schur form of the Hessenberg matrix H
+  typename internal::conditional<NumTraits<Scalar>::IsComplex, ComplexSchur<DenseMatrix>, RealSchur<DenseMatrix> >::type schurofH; 
+  bool computeU = true;
+  DenseMatrix matrixQ(it,it); 
+  matrixQ.setIdentity();
+  schurofH.computeFromHessenberg(m_Hes.topLeftCorner(it,it), matrixQ, computeU); 
+  
+  ComplexVector eig(it);
+  Matrix<StorageIndex,Dynamic,1>perm(it);
+  eig = this->schurValues(schurofH);
+  
+  // Reorder the absolute values of Schur values
+  DenseRealVector modulEig(it); 
+  for (Index j=0; j<it; ++j) modulEig(j) = std::abs(eig(j)); 
+  perm.setLinSpaced(it,0,internal::convert_index<StorageIndex>(it-1));
+  internal::sortWithPermutation(modulEig, perm, neig);
+  
+  if (!m_lambdaN)
+  {
+    m_lambdaN = (std::max)(modulEig.maxCoeff(), m_lambdaN);
+  }
+  //Count the real number of extracted eigenvalues (with complex conjugates)
+  Index nbrEig = 0; 
+  while (nbrEig < neig)
+  {
+    if(eig(perm(it-nbrEig-1)).imag() == RealScalar(0)) nbrEig++; 
+    else nbrEig += 2; 
+  }
+  // Extract the  Schur vectors corresponding to the smallest Ritz values
+  DenseMatrix Sr(it, nbrEig); 
+  Sr.setZero();
+  for (Index j = 0; j < nbrEig; j++)
+  {
+    Sr.col(j) = schurofH.matrixU().col(perm(it-j-1));
+  }
+  
+  // Form the Schur vectors of the initial matrix using the Krylov basis
+  DenseMatrix X; 
+  X = m_V.leftCols(it) * Sr;
+  if (m_r)
+  {
+   // Orthogonalize X against m_U using modified Gram-Schmidt
+   for (Index j = 0; j < nbrEig; j++)
+     for (Index k =0; k < m_r; k++)
+      X.col(j) = X.col(j) - (m_U.col(k).dot(X.col(j)))*m_U.col(k); 
+  }
+  
+  // Compute m_MX = A * M^-1 * X
+  Index m = m_V.rows();
+  if (!m_isDeflAllocated) 
+    dgmresInitDeflation(m); 
+  DenseMatrix MX(m, nbrEig);
+  DenseVector tv1(m);
+  for (Index j = 0; j < nbrEig; j++)
+  {
+    tv1 = mat * X.col(j);
+    MX.col(j) = precond.solve(tv1);
+  }
+  
+  //Update m_T = [U'MU U'MX; X'MU X'MX]
+  m_T.block(m_r, m_r, nbrEig, nbrEig) = X.transpose() * MX; 
+  if(m_r)
+  {
+    m_T.block(0, m_r, m_r, nbrEig) = m_U.leftCols(m_r).transpose() * MX; 
+    m_T.block(m_r, 0, nbrEig, m_r) = X.transpose() * m_MU.leftCols(m_r);
+  }
+  
+  // Save X into m_U and m_MX in m_MU
+  for (Index j = 0; j < nbrEig; j++) m_U.col(m_r+j) = X.col(j);
+  for (Index j = 0; j < nbrEig; j++) m_MU.col(m_r+j) = MX.col(j);
+  // Increase the size of the invariant subspace
+  m_r += nbrEig; 
+  
+  // Factorize m_T into m_luT
+  m_luT.compute(m_T.topLeftCorner(m_r, m_r));
+  
+  //FIXME CHeck if the factorization was correctly done (nonsingular matrix)
+  m_isDeflInitialized = true;
+  return 0; 
+}
+template<typename _MatrixType, typename _Preconditioner>
+template<typename RhsType, typename DestType>
+Index DGMRES<_MatrixType, _Preconditioner>::dgmresApplyDeflation(const RhsType &x, DestType &y) const
+{
+  DenseVector x1 = m_U.leftCols(m_r).transpose() * x; 
+  y = x + m_U.leftCols(m_r) * ( m_lambdaN * m_luT.solve(x1) - x1);
+  return 0; 
+}
+
+} // end namespace Eigen
+#endif 
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
new file mode 100644
index 0000000..ff91209
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/GMRES.h
@@ -0,0 +1,335 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012, 2014 Kolja Brix <brix@igpm.rwth-aaachen.de>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GMRES_H
+#define EIGEN_GMRES_H
+
+namespace Eigen {
+
+namespace internal {
+
+/**
+* Generalized Minimal Residual Algorithm based on the
+* Arnoldi algorithm implemented with Householder reflections.
+*
+* Parameters:
+*  \param mat       matrix of linear system of equations
+*  \param rhs       right hand side vector of linear system of equations
+*  \param x         on input: initial guess, on output: solution
+*  \param precond   preconditioner used
+*  \param iters     on input: maximum number of iterations to perform
+*                   on output: number of iterations performed
+*  \param restart   number of iterations for a restart
+*  \param tol_error on input: relative residual tolerance
+*                   on output: residuum achieved
+*
+* \sa IterativeMethods::bicgstab()
+*
+*
+* For references, please see:
+*
+* Saad, Y. and Schultz, M. H.
+* GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems.
+* SIAM J.Sci.Stat.Comp. 7, 1986, pp. 856 - 869.
+*
+* Saad, Y.
+* Iterative Methods for Sparse Linear Systems.
+* Society for Industrial and Applied Mathematics, Philadelphia, 2003.
+*
+* Walker, H. F.
+* Implementations of the GMRES method.
+* Comput.Phys.Comm. 53, 1989, pp. 311 - 320.
+*
+* Walker, H. F.
+* Implementation of the GMRES Method using Householder Transformations.
+* SIAM J.Sci.Stat.Comp. 9, 1988, pp. 152 - 163.
+*
+*/
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool gmres(const MatrixType & mat, const Rhs & rhs, Dest & x, const Preconditioner & precond,
+    Index &iters, const Index &restart, typename Dest::RealScalar & tol_error) {
+
+  using std::sqrt;
+  using std::abs;
+
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix < Scalar, Dynamic, 1 > VectorType;
+  typedef Matrix < Scalar, Dynamic, Dynamic, ColMajor> FMatrixType;
+
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  if(rhs.norm() <= considerAsZero) 
+  {
+    x.setZero();
+    tol_error = 0;
+    return true;
+  }
+
+  RealScalar tol = tol_error;
+  const Index maxIters = iters;
+  iters = 0;
+
+  const Index m = mat.rows();
+
+  // residual and preconditioned residual
+  VectorType p0 = rhs - mat*x;
+  VectorType r0 = precond.solve(p0);
+
+  const RealScalar r0Norm = r0.norm();
+
+  // is initial guess already good enough?
+  if(r0Norm == 0)
+  {
+    tol_error = 0;
+    return true;
+  }
+
+  // storage for Hessenberg matrix and Householder data
+  FMatrixType H   = FMatrixType::Zero(m, restart + 1);
+  VectorType w    = VectorType::Zero(restart + 1);
+  VectorType tau  = VectorType::Zero(restart + 1);
+
+  // storage for Jacobi rotations
+  std::vector < JacobiRotation < Scalar > > G(restart);
+  
+  // storage for temporaries
+  VectorType t(m), v(m), workspace(m), x_new(m);
+
+  // generate first Householder vector
+  Ref<VectorType> H0_tail = H.col(0).tail(m - 1);
+  RealScalar beta;
+  r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
+  w(0) = Scalar(beta);
+  
+  for (Index k = 1; k <= restart; ++k)
+  {
+    ++iters;
+
+    v = VectorType::Unit(m, k - 1);
+
+    // apply Householder reflections H_{1} ... H_{k-1} to v
+    // TODO: use a HouseholderSequence
+    for (Index i = k - 1; i >= 0; --i) {
+      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+    }
+
+    // apply matrix M to v:  v = mat * v;
+    t.noalias() = mat * v;
+    v = precond.solve(t);
+
+    // apply Householder reflections H_{k-1} ... H_{1} to v
+    // TODO: use a HouseholderSequence
+    for (Index i = 0; i < k; ++i) {
+      v.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+    }
+
+    if (v.tail(m - k).norm() != 0.0)
+    {
+      if (k <= restart)
+      {
+        // generate new Householder vector
+        Ref<VectorType> Hk_tail = H.col(k).tail(m - k - 1);
+        v.tail(m - k).makeHouseholder(Hk_tail, tau.coeffRef(k), beta);
+
+        // apply Householder reflection H_{k} to v
+        v.tail(m - k).applyHouseholderOnTheLeft(Hk_tail, tau.coeffRef(k), workspace.data());
+      }
+    }
+
+    if (k > 1)
+    {
+      for (Index i = 0; i < k - 1; ++i)
+      {
+        // apply old Givens rotations to v
+        v.applyOnTheLeft(i, i + 1, G[i].adjoint());
+      }
+    }
+
+    if (k<m && v(k) != (Scalar) 0)
+    {
+      // determine next Givens rotation
+      G[k - 1].makeGivens(v(k - 1), v(k));
+
+      // apply Givens rotation to v and w
+      v.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
+      w.applyOnTheLeft(k - 1, k, G[k - 1].adjoint());
+    }
+
+    // insert coefficients into upper matrix triangle
+    H.col(k-1).head(k) = v.head(k);
+
+    tol_error = abs(w(k)) / r0Norm;
+    bool stop = (k==m || tol_error < tol || iters == maxIters);
+
+    if (stop || k == restart)
+    {
+      // solve upper triangular system
+      Ref<VectorType> y = w.head(k);
+      H.topLeftCorner(k, k).template triangularView <Upper>().solveInPlace(y);
+
+      // use Horner-like scheme to calculate solution vector
+      x_new.setZero();
+      for (Index i = k - 1; i >= 0; --i)
+      {
+        x_new(i) += y(i);
+        // apply Householder reflection H_{i} to x_new
+        x_new.tail(m - i).applyHouseholderOnTheLeft(H.col(i).tail(m - i - 1), tau.coeffRef(i), workspace.data());
+      }
+
+      x += x_new;
+
+      if(stop)
+      {
+        return true;
+      }
+      else
+      {
+        k=0;
+
+        // reset data for restart
+        p0.noalias() = rhs - mat*x;
+        r0 = precond.solve(p0);
+
+        // clear Hessenberg matrix and Householder data
+        H.setZero();
+        w.setZero();
+        tau.setZero();
+
+        // generate first Householder vector
+        r0.makeHouseholder(H0_tail, tau.coeffRef(0), beta);
+        w(0) = Scalar(beta);
+      }
+    }
+  }
+
+  return false;
+
+}
+
+}
+
+template< typename _MatrixType,
+          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+class GMRES;
+
+namespace internal {
+
+template< typename _MatrixType, typename _Preconditioner>
+struct traits<GMRES<_MatrixType,_Preconditioner> >
+{
+  typedef _MatrixType MatrixType;
+  typedef _Preconditioner Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A GMRES solver for sparse square problems
+  *
+  * This class allows to solve for A.x = b sparse linear problems using a generalized minimal
+  * residual method. The vectors x and b can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  *
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+  * \code
+  * int n = 10000;
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A(n,n);
+  * // fill A and b
+  * GMRES<SparseMatrix<double> > solver(A);
+  * x = solver.solve(b);
+  * std::cout << "#iterations:     " << solver.iterations() << std::endl;
+  * std::cout << "estimated error: " << solver.error()      << std::endl;
+  * // update b, and solve again
+  * x = solver.solve(b);
+  * \endcode
+  *
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  * 
+  * GMRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+  *
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename _MatrixType, typename _Preconditioner>
+class GMRES : public IterativeSolverBase<GMRES<_MatrixType,_Preconditioner> >
+{
+  typedef IterativeSolverBase<GMRES> Base;
+  using Base::matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+
+private:
+  Index m_restart;
+
+public:
+  using Base::_solve_impl;
+  typedef _MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef _Preconditioner Preconditioner;
+
+public:
+
+  /** Default constructor. */
+  GMRES() : Base(), m_restart(30) {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    *
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit GMRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_restart(30) {}
+
+  ~GMRES() {}
+
+  /** Get the number of iterations after that a restart is performed.
+    */
+  Index get_restart() { return m_restart; }
+
+  /** Set the number of iterations after that a restart is performed.
+    *  \param restart   number of iterations for a restarti, default is 30.
+    */
+  void set_restart(const Index restart) { m_restart=restart; }
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+  {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    bool ret = internal::gmres(matrix(), b, x, Base::m_preconditioner, m_iterations, m_restart, m_error);
+    m_info = (!ret) ? NumericalIssue
+          : m_error <= Base::m_tolerance ? Success
+          : NoConvergence;
+  }
+
+protected:
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_GMRES_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
new file mode 100644
index 0000000..90d20fa
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IDRS.h
@@ -0,0 +1,436 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Chris Schoutrop <c.e.m.schoutrop@tue.nl>
+// Copyright (C) 2020 Jens Wehner <j.wehner@esciencecenter.nl>
+// Copyright (C) 2020 Jan van Dijk <j.v.dijk@tue.nl>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_IDRS_H
+#define EIGEN_IDRS_H
+
+namespace Eigen
+{
+
+	namespace internal
+	{
+		/**     \internal Low-level Induced Dimension Reduction algoritm
+		        \param A The matrix A
+		        \param b The right hand side vector b
+		        \param x On input and initial solution, on output the computed solution.
+		        \param precond A preconditioner being able to efficiently solve for an
+		                  approximation of Ax=b (regardless of b)
+		        \param iter On input the max number of iteration, on output the number of performed iterations.
+		        \param relres On input the tolerance error, on output an estimation of the relative error.
+		        \param S On input Number of the dimension of the shadow space.
+				\param smoothing switches residual smoothing on.
+				\param angle small omega lead to faster convergence at the expense of numerical stability
+				\param replacement switches on a residual replacement strategy to increase accuracy of residual at the expense of more Mat*vec products
+		        \return false in the case of numerical issue, for example a break down of IDRS.
+		*/
+		template<typename Vector, typename RealScalar>
+		typename Vector::Scalar omega(const Vector& t, const Vector& s, RealScalar angle)
+		{
+			using numext::abs;
+			typedef typename Vector::Scalar Scalar;
+			const RealScalar ns = s.norm();
+			const RealScalar nt = t.norm();
+			const Scalar ts = t.dot(s);
+			const RealScalar rho = abs(ts / (nt * ns));
+
+			if (rho < angle) {
+				if (ts == Scalar(0)) {
+					return Scalar(0);
+				}
+				// Original relation for om is given by
+				// om = om * angle / rho;
+				// To alleviate potential (near) division by zero this can be rewritten as
+				// om = angle * (ns / nt) * (ts / abs(ts)) = angle * (ns / nt) * sgn(ts)
+  				return angle * (ns / nt) * (ts / abs(ts));
+			}
+			return ts / (nt * nt);
+		}
+
+		template <typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+		bool idrs(const MatrixType& A, const Rhs& b, Dest& x, const Preconditioner& precond,
+			Index& iter,
+			typename Dest::RealScalar& relres, Index S, bool smoothing, typename Dest::RealScalar angle, bool replacement)
+		{
+			typedef typename Dest::RealScalar RealScalar;
+			typedef typename Dest::Scalar Scalar;
+			typedef Matrix<Scalar, Dynamic, 1> VectorType;
+			typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> DenseMatrixType;
+			const Index N = b.size();
+			S = S < x.rows() ? S : x.rows();
+			const RealScalar tol = relres;
+			const Index maxit = iter;
+
+			Index replacements = 0;
+			bool trueres = false;
+
+			FullPivLU<DenseMatrixType> lu_solver;
+
+			DenseMatrixType P;
+			{
+				HouseholderQR<DenseMatrixType> qr(DenseMatrixType::Random(N, S));
+			    P = (qr.householderQ() * DenseMatrixType::Identity(N, S));
+			}
+
+			const RealScalar normb = b.norm();
+
+			if (internal::isApprox(normb, RealScalar(0)))
+			{
+				//Solution is the zero vector
+				x.setZero();
+				iter = 0;
+				relres = 0;
+				return true;
+			}
+			 // from http://homepage.tudelft.nl/1w5b5/IDRS/manual.pdf
+			 // A peak in the residual is considered dangerously high if‖ri‖/‖b‖> C(tol/epsilon).
+			 // With epsilon the
+             // relative machine precision. The factor tol/epsilon corresponds to the size of a
+             // finite precision number that is so large that the absolute round-off error in
+             // this number, when propagated through the process, makes it impossible to
+             // achieve the required accuracy.The factor C accounts for the accumulation of
+             // round-off errors. This parameter has beenset to 10−3.
+			 // mp is epsilon/C
+			 // 10^3 * eps is very conservative, so normally no residual replacements will take place. 
+			 // It only happens if things go very wrong. Too many restarts may ruin the convergence.
+			const RealScalar mp = RealScalar(1e3) * NumTraits<Scalar>::epsilon();
+
+
+
+			//Compute initial residual
+			const RealScalar tolb = tol * normb; //Relative tolerance
+			VectorType r = b - A * x;
+
+			VectorType x_s, r_s;
+
+			if (smoothing)
+			{
+				x_s = x;
+				r_s = r;
+			}
+
+			RealScalar normr = r.norm();
+
+			if (normr <= tolb)
+			{
+				//Initial guess is a good enough solution
+				iter = 0;
+				relres = normr / normb;
+				return true;
+			}
+
+			DenseMatrixType G = DenseMatrixType::Zero(N, S);
+			DenseMatrixType U = DenseMatrixType::Zero(N, S);
+			DenseMatrixType M = DenseMatrixType::Identity(S, S);
+			VectorType t(N), v(N);
+			Scalar om = 1.;
+
+			//Main iteration loop, guild G-spaces:
+			iter = 0;
+
+			while (normr > tolb && iter < maxit)
+			{
+				//New right hand size for small system:
+				VectorType f = (r.adjoint() * P).adjoint();
+
+				for (Index k = 0; k < S; ++k)
+				{
+					//Solve small system and make v orthogonal to P:
+					//c = M(k:s,k:s)\f(k:s);
+					lu_solver.compute(M.block(k , k , S -k, S - k ));
+					VectorType c = lu_solver.solve(f.segment(k , S - k ));
+					//v = r - G(:,k:s)*c;
+					v = r - G.rightCols(S - k ) * c;
+					//Preconditioning
+					v = precond.solve(v);
+
+					//Compute new U(:,k) and G(:,k), G(:,k) is in space G_j
+					U.col(k) = U.rightCols(S - k ) * c + om * v;
+					G.col(k) = A * U.col(k );
+
+					//Bi-Orthogonalise the new basis vectors:
+					for (Index i = 0; i < k-1 ; ++i)
+					{
+						//alpha =  ( P(:,i)'*G(:,k) )/M(i,i);
+						Scalar alpha = P.col(i ).dot(G.col(k )) / M(i, i );
+						G.col(k ) = G.col(k ) - alpha * G.col(i );
+						U.col(k ) = U.col(k ) - alpha * U.col(i );
+					}
+
+					//New column of M = P'*G  (first k-1 entries are zero)
+					//M(k:s,k) = (G(:,k)'*P(:,k:s))';
+					M.block(k , k , S - k , 1) = (G.col(k ).adjoint() * P.rightCols(S - k )).adjoint();
+
+					if (internal::isApprox(M(k,k), Scalar(0)))
+					{
+						return false;
+					}
+
+					//Make r orthogonal to q_i, i = 0..k-1
+					Scalar beta = f(k ) / M(k , k );
+					r = r - beta * G.col(k );
+					x = x + beta * U.col(k );
+					normr = r.norm();
+
+					if (replacement && normr > tolb / mp)
+					{
+						trueres = true;
+					}
+
+					//Smoothing:
+					if (smoothing)
+					{
+						t = r_s - r;
+						//gamma is a Scalar, but the conversion is not allowed
+						Scalar gamma = t.dot(r_s) / t.norm();
+						r_s = r_s - gamma * t;
+						x_s = x_s - gamma * (x_s - x);
+						normr = r_s.norm();
+					}
+
+					if (normr < tolb || iter == maxit)
+					{
+						break;
+					}
+
+					//New f = P'*r (first k  components are zero)
+					if (k < S-1)
+					{
+						f.segment(k + 1, S - (k + 1) ) = f.segment(k + 1 , S - (k + 1)) - beta * M.block(k + 1 , k , S - (k + 1), 1);
+					}
+				}//end for
+
+				if (normr < tolb || iter == maxit)
+				{
+					break;
+				}
+
+				//Now we have sufficient vectors in G_j to compute residual in G_j+1
+				//Note: r is already perpendicular to P so v = r
+				//Preconditioning
+				v = r;
+				v = precond.solve(v);
+
+				//Matrix-vector multiplication:
+				t = A * v;
+
+				//Computation of a new omega
+				om = internal::omega(t, r, angle);
+
+				if (om == RealScalar(0.0))
+				{
+					return false;
+				}
+
+				r = r - om * t;
+				x = x + om * v;
+				normr = r.norm();
+
+				if (replacement && normr > tolb / mp)
+				{
+					trueres = true;
+				}
+
+				//Residual replacement?
+				if (trueres && normr < normb)
+				{
+					r = b - A * x;
+					trueres = false;
+					replacements++;
+				}
+
+				//Smoothing:
+				if (smoothing)
+				{
+					t = r_s - r;
+					Scalar gamma = t.dot(r_s) /t.norm();
+					r_s = r_s - gamma * t;
+					x_s = x_s - gamma * (x_s - x);
+					normr = r_s.norm();
+				}
+
+				iter++;
+
+			}//end while
+
+			if (smoothing)
+			{
+				x = x_s;
+			}
+			relres=normr/normb;
+			return true;
+		}
+
+	}  // namespace internal
+
+	template <typename _MatrixType, typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+	class IDRS;
+
+	namespace internal
+	{
+
+		template <typename _MatrixType, typename _Preconditioner>
+		struct traits<Eigen::IDRS<_MatrixType, _Preconditioner> >
+		{
+			typedef _MatrixType MatrixType;
+			typedef _Preconditioner Preconditioner;
+		};
+
+	}  // namespace internal
+
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square problems.
+  *
+  * This class allows to solve for A.x = b sparse linear problems. The vectors x and b can be either dense or sparse.
+  * he Induced Dimension Reduction method, IDR(), is a robust and efficient short-recurrence Krylov subspace method for
+  * solving large nonsymmetric systems of linear equations.
+  *
+  * For indefinite systems IDR(S) outperforms both BiCGStab and BiCGStab(L). Additionally, IDR(S) can handle matrices
+  * with complex eigenvalues more efficiently than BiCGStab.
+  *
+  * Many problems that do not converge for BiCGSTAB converge for IDR(s) (for larger values of s). And if both methods 
+  * converge the convergence for IDR(s) is typically much faster for difficult systems (for example indefinite problems). 
+  *
+  * IDR(s) is a limited memory finite termination method. In exact arithmetic it converges in at most N+N/s iterations,
+  * with N the system size.  It uses a fixed number of 4+3s vector. In comparison, BiCGSTAB terminates in 2N iterations 
+  * and uses 7 vectors. GMRES terminates in at most N iterations, and uses I+3 vectors, with I the number of iterations. 
+  * Restarting GMRES limits the memory consumption, but destroys the finite termination property.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  *
+  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+  *
+  * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
+  * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+  * See \ref TopicMultiThreading for details.
+  *
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  *
+  * IDR(s) can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+  *
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+	template <typename _MatrixType, typename _Preconditioner>
+	class IDRS : public IterativeSolverBase<IDRS<_MatrixType, _Preconditioner> >
+	{
+
+		public:
+			typedef _MatrixType MatrixType;
+			typedef typename MatrixType::Scalar Scalar;
+			typedef typename MatrixType::RealScalar RealScalar;
+			typedef _Preconditioner Preconditioner;
+
+		private:
+			typedef IterativeSolverBase<IDRS> Base;
+			using Base::m_error;
+			using Base::m_info;
+			using Base::m_isInitialized;
+			using Base::m_iterations;
+			using Base::matrix;
+			Index m_S;
+			bool m_smoothing;
+			RealScalar m_angle;
+			bool m_residual;
+
+		public:
+			/** Default constructor. */
+			IDRS(): m_S(4), m_smoothing(false), m_angle(RealScalar(0.7)), m_residual(false) {}
+
+			/**     Initialize the solver with matrix \a A for further \c Ax=b solving.
+
+			        This constructor is a shortcut for the default constructor followed
+			        by a call to compute().
+
+			        \warning this class stores a reference to the matrix A as well as some
+			        precomputed values that depend on it. Therefore, if \a A is changed
+			        this class becomes invalid. Call compute() to update it with the new
+			        matrix A, or modify a copy of A.
+			*/
+			template <typename MatrixDerived>
+			explicit IDRS(const EigenBase<MatrixDerived>& A) : Base(A.derived()), m_S(4), m_smoothing(false),
+															   m_angle(RealScalar(0.7)), m_residual(false) {}
+
+
+			/** \internal */
+			/**     Loops over the number of columns of b and does the following:
+			                1. sets the tolerence and maxIterations
+			                2. Calls the function that has the core solver routine
+			*/
+			template <typename Rhs, typename Dest>
+			void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+			{
+				m_iterations = Base::maxIterations();
+				m_error = Base::m_tolerance;
+
+				bool ret = internal::idrs(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error, m_S,m_smoothing,m_angle,m_residual);
+
+				m_info = (!ret) ? NumericalIssue : m_error <= Base::m_tolerance ? Success : NoConvergence;
+			}
+
+			/** Sets the parameter S, indicating the dimension of the shadow space. Default is 4*/
+			void setS(Index S)
+			{
+				if (S < 1)
+				{
+					S = 4;
+				}
+
+				m_S = S;
+			}
+
+			/** Switches off and on smoothing.
+			Residual smoothing results in monotonically decreasing residual norms at
+			the expense of two extra vectors of storage and a few extra vector
+			operations. Although monotonic decrease of the residual norms is a
+			desirable property, the rate of convergence of the unsmoothed process and
+			the smoothed process is basically the same. Default is off */
+			void setSmoothing(bool smoothing)
+			{
+				m_smoothing=smoothing;
+			}
+
+			/** The angle must be a real scalar. In IDR(s), a value for the
+			iteration parameter omega must be chosen in every s+1th step. The most
+			natural choice is to select a value to minimize the norm of the next residual.
+			This corresponds to the parameter omega = 0. In practice, this may lead to
+			values of omega that are so small that the other iteration parameters
+			cannot be computed with sufficient accuracy. In such cases it is better to
+			increase the value of omega sufficiently such that a compromise is reached
+			between accurate computations and reduction of the residual norm. The
+			parameter angle =0.7 (”maintaining the convergence strategy”)
+			results in such a compromise. */
+			void setAngle(RealScalar angle)
+			{
+				m_angle=angle;
+			}
+
+			/** The parameter replace is a logical that determines whether a
+			residual replacement strategy is employed to increase the accuracy of the
+			solution. */
+			void setResidualUpdate(bool update)
+			{
+				m_residual=update;
+			}
+
+	};
+
+}  // namespace Eigen
+
+#endif /* EIGEN_IDRS_H */
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
new file mode 100644
index 0000000..7d08c35
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IncompleteLU.h
@@ -0,0 +1,90 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_LU_H
+#define EIGEN_INCOMPLETE_LU_H
+
+namespace Eigen { 
+
+template <typename _Scalar>
+class IncompleteLU : public SparseSolverBase<IncompleteLU<_Scalar> >
+{
+  protected:
+    typedef SparseSolverBase<IncompleteLU<_Scalar> > Base;
+    using Base::m_isInitialized;
+    
+    typedef _Scalar Scalar;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef typename Vector::Index Index;
+    typedef SparseMatrix<Scalar,RowMajor> FactorType;
+
+  public:
+    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
+
+    IncompleteLU() {}
+
+    template<typename MatrixType>
+    IncompleteLU(const MatrixType& mat)
+    {
+      compute(mat);
+    }
+
+    Index rows() const { return m_lu.rows(); }
+    Index cols() const { return m_lu.cols(); }
+
+    template<typename MatrixType>
+    IncompleteLU& compute(const MatrixType& mat)
+    {
+      m_lu = mat;
+      int size = mat.cols();
+      Vector diag(size);
+      for(int i=0; i<size; ++i)
+      {
+        typename FactorType::InnerIterator k_it(m_lu,i);
+        for(; k_it && k_it.index()<i; ++k_it)
+        {
+          int k = k_it.index();
+          k_it.valueRef() /= diag(k);
+
+          typename FactorType::InnerIterator j_it(k_it);
+          typename FactorType::InnerIterator kj_it(m_lu, k);
+          while(kj_it && kj_it.index()<=k) ++kj_it;
+          for(++j_it; j_it; )
+          {
+            if(kj_it.index()==j_it.index())
+            {
+              j_it.valueRef() -= k_it.value() * kj_it.value();
+              ++j_it;
+              ++kj_it;
+            }
+            else if(kj_it.index()<j_it.index()) ++kj_it;
+            else                                ++j_it;
+          }
+        }
+        if(k_it && k_it.index()==i) diag(i) = k_it.value();
+        else                        diag(i) = 1;
+      }
+      m_isInitialized = true;
+      return *this;
+    }
+
+    template<typename Rhs, typename Dest>
+    void _solve_impl(const Rhs& b, Dest& x) const
+    {
+      x = m_lu.template triangularView<UnitLower>().solve(b);
+      x = m_lu.template triangularView<Upper>().solve(x);
+    }
+
+  protected:
+    FactorType m_lu;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_INCOMPLETE_LU_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
new file mode 100644
index 0000000..a116e09
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/IterationController.h
@@ -0,0 +1,154 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+
+/* NOTE The class IterationController has been adapted from the iteration
+ *      class of the GMM++ and ITL libraries.
+ */
+
+//=======================================================================
+// Copyright (C) 1997-2001
+// Authors: Andrew Lumsdaine <lums@osl.iu.edu> 
+//          Lie-Quan Lee     <llee@osl.iu.edu>
+//
+// This file is part of the Iterative Template Library
+//
+// You should have received a copy of the License Agreement for the
+// Iterative Template Library along with the software;  see the
+// file LICENSE.  
+//
+// Permission to modify the code and to distribute modified code is
+// granted, provided the text of this NOTICE is retained, a notice that
+// the code was modified is included with the above COPYRIGHT NOTICE and
+// with the COPYRIGHT NOTICE in the LICENSE file, and that the LICENSE
+// file is distributed with the modified code.
+//
+// LICENSOR MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED.
+// By way of example, but not limitation, Licensor MAKES NO
+// REPRESENTATIONS OR WARRANTIES OF MERCHANTABILITY OR FITNESS FOR ANY
+// PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE COMPONENTS
+// OR DOCUMENTATION WILL NOT INFRINGE ANY PATENTS, COPYRIGHTS, TRADEMARKS
+// OR OTHER RIGHTS.
+//=======================================================================
+
+//========================================================================
+//
+// Copyright (C) 2002-2007 Yves Renard
+//
+// This file is a part of GETFEM++
+//
+// Getfem++ is free software; you can redistribute it and/or modify
+// it under the terms of the GNU Lesser General Public License as
+// published by the Free Software Foundation; version 2.1 of the License.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU Lesser General Public License for more details.
+// You should have received a copy of the GNU Lesser General Public
+// License along with this program; if not, write to the Free Software
+// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301,
+// USA.
+//
+//========================================================================
+
+#include "../../../../Eigen/src/Core/util/NonMPL2.h"
+
+#ifndef EIGEN_ITERATION_CONTROLLER_H
+#define EIGEN_ITERATION_CONTROLLER_H
+
+namespace Eigen { 
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \class IterationController
+  *
+  * \brief Controls the iterations of the iterative solvers
+  *
+  * This class has been adapted from the iteration class of GMM++ and ITL libraries.
+  *
+  */
+class IterationController
+{
+  protected :
+    double m_rhsn;        ///< Right hand side norm
+    size_t m_maxiter;     ///< Max. number of iterations
+    int m_noise;          ///< if noise > 0 iterations are printed
+    double m_resmax;      ///< maximum residual
+    double m_resminreach, m_resadd;
+    size_t m_nit;         ///< iteration number
+    double m_res;         ///< last computed residual
+    bool m_written;
+    void (*m_callback)(const IterationController&);
+  public :
+
+    void init()
+    {
+      m_nit = 0; m_res = 0.0; m_written = false;
+      m_resminreach = 1E50; m_resadd = 0.0;
+      m_callback = 0;
+    }
+
+    IterationController(double r = 1.0E-8, int noi = 0, size_t mit = size_t(-1))
+      : m_rhsn(1.0), m_maxiter(mit), m_noise(noi), m_resmax(r) { init(); }
+
+    void operator ++(int) { m_nit++; m_written = false; m_resadd += m_res; }
+    void operator ++() { (*this)++; }
+
+    bool first() { return m_nit == 0; }
+
+    /* get/set the "noisyness" (verbosity) of the solvers */
+    int noiseLevel() const { return m_noise; }
+    void setNoiseLevel(int n) { m_noise = n; }
+    void reduceNoiseLevel() { if (m_noise > 0) m_noise--; }
+
+    double maxResidual() const { return m_resmax; }
+    void setMaxResidual(double r) { m_resmax = r; }
+
+    double residual() const { return m_res; }
+
+    /* change the user-definable callback, called after each iteration */
+    void setCallback(void (*t)(const IterationController&))
+    {
+      m_callback = t;
+    }
+
+    size_t iteration() const { return m_nit; }
+    void setIteration(size_t i) { m_nit = i; }
+
+    size_t maxIterarions() const { return m_maxiter; }
+    void setMaxIterations(size_t i) { m_maxiter = i; }
+
+    double rhsNorm() const { return m_rhsn; }
+    void setRhsNorm(double r) { m_rhsn = r; }
+
+    bool converged() const { return m_res <= m_rhsn * m_resmax; }
+    bool converged(double nr)
+    {
+      using std::abs;
+      m_res = abs(nr); 
+      m_resminreach = (std::min)(m_resminreach, m_res);
+      return converged();
+    }
+    template<typename VectorType> bool converged(const VectorType &v)
+    { return converged(v.squaredNorm()); }
+
+    bool finished(double nr)
+    {
+      if (m_callback) m_callback(*this);
+      if (m_noise > 0 && !m_written)
+      {
+        converged(nr);
+        m_written = true;
+      }
+      return (m_nit >= m_maxiter || converged(nr));
+    }
+    template <typename VectorType>
+    bool finished(const MatrixBase<VectorType> &v)
+    { return finished(double(v.squaredNorm())); }
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_ITERATION_CONTROLLER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
new file mode 100644
index 0000000..5db454d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/MINRES.h
@@ -0,0 +1,267 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Giacomo Po <gpo@ucla.edu>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2018 David Hyde <dabh@stanford.edu>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_MINRES_H_
+#define EIGEN_MINRES_H_
+
+
+namespace Eigen {
+    
+    namespace internal {
+        
+        /** \internal Low-level MINRES algorithm
+         * \param mat The matrix A
+         * \param rhs The right hand side vector b
+         * \param x On input and initial solution, on output the computed solution.
+         * \param precond A right preconditioner being able to efficiently solve for an
+         *                approximation of Ax=b (regardless of b)
+         * \param iters On input the max number of iteration, on output the number of performed iterations.
+         * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+         */
+        template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+        EIGEN_DONT_INLINE
+        void minres(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                    const Preconditioner& precond, Index& iters,
+                    typename Dest::RealScalar& tol_error)
+        {
+            using std::sqrt;
+            typedef typename Dest::RealScalar RealScalar;
+            typedef typename Dest::Scalar Scalar;
+            typedef Matrix<Scalar,Dynamic,1> VectorType;
+
+            // Check for zero rhs
+            const RealScalar rhsNorm2(rhs.squaredNorm());
+            if(rhsNorm2 == 0)
+            {
+                x.setZero();
+                iters = 0;
+                tol_error = 0;
+                return;
+            }
+            
+            // initialize
+            const Index maxIters(iters);  // initialize maxIters to iters
+            const Index N(mat.cols());    // the size of the matrix
+            const RealScalar threshold2(tol_error*tol_error*rhsNorm2); // convergence threshold (compared to residualNorm2)
+            
+            // Initialize preconditioned Lanczos
+            VectorType v_old(N); // will be initialized inside loop
+            VectorType v( VectorType::Zero(N) ); //initialize v
+            VectorType v_new(rhs-mat*x); //initialize v_new
+            RealScalar residualNorm2(v_new.squaredNorm());
+            VectorType w(N); // will be initialized inside loop
+            VectorType w_new(precond.solve(v_new)); // initialize w_new
+//            RealScalar beta; // will be initialized inside loop
+            RealScalar beta_new2(v_new.dot(w_new));
+            eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+            RealScalar beta_new(sqrt(beta_new2));
+            const RealScalar beta_one(beta_new);
+            // Initialize other variables
+            RealScalar c(1.0); // the cosine of the Givens rotation
+            RealScalar c_old(1.0);
+            RealScalar s(0.0); // the sine of the Givens rotation
+            RealScalar s_old(0.0); // the sine of the Givens rotation
+            VectorType p_oold(N); // will be initialized in loop
+            VectorType p_old(VectorType::Zero(N)); // initialize p_old=0
+            VectorType p(p_old); // initialize p=0
+            RealScalar eta(1.0);
+                        
+            iters = 0; // reset iters
+            while ( iters < maxIters )
+            {
+                // Preconditioned Lanczos
+                /* Note that there are 4 variants on the Lanczos algorithm. These are
+                 * described in Paige, C. C. (1972). Computational variants of
+                 * the Lanczos method for the eigenproblem. IMA Journal of Applied
+                 * Mathematics, 10(3), 373-381. The current implementation corresponds 
+                 * to the case A(2,7) in the paper. It also corresponds to 
+                 * algorithm 6.14 in Y. Saad, Iterative Methods for Sparse Linear
+                 * Systems, 2003 p.173. For the preconditioned version see 
+                 * A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM (1987).
+                 */
+                const RealScalar beta(beta_new);
+                v_old = v; // update: at first time step, this makes v_old = 0 so value of beta doesn't matter
+                v_new /= beta_new; // overwrite v_new for next iteration
+                w_new /= beta_new; // overwrite w_new for next iteration
+                v = v_new; // update
+                w = w_new; // update
+                v_new.noalias() = mat*w - beta*v_old; // compute v_new
+                const RealScalar alpha = v_new.dot(w);
+                v_new -= alpha*v; // overwrite v_new
+                w_new = precond.solve(v_new); // overwrite w_new
+                beta_new2 = v_new.dot(w_new); // compute beta_new
+                eigen_assert(beta_new2 >= 0.0 && "PRECONDITIONER IS NOT POSITIVE DEFINITE");
+                beta_new = sqrt(beta_new2); // compute beta_new
+                
+                // Givens rotation
+                const RealScalar r2 =s*alpha+c*c_old*beta; // s, s_old, c and c_old are still from previous iteration
+                const RealScalar r3 =s_old*beta; // s, s_old, c and c_old are still from previous iteration
+                const RealScalar r1_hat=c*alpha-c_old*s*beta;
+                const RealScalar r1 =sqrt( std::pow(r1_hat,2) + std::pow(beta_new,2) );
+                c_old = c; // store for next iteration
+                s_old = s; // store for next iteration
+                c=r1_hat/r1; // new cosine
+                s=beta_new/r1; // new sine
+                
+                // Update solution
+                p_oold = p_old;
+                p_old = p;
+                p.noalias()=(w-r2*p_old-r3*p_oold) /r1; // IS NOALIAS REQUIRED?
+                x += beta_one*c*eta*p;
+                
+                /* Update the squared residual. Note that this is the estimated residual.
+                The real residual |Ax-b|^2 may be slightly larger */
+                residualNorm2 *= s*s;
+                
+                if ( residualNorm2 < threshold2)
+                {
+                    break;
+                }
+                
+                eta=-s*eta; // update eta
+                iters++; // increment iteration number (for output purposes)
+            }
+            
+            /* Compute error. Note that this is the estimated error. The real 
+             error |Ax-b|/|b| may be slightly larger */
+            tol_error = std::sqrt(residualNorm2 / rhsNorm2);
+        }
+        
+    }
+    
+    template< typename _MatrixType, int _UpLo=Lower,
+    typename _Preconditioner = IdentityPreconditioner>
+    class MINRES;
+    
+    namespace internal {
+        
+        template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+        struct traits<MINRES<_MatrixType,_UpLo,_Preconditioner> >
+        {
+            typedef _MatrixType MatrixType;
+            typedef _Preconditioner Preconditioner;
+        };
+        
+    }
+    
+    /** \ingroup IterativeLinearSolvers_Module
+     * \brief A minimal residual solver for sparse symmetric problems
+     *
+     * This class allows to solve for A.x = b sparse linear problems using the MINRES algorithm
+     * of Paige and Saunders (1975). The sparse matrix A must be symmetric (possibly indefinite).
+     * The vectors x and b can be either dense or sparse.
+     *
+     * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+     * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower,
+     *               Upper, or Lower|Upper in which the full matrix entries will be considered. Default is Lower.
+     * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+     *
+     * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+     * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+     * and NumTraits<Scalar>::epsilon() for the tolerance.
+     *
+     * This class can be used as the direct solver classes. Here is a typical usage example:
+     * \code
+     * int n = 10000;
+     * VectorXd x(n), b(n);
+     * SparseMatrix<double> A(n,n);
+     * // fill A and b
+     * MINRES<SparseMatrix<double> > mr;
+     * mr.compute(A);
+     * x = mr.solve(b);
+     * std::cout << "#iterations:     " << mr.iterations() << std::endl;
+     * std::cout << "estimated error: " << mr.error()      << std::endl;
+     * // update b, and solve again
+     * x = mr.solve(b);
+     * \endcode
+     *
+     * By default the iterations start with x=0 as an initial guess of the solution.
+     * One can control the start using the solveWithGuess() method.
+     *
+     * MINRES can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+     *
+     * \sa class ConjugateGradient, BiCGSTAB, SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+     */
+    template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+    class MINRES : public IterativeSolverBase<MINRES<_MatrixType,_UpLo,_Preconditioner> >
+    {
+        
+        typedef IterativeSolverBase<MINRES> Base;
+        using Base::matrix;
+        using Base::m_error;
+        using Base::m_iterations;
+        using Base::m_info;
+        using Base::m_isInitialized;
+    public:
+        using Base::_solve_impl;
+        typedef _MatrixType MatrixType;
+        typedef typename MatrixType::Scalar Scalar;
+        typedef typename MatrixType::RealScalar RealScalar;
+        typedef _Preconditioner Preconditioner;
+        
+        enum {UpLo = _UpLo};
+        
+    public:
+        
+        /** Default constructor. */
+        MINRES() : Base() {}
+        
+        /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+         *
+         * This constructor is a shortcut for the default constructor followed
+         * by a call to compute().
+         *
+         * \warning this class stores a reference to the matrix A as well as some
+         * precomputed values that depend on it. Therefore, if \a A is changed
+         * this class becomes invalid. Call compute() to update it with the new
+         * matrix A, or modify a copy of A.
+         */
+        template<typename MatrixDerived>
+        explicit MINRES(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+        
+        /** Destructor. */
+        ~MINRES(){}
+
+        /** \internal */
+        template<typename Rhs,typename Dest>
+        void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+        {
+            typedef typename Base::MatrixWrapper MatrixWrapper;
+            typedef typename Base::ActualMatrixType ActualMatrixType;
+            enum {
+              TransposeInput  =   (!MatrixWrapper::MatrixFree)
+                              &&  (UpLo==(Lower|Upper))
+                              &&  (!MatrixType::IsRowMajor)
+                              &&  (!NumTraits<Scalar>::IsComplex)
+            };
+            typedef typename internal::conditional<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&>::type RowMajorWrapper;
+            EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
+            typedef typename internal::conditional<UpLo==(Lower|Upper),
+                                                  RowMajorWrapper,
+                                                  typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
+                                            >::type SelfAdjointWrapper;
+
+            m_iterations = Base::maxIterations();
+            m_error = Base::m_tolerance;
+            RowMajorWrapper row_mat(matrix());
+            internal::minres(SelfAdjointWrapper(row_mat), b, x,
+                             Base::m_preconditioner, m_iterations, m_error);
+            m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+        }
+        
+    protected:
+        
+    };
+
+} // end namespace Eigen
+
+#endif // EIGEN_MINRES_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
new file mode 100644
index 0000000..9b3eb53
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/IterativeSolvers/Scaling.h
@@ -0,0 +1,193 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERSCALING_H
+#define EIGEN_ITERSCALING_H
+
+namespace Eigen {
+
+/**
+  * \ingroup IterativeSolvers_Module
+  * \brief iterative scaling algorithm to equilibrate rows and column norms in matrices
+  * 
+  * This class can be used as a preprocessing tool to accelerate the convergence of iterative methods 
+  * 
+  * This feature is  useful to limit the pivoting amount during LU/ILU factorization
+  * The  scaling strategy as presented here preserves the symmetry of the problem
+  * NOTE It is assumed that the matrix does not have empty row or column, 
+  * 
+  * Example with key steps 
+  * \code
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A;
+  * // fill A and b;
+  * IterScaling<SparseMatrix<double> > scal; 
+  * // Compute the left and right scaling vectors. The matrix is equilibrated at output
+  * scal.computeRef(A); 
+  * // Scale the right hand side
+  * b = scal.LeftScaling().cwiseProduct(b); 
+  * // Now, solve the equilibrated linear system with any available solver
+  * 
+  * // Scale back the computed solution
+  * x = scal.RightScaling().cwiseProduct(x); 
+  * \endcode
+  * 
+  * \tparam _MatrixType the type of the matrix. It should be a real square sparsematrix
+  * 
+  * References : D. Ruiz and B. Ucar, A Symmetry Preserving Algorithm for Matrix Scaling, INRIA Research report RR-7552
+  * 
+  * \sa \ref IncompleteLUT 
+  */
+template<typename _MatrixType>
+class IterScaling
+{
+  public:
+    typedef _MatrixType MatrixType; 
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    
+  public:
+    IterScaling() { init(); }
+    
+    IterScaling(const MatrixType& matrix)
+    {
+      init();
+      compute(matrix);
+    }
+    
+    ~IterScaling() { }
+    
+    /** 
+     * Compute the left and right diagonal matrices to scale the input matrix @p mat
+     * 
+     * FIXME This algorithm will be modified such that the diagonal elements are permuted on the diagonal. 
+     * 
+     * \sa LeftScaling() RightScaling()
+     */
+    void compute (const MatrixType& mat)
+    {
+      using std::abs;
+      int m = mat.rows(); 
+      int n = mat.cols();
+      eigen_assert((m>0 && m == n) && "Please give a non - empty matrix");
+      m_left.resize(m); 
+      m_right.resize(n);
+      m_left.setOnes();
+      m_right.setOnes();
+      m_matrix = mat;
+      VectorXd Dr, Dc, DrRes, DcRes; // Temporary Left and right scaling vectors
+      Dr.resize(m); Dc.resize(n);
+      DrRes.resize(m); DcRes.resize(n);
+      double EpsRow = 1.0, EpsCol = 1.0;
+      int its = 0; 
+      do
+      { // Iterate until the infinite norm of each row and column is approximately 1
+        // Get the maximum value in each row and column
+        Dr.setZero(); Dc.setZero();
+        for (int k=0; k<m_matrix.outerSize(); ++k)
+        {
+          for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it)
+          {
+            if ( Dr(it.row()) < abs(it.value()) )
+              Dr(it.row()) = abs(it.value());
+            
+            if ( Dc(it.col()) < abs(it.value()) )
+              Dc(it.col()) = abs(it.value());
+          }
+        }
+        for (int i = 0; i < m; ++i) 
+        {
+          Dr(i) = std::sqrt(Dr(i));
+        }
+        for (int i = 0; i < n; ++i) 
+        {
+          Dc(i) = std::sqrt(Dc(i));
+        }
+        // Save the scaling factors 
+        for (int i = 0; i < m; ++i) 
+        {
+          m_left(i) /= Dr(i);
+        }
+        for (int i = 0; i < n; ++i) 
+        {
+          m_right(i) /= Dc(i);
+        }
+        // Scale the rows and the columns of the matrix
+        DrRes.setZero(); DcRes.setZero(); 
+        for (int k=0; k<m_matrix.outerSize(); ++k)
+        {
+          for (typename MatrixType::InnerIterator it(m_matrix, k); it; ++it)
+          {
+            it.valueRef() = it.value()/( Dr(it.row()) * Dc(it.col()) );
+            // Accumulate the norms of the row and column vectors   
+            if ( DrRes(it.row()) < abs(it.value()) )
+              DrRes(it.row()) = abs(it.value());
+            
+            if ( DcRes(it.col()) < abs(it.value()) )
+              DcRes(it.col()) = abs(it.value());
+          }
+        }  
+        DrRes.array() = (1-DrRes.array()).abs();
+        EpsRow = DrRes.maxCoeff();
+        DcRes.array() = (1-DcRes.array()).abs();
+        EpsCol = DcRes.maxCoeff();
+        its++;
+      }while ( (EpsRow >m_tol || EpsCol > m_tol) && (its < m_maxits) );
+      m_isInitialized = true;
+    }
+    /** Compute the left and right vectors to scale the vectors
+     * the input matrix is scaled with the computed vectors at output
+     * 
+     * \sa compute()
+     */
+    void computeRef (MatrixType& mat)
+    {
+      compute (mat);
+      mat = m_matrix;
+    }
+    /** Get the vector to scale the rows of the matrix 
+     */
+    VectorXd& LeftScaling()
+    {
+      return m_left;
+    }
+    
+    /** Get the vector to scale the columns of the matrix 
+     */
+    VectorXd& RightScaling()
+    {
+      return m_right;
+    }
+    
+    /** Set the tolerance for the convergence of the iterative scaling algorithm
+     */
+    void setTolerance(double tol)
+    {
+      m_tol = tol; 
+    }
+      
+  protected:
+    
+    void init()
+    {
+      m_tol = 1e-10;
+      m_maxits = 5;
+      m_isInitialized = false;
+    }
+    
+    MatrixType m_matrix;
+    mutable ComputationInfo m_info; 
+    bool m_isInitialized; 
+    VectorXd m_left; // Left scaling vector
+    VectorXd m_right; // m_right scaling vector
+    double m_tol; 
+    int m_maxits; // Maximum number of iterations allowed
+};
+}
+#endif
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
new file mode 100644
index 0000000..6a9b0be
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/KroneckerProduct/KroneckerTensorProduct.h
@@ -0,0 +1,305 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Kolja Brix <brix@igpm.rwth-aachen.de>
+// Copyright (C) 2011 Andreas Platen <andiplaten@gmx.de>
+// Copyright (C) 2012 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef KRONECKER_TENSOR_PRODUCT_H
+#define KRONECKER_TENSOR_PRODUCT_H
+
+namespace Eigen {
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief The base class of dense and sparse Kronecker product.
+ *
+ * \tparam Derived is the derived type.
+ */
+template<typename Derived>
+class KroneckerProductBase : public ReturnByValue<Derived>
+{
+  private:
+    typedef typename internal::traits<Derived> Traits;
+    typedef typename Traits::Scalar Scalar;
+
+  protected:
+    typedef typename Traits::Lhs Lhs;
+    typedef typename Traits::Rhs Rhs;
+
+  public:
+    /*! \brief Constructor. */
+    KroneckerProductBase(const Lhs& A, const Rhs& B)
+      : m_A(A), m_B(B)
+    {}
+
+    inline Index rows() const { return m_A.rows() * m_B.rows(); }
+    inline Index cols() const { return m_A.cols() * m_B.cols(); }
+
+    /*!
+     * This overrides ReturnByValue::coeff because this function is
+     * efficient enough.
+     */
+    Scalar coeff(Index row, Index col) const
+    {
+      return m_A.coeff(row / m_B.rows(), col / m_B.cols()) *
+             m_B.coeff(row % m_B.rows(), col % m_B.cols());
+    }
+
+    /*!
+     * This overrides ReturnByValue::coeff because this function is
+     * efficient enough.
+     */
+    Scalar coeff(Index i) const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+      return m_A.coeff(i / m_A.size()) * m_B.coeff(i % m_A.size());
+    }
+
+  protected:
+    typename Lhs::Nested m_A;
+    typename Rhs::Nested m_B;
+};
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief Kronecker tensor product helper class for dense matrices
+ *
+ * This class is the return value of kroneckerProduct(MatrixBase,
+ * MatrixBase). Use the function rather than construct this class
+ * directly to avoid specifying template prarameters.
+ *
+ * \tparam Lhs  Type of the left-hand side, a matrix expression.
+ * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
+ */
+template<typename Lhs, typename Rhs>
+class KroneckerProduct : public KroneckerProductBase<KroneckerProduct<Lhs,Rhs> >
+{
+  private:
+    typedef KroneckerProductBase<KroneckerProduct> Base;
+    using Base::m_A;
+    using Base::m_B;
+
+  public:
+    /*! \brief Constructor. */
+    KroneckerProduct(const Lhs& A, const Rhs& B)
+      : Base(A, B)
+    {}
+
+    /*! \brief Evaluate the Kronecker tensor product. */
+    template<typename Dest> void evalTo(Dest& dst) const;
+};
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * \brief Kronecker tensor product helper class for sparse matrices
+ *
+ * If at least one of the operands is a sparse matrix expression,
+ * then this class is returned and evaluates into a sparse matrix.
+ *
+ * This class is the return value of kroneckerProduct(EigenBase,
+ * EigenBase). Use the function rather than construct this class
+ * directly to avoid specifying template prarameters.
+ *
+ * \tparam Lhs  Type of the left-hand side, a matrix expression.
+ * \tparam Rhs  Type of the rignt-hand side, a matrix expression.
+ */
+template<typename Lhs, typename Rhs>
+class KroneckerProductSparse : public KroneckerProductBase<KroneckerProductSparse<Lhs,Rhs> >
+{
+  private:
+    typedef KroneckerProductBase<KroneckerProductSparse> Base;
+    using Base::m_A;
+    using Base::m_B;
+
+  public:
+    /*! \brief Constructor. */
+    KroneckerProductSparse(const Lhs& A, const Rhs& B)
+      : Base(A, B)
+    {}
+
+    /*! \brief Evaluate the Kronecker tensor product. */
+    template<typename Dest> void evalTo(Dest& dst) const;
+};
+
+template<typename Lhs, typename Rhs>
+template<typename Dest>
+void KroneckerProduct<Lhs,Rhs>::evalTo(Dest& dst) const
+{
+  const int BlockRows = Rhs::RowsAtCompileTime,
+            BlockCols = Rhs::ColsAtCompileTime;
+  const Index Br = m_B.rows(),
+              Bc = m_B.cols();
+  for (Index i=0; i < m_A.rows(); ++i)
+    for (Index j=0; j < m_A.cols(); ++j)
+      Block<Dest,BlockRows,BlockCols>(dst,i*Br,j*Bc,Br,Bc) = m_A.coeff(i,j) * m_B;
+}
+
+template<typename Lhs, typename Rhs>
+template<typename Dest>
+void KroneckerProductSparse<Lhs,Rhs>::evalTo(Dest& dst) const
+{
+  Index Br = m_B.rows(), Bc = m_B.cols();
+  dst.resize(this->rows(), this->cols());
+  dst.resizeNonZeros(0);
+  
+  // 1 - evaluate the operands if needed:
+  typedef typename internal::nested_eval<Lhs,Dynamic>::type Lhs1;
+  typedef typename internal::remove_all<Lhs1>::type Lhs1Cleaned;
+  const Lhs1 lhs1(m_A);
+  typedef typename internal::nested_eval<Rhs,Dynamic>::type Rhs1;
+  typedef typename internal::remove_all<Rhs1>::type Rhs1Cleaned;
+  const Rhs1 rhs1(m_B);
+    
+  // 2 - construct respective iterators
+  typedef Eigen::InnerIterator<Lhs1Cleaned> LhsInnerIterator;
+  typedef Eigen::InnerIterator<Rhs1Cleaned> RhsInnerIterator;
+  
+  // compute number of non-zeros per innervectors of dst
+  {
+    // TODO VectorXi is not necessarily big enough!
+    VectorXi nnzA = VectorXi::Zero(Dest::IsRowMajor ? m_A.rows() : m_A.cols());
+    for (Index kA=0; kA < m_A.outerSize(); ++kA)
+      for (LhsInnerIterator itA(lhs1,kA); itA; ++itA)
+        nnzA(Dest::IsRowMajor ? itA.row() : itA.col())++;
+      
+    VectorXi nnzB = VectorXi::Zero(Dest::IsRowMajor ? m_B.rows() : m_B.cols());
+    for (Index kB=0; kB < m_B.outerSize(); ++kB)
+      for (RhsInnerIterator itB(rhs1,kB); itB; ++itB)
+        nnzB(Dest::IsRowMajor ? itB.row() : itB.col())++;
+    
+    Matrix<int,Dynamic,Dynamic,ColMajor> nnzAB = nnzB * nnzA.transpose();
+    dst.reserve(VectorXi::Map(nnzAB.data(), nnzAB.size()));
+  }
+
+  for (Index kA=0; kA < m_A.outerSize(); ++kA)
+  {
+    for (Index kB=0; kB < m_B.outerSize(); ++kB)
+    {
+      for (LhsInnerIterator itA(lhs1,kA); itA; ++itA)
+      {
+        for (RhsInnerIterator itB(rhs1,kB); itB; ++itB)
+        {
+          Index i = itA.row() * Br + itB.row(),
+                j = itA.col() * Bc + itB.col();
+          dst.insert(i,j) = itA.value() * itB.value();
+        }
+      }
+    }
+  }
+}
+
+namespace internal {
+
+template<typename _Lhs, typename _Rhs>
+struct traits<KroneckerProduct<_Lhs,_Rhs> >
+{
+  typedef typename remove_all<_Lhs>::type Lhs;
+  typedef typename remove_all<_Rhs>::type Rhs;
+  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
+  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
+
+  enum {
+    Rows = size_at_compile_time<traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime>::ret,
+    Cols = size_at_compile_time<traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime>::ret,
+    MaxRows = size_at_compile_time<traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime>::ret,
+    MaxCols = size_at_compile_time<traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime>::ret
+  };
+
+  typedef Matrix<Scalar,Rows,Cols> ReturnType;
+};
+
+template<typename _Lhs, typename _Rhs>
+struct traits<KroneckerProductSparse<_Lhs,_Rhs> >
+{
+  typedef MatrixXpr XprKind;
+  typedef typename remove_all<_Lhs>::type Lhs;
+  typedef typename remove_all<_Rhs>::type Rhs;
+  typedef typename ScalarBinaryOpTraits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar;
+  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind, typename traits<Rhs>::StorageKind, scalar_product_op<typename Lhs::Scalar, typename Rhs::Scalar> >::ret StorageKind;
+  typedef typename promote_index_type<typename Lhs::StorageIndex, typename Rhs::StorageIndex>::type StorageIndex;
+
+  enum {
+    LhsFlags = Lhs::Flags,
+    RhsFlags = Rhs::Flags,
+
+    RowsAtCompileTime = size_at_compile_time<traits<Lhs>::RowsAtCompileTime, traits<Rhs>::RowsAtCompileTime>::ret,
+    ColsAtCompileTime = size_at_compile_time<traits<Lhs>::ColsAtCompileTime, traits<Rhs>::ColsAtCompileTime>::ret,
+    MaxRowsAtCompileTime = size_at_compile_time<traits<Lhs>::MaxRowsAtCompileTime, traits<Rhs>::MaxRowsAtCompileTime>::ret,
+    MaxColsAtCompileTime = size_at_compile_time<traits<Lhs>::MaxColsAtCompileTime, traits<Rhs>::MaxColsAtCompileTime>::ret,
+
+    EvalToRowMajor = (int(LhsFlags) & int(RhsFlags) & RowMajorBit),
+    RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
+
+    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & RemovedBits)
+          | EvalBeforeNestingBit,
+    CoeffReadCost = HugeCost
+  };
+
+  typedef SparseMatrix<Scalar, 0, StorageIndex> ReturnType;
+};
+
+} // end namespace internal
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * Computes Kronecker tensor product of two dense matrices
+ *
+ * \warning If you want to replace a matrix by its Kronecker product
+ *          with some matrix, do \b NOT do this:
+ * \code
+ * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
+ * \endcode
+ * instead, use eval() to work around this:
+ * \code
+ * A = kroneckerProduct(A,B).eval();
+ * \endcode
+ *
+ * \param a  Dense matrix a
+ * \param b  Dense matrix b
+ * \return   Kronecker tensor product of a and b
+ */
+template<typename A, typename B>
+KroneckerProduct<A,B> kroneckerProduct(const MatrixBase<A>& a, const MatrixBase<B>& b)
+{
+  return KroneckerProduct<A, B>(a.derived(), b.derived());
+}
+
+/*!
+ * \ingroup KroneckerProduct_Module
+ *
+ * Computes Kronecker tensor product of two matrices, at least one of
+ * which is sparse
+ *
+ * \warning If you want to replace a matrix by its Kronecker product
+ *          with some matrix, do \b NOT do this:
+ * \code
+ * A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
+ * \endcode
+ * instead, use eval() to work around this:
+ * \code
+ * A = kroneckerProduct(A,B).eval();
+ * \endcode
+ *
+ * \param a  Dense/sparse matrix a
+ * \param b  Dense/sparse matrix b
+ * \return   Kronecker tensor product of a and b, stored in a sparse
+ *           matrix
+ */
+template<typename A, typename B>
+KroneckerProductSparse<A,B> kroneckerProduct(const EigenBase<A>& a, const EigenBase<B>& b)
+{
+  return KroneckerProductSparse<A,B>(a.derived(), b.derived());
+}
+
+} // end namespace Eigen
+
+#endif // KRONECKER_TENSOR_PRODUCT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
new file mode 100644
index 0000000..b75bea2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMcovar.h
@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMCOVAR_H
+#define EIGEN_LMCOVAR_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void covar(
+        Matrix< Scalar, Dynamic, Dynamic > &r,
+        const VectorXi& ipvt,
+        Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) )
+{
+    using std::abs;
+    /* Local variables */
+    Index i, j, k, l, ii, jj;
+    bool sing;
+    Scalar temp;
+
+    /* Function Body */
+    const Index n = r.cols();
+    const Scalar tolr = tol * abs(r(0,0));
+    Matrix< Scalar, Dynamic, 1 > wa(n);
+    eigen_assert(ipvt.size()==n);
+
+    /* form the inverse of r in the full upper triangle of r. */
+    l = -1;
+    for (k = 0; k < n; ++k)
+        if (abs(r(k,k)) > tolr) {
+            r(k,k) = 1. / r(k,k);
+            for (j = 0; j <= k-1; ++j) {
+                temp = r(k,k) * r(j,k);
+                r(j,k) = 0.;
+                r.col(k).head(j+1) -= r.col(j).head(j+1) * temp;
+            }
+            l = k;
+        }
+
+    /* form the full upper triangle of the inverse of (r transpose)*r */
+    /* in the full upper triangle of r. */
+    for (k = 0; k <= l; ++k) {
+        for (j = 0; j <= k-1; ++j)
+            r.col(j).head(j+1) += r.col(k).head(j+1) * r(j,k);
+        r.col(k).head(k+1) *= r(k,k);
+    }
+
+    /* form the full lower triangle of the covariance matrix */
+    /* in the strict lower triangle of r and in wa. */
+    for (j = 0; j < n; ++j) {
+        jj = ipvt[j];
+        sing = j > l;
+        for (i = 0; i <= j; ++i) {
+            if (sing)
+                r(i,j) = 0.;
+            ii = ipvt[i];
+            if (ii > jj)
+                r(ii,jj) = r(i,j);
+            if (ii < jj)
+                r(jj,ii) = r(i,j);
+        }
+        wa[jj] = r(j,j);
+    }
+
+    /* symmetrize the covariance matrix in r. */
+    r.topLeftCorner(n,n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n,n).transpose();
+    r.diagonal() = wa;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_LMCOVAR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
new file mode 100644
index 0000000..25b32ec
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMonestep.h
@@ -0,0 +1,202 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMONESTEP_H
+#define EIGEN_LMONESTEP_H
+
+namespace Eigen {
+
+template<typename FunctorType>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType>::minimizeOneStep(FVectorType  &x)
+{
+  using std::abs;
+  using std::sqrt;
+  RealScalar temp, temp1,temp2; 
+  RealScalar ratio; 
+  RealScalar pnorm, xnorm, fnorm1, actred, dirder, prered;
+  eigen_assert(x.size()==n); // check the caller is not cheating us
+
+  temp = 0.0; xnorm = 0.0;
+  /* calculate the jacobian matrix. */
+  Index df_ret = m_functor.df(x, m_fjac);
+  if (df_ret<0)
+      return LevenbergMarquardtSpace::UserAsked;
+  if (df_ret>0)
+      // numerical diff, we evaluated the function df_ret times
+      m_nfev += df_ret;
+  else m_njev++;
+
+  /* compute the qr factorization of the jacobian. */
+  for (int j = 0; j < x.size(); ++j)
+    m_wa2(j) = m_fjac.col(j).blueNorm();
+  QRSolver qrfac(m_fjac);
+  if(qrfac.info() != Success) {
+    m_info = NumericalIssue;
+    return LevenbergMarquardtSpace::ImproperInputParameters;
+  }
+  // Make a copy of the first factor with the associated permutation
+  m_rfactor = qrfac.matrixR();
+  m_permutation = (qrfac.colsPermutation());
+
+  /* on the first iteration and if external scaling is not used, scale according */
+  /* to the norms of the columns of the initial jacobian. */
+  if (m_iter == 1) {
+      if (!m_useExternalScaling)
+          for (Index j = 0; j < n; ++j)
+              m_diag[j] = (m_wa2[j]==0.)? 1. : m_wa2[j];
+
+      /* on the first iteration, calculate the norm of the scaled x */
+      /* and initialize the step bound m_delta. */
+      xnorm = m_diag.cwiseProduct(x).stableNorm();
+      m_delta = m_factor * xnorm;
+      if (m_delta == 0.)
+          m_delta = m_factor;
+  }
+
+  /* form (q transpose)*m_fvec and store the first n components in */
+  /* m_qtf. */
+  m_wa4 = m_fvec;
+  m_wa4 = qrfac.matrixQ().adjoint() * m_fvec; 
+  m_qtf = m_wa4.head(n);
+
+  /* compute the norm of the scaled gradient. */
+  m_gnorm = 0.;
+  if (m_fnorm != 0.)
+      for (Index j = 0; j < n; ++j)
+          if (m_wa2[m_permutation.indices()[j]] != 0.)
+              m_gnorm = (std::max)(m_gnorm, abs( m_rfactor.col(j).head(j+1).dot(m_qtf.head(j+1)/m_fnorm) / m_wa2[m_permutation.indices()[j]]));
+
+  /* test for convergence of the gradient norm. */
+  if (m_gnorm <= m_gtol) {
+    m_info = Success;
+    return LevenbergMarquardtSpace::CosinusTooSmall;
+  }
+
+  /* rescale if necessary. */
+  if (!m_useExternalScaling)
+      m_diag = m_diag.cwiseMax(m_wa2);
+
+  do {
+    /* determine the levenberg-marquardt parameter. */
+    internal::lmpar2(qrfac, m_diag, m_qtf, m_delta, m_par, m_wa1);
+
+    /* store the direction p and x + p. calculate the norm of p. */
+    m_wa1 = -m_wa1;
+    m_wa2 = x + m_wa1;
+    pnorm = m_diag.cwiseProduct(m_wa1).stableNorm();
+
+    /* on the first iteration, adjust the initial step bound. */
+    if (m_iter == 1)
+        m_delta = (std::min)(m_delta,pnorm);
+
+    /* evaluate the function at x + p and calculate its norm. */
+    if ( m_functor(m_wa2, m_wa4) < 0)
+        return LevenbergMarquardtSpace::UserAsked;
+    ++m_nfev;
+    fnorm1 = m_wa4.stableNorm();
+
+    /* compute the scaled actual reduction. */
+    actred = -1.;
+    if (Scalar(.1) * fnorm1 < m_fnorm)
+        actred = 1. - numext::abs2(fnorm1 / m_fnorm);
+
+    /* compute the scaled predicted reduction and */
+    /* the scaled directional derivative. */
+    m_wa3 = m_rfactor.template triangularView<Upper>() * (m_permutation.inverse() *m_wa1);
+    temp1 = numext::abs2(m_wa3.stableNorm() / m_fnorm);
+    temp2 = numext::abs2(sqrt(m_par) * pnorm / m_fnorm);
+    prered = temp1 + temp2 / Scalar(.5);
+    dirder = -(temp1 + temp2);
+
+    /* compute the ratio of the actual to the predicted */
+    /* reduction. */
+    ratio = 0.;
+    if (prered != 0.)
+        ratio = actred / prered;
+
+    /* update the step bound. */
+    if (ratio <= Scalar(.25)) {
+        if (actred >= 0.)
+            temp = RealScalar(.5);
+        if (actred < 0.)
+            temp = RealScalar(.5) * dirder / (dirder + RealScalar(.5) * actred);
+        if (RealScalar(.1) * fnorm1 >= m_fnorm || temp < RealScalar(.1))
+            temp = Scalar(.1);
+        /* Computing MIN */
+        m_delta = temp * (std::min)(m_delta, pnorm / RealScalar(.1));
+        m_par /= temp;
+    } else if (!(m_par != 0. && ratio < RealScalar(.75))) {
+        m_delta = pnorm / RealScalar(.5);
+        m_par = RealScalar(.5) * m_par;
+    }
+
+    /* test for successful iteration. */
+    if (ratio >= RealScalar(1e-4)) {
+        /* successful iteration. update x, m_fvec, and their norms. */
+        x = m_wa2;
+        m_wa2 = m_diag.cwiseProduct(x);
+        m_fvec = m_wa4;
+        xnorm = m_wa2.stableNorm();
+        m_fnorm = fnorm1;
+        ++m_iter;
+    }
+
+    /* tests for convergence. */
+    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1. && m_delta <= m_xtol * xnorm)
+    {
+       m_info = Success;
+      return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+    }
+    if (abs(actred) <= m_ftol && prered <= m_ftol && Scalar(.5) * ratio <= 1.) 
+    {
+      m_info = Success;
+      return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+    }
+    if (m_delta <= m_xtol * xnorm)
+    {
+      m_info = Success;
+      return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+    }
+
+    /* tests for termination and stringent tolerances. */
+    if (m_nfev >= m_maxfev) 
+    {
+      m_info = NoConvergence;
+      return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+    }
+    if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
+    {
+      m_info = Success;
+      return LevenbergMarquardtSpace::FtolTooSmall;
+    }
+    if (m_delta <= NumTraits<Scalar>::epsilon() * xnorm) 
+    {
+      m_info = Success;
+      return LevenbergMarquardtSpace::XtolTooSmall;
+    }
+    if (m_gnorm <= NumTraits<Scalar>::epsilon())
+    {
+      m_info = Success;
+      return LevenbergMarquardtSpace::GtolTooSmall;
+    }
+
+  } while (ratio < Scalar(1e-4));
+
+  return LevenbergMarquardtSpace::Running;
+}
+
+  
+} // end namespace Eigen
+
+#endif // EIGEN_LMONESTEP_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
new file mode 100644
index 0000000..9a48365
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMpar.h
@@ -0,0 +1,160 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMPAR_H
+#define EIGEN_LMPAR_H
+
+namespace Eigen {
+
+namespace internal {
+  
+  template <typename QRSolver, typename VectorType>
+    void lmpar2(
+    const QRSolver &qr,
+    const VectorType  &diag,
+    const VectorType  &qtb,
+    typename VectorType::Scalar m_delta,
+    typename VectorType::Scalar &par,
+    VectorType  &x)
+
+  {
+    using std::sqrt;
+    using std::abs;
+    typedef typename QRSolver::MatrixType MatrixType;
+    typedef typename QRSolver::Scalar Scalar;
+//    typedef typename QRSolver::StorageIndex StorageIndex;
+
+    /* Local variables */
+    Index j;
+    Scalar fp;
+    Scalar parc, parl;
+    Index iter;
+    Scalar temp, paru;
+    Scalar gnorm;
+    Scalar dxnorm;
+    
+    // Make a copy of the triangular factor. 
+    // This copy is modified during call the qrsolv
+    MatrixType s;
+    s = qr.matrixR();
+
+    /* Function Body */
+    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+    const Index n = qr.matrixR().cols();
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
+
+    VectorType  wa1, wa2;
+
+    /* compute and store in x the gauss-newton direction. if the */
+    /* jacobian is rank-deficient, obtain a least squares solution. */
+
+    //    const Index rank = qr.nonzeroPivots(); // exactly double(0.)
+    const Index rank = qr.rank(); // use a threshold
+    wa1 = qtb;
+    wa1.tail(n-rank).setZero();
+    //FIXME There is no solve in place for sparse triangularView
+    wa1.head(rank) = s.topLeftCorner(rank,rank).template triangularView<Upper>().solve(qtb.head(rank));
+
+    x = qr.colsPermutation()*wa1;
+
+    /* initialize the iteration counter. */
+    /* evaluate the function at the origin, and test */
+    /* for acceptance of the gauss-newton direction. */
+    iter = 0;
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    fp = dxnorm - m_delta;
+    if (fp <= Scalar(0.1) * m_delta) {
+      par = 0;
+      return;
+    }
+
+    /* if the jacobian is not rank deficient, the newton */
+    /* step provides a lower bound, parl, for the zero of */
+    /* the function. otherwise set this bound to zero. */
+    parl = 0.;
+    if (rank==n) {
+      wa1 = qr.colsPermutation().inverse() *  diag.cwiseProduct(wa2)/dxnorm;
+      s.topLeftCorner(n,n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+      temp = wa1.blueNorm();
+      parl = fp / m_delta / temp / temp;
+    }
+
+    /* calculate an upper bound, paru, for the zero of the function. */
+    for (j = 0; j < n; ++j)
+      wa1[j] = s.col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
+
+    gnorm = wa1.stableNorm();
+    paru = gnorm / m_delta;
+    if (paru == 0.)
+      paru = dwarf / (std::min)(m_delta,Scalar(0.1));
+
+    /* if the input par lies outside of the interval (parl,paru), */
+    /* set par to the closer endpoint. */
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
+    if (par == 0.)
+      par = gnorm / dxnorm;
+
+    /* beginning of an iteration. */
+    while (true) {
+      ++iter;
+
+      /* evaluate the function at the current value of par. */
+      if (par == 0.)
+        par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
+      wa1 = sqrt(par)* diag;
+
+      VectorType sdiag(n);
+      lmqrsolv(s, qr.colsPermutation(), wa1, qtb, x, sdiag);
+
+      wa2 = diag.cwiseProduct(x);
+      dxnorm = wa2.blueNorm();
+      temp = fp;
+      fp = dxnorm - m_delta;
+
+      /* if the function is small enough, accept the current value */
+      /* of par. also test for the exceptional cases where parl */
+      /* is zero or the number of iterations has reached 10. */
+      if (abs(fp) <= Scalar(0.1) * m_delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
+        break;
+
+      /* compute the newton correction. */
+      wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
+      // we could almost use this here, but the diagonal is outside qr, in sdiag[]
+      for (j = 0; j < n; ++j) {
+        wa1[j] /= sdiag[j];
+        temp = wa1[j];
+        for (Index i = j+1; i < n; ++i)
+          wa1[i] -= s.coeff(i,j) * temp;
+      }
+      temp = wa1.blueNorm();
+      parc = fp / m_delta / temp / temp;
+
+      /* depending on the sign of the function, update parl or paru. */
+      if (fp > 0.)
+        parl = (std::max)(parl,par);
+      if (fp < 0.)
+        paru = (std::min)(paru,par);
+
+      /* compute an improved estimate for par. */
+      par = (std::max)(parl,par+parc);
+    }
+    if (iter == 0)
+      par = 0.;
+    return;
+  }
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_LMPAR_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
new file mode 100644
index 0000000..1234858
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LMqrsolv.h
@@ -0,0 +1,188 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+//
+// This code initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+
+#ifndef EIGEN_LMQRSOLV_H
+#define EIGEN_LMQRSOLV_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar,int Rows, int Cols, typename PermIndex>
+void lmqrsolv(
+  Matrix<Scalar,Rows,Cols> &s,
+  const PermutationMatrix<Dynamic,Dynamic,PermIndex> &iPerm,
+  const Matrix<Scalar,Dynamic,1> &diag,
+  const Matrix<Scalar,Dynamic,1> &qtb,
+  Matrix<Scalar,Dynamic,1> &x,
+  Matrix<Scalar,Dynamic,1> &sdiag)
+{
+    /* Local variables */
+    Index i, j, k;
+    Scalar temp;
+    Index n = s.cols();
+    Matrix<Scalar,Dynamic,1>  wa(n);
+    JacobiRotation<Scalar> givens;
+
+    /* Function Body */
+    // the following will only change the lower triangular part of s, including
+    // the diagonal, though the diagonal is restored afterward
+
+    /*     copy r and (q transpose)*b to preserve input and initialize s. */
+    /*     in particular, save the diagonal elements of r in x. */
+    x = s.diagonal();
+    wa = qtb;
+    
+   
+    s.topLeftCorner(n,n).template triangularView<StrictlyLower>() = s.topLeftCorner(n,n).transpose();
+    /*     eliminate the diagonal matrix d using a givens rotation. */
+    for (j = 0; j < n; ++j) {
+
+        /*        prepare the row of d to be eliminated, locating the */
+        /*        diagonal element using p from the qr factorization. */
+        const PermIndex l = iPerm.indices()(j);
+        if (diag[l] == 0.)
+            break;
+        sdiag.tail(n-j).setZero();
+        sdiag[j] = diag[l];
+
+        /*        the transformations to eliminate the row of d */
+        /*        modify only a single element of (q transpose)*b */
+        /*        beyond the first n, which is initially zero. */
+        Scalar qtbpj = 0.;
+        for (k = j; k < n; ++k) {
+            /*           determine a givens rotation which eliminates the */
+            /*           appropriate element in the current row of d. */
+            givens.makeGivens(-s(k,k), sdiag[k]);
+
+            /*           compute the modified diagonal element of r and */
+            /*           the modified element of ((q transpose)*b,0). */
+            s(k,k) = givens.c() * s(k,k) + givens.s() * sdiag[k];
+            temp = givens.c() * wa[k] + givens.s() * qtbpj;
+            qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
+            wa[k] = temp;
+
+            /*           accumulate the transformation in the row of s. */
+            for (i = k+1; i<n; ++i) {
+                temp = givens.c() * s(i,k) + givens.s() * sdiag[i];
+                sdiag[i] = -givens.s() * s(i,k) + givens.c() * sdiag[i];
+                s(i,k) = temp;
+            }
+        }
+    }
+  
+    /*     solve the triangular system for z. if the system is */
+    /*     singular, then obtain a least squares solution. */
+    Index nsing;
+    for(nsing=0; nsing<n && sdiag[nsing]!=0; nsing++) {}
+
+    wa.tail(n-nsing).setZero();
+    s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
+  
+    // restore
+    sdiag = s.diagonal();
+    s.diagonal() = x;
+
+    /* permute the components of z back to components of x. */
+    x = iPerm * wa; 
+}
+
+template <typename Scalar, int _Options, typename Index>
+void lmqrsolv(
+  SparseMatrix<Scalar,_Options,Index> &s,
+  const PermutationMatrix<Dynamic,Dynamic> &iPerm,
+  const Matrix<Scalar,Dynamic,1> &diag,
+  const Matrix<Scalar,Dynamic,1> &qtb,
+  Matrix<Scalar,Dynamic,1> &x,
+  Matrix<Scalar,Dynamic,1> &sdiag)
+{
+  /* Local variables */
+  typedef SparseMatrix<Scalar,RowMajor,Index> FactorType;
+    Index i, j, k, l;
+    Scalar temp;
+    Index n = s.cols();
+    Matrix<Scalar,Dynamic,1>  wa(n);
+    JacobiRotation<Scalar> givens;
+
+    /* Function Body */
+    // the following will only change the lower triangular part of s, including
+    // the diagonal, though the diagonal is restored afterward
+
+    /*     copy r and (q transpose)*b to preserve input and initialize R. */
+    wa = qtb;
+    FactorType R(s);
+    // Eliminate the diagonal matrix d using a givens rotation
+    for (j = 0; j < n; ++j)
+    {
+      // Prepare the row of d to be eliminated, locating the 
+      // diagonal element using p from the qr factorization
+      l = iPerm.indices()(j);
+      if (diag(l) == Scalar(0)) 
+        break; 
+      sdiag.tail(n-j).setZero();
+      sdiag[j] = diag[l];
+      // the transformations to eliminate the row of d
+      // modify only a single element of (q transpose)*b
+      // beyond the first n, which is initially zero. 
+      
+      Scalar qtbpj = 0; 
+      // Browse the nonzero elements of row j of the upper triangular s
+      for (k = j; k < n; ++k)
+      {
+        typename FactorType::InnerIterator itk(R,k);
+        for (; itk; ++itk){
+          if (itk.index() < k) continue;
+          else break;
+        }
+        //At this point, we have the diagonal element R(k,k)
+        // Determine a givens rotation which eliminates 
+        // the appropriate element in the current row of d
+        givens.makeGivens(-itk.value(), sdiag(k));
+        
+        // Compute the modified diagonal element of r and 
+        // the modified element of ((q transpose)*b,0).
+        itk.valueRef() = givens.c() * itk.value() + givens.s() * sdiag(k);
+        temp = givens.c() * wa(k) + givens.s() * qtbpj; 
+        qtbpj = -givens.s() * wa(k) + givens.c() * qtbpj;
+        wa(k) = temp;
+        
+        // Accumulate the transformation in the remaining k row/column of R
+        for (++itk; itk; ++itk)
+        {
+          i = itk.index();
+          temp = givens.c() *  itk.value() + givens.s() * sdiag(i);
+          sdiag(i) = -givens.s() * itk.value() + givens.c() * sdiag(i);
+          itk.valueRef() = temp;
+        }
+      }
+    }
+    
+    // Solve the triangular system for z. If the system is 
+    // singular, then obtain a least squares solution
+    Index nsing;
+    for(nsing = 0; nsing<n && sdiag(nsing) !=0; nsing++) {}
+    
+    wa.tail(n-nsing).setZero();
+//     x = wa; 
+    wa.head(nsing) = R.topLeftCorner(nsing,nsing).template triangularView<Upper>().solve/*InPlace*/(wa.head(nsing));
+    
+    sdiag = R.diagonal();
+    // Permute the components of z back to components of x
+    x = iPerm * wa; 
+}
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_LMQRSOLV_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
new file mode 100644
index 0000000..62561da
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/LevenbergMarquardt/LevenbergMarquardt.h
@@ -0,0 +1,396 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+//
+// The algorithm of this class initially comes from MINPACK whose original authors are:
+// Copyright Jorge More - Argonne National Laboratory
+// Copyright Burt Garbow - Argonne National Laboratory
+// Copyright Ken Hillstrom - Argonne National Laboratory
+//
+// This Source Code Form is subject to the terms of the Minpack license
+// (a BSD-like license) described in the campaigned CopyrightMINPACK.txt file.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT_H
+#define EIGEN_LEVENBERGMARQUARDT_H
+
+
+namespace Eigen {
+namespace LevenbergMarquardtSpace {
+    enum Status {
+        NotStarted = -2,
+        Running = -1,
+        ImproperInputParameters = 0,
+        RelativeReductionTooSmall = 1,
+        RelativeErrorTooSmall = 2,
+        RelativeErrorAndReductionTooSmall = 3,
+        CosinusTooSmall = 4,
+        TooManyFunctionEvaluation = 5,
+        FtolTooSmall = 6,
+        XtolTooSmall = 7,
+        GtolTooSmall = 8,
+        UserAsked = 9
+    };
+}
+
+template <typename _Scalar, int NX=Dynamic, int NY=Dynamic>
+struct DenseFunctor
+{
+  typedef _Scalar Scalar;
+  enum {
+    InputsAtCompileTime = NX,
+    ValuesAtCompileTime = NY
+  };
+  typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
+  typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
+  typedef ColPivHouseholderQR<JacobianType> QRSolver;
+  const int m_inputs, m_values;
+
+  DenseFunctor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
+  DenseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+
+  //int operator()(const InputType &x, ValueType& fvec) { }
+  // should be defined in derived classes
+  
+  //int df(const InputType &x, JacobianType& fjac) { }
+  // should be defined in derived classes
+};
+
+template <typename _Scalar, typename _Index>
+struct SparseFunctor
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Matrix<Scalar,Dynamic,1> InputType;
+  typedef Matrix<Scalar,Dynamic,1> ValueType;
+  typedef SparseMatrix<Scalar, ColMajor, Index> JacobianType;
+  typedef SparseQR<JacobianType, COLAMDOrdering<int> > QRSolver;
+  enum {
+    InputsAtCompileTime = Dynamic,
+    ValuesAtCompileTime = Dynamic
+  };
+  
+  SparseFunctor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
+
+  int inputs() const { return m_inputs; }
+  int values() const { return m_values; }
+  
+  const int m_inputs, m_values;
+  //int operator()(const InputType &x, ValueType& fvec) { }
+  // to be defined in the functor
+  
+  //int df(const InputType &x, JacobianType& fjac) { }
+  // to be defined in the functor if no automatic differentiation
+  
+};
+namespace internal {
+template <typename QRSolver, typename VectorType>
+void lmpar2(const QRSolver &qr, const VectorType  &diag, const VectorType  &qtb,
+	    typename VectorType::Scalar m_delta, typename VectorType::Scalar &par,
+	    VectorType  &x);
+    }
+/**
+  * \ingroup NonLinearOptimization_Module
+  * \brief Performs non linear optimization over a non-linear function,
+  * using a variant of the Levenberg Marquardt algorithm.
+  *
+  * Check wikipedia for more information.
+  * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
+  */
+template<typename _FunctorType>
+class LevenbergMarquardt : internal::no_assignment_operator
+{
+  public:
+    typedef _FunctorType FunctorType;
+    typedef typename FunctorType::QRSolver QRSolver;
+    typedef typename FunctorType::JacobianType JacobianType;
+    typedef typename JacobianType::Scalar Scalar;
+    typedef typename JacobianType::RealScalar RealScalar; 
+    typedef typename QRSolver::StorageIndex PermIndex;
+    typedef Matrix<Scalar,Dynamic,1> FVectorType;
+    typedef PermutationMatrix<Dynamic,Dynamic,int> PermutationType;
+  public:
+    LevenbergMarquardt(FunctorType& functor) 
+    : m_functor(functor),m_nfev(0),m_njev(0),m_fnorm(0.0),m_gnorm(0),
+      m_isInitialized(false),m_info(InvalidInput)
+    {
+      resetParameters();
+      m_useExternalScaling=false; 
+    }
+    
+    LevenbergMarquardtSpace::Status minimize(FVectorType &x);
+    LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
+    LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
+    LevenbergMarquardtSpace::Status lmder1(
+      FVectorType  &x, 
+      const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
+    );
+    static LevenbergMarquardtSpace::Status lmdif1(
+            FunctorType &functor,
+            FVectorType  &x,
+            Index *nfev,
+            const Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon())
+            );
+    
+    /** Sets the default parameters */
+    void resetParameters() 
+    {
+      using std::sqrt;        
+
+      m_factor = 100.; 
+      m_maxfev = 400; 
+      m_ftol = sqrt(NumTraits<RealScalar>::epsilon());
+      m_xtol = sqrt(NumTraits<RealScalar>::epsilon());
+      m_gtol = 0. ; 
+      m_epsfcn = 0. ;
+    }
+    
+    /** Sets the tolerance for the norm of the solution vector*/
+    void setXtol(RealScalar xtol) { m_xtol = xtol; }
+    
+    /** Sets the tolerance for the norm of the vector function*/
+    void setFtol(RealScalar ftol) { m_ftol = ftol; }
+    
+    /** Sets the tolerance for the norm of the gradient of the error vector*/
+    void setGtol(RealScalar gtol) { m_gtol = gtol; }
+    
+    /** Sets the step bound for the diagonal shift */
+    void setFactor(RealScalar factor) { m_factor = factor; }    
+    
+    /** Sets the error precision  */
+    void setEpsilon (RealScalar epsfcn) { m_epsfcn = epsfcn; }
+    
+    /** Sets the maximum number of function evaluation */
+    void setMaxfev(Index maxfev) {m_maxfev = maxfev; }
+    
+    /** Use an external Scaling. If set to true, pass a nonzero diagonal to diag() */
+    void setExternalScaling(bool value) {m_useExternalScaling  = value; }
+    
+    /** \returns the tolerance for the norm of the solution vector */
+    RealScalar xtol() const {return m_xtol; }
+    
+    /** \returns the tolerance for the norm of the vector function */
+    RealScalar ftol() const {return m_ftol; }
+    
+    /** \returns the tolerance for the norm of the gradient of the error vector */
+    RealScalar gtol() const {return m_gtol; }
+    
+    /** \returns the step bound for the diagonal shift */
+    RealScalar factor() const {return m_factor; }
+    
+    /** \returns the error precision */
+    RealScalar epsilon() const {return m_epsfcn; }
+    
+    /** \returns the maximum number of function evaluation */
+    Index maxfev() const {return m_maxfev; }
+    
+    /** \returns a reference to the diagonal of the jacobian */
+    FVectorType& diag() {return m_diag; }
+    
+    /** \returns the number of iterations performed */
+    Index iterations() { return m_iter; }
+    
+    /** \returns the number of functions evaluation */
+    Index nfev() { return m_nfev; }
+    
+    /** \returns the number of jacobian evaluation */
+    Index njev() { return m_njev; }
+    
+    /** \returns the norm of current vector function */
+    RealScalar fnorm() {return m_fnorm; }
+    
+    /** \returns the norm of the gradient of the error */
+    RealScalar gnorm() {return m_gnorm; }
+    
+    /** \returns the LevenbergMarquardt parameter */
+    RealScalar lm_param(void) { return m_par; }
+    
+    /** \returns a reference to the  current vector function 
+     */
+    FVectorType& fvec() {return m_fvec; }
+    
+    /** \returns a reference to the matrix where the current Jacobian matrix is stored
+     */
+    JacobianType& jacobian() {return m_fjac; }
+    
+    /** \returns a reference to the triangular matrix R from the QR of the jacobian matrix.
+     * \sa jacobian()
+     */
+    JacobianType& matrixR() {return m_rfactor; }
+    
+    /** the permutation used in the QR factorization
+     */
+    PermutationType permutation() {return m_permutation; }
+    
+    /** 
+     * \brief Reports whether the minimization was successful
+     * \returns \c Success if the minimization was successful,
+     *         \c NumericalIssue if a numerical problem arises during the 
+     *          minimization process, for example during the QR factorization
+     *         \c NoConvergence if the minimization did not converge after 
+     *          the maximum number of function evaluation allowed
+     *          \c InvalidInput if the input matrix is invalid
+     */
+    ComputationInfo info() const
+    {
+      
+      return m_info;
+    }
+  private:
+    JacobianType m_fjac; 
+    JacobianType m_rfactor; // The triangular matrix R from the QR of the jacobian matrix m_fjac
+    FunctorType &m_functor;
+    FVectorType m_fvec, m_qtf, m_diag; 
+    Index n;
+    Index m; 
+    Index m_nfev;
+    Index m_njev; 
+    RealScalar m_fnorm; // Norm of the current vector function
+    RealScalar m_gnorm; //Norm of the gradient of the error 
+    RealScalar m_factor; //
+    Index m_maxfev; // Maximum number of function evaluation
+    RealScalar m_ftol; //Tolerance in the norm of the vector function
+    RealScalar m_xtol; // 
+    RealScalar m_gtol; //tolerance of the norm of the error gradient
+    RealScalar m_epsfcn; //
+    Index m_iter; // Number of iterations performed
+    RealScalar m_delta;
+    bool m_useExternalScaling;
+    PermutationType m_permutation;
+    FVectorType m_wa1, m_wa2, m_wa3, m_wa4; //Temporary vectors
+    RealScalar m_par;
+    bool m_isInitialized; // Check whether the minimization step has been called
+    ComputationInfo m_info; 
+};
+
+template<typename FunctorType>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType>::minimize(FVectorType  &x)
+{
+    LevenbergMarquardtSpace::Status status = minimizeInit(x);
+    if (status==LevenbergMarquardtSpace::ImproperInputParameters) {
+      m_isInitialized = true;
+      return status;
+    }
+    do {
+//       std::cout << " uv " << x.transpose() << "\n";
+        status = minimizeOneStep(x);
+    } while (status==LevenbergMarquardtSpace::Running);
+     m_isInitialized = true;
+     return status;
+}
+
+template<typename FunctorType>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType>::minimizeInit(FVectorType  &x)
+{
+    n = x.size();
+    m = m_functor.values();
+
+    m_wa1.resize(n); m_wa2.resize(n); m_wa3.resize(n);
+    m_wa4.resize(m);
+    m_fvec.resize(m);
+    //FIXME Sparse Case : Allocate space for the jacobian
+    m_fjac.resize(m, n);
+//     m_fjac.reserve(VectorXi::Constant(n,5)); // FIXME Find a better alternative
+    if (!m_useExternalScaling)
+        m_diag.resize(n);
+    eigen_assert( (!m_useExternalScaling || m_diag.size()==n) && "When m_useExternalScaling is set, the caller must provide a valid 'm_diag'");
+    m_qtf.resize(n);
+
+    /* Function Body */
+    m_nfev = 0;
+    m_njev = 0;
+
+    /*     check the input parameters for errors. */
+    if (n <= 0 || m < n || m_ftol < 0. || m_xtol < 0. || m_gtol < 0. || m_maxfev <= 0 || m_factor <= 0.){
+      m_info = InvalidInput;
+      return LevenbergMarquardtSpace::ImproperInputParameters;
+    }
+
+    if (m_useExternalScaling)
+        for (Index j = 0; j < n; ++j)
+            if (m_diag[j] <= 0.) 
+            {
+              m_info = InvalidInput;
+              return LevenbergMarquardtSpace::ImproperInputParameters;
+            }
+
+    /*     evaluate the function at the starting point */
+    /*     and calculate its norm. */
+    m_nfev = 1;
+    if ( m_functor(x, m_fvec) < 0)
+        return LevenbergMarquardtSpace::UserAsked;
+    m_fnorm = m_fvec.stableNorm();
+
+    /*     initialize levenberg-marquardt parameter and iteration counter. */
+    m_par = 0.;
+    m_iter = 1;
+
+    return LevenbergMarquardtSpace::NotStarted;
+}
+
+template<typename FunctorType>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType>::lmder1(
+        FVectorType  &x,
+        const Scalar tol
+        )
+{
+    n = x.size();
+    m = m_functor.values();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || m < n || tol < 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    resetParameters();
+    m_ftol = tol;
+    m_xtol = tol;
+    m_maxfev = 100*(n+1);
+
+    return minimize(x);
+}
+
+
+template<typename FunctorType>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType>::lmdif1(
+        FunctorType &functor,
+        FVectorType  &x,
+        Index *nfev,
+        const Scalar tol
+        )
+{
+    Index n = x.size();
+    Index m = functor.values();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || m < n || tol < 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    NumericalDiff<FunctorType> numDiff(functor);
+    // embedded LevenbergMarquardt
+    LevenbergMarquardt<NumericalDiff<FunctorType> > lm(numDiff);
+    lm.setFtol(tol);
+    lm.setXtol(tol);
+    lm.setMaxfev(200*(n+1));
+
+    LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
+    if (nfev)
+        * nfev = lm.nfev();
+    return info;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LEVENBERGMARQUARDT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
new file mode 100644
index 0000000..02284b0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
@@ -0,0 +1,441 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009, 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2011, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_EXPONENTIAL
+#define EIGEN_MATRIX_EXPONENTIAL
+
+#include "StemFunction.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \brief Scaling operator.
+ *
+ * This struct is used by CwiseUnaryOp to scale a matrix by \f$ 2^{-s} \f$.
+ */
+template <typename RealScalar>
+struct MatrixExponentialScalingOp
+{
+  /** \brief Constructor.
+   *
+   * \param[in] squarings  The integer \f$ s \f$ in this document.
+   */
+  MatrixExponentialScalingOp(int squarings) : m_squarings(squarings) { }
+
+
+  /** \brief Scale a matrix coefficient.
+   *
+   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
+   */
+  inline const RealScalar operator() (const RealScalar& x) const
+  {
+    using std::ldexp;
+    return ldexp(x, -m_squarings);
+  }
+
+  typedef std::complex<RealScalar> ComplexScalar;
+
+  /** \brief Scale a matrix coefficient.
+   *
+   * \param[in,out] x  The scalar to be scaled, becoming \f$ 2^{-s} x \f$.
+   */
+  inline const ComplexScalar operator() (const ComplexScalar& x) const
+  {
+    using std::ldexp;
+    return ComplexScalar(ldexp(x.real(), -m_squarings), ldexp(x.imag(), -m_squarings));
+  }
+
+  private:
+    int m_squarings;
+};
+
+/** \brief Compute the (3,3)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade3(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatA>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {120.L, 60.L, 12.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType tmp = b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (5,5)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade5(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {30240.L, 15120.L, 3360.L, 420.L, 30.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType tmp = b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (7,7)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade7(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {17297280.L, 8648640.L, 1995840.L, 277200.L, 25200.L, 1512.L, 56.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType tmp = b[7] * A6 + b[5] * A4 + b[3] * A2 
+    + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+
+}
+
+/** \brief Compute the (9,9)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade9(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {17643225600.L, 8821612800.L, 2075673600.L, 302702400.L, 30270240.L,
+                          2162160.L, 110880.L, 3960.L, 90.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType A8 = A6 * A2;
+  const MatrixType tmp = b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 
+    + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  V = b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (13,13)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ */
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade13(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {64764752532480000.L, 32382376266240000.L, 7771770303897600.L,
+                          1187353796428800.L, 129060195264000.L, 10559470521600.L, 670442572800.L,
+                          33522128640.L, 1323241920.L, 40840800.L, 960960.L, 16380.L, 182.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  V = b[13] * A6 + b[11] * A4 + b[9] * A2; // used for temporary storage
+  MatrixType tmp = A6 * V;
+  tmp += b[7] * A6 + b[5] * A4 + b[3] * A2 + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  tmp = b[12] * A6 + b[10] * A4 + b[8] * A2;
+  V.noalias() = A6 * tmp;
+  V += b[6] * A6 + b[4] * A4 + b[2] * A2 + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+
+/** \brief Compute the (17,17)-Pad&eacute; approximant to the exponential.
+ *
+ *  After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Pad&eacute;
+ *  approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ *  This function activates only if your long double is double-double or quadruple.
+ */
+#if LDBL_MANT_DIG > 64
+template <typename MatA, typename MatU, typename MatV>
+void matrix_exp_pade17(const MatA& A, MatU& U, MatV& V)
+{
+  typedef typename MatA::PlainObject MatrixType;
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  const RealScalar b[] = {830034394580628357120000.L, 415017197290314178560000.L,
+                          100610229646136770560000.L, 15720348382208870400000.L,
+                          1774878043152614400000.L, 153822763739893248000.L, 10608466464820224000.L,
+                          595373117923584000.L, 27563570274240000.L, 1060137318240000.L,
+                          33924394183680.L, 899510451840.L, 19554575040.L, 341863200.L, 4651200.L,
+                          46512.L, 306.L, 1.L};
+  const MatrixType A2 = A * A;
+  const MatrixType A4 = A2 * A2;
+  const MatrixType A6 = A4 * A2;
+  const MatrixType A8 = A4 * A4;
+  V = b[17] * A8 + b[15] * A6 + b[13] * A4 + b[11] * A2; // used for temporary storage
+  MatrixType tmp = A8 * V;
+  tmp += b[9] * A8 + b[7] * A6 + b[5] * A4 + b[3] * A2 
+    + b[1] * MatrixType::Identity(A.rows(), A.cols());
+  U.noalias() = A * tmp;
+  tmp = b[16] * A8 + b[14] * A6 + b[12] * A4 + b[10] * A2;
+  V.noalias() = tmp * A8;
+  V += b[8] * A8 + b[6] * A6 + b[4] * A4 + b[2] * A2 
+    + b[0] * MatrixType::Identity(A.rows(), A.cols());
+}
+#endif
+
+template <typename MatrixType, typename RealScalar = typename NumTraits<typename traits<MatrixType>::Scalar>::Real>
+struct matrix_exp_computeUV
+{
+  /** \brief Compute Pad&eacute; approximant to the exponential.
+    *
+    * Computes \c U, \c V and \c squarings such that \f$ (V+U)(V-U)^{-1} \f$ is a Pad&eacute;
+    * approximant of \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$, where \f$ M \f$
+    * denotes the matrix \c arg. The degree of the Pad&eacute; approximant and the value of squarings
+    * are chosen such that the approximation error is no more than the round-off error.
+    */
+  static void run(const MatrixType& arg, MatrixType& U, MatrixType& V, int& squarings);
+};
+
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, float>
+{
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
+  {
+    using std::frexp;
+    using std::pow;
+    const float l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+    if (l1norm < 4.258730016922831e-001f) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 1.880152677804762e+000f) {
+      matrix_exp_pade5(arg, U, V);
+    } else {
+      const float maxnorm = 3.925724783138660f;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<float>(squarings));
+      matrix_exp_pade7(A, U, V);
+    }
+  }
+};
+
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, double>
+{
+  typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar;
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
+  {
+    using std::frexp;
+    using std::pow;
+    const RealScalar l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+    if (l1norm < 1.495585217958292e-002) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 2.539398330063230e-001) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 9.504178996162932e-001) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.097847961257068e+000) {
+      matrix_exp_pade9(arg, U, V);
+    } else {
+      const RealScalar maxnorm = 5.371920351148152;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<RealScalar>(squarings));
+      matrix_exp_pade13(A, U, V);
+    }
+  }
+};
+  
+template <typename MatrixType>
+struct matrix_exp_computeUV<MatrixType, long double>
+{
+  template <typename ArgType>
+  static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings)
+  {
+#if   LDBL_MANT_DIG == 53   // double precision
+    matrix_exp_computeUV<MatrixType, double>::run(arg, U, V, squarings);
+  
+#else
+  
+    using std::frexp;
+    using std::pow;
+    const long double l1norm = arg.cwiseAbs().colwise().sum().maxCoeff();
+    squarings = 0;
+  
+#if LDBL_MANT_DIG <= 64   // extended precision
+  
+    if (l1norm < 4.1968497232266989671e-003L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 1.1848116734693823091e-001L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 5.5170388480686700274e-001L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 1.3759868875587845383e+000L) {
+      matrix_exp_pade9(arg, U, V);
+    } else {
+      const long double maxnorm = 4.0246098906697353063L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
+      matrix_exp_pade13(A, U, V);
+    }
+  
+#elif LDBL_MANT_DIG <= 106  // double-double
+  
+    if (l1norm < 3.2787892205607026992947488108213e-005L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 6.4467025060072760084130906076332e-003L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 6.8988028496595374751374122881143e-002L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.7339737518502231741495857201670e-001L) {
+      matrix_exp_pade9(arg, U, V);
+    } else if (l1norm < 1.3203382096514474905666448850278e+000L) {
+      matrix_exp_pade13(arg, U, V);
+    } else {
+      const long double maxnorm = 3.2579440895405400856599663723517L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
+      matrix_exp_pade17(A, U, V);
+    }
+  
+#elif LDBL_MANT_DIG <= 113  // quadruple precision
+  
+    if (l1norm < 1.639394610288918690547467954466970e-005L) {
+      matrix_exp_pade3(arg, U, V);
+    } else if (l1norm < 4.253237712165275566025884344433009e-003L) {
+      matrix_exp_pade5(arg, U, V);
+    } else if (l1norm < 5.125804063165764409885122032933142e-002L) {
+      matrix_exp_pade7(arg, U, V);
+    } else if (l1norm < 2.170000765161155195453205651889853e-001L) {
+      matrix_exp_pade9(arg, U, V);
+    } else if (l1norm < 1.125358383453143065081397882891878e+000L) {
+      matrix_exp_pade13(arg, U, V);
+    } else {
+      const long double maxnorm = 2.884233277829519311757165057717815L;
+      frexp(l1norm / maxnorm, &squarings);
+      if (squarings < 0) squarings = 0;
+      MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<long double>(squarings));
+      matrix_exp_pade17(A, U, V);
+    }
+  
+#else
+  
+    // this case should be handled in compute()
+    eigen_assert(false && "Bug in MatrixExponential"); 
+  
+#endif
+#endif  // LDBL_MANT_DIG
+  }
+};
+
+template<typename T> struct is_exp_known_type : false_type {};
+template<> struct is_exp_known_type<float> : true_type {};
+template<> struct is_exp_known_type<double> : true_type {};
+#if LDBL_MANT_DIG <= 113
+template<> struct is_exp_known_type<long double> : true_type {};
+#endif
+
+template <typename ArgType, typename ResultType>
+void matrix_exp_compute(const ArgType& arg, ResultType &result, true_type) // natively supported scalar type
+{
+  typedef typename ArgType::PlainObject MatrixType;
+  MatrixType U, V;
+  int squarings;
+  matrix_exp_computeUV<MatrixType>::run(arg, U, V, squarings); // Pade approximant is (U+V) / (-U+V)
+  MatrixType numer = U + V;
+  MatrixType denom = -U + V;
+  result = denom.partialPivLu().solve(numer);
+  for (int i=0; i<squarings; i++)
+    result *= result;   // undo scaling by repeated squaring
+}
+
+
+/* Computes the matrix exponential
+ *
+ * \param arg    argument of matrix exponential (should be plain object)
+ * \param result variable in which result will be stored
+ */
+template <typename ArgType, typename ResultType>
+void matrix_exp_compute(const ArgType& arg, ResultType &result, false_type) // default
+{
+  typedef typename ArgType::PlainObject MatrixType;
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename std::complex<RealScalar> ComplexScalar;
+  result = arg.matrixFunction(internal::stem_function_exp<ComplexScalar>);
+}
+
+} // end namespace Eigen::internal
+
+/** \ingroup MatrixFunctions_Module
+  *
+  * \brief Proxy for the matrix exponential of some matrix (expression).
+  *
+  * \tparam Derived  Type of the argument to the matrix exponential.
+  *
+  * This class holds the argument to the matrix exponential until it is assigned or evaluated for
+  * some other reason (so the argument should not be changed in the meantime). It is the return type
+  * of MatrixBase::exp() and most of the time this is the only way it is used.
+  */
+template<typename Derived> struct MatrixExponentialReturnValue
+: public ReturnByValue<MatrixExponentialReturnValue<Derived> >
+{
+  public:
+    /** \brief Constructor.
+      *
+      * \param src %Matrix (expression) forming the argument of the matrix exponential.
+      */
+    MatrixExponentialReturnValue(const Derived& src) : m_src(src) { }
+
+    /** \brief Compute the matrix exponential.
+      *
+      * \param result the matrix exponential of \p src in the constructor.
+      */
+    template <typename ResultType>
+    inline void evalTo(ResultType& result) const
+    {
+      const typename internal::nested_eval<Derived, 10>::type tmp(m_src);
+      internal::matrix_exp_compute(tmp, result, internal::is_exp_known_type<typename Derived::RealScalar>());
+    }
+
+    Index rows() const { return m_src.rows(); }
+    Index cols() const { return m_src.cols(); }
+
+  protected:
+    const typename internal::ref_selector<Derived>::type m_src;
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixExponentialReturnValue<Derived> >
+{
+  typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+template <typename Derived>
+const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
+{
+  eigen_assert(rows() == cols());
+  return MatrixExponentialReturnValue<Derived>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_EXPONENTIAL
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
new file mode 100644
index 0000000..cc12ab6
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
@@ -0,0 +1,569 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_FUNCTION_H
+#define EIGEN_MATRIX_FUNCTION_H
+
+#include "StemFunction.h"
+
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \brief Maximum distance allowed between eigenvalues to be considered "close". */
+static const float matrix_function_separation = 0.1f;
+
+/** \ingroup MatrixFunctions_Module
+  * \class MatrixFunctionAtomic
+  * \brief Helper class for computing matrix functions of atomic matrices.
+  *
+  * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
+  */
+template <typename MatrixType>
+class MatrixFunctionAtomic 
+{
+  public:
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename stem_function<Scalar>::type StemFunction;
+
+    /** \brief Constructor
+      * \param[in]  f  matrix function to compute.
+      */
+    MatrixFunctionAtomic(StemFunction f) : m_f(f) { }
+
+    /** \brief Compute matrix function of atomic matrix
+      * \param[in]  A  argument of matrix function, should be upper triangular and atomic
+      * \returns  f(A), the matrix function evaluated at the given matrix
+      */
+    MatrixType compute(const MatrixType& A);
+
+  private:
+    StemFunction* m_f;
+};
+
+template <typename MatrixType>
+typename NumTraits<typename MatrixType::Scalar>::Real matrix_function_compute_mu(const MatrixType& A)
+{
+  typedef typename plain_col_type<MatrixType>::type VectorType;
+  Index rows = A.rows();
+  const MatrixType N = MatrixType::Identity(rows, rows) - A;
+  VectorType e = VectorType::Ones(rows);
+  N.template triangularView<Upper>().solveInPlace(e);
+  return e.cwiseAbs().maxCoeff();
+}
+
+template <typename MatrixType>
+MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
+{
+  // TODO: Use that A is upper triangular
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  Index rows = A.rows();
+  Scalar avgEival = A.trace() / Scalar(RealScalar(rows));
+  MatrixType Ashifted = A - avgEival * MatrixType::Identity(rows, rows);
+  RealScalar mu = matrix_function_compute_mu(Ashifted);
+  MatrixType F = m_f(avgEival, 0) * MatrixType::Identity(rows, rows);
+  MatrixType P = Ashifted;
+  MatrixType Fincr;
+  for (Index s = 1; double(s) < 1.1 * double(rows) + 10.0; s++) { // upper limit is fairly arbitrary
+    Fincr = m_f(avgEival, static_cast<int>(s)) * P;
+    F += Fincr;
+    P = Scalar(RealScalar(1)/RealScalar(s + 1)) * P * Ashifted;
+
+    // test whether Taylor series converged
+    const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
+    const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
+    if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
+      RealScalar delta = 0;
+      RealScalar rfactorial = 1;
+      for (Index r = 0; r < rows; r++) {
+        RealScalar mx = 0;
+        for (Index i = 0; i < rows; i++)
+          mx = (std::max)(mx, std::abs(m_f(Ashifted(i, i) + avgEival, static_cast<int>(s+r))));
+        if (r != 0)
+          rfactorial *= RealScalar(r);
+        delta = (std::max)(delta, mx / rfactorial);
+      }
+      const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
+      if (mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm) // series converged
+        break;
+    }
+  }
+  return F;
+}
+
+/** \brief Find cluster in \p clusters containing some value 
+  * \param[in] key Value to find
+  * \returns Iterator to cluster containing \p key, or \c clusters.end() if no cluster in \p m_clusters
+  * contains \p key.
+  */
+template <typename Index, typename ListOfClusters>
+typename ListOfClusters::iterator matrix_function_find_cluster(Index key, ListOfClusters& clusters)
+{
+  typename std::list<Index>::iterator j;
+  for (typename ListOfClusters::iterator i = clusters.begin(); i != clusters.end(); ++i) {
+    j = std::find(i->begin(), i->end(), key);
+    if (j != i->end())
+      return i;
+  }
+  return clusters.end();
+}
+
+/** \brief Partition eigenvalues in clusters of ei'vals close to each other
+  * 
+  * \param[in]  eivals    Eigenvalues
+  * \param[out] clusters  Resulting partition of eigenvalues
+  *
+  * The partition satisfies the following two properties:
+  * # Any eigenvalue in a certain cluster is at most matrix_function_separation() away from another eigenvalue
+  *   in the same cluster.
+  * # The distance between two eigenvalues in different clusters is more than matrix_function_separation().  
+  * The implementation follows Algorithm 4.1 in the paper of Davies and Higham.
+  */
+template <typename EivalsType, typename Cluster>
+void matrix_function_partition_eigenvalues(const EivalsType& eivals, std::list<Cluster>& clusters)
+{
+  typedef typename EivalsType::RealScalar RealScalar;
+  for (Index i=0; i<eivals.rows(); ++i) {
+    // Find cluster containing i-th ei'val, adding a new cluster if necessary
+    typename std::list<Cluster>::iterator qi = matrix_function_find_cluster(i, clusters);
+    if (qi == clusters.end()) {
+      Cluster l;
+      l.push_back(i);
+      clusters.push_back(l);
+      qi = clusters.end();
+      --qi;
+    }
+
+    // Look for other element to add to the set
+    for (Index j=i+1; j<eivals.rows(); ++j) {
+      if (abs(eivals(j) - eivals(i)) <= RealScalar(matrix_function_separation)
+          && std::find(qi->begin(), qi->end(), j) == qi->end()) {
+        typename std::list<Cluster>::iterator qj = matrix_function_find_cluster(j, clusters);
+        if (qj == clusters.end()) {
+          qi->push_back(j);
+        } else {
+          qi->insert(qi->end(), qj->begin(), qj->end());
+          clusters.erase(qj);
+        }
+      }
+    }
+  }
+}
+
+/** \brief Compute size of each cluster given a partitioning */
+template <typename ListOfClusters, typename Index>
+void matrix_function_compute_cluster_size(const ListOfClusters& clusters, Matrix<Index, Dynamic, 1>& clusterSize)
+{
+  const Index numClusters = static_cast<Index>(clusters.size());
+  clusterSize.setZero(numClusters);
+  Index clusterIndex = 0;
+  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
+    clusterSize[clusterIndex] = cluster->size();
+    ++clusterIndex;
+  }
+}
+
+/** \brief Compute start of each block using clusterSize */
+template <typename VectorType>
+void matrix_function_compute_block_start(const VectorType& clusterSize, VectorType& blockStart)
+{
+  blockStart.resize(clusterSize.rows());
+  blockStart(0) = 0;
+  for (Index i = 1; i < clusterSize.rows(); i++) {
+    blockStart(i) = blockStart(i-1) + clusterSize(i-1);
+  }
+}
+
+/** \brief Compute mapping of eigenvalue indices to cluster indices */
+template <typename EivalsType, typename ListOfClusters, typename VectorType>
+void matrix_function_compute_map(const EivalsType& eivals, const ListOfClusters& clusters, VectorType& eivalToCluster)
+{
+  eivalToCluster.resize(eivals.rows());
+  Index clusterIndex = 0;
+  for (typename ListOfClusters::const_iterator cluster = clusters.begin(); cluster != clusters.end(); ++cluster) {
+    for (Index i = 0; i < eivals.rows(); ++i) {
+      if (std::find(cluster->begin(), cluster->end(), i) != cluster->end()) {
+        eivalToCluster[i] = clusterIndex;
+      }
+    }
+    ++clusterIndex;
+  }
+}
+
+/** \brief Compute permutation which groups ei'vals in same cluster together */
+template <typename DynVectorType, typename VectorType>
+void matrix_function_compute_permutation(const DynVectorType& blockStart, const DynVectorType& eivalToCluster, VectorType& permutation)
+{
+  DynVectorType indexNextEntry = blockStart;
+  permutation.resize(eivalToCluster.rows());
+  for (Index i = 0; i < eivalToCluster.rows(); i++) {
+    Index cluster = eivalToCluster[i];
+    permutation[i] = indexNextEntry[cluster];
+    ++indexNextEntry[cluster];
+  }
+}  
+
+/** \brief Permute Schur decomposition in U and T according to permutation */
+template <typename VectorType, typename MatrixType>
+void matrix_function_permute_schur(VectorType& permutation, MatrixType& U, MatrixType& T)
+{
+  for (Index i = 0; i < permutation.rows() - 1; i++) {
+    Index j;
+    for (j = i; j < permutation.rows(); j++) {
+      if (permutation(j) == i) break;
+    }
+    eigen_assert(permutation(j) == i);
+    for (Index k = j-1; k >= i; k--) {
+      JacobiRotation<typename MatrixType::Scalar> rotation;
+      rotation.makeGivens(T(k, k+1), T(k+1, k+1) - T(k, k));
+      T.applyOnTheLeft(k, k+1, rotation.adjoint());
+      T.applyOnTheRight(k, k+1, rotation);
+      U.applyOnTheRight(k, k+1, rotation);
+      std::swap(permutation.coeffRef(k), permutation.coeffRef(k+1));
+    }
+  }
+}
+
+/** \brief Compute block diagonal part of matrix function.
+  *
+  * This routine computes the matrix function applied to the block diagonal part of \p T (which should be
+  * upper triangular), with the blocking given by \p blockStart and \p clusterSize. The matrix function of
+  * each diagonal block is computed by \p atomic. The off-diagonal parts of \p fT are set to zero.
+  */
+template <typename MatrixType, typename AtomicType, typename VectorType>
+void matrix_function_compute_block_atomic(const MatrixType& T, AtomicType& atomic, const VectorType& blockStart, const VectorType& clusterSize, MatrixType& fT)
+{ 
+  fT.setZero(T.rows(), T.cols());
+  for (Index i = 0; i < clusterSize.rows(); ++i) {
+    fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i))
+      = atomic.compute(T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)));
+  }
+}
+
+/** \brief Solve a triangular Sylvester equation AX + XB = C 
+  *
+  * \param[in]  A  the matrix A; should be square and upper triangular
+  * \param[in]  B  the matrix B; should be square and upper triangular
+  * \param[in]  C  the matrix C; should have correct size.
+  *
+  * \returns the solution X.
+  *
+  * If A is m-by-m and B is n-by-n, then both C and X are m-by-n.  The (i,j)-th component of the Sylvester
+  * equation is
+  * \f[ 
+  *     \sum_{k=i}^m A_{ik} X_{kj} + \sum_{k=1}^j X_{ik} B_{kj} = C_{ij}. 
+  * \f]
+  * This can be re-arranged to yield:
+  * \f[ 
+  *     X_{ij} = \frac{1}{A_{ii} + B_{jj}} \Bigl( C_{ij}
+  *     - \sum_{k=i+1}^m A_{ik} X_{kj} - \sum_{k=1}^{j-1} X_{ik} B_{kj} \Bigr).
+  * \f]
+  * It is assumed that A and B are such that the numerator is never zero (otherwise the Sylvester equation
+  * does not have a unique solution). In that case, these equations can be evaluated in the order 
+  * \f$ i=m,\ldots,1 \f$ and \f$ j=1,\ldots,n \f$.
+  */
+template <typename MatrixType>
+MatrixType matrix_function_solve_triangular_sylvester(const MatrixType& A, const MatrixType& B, const MatrixType& C)
+{
+  eigen_assert(A.rows() == A.cols());
+  eigen_assert(A.isUpperTriangular());
+  eigen_assert(B.rows() == B.cols());
+  eigen_assert(B.isUpperTriangular());
+  eigen_assert(C.rows() == A.rows());
+  eigen_assert(C.cols() == B.rows());
+
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index m = A.rows();
+  Index n = B.rows();
+  MatrixType X(m, n);
+
+  for (Index i = m - 1; i >= 0; --i) {
+    for (Index j = 0; j < n; ++j) {
+
+      // Compute AX = \sum_{k=i+1}^m A_{ik} X_{kj}
+      Scalar AX;
+      if (i == m - 1) {
+	AX = 0; 
+      } else {
+	Matrix<Scalar,1,1> AXmatrix = A.row(i).tail(m-1-i) * X.col(j).tail(m-1-i);
+	AX = AXmatrix(0,0);
+      }
+
+      // Compute XB = \sum_{k=1}^{j-1} X_{ik} B_{kj}
+      Scalar XB;
+      if (j == 0) {
+	XB = 0; 
+      } else {
+	Matrix<Scalar,1,1> XBmatrix = X.row(i).head(j) * B.col(j).head(j);
+	XB = XBmatrix(0,0);
+      }
+
+      X(i,j) = (C(i,j) - AX - XB) / (A(i,i) + B(j,j));
+    }
+  }
+  return X;
+}
+
+/** \brief Compute part of matrix function above block diagonal.
+  *
+  * This routine completes the computation of \p fT, denoting a matrix function applied to the triangular
+  * matrix \p T. It assumes that the block diagonal part of \p fT has already been computed. The part below
+  * the diagonal is zero, because \p T is upper triangular.
+  */
+template <typename MatrixType, typename VectorType>
+void matrix_function_compute_above_diagonal(const MatrixType& T, const VectorType& blockStart, const VectorType& clusterSize, MatrixType& fT)
+{ 
+  typedef internal::traits<MatrixType> Traits;
+  typedef typename MatrixType::Scalar Scalar;
+  static const int Options = MatrixType::Options;
+  typedef Matrix<Scalar, Dynamic, Dynamic, Options, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
+
+  for (Index k = 1; k < clusterSize.rows(); k++) {
+    for (Index i = 0; i < clusterSize.rows() - k; i++) {
+      // compute (i, i+k) block
+      DynMatrixType A = T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i));
+      DynMatrixType B = -T.block(blockStart(i+k), blockStart(i+k), clusterSize(i+k), clusterSize(i+k));
+      DynMatrixType C = fT.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i))
+        * T.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k));
+      C -= T.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k))
+        * fT.block(blockStart(i+k), blockStart(i+k), clusterSize(i+k), clusterSize(i+k));
+      for (Index m = i + 1; m < i + k; m++) {
+        C += fT.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m))
+          * T.block(blockStart(m), blockStart(i+k), clusterSize(m), clusterSize(i+k));
+        C -= T.block(blockStart(i), blockStart(m), clusterSize(i), clusterSize(m))
+          * fT.block(blockStart(m), blockStart(i+k), clusterSize(m), clusterSize(i+k));
+      }
+      fT.block(blockStart(i), blockStart(i+k), clusterSize(i), clusterSize(i+k))
+        = matrix_function_solve_triangular_sylvester(A, B, C);
+    }
+  }
+}
+
+/** \ingroup MatrixFunctions_Module
+  * \brief Class for computing matrix functions.
+  * \tparam  MatrixType  type of the argument of the matrix function,
+  *                      expected to be an instantiation of the Matrix class template.
+  * \tparam  AtomicType  type for computing matrix function of atomic blocks.
+  * \tparam  IsComplex   used internally to select correct specialization.
+  *
+  * This class implements the Schur-Parlett algorithm for computing matrix functions. The spectrum of the
+  * matrix is divided in clustered of eigenvalues that lies close together. This class delegates the
+  * computation of the matrix function on every block corresponding to these clusters to an object of type
+  * \p AtomicType and uses these results to compute the matrix function of the whole matrix. The class
+  * \p AtomicType should have a \p compute() member function for computing the matrix function of a block.
+  *
+  * \sa class MatrixFunctionAtomic, class MatrixLogarithmAtomic
+  */
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct matrix_function_compute
+{  
+    /** \brief Compute the matrix function.
+      *
+      * \param[in]  A       argument of matrix function, should be a square matrix.
+      * \param[in]  atomic  class for computing matrix function of atomic blocks.
+      * \param[out] result  the function \p f applied to \p A, as
+      * specified in the constructor.
+      *
+      * See MatrixBase::matrixFunction() for details on how this computation
+      * is implemented.
+      */
+    template <typename AtomicType, typename ResultType> 
+    static void run(const MatrixType& A, AtomicType& atomic, ResultType &result);    
+};
+
+/** \internal \ingroup MatrixFunctions_Module 
+  * \brief Partial specialization of MatrixFunction for real matrices
+  *
+  * This converts the real matrix to a complex matrix, compute the matrix function of that matrix, and then
+  * converts the result back to a real matrix.
+  */
+template <typename MatrixType>
+struct matrix_function_compute<MatrixType, 0>
+{  
+  template <typename MatA, typename AtomicType, typename ResultType>
+  static void run(const MatA& A, AtomicType& atomic, ResultType &result)
+  {
+    typedef internal::traits<MatrixType> Traits;
+    typedef typename Traits::Scalar Scalar;
+    static const int Rows = Traits::RowsAtCompileTime, Cols = Traits::ColsAtCompileTime;
+    static const int MaxRows = Traits::MaxRowsAtCompileTime, MaxCols = Traits::MaxColsAtCompileTime;
+
+    typedef std::complex<Scalar> ComplexScalar;
+    typedef Matrix<ComplexScalar, Rows, Cols, 0, MaxRows, MaxCols> ComplexMatrix;
+
+    ComplexMatrix CA = A.template cast<ComplexScalar>();
+    ComplexMatrix Cresult;
+    matrix_function_compute<ComplexMatrix>::run(CA, atomic, Cresult);
+    result = Cresult.real();
+  }
+};
+
+/** \internal \ingroup MatrixFunctions_Module 
+  * \brief Partial specialization of MatrixFunction for complex matrices
+  */
+template <typename MatrixType>
+struct matrix_function_compute<MatrixType, 1>
+{
+  template <typename MatA, typename AtomicType, typename ResultType>
+  static void run(const MatA& A, AtomicType& atomic, ResultType &result)
+  {
+    typedef internal::traits<MatrixType> Traits;
+    
+    // compute Schur decomposition of A
+    const ComplexSchur<MatrixType> schurOfA(A);
+    eigen_assert(schurOfA.info()==Success);
+    MatrixType T = schurOfA.matrixT();
+    MatrixType U = schurOfA.matrixU();
+
+    // partition eigenvalues into clusters of ei'vals "close" to each other
+    std::list<std::list<Index> > clusters; 
+    matrix_function_partition_eigenvalues(T.diagonal(), clusters);
+
+    // compute size of each cluster
+    Matrix<Index, Dynamic, 1> clusterSize;
+    matrix_function_compute_cluster_size(clusters, clusterSize);
+
+    // blockStart[i] is row index at which block corresponding to i-th cluster starts 
+    Matrix<Index, Dynamic, 1> blockStart; 
+    matrix_function_compute_block_start(clusterSize, blockStart);
+
+    // compute map so that eivalToCluster[i] = j means that i-th ei'val is in j-th cluster 
+    Matrix<Index, Dynamic, 1> eivalToCluster;
+    matrix_function_compute_map(T.diagonal(), clusters, eivalToCluster);
+
+    // compute permutation which groups ei'vals in same cluster together 
+    Matrix<Index, Traits::RowsAtCompileTime, 1> permutation;
+    matrix_function_compute_permutation(blockStart, eivalToCluster, permutation);
+
+    // permute Schur decomposition
+    matrix_function_permute_schur(permutation, U, T);
+
+    // compute result
+    MatrixType fT; // matrix function applied to T
+    matrix_function_compute_block_atomic(T, atomic, blockStart, clusterSize, fT);
+    matrix_function_compute_above_diagonal(T, blockStart, clusterSize, fT);
+    result = U * (fT.template triangularView<Upper>() * U.adjoint());
+  }
+};
+
+} // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+  *
+  * \brief Proxy for the matrix function of some matrix (expression).
+  *
+  * \tparam Derived  Type of the argument to the matrix function.
+  *
+  * This class holds the argument to the matrix function until it is assigned or evaluated for some other
+  * reason (so the argument should not be changed in the meantime). It is the return type of
+  * matrixBase::matrixFunction() and related functions and most of the time this is the only way it is used.
+  */
+template<typename Derived> class MatrixFunctionReturnValue
+: public ReturnByValue<MatrixFunctionReturnValue<Derived> >
+{
+  public:
+    typedef typename Derived::Scalar Scalar;
+    typedef typename internal::stem_function<Scalar>::type StemFunction;
+
+  protected:
+    typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+  public:
+
+    /** \brief Constructor.
+      *
+      * \param[in] A  %Matrix (expression) forming the argument of the matrix function.
+      * \param[in] f  Stem function for matrix function under consideration.
+      */
+    MatrixFunctionReturnValue(const Derived& A, StemFunction f) : m_A(A), m_f(f) { }
+
+    /** \brief Compute the matrix function.
+      *
+      * \param[out] result \p f applied to \p A, where \p f and \p A are as in the constructor.
+      */
+    template <typename ResultType>
+    inline void evalTo(ResultType& result) const
+    {
+      typedef typename internal::nested_eval<Derived, 10>::type NestedEvalType;
+      typedef typename internal::remove_all<NestedEvalType>::type NestedEvalTypeClean;
+      typedef internal::traits<NestedEvalTypeClean> Traits;
+      typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
+      typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
+
+      typedef internal::MatrixFunctionAtomic<DynMatrixType> AtomicType;
+      AtomicType atomic(m_f);
+
+      internal::matrix_function_compute<typename NestedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
+    }
+
+    Index rows() const { return m_A.rows(); }
+    Index cols() const { return m_A.cols(); }
+
+  private:
+    const DerivedNested m_A;
+    StemFunction *m_f;
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixFunctionReturnValue<Derived> >
+{
+  typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+
+/********** MatrixBase methods **********/
+
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const
+{
+  eigen_assert(rows() == cols());
+  return MatrixFunctionReturnValue<Derived>(derived(), f);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
+{
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sin<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
+{
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cos<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
+{
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_sinh<ComplexScalar>);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
+{
+  eigen_assert(rows() == cols());
+  typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+  return MatrixFunctionReturnValue<Derived>(derived(), internal::stem_function_cosh<ComplexScalar>);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
new file mode 100644
index 0000000..e917013
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
@@ -0,0 +1,373 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2011 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_LOGARITHM
+#define EIGEN_MATRIX_LOGARITHM
+
+namespace Eigen { 
+
+namespace internal { 
+
+template <typename Scalar>
+struct matrix_log_min_pade_degree 
+{
+  static const int value = 3;
+};
+
+template <typename Scalar>
+struct matrix_log_max_pade_degree 
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  static const int value = std::numeric_limits<RealScalar>::digits<= 24?  5:  // single precision
+                           std::numeric_limits<RealScalar>::digits<= 53?  7:  // double precision
+                           std::numeric_limits<RealScalar>::digits<= 64?  8:  // extended precision
+                           std::numeric_limits<RealScalar>::digits<=106? 10:  // double-double
+                                                                         11;  // quadruple precision
+};
+
+/** \brief Compute logarithm of 2x2 triangular matrix. */
+template <typename MatrixType>
+void matrix_log_compute_2x2(const MatrixType& A, MatrixType& result)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  using std::abs;
+  using std::ceil;
+  using std::imag;
+  using std::log;
+
+  Scalar logA00 = log(A(0,0));
+  Scalar logA11 = log(A(1,1));
+
+  result(0,0) = logA00;
+  result(1,0) = Scalar(0);
+  result(1,1) = logA11;
+
+  Scalar y = A(1,1) - A(0,0);
+  if (y==Scalar(0))
+  {
+    result(0,1) = A(0,1) / A(0,0);
+  }
+  else if ((abs(A(0,0)) < RealScalar(0.5)*abs(A(1,1))) || (abs(A(0,0)) > 2*abs(A(1,1))))
+  {
+    result(0,1) = A(0,1) * (logA11 - logA00) / y;
+  }
+  else
+  {
+    // computation in previous branch is inaccurate if A(1,1) \approx A(0,0)
+    RealScalar unwindingNumber = ceil((imag(logA11 - logA00) - RealScalar(EIGEN_PI)) / RealScalar(2*EIGEN_PI));
+    result(0,1) = A(0,1) * (numext::log1p(y/A(0,0)) + Scalar(0,RealScalar(2*EIGEN_PI)*unwindingNumber)) / y;
+  }
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = float) */
+inline int matrix_log_get_pade_degree(float normTminusI)
+{
+  const float maxNormForPade[] = { 2.5111573934555054e-1 /* degree = 3 */ , 4.0535837411880493e-1,
+            5.3149729967117310e-1 };
+  const int minPadeDegree = matrix_log_min_pade_degree<float>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<float>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree) 
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
+      break;
+  return degree;
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = double) */
+inline int matrix_log_get_pade_degree(double normTminusI)
+{
+  const double maxNormForPade[] = { 1.6206284795015624e-2 /* degree = 3 */ , 5.3873532631381171e-2,
+            1.1352802267628681e-1, 1.8662860613541288e-1, 2.642960831111435e-1 };
+  const int minPadeDegree = matrix_log_min_pade_degree<double>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<double>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
+      break;
+  return degree;
+}
+
+/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = long double) */
+inline int matrix_log_get_pade_degree(long double normTminusI)
+{
+#if   LDBL_MANT_DIG == 53         // double precision
+  const long double maxNormForPade[] = { 1.6206284795015624e-2L /* degree = 3 */ , 5.3873532631381171e-2L,
+            1.1352802267628681e-1L, 1.8662860613541288e-1L, 2.642960831111435e-1L };
+#elif LDBL_MANT_DIG <= 64         // extended precision
+  const long double maxNormForPade[] = { 5.48256690357782863103e-3L /* degree = 3 */, 2.34559162387971167321e-2L,
+            5.84603923897347449857e-2L, 1.08486423756725170223e-1L, 1.68385767881294446649e-1L,
+            2.32777776523703892094e-1L };
+#elif LDBL_MANT_DIG <= 106        // double-double
+  const long double maxNormForPade[] = { 8.58970550342939562202529664318890e-5L /* degree = 3 */,
+            9.34074328446359654039446552677759e-4L, 4.26117194647672175773064114582860e-3L,
+            1.21546224740281848743149666560464e-2L, 2.61100544998339436713088248557444e-2L,
+            4.66170074627052749243018566390567e-2L, 7.32585144444135027565872014932387e-2L,
+            1.05026503471351080481093652651105e-1L };
+#else                             // quadruple precision
+  const long double maxNormForPade[] = { 4.7419931187193005048501568167858103e-5L /* degree = 3 */,
+            5.8853168473544560470387769480192666e-4L, 2.9216120366601315391789493628113520e-3L,
+            8.8415758124319434347116734705174308e-3L, 1.9850836029449446668518049562565291e-2L,
+            3.6688019729653446926585242192447447e-2L, 5.9290962294020186998954055264528393e-2L,
+            8.6998436081634343903250580992127677e-2L, 1.1880960220216759245467951592883642e-1L };
+#endif
+  const int minPadeDegree = matrix_log_min_pade_degree<long double>::value;
+  const int maxPadeDegree = matrix_log_max_pade_degree<long double>::value;
+  int degree = minPadeDegree;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normTminusI <= maxNormForPade[degree - minPadeDegree])
+      break;
+  return degree;
+}
+
+/* \brief Compute Pade approximation to matrix logarithm */
+template <typename MatrixType>
+void matrix_log_compute_pade(MatrixType& result, const MatrixType& T, int degree)
+{
+  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+  const int minPadeDegree = 3;
+  const int maxPadeDegree = 11;
+  assert(degree >= minPadeDegree && degree <= maxPadeDegree);
+  // FIXME this creates float-conversion-warnings if these are enabled.
+  // Either manually convert each value, or disable the warning locally
+  const RealScalar nodes[][maxPadeDegree] = { 
+    { 0.1127016653792583114820734600217600L, 0.5000000000000000000000000000000000L,  // degree 3
+      0.8872983346207416885179265399782400L }, 
+    { 0.0694318442029737123880267555535953L, 0.3300094782075718675986671204483777L,  // degree 4
+      0.6699905217924281324013328795516223L, 0.9305681557970262876119732444464048L },
+    { 0.0469100770306680036011865608503035L, 0.2307653449471584544818427896498956L,  // degree 5
+      0.5000000000000000000000000000000000L, 0.7692346550528415455181572103501044L,
+      0.9530899229693319963988134391496965L },
+    { 0.0337652428984239860938492227530027L, 0.1693953067668677431693002024900473L,  // degree 6
+      0.3806904069584015456847491391596440L, 0.6193095930415984543152508608403560L,
+      0.8306046932331322568306997975099527L, 0.9662347571015760139061507772469973L },
+    { 0.0254460438286207377369051579760744L, 0.1292344072003027800680676133596058L,  // degree 7
+      0.2970774243113014165466967939615193L, 0.5000000000000000000000000000000000L,
+      0.7029225756886985834533032060384807L, 0.8707655927996972199319323866403942L,
+      0.9745539561713792622630948420239256L },
+    { 0.0198550717512318841582195657152635L, 0.1016667612931866302042230317620848L,  // degree 8
+      0.2372337950418355070911304754053768L, 0.4082826787521750975302619288199080L,
+      0.5917173212478249024697380711800920L, 0.7627662049581644929088695245946232L,
+      0.8983332387068133697957769682379152L, 0.9801449282487681158417804342847365L },
+    { 0.0159198802461869550822118985481636L, 0.0819844463366821028502851059651326L,  // degree 9
+      0.1933142836497048013456489803292629L, 0.3378732882980955354807309926783317L,
+      0.5000000000000000000000000000000000L, 0.6621267117019044645192690073216683L,
+      0.8066857163502951986543510196707371L, 0.9180155536633178971497148940348674L,
+      0.9840801197538130449177881014518364L },
+    { 0.0130467357414141399610179939577740L, 0.0674683166555077446339516557882535L,  // degree 10
+      0.1602952158504877968828363174425632L, 0.2833023029353764046003670284171079L,
+      0.4255628305091843945575869994351400L, 0.5744371694908156054424130005648600L,
+      0.7166976970646235953996329715828921L, 0.8397047841495122031171636825574368L,
+      0.9325316833444922553660483442117465L, 0.9869532642585858600389820060422260L },
+    { 0.0108856709269715035980309994385713L, 0.0564687001159523504624211153480364L,  // degree 11
+      0.1349239972129753379532918739844233L, 0.2404519353965940920371371652706952L,
+      0.3652284220238275138342340072995692L, 0.5000000000000000000000000000000000L,
+      0.6347715779761724861657659927004308L, 0.7595480646034059079628628347293048L,
+      0.8650760027870246620467081260155767L, 0.9435312998840476495375788846519636L,
+      0.9891143290730284964019690005614287L } };
+
+  const RealScalar weights[][maxPadeDegree] = { 
+    { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L,  // degree 3
+      0.2777777777777777777777777777777778L },
+    { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L,  // degree 4
+      0.3260725774312730713134680253890003L, 0.1739274225687269286865319746109997L },
+    { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L,  // degree 5
+      0.2844444444444444444444444444444444L, 0.2393143352496832340206457574178191L,
+      0.1184634425280945437571320203599587L },
+    { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L,  // degree 6
+      0.2339569672863455236949351719947755L, 0.2339569672863455236949351719947755L,
+      0.1803807865240693037849167569188581L, 0.0856622461895851725201480710863665L },
+    { 0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L,  // degree 7
+      0.1909150252525594724751848877444876L, 0.2089795918367346938775510204081633L,
+      0.1909150252525594724751848877444876L, 0.1398526957446383339507338857118898L,
+      0.0647424830844348466353057163395410L },
+    { 0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L,  // degree 8
+      0.1568533229389436436689811009933007L, 0.1813418916891809914825752246385978L,
+      0.1813418916891809914825752246385978L, 0.1568533229389436436689811009933007L,
+      0.1111905172266872352721779972131204L, 0.0506142681451881295762656771549811L },
+    { 0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L,  // degree 9
+      0.1303053482014677311593714347093164L, 0.1561735385200014200343152032922218L,
+      0.1651196775006298815822625346434870L, 0.1561735385200014200343152032922218L,
+      0.1303053482014677311593714347093164L, 0.0903240803474287020292360156214564L,
+      0.0406371941807872059859460790552618L },
+    { 0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L,  // degree 10
+      0.1095431812579910219977674671140816L, 0.1346333596549981775456134607847347L,
+      0.1477621123573764350869464973256692L, 0.1477621123573764350869464973256692L,
+      0.1346333596549981775456134607847347L, 0.1095431812579910219977674671140816L,
+      0.0747256745752902965728881698288487L, 0.0333356721543440687967844049466659L },
+    { 0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L,  // degree 11
+      0.0931451054638671257130488207158280L, 0.1165968822959952399592618524215876L,
+      0.1314022722551233310903444349452546L, 0.1364625433889503153572417641681711L,
+      0.1314022722551233310903444349452546L, 0.1165968822959952399592618524215876L,
+      0.0931451054638671257130488207158280L, 0.0627901847324523123173471496119701L,
+      0.0278342835580868332413768602212743L } };
+
+  MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows());
+  result.setZero(T.rows(), T.rows());
+  for (int k = 0; k < degree; ++k) {
+    RealScalar weight = weights[degree-minPadeDegree][k];
+    RealScalar node = nodes[degree-minPadeDegree][k];
+    result += weight * (MatrixType::Identity(T.rows(), T.rows()) + node * TminusI)
+                       .template triangularView<Upper>().solve(TminusI);
+  }
+} 
+
+/** \brief Compute logarithm of triangular matrices with size > 2. 
+  * \details This uses a inverse scale-and-square algorithm. */
+template <typename MatrixType>
+void matrix_log_compute_big(const MatrixType& A, MatrixType& result)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  using std::pow;
+
+  int numberOfSquareRoots = 0;
+  int numberOfExtraSquareRoots = 0;
+  int degree;
+  MatrixType T = A, sqrtT;
+
+  const int maxPadeDegree = matrix_log_max_pade_degree<Scalar>::value;
+  const RealScalar maxNormForPade = RealScalar(
+                                    maxPadeDegree<= 5? 5.3149729967117310e-1L:                    // single precision
+                                    maxPadeDegree<= 7? 2.6429608311114350e-1L:                    // double precision
+                                    maxPadeDegree<= 8? 2.32777776523703892094e-1L:                // extended precision
+                                    maxPadeDegree<=10? 1.05026503471351080481093652651105e-1L:    // double-double
+                                                       1.1880960220216759245467951592883642e-1L); // quadruple precision
+
+  while (true) {
+    RealScalar normTminusI = (T - MatrixType::Identity(T.rows(), T.rows())).cwiseAbs().colwise().sum().maxCoeff();
+    if (normTminusI < maxNormForPade) {
+      degree = matrix_log_get_pade_degree(normTminusI);
+      int degree2 = matrix_log_get_pade_degree(normTminusI / RealScalar(2));
+      if ((degree - degree2 <= 1) || (numberOfExtraSquareRoots == 1)) 
+        break;
+      ++numberOfExtraSquareRoots;
+    }
+    matrix_sqrt_triangular(T, sqrtT);
+    T = sqrtT.template triangularView<Upper>();
+    ++numberOfSquareRoots;
+  }
+
+  matrix_log_compute_pade(result, T, degree);
+  result *= pow(RealScalar(2), RealScalar(numberOfSquareRoots)); // TODO replace by bitshift if possible
+}
+
+/** \ingroup MatrixFunctions_Module
+  * \class MatrixLogarithmAtomic
+  * \brief Helper class for computing matrix logarithm of atomic matrices.
+  *
+  * Here, an atomic matrix is a triangular matrix whose diagonal entries are close to each other.
+  *
+  * \sa class MatrixFunctionAtomic, MatrixBase::log()
+  */
+template <typename MatrixType>
+class MatrixLogarithmAtomic
+{
+public:
+  /** \brief Compute matrix logarithm of atomic matrix
+    * \param[in]  A  argument of matrix logarithm, should be upper triangular and atomic
+    * \returns  The logarithm of \p A.
+    */
+  MatrixType compute(const MatrixType& A);
+};
+
+template <typename MatrixType>
+MatrixType MatrixLogarithmAtomic<MatrixType>::compute(const MatrixType& A)
+{
+  using std::log;
+  MatrixType result(A.rows(), A.rows());
+  if (A.rows() == 1)
+    result(0,0) = log(A(0,0));
+  else if (A.rows() == 2)
+    matrix_log_compute_2x2(A, result);
+  else
+    matrix_log_compute_big(A, result);
+  return result;
+}
+
+} // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+  *
+  * \brief Proxy for the matrix logarithm of some matrix (expression).
+  *
+  * \tparam Derived  Type of the argument to the matrix function.
+  *
+  * This class holds the argument to the matrix function until it is
+  * assigned or evaluated for some other reason (so the argument
+  * should not be changed in the meantime). It is the return type of
+  * MatrixBase::log() and most of the time this is the only way it
+  * is used.
+  */
+template<typename Derived> class MatrixLogarithmReturnValue
+: public ReturnByValue<MatrixLogarithmReturnValue<Derived> >
+{
+public:
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::Index Index;
+
+protected:
+  typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+public:
+
+  /** \brief Constructor.
+    *
+    * \param[in]  A  %Matrix (expression) forming the argument of the matrix logarithm.
+    */
+  explicit MatrixLogarithmReturnValue(const Derived& A) : m_A(A) { }
+  
+  /** \brief Compute the matrix logarithm.
+    *
+    * \param[out]  result  Logarithm of \c A, where \c A is as specified in the constructor.
+    */
+  template <typename ResultType>
+  inline void evalTo(ResultType& result) const
+  {
+    typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
+    typedef typename internal::remove_all<DerivedEvalType>::type DerivedEvalTypeClean;
+    typedef internal::traits<DerivedEvalTypeClean> Traits;
+    typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
+    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0, Traits::RowsAtCompileTime, Traits::ColsAtCompileTime> DynMatrixType;
+    typedef internal::MatrixLogarithmAtomic<DynMatrixType> AtomicType;
+    AtomicType atomic;
+    
+    internal::matrix_function_compute<typename DerivedEvalTypeClean::PlainObject>::run(m_A, atomic, result);
+  }
+
+  Index rows() const { return m_A.rows(); }
+  Index cols() const { return m_A.cols(); }
+  
+private:
+  const DerivedNested m_A;
+};
+
+namespace internal {
+  template<typename Derived>
+  struct traits<MatrixLogarithmReturnValue<Derived> >
+  {
+    typedef typename Derived::PlainObject ReturnType;
+  };
+}
+
+
+/********** MatrixBase method **********/
+
+
+template <typename Derived>
+const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const
+{
+  eigen_assert(rows() == cols());
+  return MatrixLogarithmReturnValue<Derived>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_LOGARITHM
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
new file mode 100644
index 0000000..d7672d7
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
@@ -0,0 +1,705 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012, 2013 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_POWER
+#define EIGEN_MATRIX_POWER
+
+namespace Eigen {
+
+template<typename MatrixType> class MatrixPower;
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix.
+ *
+ * \tparam MatrixType  type of the base, a matrix.
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixPower::operator() and related functions and most of the
+ * time this is the only way it is used.
+ */
+/* TODO This class is only used by MatrixPower, so it should be nested
+ * into MatrixPower, like MatrixPower::ReturnValue. However, my
+ * compiler complained about unused template parameter in the
+ * following declaration in namespace internal.
+ *
+ * template<typename MatrixType>
+ * struct traits<MatrixPower<MatrixType>::ReturnValue>;
+ */
+template<typename MatrixType>
+class MatrixPowerParenthesesReturnValue : public ReturnByValue< MatrixPowerParenthesesReturnValue<MatrixType> >
+{
+  public:
+    typedef typename MatrixType::RealScalar RealScalar;
+
+    /**
+     * \brief Constructor.
+     *
+     * \param[in] pow  %MatrixPower storing the base.
+     * \param[in] p    scalar, the exponent of the matrix power.
+     */
+    MatrixPowerParenthesesReturnValue(MatrixPower<MatrixType>& pow, RealScalar p) : m_pow(pow), m_p(p)
+    { }
+
+    /**
+     * \brief Compute the matrix power.
+     *
+     * \param[out] result
+     */
+    template<typename ResultType>
+    inline void evalTo(ResultType& result) const
+    { m_pow.compute(result, m_p); }
+
+    Index rows() const { return m_pow.rows(); }
+    Index cols() const { return m_pow.cols(); }
+
+  private:
+    MatrixPower<MatrixType>& m_pow;
+    const RealScalar m_p;
+};
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Class for computing matrix powers.
+ *
+ * \tparam MatrixType  type of the base, expected to be an instantiation
+ * of the Matrix class template.
+ *
+ * This class is capable of computing triangular real/complex matrices
+ * raised to a power in the interval \f$ (-1, 1) \f$.
+ *
+ * \note Currently this class is only used by MatrixPower. One may
+ * insist that this be nested into MatrixPower. This class is here to
+ * facilitate future development of triangular matrix functions.
+ */
+template<typename MatrixType>
+class MatrixPowerAtomic : internal::noncopyable
+{
+  private:
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
+    };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef std::complex<RealScalar> ComplexScalar;
+    typedef Block<MatrixType,Dynamic,Dynamic> ResultType;
+
+    const MatrixType& m_A;
+    RealScalar m_p;
+
+    void computePade(int degree, const MatrixType& IminusT, ResultType& res) const;
+    void compute2x2(ResultType& res, RealScalar p) const;
+    void computeBig(ResultType& res) const;
+    static int getPadeDegree(float normIminusT);
+    static int getPadeDegree(double normIminusT);
+    static int getPadeDegree(long double normIminusT);
+    static ComplexScalar computeSuperDiag(const ComplexScalar&, const ComplexScalar&, RealScalar p);
+    static RealScalar computeSuperDiag(RealScalar, RealScalar, RealScalar p);
+
+  public:
+    /**
+     * \brief Constructor.
+     *
+     * \param[in] T  the base of the matrix power.
+     * \param[in] p  the exponent of the matrix power, should be in
+     * \f$ (-1, 1) \f$.
+     *
+     * The class stores a reference to T, so it should not be changed
+     * (or destroyed) before evaluation. Only the upper triangular
+     * part of T is read.
+     */
+    MatrixPowerAtomic(const MatrixType& T, RealScalar p);
+    
+    /**
+     * \brief Compute the matrix power.
+     *
+     * \param[out] res  \f$ A^p \f$ where A and p are specified in the
+     * constructor.
+     */
+    void compute(ResultType& res) const;
+};
+
+template<typename MatrixType>
+MatrixPowerAtomic<MatrixType>::MatrixPowerAtomic(const MatrixType& T, RealScalar p) :
+  m_A(T), m_p(p)
+{
+  eigen_assert(T.rows() == T.cols());
+  eigen_assert(p > -1 && p < 1);
+}
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute(ResultType& res) const
+{
+  using std::pow;
+  switch (m_A.rows()) {
+    case 0:
+      break;
+    case 1:
+      res(0,0) = pow(m_A(0,0), m_p);
+      break;
+    case 2:
+      compute2x2(res, m_p);
+      break;
+    default:
+      computeBig(res);
+  }
+}
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computePade(int degree, const MatrixType& IminusT, ResultType& res) const
+{
+  int i = 2*degree;
+  res = (m_p-RealScalar(degree)) / RealScalar(2*i-2) * IminusT;
+
+  for (--i; i; --i) {
+    res = (MatrixType::Identity(IminusT.rows(), IminusT.cols()) + res).template triangularView<Upper>()
+	.solve((i==1 ? -m_p : i&1 ? (-m_p-RealScalar(i/2))/RealScalar(2*i) : (m_p-RealScalar(i/2))/RealScalar(2*i-2)) * IminusT).eval();
+  }
+  res += MatrixType::Identity(IminusT.rows(), IminusT.cols());
+}
+
+// This function assumes that res has the correct size (see bug 614)
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute2x2(ResultType& res, RealScalar p) const
+{
+  using std::abs;
+  using std::pow;
+  res.coeffRef(0,0) = pow(m_A.coeff(0,0), p);
+
+  for (Index i=1; i < m_A.cols(); ++i) {
+    res.coeffRef(i,i) = pow(m_A.coeff(i,i), p);
+    if (m_A.coeff(i-1,i-1) == m_A.coeff(i,i))
+      res.coeffRef(i-1,i) = p * pow(m_A.coeff(i,i), p-1);
+    else if (2*abs(m_A.coeff(i-1,i-1)) < abs(m_A.coeff(i,i)) || 2*abs(m_A.coeff(i,i)) < abs(m_A.coeff(i-1,i-1)))
+      res.coeffRef(i-1,i) = (res.coeff(i,i)-res.coeff(i-1,i-1)) / (m_A.coeff(i,i)-m_A.coeff(i-1,i-1));
+    else
+      res.coeffRef(i-1,i) = computeSuperDiag(m_A.coeff(i,i), m_A.coeff(i-1,i-1), p);
+    res.coeffRef(i-1,i) *= m_A.coeff(i-1,i);
+  }
+}
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computeBig(ResultType& res) const
+{
+  using std::ldexp;
+  const int digits = std::numeric_limits<RealScalar>::digits;
+  const RealScalar maxNormForPade = RealScalar(
+                                    digits <=  24? 4.3386528e-1L                            // single precision
+                                  : digits <=  53? 2.789358995219730e-1L                    // double precision
+                                  : digits <=  64? 2.4471944416607995472e-1L                // extended precision
+                                  : digits <= 106? 1.1016843812851143391275867258512e-1L    // double-double
+                                  :                9.134603732914548552537150753385375e-2L); // quadruple precision
+  MatrixType IminusT, sqrtT, T = m_A.template triangularView<Upper>();
+  RealScalar normIminusT;
+  int degree, degree2, numberOfSquareRoots = 0;
+  bool hasExtraSquareRoot = false;
+
+  for (Index i=0; i < m_A.cols(); ++i)
+    eigen_assert(m_A(i,i) != RealScalar(0));
+
+  while (true) {
+    IminusT = MatrixType::Identity(m_A.rows(), m_A.cols()) - T;
+    normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
+    if (normIminusT < maxNormForPade) {
+      degree = getPadeDegree(normIminusT);
+      degree2 = getPadeDegree(normIminusT/2);
+      if (degree - degree2 <= 1 || hasExtraSquareRoot)
+	break;
+      hasExtraSquareRoot = true;
+    }
+    matrix_sqrt_triangular(T, sqrtT);
+    T = sqrtT.template triangularView<Upper>();
+    ++numberOfSquareRoots;
+  }
+  computePade(degree, IminusT, res);
+
+  for (; numberOfSquareRoots; --numberOfSquareRoots) {
+    compute2x2(res, ldexp(m_p, -numberOfSquareRoots));
+    res = res.template triangularView<Upper>() * res;
+  }
+  compute2x2(res, m_p);
+}
+  
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(float normIminusT)
+{
+  const float maxNormForPade[] = { 2.8064004e-1f /* degree = 3 */ , 4.3386528e-1f };
+  int degree = 3;
+  for (; degree <= 4; ++degree)
+    if (normIminusT <= maxNormForPade[degree - 3])
+      break;
+  return degree;
+}
+
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(double normIminusT)
+{
+  const double maxNormForPade[] = { 1.884160592658218e-2 /* degree = 3 */ , 6.038881904059573e-2, 1.239917516308172e-1,
+      1.999045567181744e-1, 2.789358995219730e-1 };
+  int degree = 3;
+  for (; degree <= 7; ++degree)
+    if (normIminusT <= maxNormForPade[degree - 3])
+      break;
+  return degree;
+}
+
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(long double normIminusT)
+{
+#if   LDBL_MANT_DIG == 53
+  const int maxPadeDegree = 7;
+  const double maxNormForPade[] = { 1.884160592658218e-2L /* degree = 3 */ , 6.038881904059573e-2L, 1.239917516308172e-1L,
+      1.999045567181744e-1L, 2.789358995219730e-1L };
+#elif LDBL_MANT_DIG <= 64
+  const int maxPadeDegree = 8;
+  const long double maxNormForPade[] = { 6.3854693117491799460e-3L /* degree = 3 */ , 2.6394893435456973676e-2L,
+      6.4216043030404063729e-2L, 1.1701165502926694307e-1L, 1.7904284231268670284e-1L, 2.4471944416607995472e-1L };
+#elif LDBL_MANT_DIG <= 106
+  const int maxPadeDegree = 10;
+  const double maxNormForPade[] = { 1.0007161601787493236741409687186e-4L /* degree = 3 */ ,
+      1.0007161601787493236741409687186e-3L, 4.7069769360887572939882574746264e-3L, 1.3220386624169159689406653101695e-2L,
+      2.8063482381631737920612944054906e-2L, 4.9625993951953473052385361085058e-2L, 7.7367040706027886224557538328171e-2L,
+      1.1016843812851143391275867258512e-1L };
+#else
+  const int maxPadeDegree = 10;
+  const double maxNormForPade[] = { 5.524506147036624377378713555116378e-5L /* degree = 3 */ ,
+      6.640600568157479679823602193345995e-4L, 3.227716520106894279249709728084626e-3L,
+      9.619593944683432960546978734646284e-3L, 2.134595382433742403911124458161147e-2L,
+      3.908166513900489428442993794761185e-2L, 6.266780814639442865832535460550138e-2L,
+      9.134603732914548552537150753385375e-2L };
+#endif
+  int degree = 3;
+  for (; degree <= maxPadeDegree; ++degree)
+    if (normIminusT <= maxNormForPade[degree - 3])
+      break;
+  return degree;
+}
+
+template<typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::ComplexScalar
+MatrixPowerAtomic<MatrixType>::computeSuperDiag(const ComplexScalar& curr, const ComplexScalar& prev, RealScalar p)
+{
+  using std::ceil;
+  using std::exp;
+  using std::log;
+  using std::sinh;
+
+  ComplexScalar logCurr = log(curr);
+  ComplexScalar logPrev = log(prev);
+  RealScalar unwindingNumber = ceil((numext::imag(logCurr - logPrev) - RealScalar(EIGEN_PI)) / RealScalar(2*EIGEN_PI));
+  ComplexScalar w = numext::log1p((curr-prev)/prev)/RealScalar(2) + ComplexScalar(0, RealScalar(EIGEN_PI)*unwindingNumber);
+  return RealScalar(2) * exp(RealScalar(0.5) * p * (logCurr + logPrev)) * sinh(p * w) / (curr - prev);
+}
+
+template<typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::RealScalar
+MatrixPowerAtomic<MatrixType>::computeSuperDiag(RealScalar curr, RealScalar prev, RealScalar p)
+{
+  using std::exp;
+  using std::log;
+  using std::sinh;
+
+  RealScalar w = numext::log1p((curr-prev)/prev)/RealScalar(2);
+  return 2 * exp(p * (log(curr) + log(prev)) / 2) * sinh(p * w) / (curr - prev);
+}
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Class for computing matrix powers.
+ *
+ * \tparam MatrixType  type of the base, expected to be an instantiation
+ * of the Matrix class template.
+ *
+ * This class is capable of computing real/complex matrices raised to
+ * an arbitrary real power. Meanwhile, it saves the result of Schur
+ * decomposition if an non-integral power has even been calculated.
+ * Therefore, if you want to compute multiple (>= 2) matrix powers
+ * for the same matrix, using the class directly is more efficient than
+ * calling MatrixBase::pow().
+ *
+ * Example:
+ * \include MatrixPower_optimal.cpp
+ * Output: \verbinclude MatrixPower_optimal.out
+ */
+template<typename MatrixType>
+class MatrixPower : internal::noncopyable
+{
+  private:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+
+  public:
+    /**
+     * \brief Constructor.
+     *
+     * \param[in] A  the base of the matrix power.
+     *
+     * The class stores a reference to A, so it should not be changed
+     * (or destroyed) before evaluation.
+     */
+    explicit MatrixPower(const MatrixType& A) :
+      m_A(A),
+      m_conditionNumber(0),
+      m_rank(A.cols()),
+      m_nulls(0)
+    { eigen_assert(A.rows() == A.cols()); }
+
+    /**
+     * \brief Returns the matrix power.
+     *
+     * \param[in] p  exponent, a real scalar.
+     * \return The expression \f$ A^p \f$, where A is specified in the
+     * constructor.
+     */
+    const MatrixPowerParenthesesReturnValue<MatrixType> operator()(RealScalar p)
+    { return MatrixPowerParenthesesReturnValue<MatrixType>(*this, p); }
+
+    /**
+     * \brief Compute the matrix power.
+     *
+     * \param[in]  p    exponent, a real scalar.
+     * \param[out] res  \f$ A^p \f$ where A is specified in the
+     * constructor.
+     */
+    template<typename ResultType>
+    void compute(ResultType& res, RealScalar p);
+    
+    Index rows() const { return m_A.rows(); }
+    Index cols() const { return m_A.cols(); }
+
+  private:
+    typedef std::complex<RealScalar> ComplexScalar;
+    typedef Matrix<ComplexScalar, Dynamic, Dynamic, 0,
+              MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> ComplexMatrix;
+
+    /** \brief Reference to the base of matrix power. */
+    typename MatrixType::Nested m_A;
+
+    /** \brief Temporary storage. */
+    MatrixType m_tmp;
+
+    /** \brief Store the result of Schur decomposition. */
+    ComplexMatrix m_T, m_U;
+    
+    /** \brief Store fractional power of m_T. */
+    ComplexMatrix m_fT;
+
+    /**
+     * \brief Condition number of m_A.
+     *
+     * It is initialized as 0 to avoid performing unnecessary Schur
+     * decomposition, which is the bottleneck.
+     */
+    RealScalar m_conditionNumber;
+
+    /** \brief Rank of m_A. */
+    Index m_rank;
+    
+    /** \brief Rank deficiency of m_A. */
+    Index m_nulls;
+
+    /**
+     * \brief Split p into integral part and fractional part.
+     *
+     * \param[in]  p        The exponent.
+     * \param[out] p        The fractional part ranging in \f$ (-1, 1) \f$.
+     * \param[out] intpart  The integral part.
+     *
+     * Only if the fractional part is nonzero, it calls initialize().
+     */
+    void split(RealScalar& p, RealScalar& intpart);
+
+    /** \brief Perform Schur decomposition for fractional power. */
+    void initialize();
+
+    template<typename ResultType>
+    void computeIntPower(ResultType& res, RealScalar p);
+
+    template<typename ResultType>
+    void computeFracPower(ResultType& res, RealScalar p);
+
+    template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+    static void revertSchur(
+        Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+        const ComplexMatrix& T,
+        const ComplexMatrix& U);
+
+    template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+    static void revertSchur(
+        Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+        const ComplexMatrix& T,
+        const ComplexMatrix& U);
+};
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::compute(ResultType& res, RealScalar p)
+{
+  using std::pow;
+  switch (cols()) {
+    case 0:
+      break;
+    case 1:
+      res(0,0) = pow(m_A.coeff(0,0), p);
+      break;
+    default:
+      RealScalar intpart;
+      split(p, intpart);
+
+      res = MatrixType::Identity(rows(), cols());
+      computeIntPower(res, intpart);
+      if (p) computeFracPower(res, p);
+  }
+}
+
+template<typename MatrixType>
+void MatrixPower<MatrixType>::split(RealScalar& p, RealScalar& intpart)
+{
+  using std::floor;
+  using std::pow;
+
+  intpart = floor(p);
+  p -= intpart;
+
+  // Perform Schur decomposition if it is not yet performed and the power is
+  // not an integer.
+  if (!m_conditionNumber && p)
+    initialize();
+
+  // Choose the more stable of intpart = floor(p) and intpart = ceil(p).
+  if (p > RealScalar(0.5) && p > (1-p) * pow(m_conditionNumber, p)) {
+    --p;
+    ++intpart;
+  }
+}
+
+template<typename MatrixType>
+void MatrixPower<MatrixType>::initialize()
+{
+  const ComplexSchur<MatrixType> schurOfA(m_A);
+  JacobiRotation<ComplexScalar> rot;
+  ComplexScalar eigenvalue;
+
+  m_fT.resizeLike(m_A);
+  m_T = schurOfA.matrixT();
+  m_U = schurOfA.matrixU();
+  m_conditionNumber = m_T.diagonal().array().abs().maxCoeff() / m_T.diagonal().array().abs().minCoeff();
+
+  // Move zero eigenvalues to the bottom right corner.
+  for (Index i = cols()-1; i>=0; --i) {
+    if (m_rank <= 2)
+      return;
+    if (m_T.coeff(i,i) == RealScalar(0)) {
+      for (Index j=i+1; j < m_rank; ++j) {
+        eigenvalue = m_T.coeff(j,j);
+        rot.makeGivens(m_T.coeff(j-1,j), eigenvalue);
+        m_T.applyOnTheRight(j-1, j, rot);
+        m_T.applyOnTheLeft(j-1, j, rot.adjoint());
+        m_T.coeffRef(j-1,j-1) = eigenvalue;
+        m_T.coeffRef(j,j) = RealScalar(0);
+        m_U.applyOnTheRight(j-1, j, rot);
+      }
+      --m_rank;
+    }
+  }
+
+  m_nulls = rows() - m_rank;
+  if (m_nulls) {
+    eigen_assert(m_T.bottomRightCorner(m_nulls, m_nulls).isZero()
+        && "Base of matrix power should be invertible or with a semisimple zero eigenvalue.");
+    m_fT.bottomRows(m_nulls).fill(RealScalar(0));
+  }
+}
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::computeIntPower(ResultType& res, RealScalar p)
+{
+  using std::abs;
+  using std::fmod;
+  RealScalar pp = abs(p);
+
+  if (p<0) 
+    m_tmp = m_A.inverse();
+  else     
+    m_tmp = m_A;
+
+  while (true) {
+    if (fmod(pp, 2) >= 1)
+      res = m_tmp * res;
+    pp /= 2;
+    if (pp < 1)
+      break;
+    m_tmp *= m_tmp;
+  }
+}
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::computeFracPower(ResultType& res, RealScalar p)
+{
+  Block<ComplexMatrix,Dynamic,Dynamic> blockTp(m_fT, 0, 0, m_rank, m_rank);
+  eigen_assert(m_conditionNumber);
+  eigen_assert(m_rank + m_nulls == rows());
+
+  MatrixPowerAtomic<ComplexMatrix>(m_T.topLeftCorner(m_rank, m_rank), p).compute(blockTp);
+  if (m_nulls) {
+    m_fT.topRightCorner(m_rank, m_nulls) = m_T.topLeftCorner(m_rank, m_rank).template triangularView<Upper>()
+        .solve(blockTp * m_T.topRightCorner(m_rank, m_nulls));
+  }
+  revertSchur(m_tmp, m_fT, m_U);
+  res = m_tmp * res;
+}
+
+template<typename MatrixType>
+template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(
+    Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+    const ComplexMatrix& T,
+    const ComplexMatrix& U)
+{ res.noalias() = U * (T.template triangularView<Upper>() * U.adjoint()); }
+
+template<typename MatrixType>
+template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(
+    Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+    const ComplexMatrix& T,
+    const ComplexMatrix& U)
+{ res.noalias() = (U * (T.template triangularView<Upper>() * U.adjoint())).real(); }
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix (expression).
+ *
+ * \tparam Derived  type of the base, a matrix (expression).
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::pow() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template<typename Derived>
+class MatrixPowerReturnValue : public ReturnByValue< MatrixPowerReturnValue<Derived> >
+{
+  public:
+    typedef typename Derived::PlainObject PlainObject;
+    typedef typename Derived::RealScalar RealScalar;
+
+    /**
+     * \brief Constructor.
+     *
+     * \param[in] A  %Matrix (expression), the base of the matrix power.
+     * \param[in] p  real scalar, the exponent of the matrix power.
+     */
+    MatrixPowerReturnValue(const Derived& A, RealScalar p) : m_A(A), m_p(p)
+    { }
+
+    /**
+     * \brief Compute the matrix power.
+     *
+     * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
+     * constructor.
+     */
+    template<typename ResultType>
+    inline void evalTo(ResultType& result) const
+    { MatrixPower<PlainObject>(m_A.eval()).compute(result, m_p); }
+
+    Index rows() const { return m_A.rows(); }
+    Index cols() const { return m_A.cols(); }
+
+  private:
+    const Derived& m_A;
+    const RealScalar m_p;
+};
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix (expression).
+ *
+ * \tparam Derived  type of the base, a matrix (expression).
+ *
+ * This class holds the arguments to the matrix power until it is
+ * assigned or evaluated for some other reason (so the argument
+ * should not be changed in the meantime). It is the return type of
+ * MatrixBase::pow() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template<typename Derived>
+class MatrixComplexPowerReturnValue : public ReturnByValue< MatrixComplexPowerReturnValue<Derived> >
+{
+  public:
+    typedef typename Derived::PlainObject PlainObject;
+    typedef typename std::complex<typename Derived::RealScalar> ComplexScalar;
+
+    /**
+     * \brief Constructor.
+     *
+     * \param[in] A  %Matrix (expression), the base of the matrix power.
+     * \param[in] p  complex scalar, the exponent of the matrix power.
+     */
+    MatrixComplexPowerReturnValue(const Derived& A, const ComplexScalar& p) : m_A(A), m_p(p)
+    { }
+
+    /**
+     * \brief Compute the matrix power.
+     *
+     * Because \p p is complex, \f$ A^p \f$ is simply evaluated as \f$
+     * \exp(p \log(A)) \f$.
+     *
+     * \param[out] result  \f$ A^p \f$ where \p A and \p p are as in the
+     * constructor.
+     */
+    template<typename ResultType>
+    inline void evalTo(ResultType& result) const
+    { result = (m_p * m_A.log()).exp(); }
+
+    Index rows() const { return m_A.rows(); }
+    Index cols() const { return m_A.cols(); }
+
+  private:
+    const Derived& m_A;
+    const ComplexScalar m_p;
+};
+
+namespace internal {
+
+template<typename MatrixPowerType>
+struct traits< MatrixPowerParenthesesReturnValue<MatrixPowerType> >
+{ typedef typename MatrixPowerType::PlainObject ReturnType; };
+
+template<typename Derived>
+struct traits< MatrixPowerReturnValue<Derived> >
+{ typedef typename Derived::PlainObject ReturnType; };
+
+template<typename Derived>
+struct traits< MatrixComplexPowerReturnValue<Derived> >
+{ typedef typename Derived::PlainObject ReturnType; };
+
+}
+
+template<typename Derived>
+const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(const RealScalar& p) const
+{ return MatrixPowerReturnValue<Derived>(derived(), p); }
+
+template<typename Derived>
+const MatrixComplexPowerReturnValue<Derived> MatrixBase<Derived>::pow(const std::complex<RealScalar>& p) const
+{ return MatrixComplexPowerReturnValue<Derived>(derived(), p); }
+
+} // namespace Eigen
+
+#endif // EIGEN_MATRIX_POWER
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
new file mode 100644
index 0000000..e363e77
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
@@ -0,0 +1,368 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_SQUARE_ROOT
+#define EIGEN_MATRIX_SQUARE_ROOT
+
+namespace Eigen { 
+
+namespace internal {
+
+// pre:  T.block(i,i,2,2) has complex conjugate eigenvalues
+// post: sqrtT.block(i,i,2,2) is square root of T.block(i,i,2,2)
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x2_diagonal_block(const MatrixType& T, Index i, ResultType& sqrtT)
+{
+  // TODO: This case (2-by-2 blocks with complex conjugate eigenvalues) is probably hidden somewhere
+  //       in EigenSolver. If we expose it, we could call it directly from here.
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar,2,2> block = T.template block<2,2>(i,i);
+  EigenSolver<Matrix<Scalar,2,2> > es(block);
+  sqrtT.template block<2,2>(i,i)
+    = (es.eigenvectors() * es.eigenvalues().cwiseSqrt().asDiagonal() * es.eigenvectors().inverse()).real();
+}
+
+// pre:  block structure of T is such that (i,j) is a 1x1 block,
+//       all blocks of sqrtT to left of and below (i,j) are correct
+// post: sqrtT(i,j) has the correct value
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Scalar tmp = (sqrtT.row(i).segment(i+1,j-i-1) * sqrtT.col(j).segment(i+1,j-i-1)).value();
+  sqrtT.coeffRef(i,j) = (T.coeff(i,j) - tmp) / (sqrtT.coeff(i,i) + sqrtT.coeff(j,j));
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar,1,2> rhs = T.template block<1,2>(i,j);
+  if (j-i > 1)
+    rhs -= sqrtT.block(i, i+1, 1, j-i-1) * sqrtT.block(i+1, j, j-i-1, 2);
+  Matrix<Scalar,2,2> A = sqrtT.coeff(i,i) * Matrix<Scalar,2,2>::Identity();
+  A += sqrtT.template block<2,2>(j,j).transpose();
+  sqrtT.template block<1,2>(i,j).transpose() = A.fullPivLu().solve(rhs.transpose());
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar,2,1> rhs = T.template block<2,1>(i,j);
+  if (j-i > 2)
+    rhs -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 1);
+  Matrix<Scalar,2,2> A = sqrtT.coeff(j,j) * Matrix<Scalar,2,2>::Identity();
+  A += sqrtT.template block<2,2>(i,i);
+  sqrtT.template block<2,1>(i,j) = A.fullPivLu().solve(rhs);
+}
+
+// solves the equation A X + X B = C where all matrices are 2-by-2
+template <typename MatrixType>
+void matrix_sqrt_quasi_triangular_solve_auxiliary_equation(MatrixType& X, const MatrixType& A, const MatrixType& B, const MatrixType& C)
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar,4,4> coeffMatrix = Matrix<Scalar,4,4>::Zero();
+  coeffMatrix.coeffRef(0,0) = A.coeff(0,0) + B.coeff(0,0);
+  coeffMatrix.coeffRef(1,1) = A.coeff(0,0) + B.coeff(1,1);
+  coeffMatrix.coeffRef(2,2) = A.coeff(1,1) + B.coeff(0,0);
+  coeffMatrix.coeffRef(3,3) = A.coeff(1,1) + B.coeff(1,1);
+  coeffMatrix.coeffRef(0,1) = B.coeff(1,0);
+  coeffMatrix.coeffRef(0,2) = A.coeff(0,1);
+  coeffMatrix.coeffRef(1,0) = B.coeff(0,1);
+  coeffMatrix.coeffRef(1,3) = A.coeff(0,1);
+  coeffMatrix.coeffRef(2,0) = A.coeff(1,0);
+  coeffMatrix.coeffRef(2,3) = B.coeff(1,0);
+  coeffMatrix.coeffRef(3,1) = A.coeff(1,0);
+  coeffMatrix.coeffRef(3,2) = B.coeff(0,1);
+
+  Matrix<Scalar,4,1> rhs;
+  rhs.coeffRef(0) = C.coeff(0,0);
+  rhs.coeffRef(1) = C.coeff(0,1);
+  rhs.coeffRef(2) = C.coeff(1,0);
+  rhs.coeffRef(3) = C.coeff(1,1);
+
+  Matrix<Scalar,4,1> result;
+  result = coeffMatrix.fullPivLu().solve(rhs);
+
+  X.coeffRef(0,0) = result.coeff(0);
+  X.coeffRef(0,1) = result.coeff(1);
+  X.coeffRef(1,0) = result.coeff(2);
+  X.coeffRef(1,1) = result.coeff(3);
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(const MatrixType& T, Index i, Index j, ResultType& sqrtT)
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  Matrix<Scalar,2,2> A = sqrtT.template block<2,2>(i,i);
+  Matrix<Scalar,2,2> B = sqrtT.template block<2,2>(j,j);
+  Matrix<Scalar,2,2> C = T.template block<2,2>(i,j);
+  if (j-i > 2)
+    C -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 2);
+  Matrix<Scalar,2,2> X;
+  matrix_sqrt_quasi_triangular_solve_auxiliary_equation(X, A, B, C);
+  sqrtT.template block<2,2>(i,j) = X;
+}
+
+// pre:  T is quasi-upper-triangular and sqrtT is a zero matrix of the same size
+// post: the diagonal blocks of sqrtT are the square roots of the diagonal blocks of T
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_diagonal(const MatrixType& T, ResultType& sqrtT)
+{
+  using std::sqrt;
+  const Index size = T.rows();
+  for (Index i = 0; i < size; i++) {
+    if (i == size - 1 || T.coeff(i+1, i) == 0) {
+      eigen_assert(T(i,i) >= 0);
+      sqrtT.coeffRef(i,i) = sqrt(T.coeff(i,i));
+    }
+    else {
+      matrix_sqrt_quasi_triangular_2x2_diagonal_block(T, i, sqrtT);
+      ++i;
+    }
+  }
+}
+
+// pre:  T is quasi-upper-triangular and diagonal blocks of sqrtT are square root of diagonal blocks of T.
+// post: sqrtT is the square root of T.
+template <typename MatrixType, typename ResultType>
+void matrix_sqrt_quasi_triangular_off_diagonal(const MatrixType& T, ResultType& sqrtT)
+{
+  const Index size = T.rows();
+  for (Index j = 1; j < size; j++) {
+      if (T.coeff(j, j-1) != 0)  // if T(j-1:j, j-1:j) is a 2-by-2 block
+	continue;
+    for (Index i = j-1; i >= 0; i--) {
+      if (i > 0 && T.coeff(i, i-1) != 0)  // if T(i-1:i, i-1:i) is a 2-by-2 block
+	continue;
+      bool iBlockIs2x2 = (i < size - 1) && (T.coeff(i+1, i) != 0);
+      bool jBlockIs2x2 = (j < size - 1) && (T.coeff(j+1, j) != 0);
+      if (iBlockIs2x2 && jBlockIs2x2) 
+        matrix_sqrt_quasi_triangular_2x2_off_diagonal_block(T, i, j, sqrtT);
+      else if (iBlockIs2x2 && !jBlockIs2x2) 
+        matrix_sqrt_quasi_triangular_2x1_off_diagonal_block(T, i, j, sqrtT);
+      else if (!iBlockIs2x2 && jBlockIs2x2) 
+        matrix_sqrt_quasi_triangular_1x2_off_diagonal_block(T, i, j, sqrtT);
+      else if (!iBlockIs2x2 && !jBlockIs2x2) 
+        matrix_sqrt_quasi_triangular_1x1_off_diagonal_block(T, i, j, sqrtT);
+    }
+  }
+}
+
+} // end of namespace internal
+
+/** \ingroup MatrixFunctions_Module
+  * \brief Compute matrix square root of quasi-triangular matrix.
+  *
+  * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
+  *                      expected to be an instantiation of the Matrix class template.
+  * \tparam  ResultType  type of \p result, where result is to be stored.
+  * \param[in]  arg      argument of matrix square root.
+  * \param[out] result   matrix square root of upper Hessenberg part of \p arg.
+  *
+  * This function computes the square root of the upper quasi-triangular matrix stored in the upper
+  * Hessenberg part of \p arg.  Only the upper Hessenberg part of \p result is updated, the rest is
+  * not touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
+  *
+  * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
+  */
+template <typename MatrixType, typename ResultType> 
+void matrix_sqrt_quasi_triangular(const MatrixType &arg, ResultType &result)
+{
+  eigen_assert(arg.rows() == arg.cols());
+  result.resize(arg.rows(), arg.cols());
+  internal::matrix_sqrt_quasi_triangular_diagonal(arg, result);
+  internal::matrix_sqrt_quasi_triangular_off_diagonal(arg, result);
+}
+
+
+/** \ingroup MatrixFunctions_Module
+  * \brief Compute matrix square root of triangular matrix.
+  *
+  * \tparam  MatrixType  type of \p arg, the argument of matrix square root,
+  *                      expected to be an instantiation of the Matrix class template.
+  * \tparam  ResultType  type of \p result, where result is to be stored.
+  * \param[in]  arg      argument of matrix square root.
+  * \param[out] result   matrix square root of upper triangular part of \p arg.
+  *
+  * Only the upper triangular part (including the diagonal) of \p result is updated, the rest is not
+  * touched.  See MatrixBase::sqrt() for details on how this computation is implemented.
+  *
+  * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
+  */
+template <typename MatrixType, typename ResultType> 
+void matrix_sqrt_triangular(const MatrixType &arg, ResultType &result)
+{
+  using std::sqrt;
+  typedef typename MatrixType::Scalar Scalar;
+
+  eigen_assert(arg.rows() == arg.cols());
+
+  // Compute square root of arg and store it in upper triangular part of result
+  // This uses that the square root of triangular matrices can be computed directly.
+  result.resize(arg.rows(), arg.cols());
+  for (Index i = 0; i < arg.rows(); i++) {
+    result.coeffRef(i,i) = sqrt(arg.coeff(i,i));
+  }
+  for (Index j = 1; j < arg.cols(); j++) {
+    for (Index i = j-1; i >= 0; i--) {
+      // if i = j-1, then segment has length 0 so tmp = 0
+      Scalar tmp = (result.row(i).segment(i+1,j-i-1) * result.col(j).segment(i+1,j-i-1)).value();
+      // denominator may be zero if original matrix is singular
+      result.coeffRef(i,j) = (arg.coeff(i,j) - tmp) / (result.coeff(i,i) + result.coeff(j,j));
+    }
+  }
+}
+
+
+namespace internal {
+
+/** \ingroup MatrixFunctions_Module
+  * \brief Helper struct for computing matrix square roots of general matrices.
+  * \tparam  MatrixType  type of the argument of the matrix square root,
+  *                      expected to be an instantiation of the Matrix class template.
+  *
+  * \sa MatrixSquareRootTriangular, MatrixSquareRootQuasiTriangular, MatrixBase::sqrt()
+  */
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+struct matrix_sqrt_compute
+{
+  /** \brief Compute the matrix square root
+    *
+    * \param[in]  arg     matrix whose square root is to be computed.
+    * \param[out] result  square root of \p arg.
+    *
+    * See MatrixBase::sqrt() for details on how this computation is implemented.
+    */
+  template <typename ResultType> static void run(const MatrixType &arg, ResultType &result);    
+};
+
+
+// ********** Partial specialization for real matrices **********
+
+template <typename MatrixType>
+struct matrix_sqrt_compute<MatrixType, 0>
+{
+  typedef typename MatrixType::PlainObject PlainType;
+  template <typename ResultType>
+  static void run(const MatrixType &arg, ResultType &result)
+  {
+    eigen_assert(arg.rows() == arg.cols());
+
+    // Compute Schur decomposition of arg
+    const RealSchur<PlainType> schurOfA(arg);
+    const PlainType& T = schurOfA.matrixT();
+    const PlainType& U = schurOfA.matrixU();
+    
+    // Compute square root of T
+    PlainType sqrtT = PlainType::Zero(arg.rows(), arg.cols());
+    matrix_sqrt_quasi_triangular(T, sqrtT);
+    
+    // Compute square root of arg
+    result = U * sqrtT * U.adjoint();
+  }
+};
+
+
+// ********** Partial specialization for complex matrices **********
+
+template <typename MatrixType>
+struct matrix_sqrt_compute<MatrixType, 1>
+{
+  typedef typename MatrixType::PlainObject PlainType;
+  template <typename ResultType>
+  static void run(const MatrixType &arg, ResultType &result)
+  {
+    eigen_assert(arg.rows() == arg.cols());
+
+    // Compute Schur decomposition of arg
+    const ComplexSchur<PlainType> schurOfA(arg);
+    const PlainType& T = schurOfA.matrixT();
+    const PlainType& U = schurOfA.matrixU();
+    
+    // Compute square root of T
+    PlainType sqrtT;
+    matrix_sqrt_triangular(T, sqrtT);
+    
+    // Compute square root of arg
+    result = U * (sqrtT.template triangularView<Upper>() * U.adjoint());
+  }
+};
+
+} // end namespace internal
+
+/** \ingroup MatrixFunctions_Module
+  *
+  * \brief Proxy for the matrix square root of some matrix (expression).
+  *
+  * \tparam Derived  Type of the argument to the matrix square root.
+  *
+  * This class holds the argument to the matrix square root until it
+  * is assigned or evaluated for some other reason (so the argument
+  * should not be changed in the meantime). It is the return type of
+  * MatrixBase::sqrt() and most of the time this is the only way it is
+  * used.
+  */
+template<typename Derived> class MatrixSquareRootReturnValue
+: public ReturnByValue<MatrixSquareRootReturnValue<Derived> >
+{
+  protected:
+    typedef typename internal::ref_selector<Derived>::type DerivedNested;
+
+  public:
+    /** \brief Constructor.
+      *
+      * \param[in]  src  %Matrix (expression) forming the argument of the
+      * matrix square root.
+      */
+    explicit MatrixSquareRootReturnValue(const Derived& src) : m_src(src) { }
+
+    /** \brief Compute the matrix square root.
+      *
+      * \param[out]  result  the matrix square root of \p src in the
+      * constructor.
+      */
+    template <typename ResultType>
+    inline void evalTo(ResultType& result) const
+    {
+      typedef typename internal::nested_eval<Derived, 10>::type DerivedEvalType;
+      typedef typename internal::remove_all<DerivedEvalType>::type DerivedEvalTypeClean;
+      DerivedEvalType tmp(m_src);
+      internal::matrix_sqrt_compute<DerivedEvalTypeClean>::run(tmp, result);
+    }
+
+    Index rows() const { return m_src.rows(); }
+    Index cols() const { return m_src.cols(); }
+
+  protected:
+    const DerivedNested m_src;
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixSquareRootReturnValue<Derived> >
+{
+  typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+template <typename Derived>
+const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
+{
+  eigen_assert(rows() == cols());
+  return MatrixSquareRootReturnValue<Derived>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
new file mode 100644
index 0000000..7604df9
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
@@ -0,0 +1,117 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010, 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STEM_FUNCTION
+#define EIGEN_STEM_FUNCTION
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \brief The exponential function (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_exp(Scalar x, int)
+{
+  using std::exp;
+  return exp(x);
+}
+
+/** \brief Cosine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_cos(Scalar x, int n)
+{
+  using std::cos;
+  using std::sin;
+  Scalar res;
+
+  switch (n % 4) {
+  case 0: 
+    res = std::cos(x);
+    break;
+  case 1:
+    res = -std::sin(x);
+    break;
+  case 2:
+    res = -std::cos(x);
+    break;
+  case 3:
+    res = std::sin(x);
+    break;
+  }
+  return res;
+}
+
+/** \brief Sine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_sin(Scalar x, int n)
+{
+  using std::cos;
+  using std::sin;
+  Scalar res;
+
+  switch (n % 4) {
+  case 0:
+    res = std::sin(x);
+    break;
+  case 1:
+    res = std::cos(x);
+    break;
+  case 2:
+    res = -std::sin(x);
+    break;
+  case 3:
+    res = -std::cos(x);
+    break;
+  }
+  return res;
+}
+
+/** \brief Hyperbolic cosine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_cosh(Scalar x, int n)
+{
+  using std::cosh;
+  using std::sinh;
+  Scalar res;
+  
+  switch (n % 2) {
+  case 0:
+    res = std::cosh(x);
+    break;
+  case 1:
+    res = std::sinh(x);
+    break;
+  }
+  return res;
+}
+	
+/** \brief Hyperbolic sine (and its derivatives). */
+template <typename Scalar>
+Scalar stem_function_sinh(Scalar x, int n)
+{
+  using std::cosh;
+  using std::sinh;
+  Scalar res;
+  
+  switch (n % 2) {
+  case 0:
+    res = std::sinh(x);
+    break;
+  case 1:
+    res = std::cosh(x);
+    break;
+  }
+  return res;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_STEM_FUNCTION
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
new file mode 100644
index 0000000..63cb28d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/MoreVectorization/MathFunctions.h
@@ -0,0 +1,95 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
+#define EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal \returns the arcsin of \a a (coeff-wise) */
+template<typename Packet> inline static Packet pasin(Packet a) { return std::asin(a); }
+
+#ifdef EIGEN_VECTORIZE_SSE
+
+template<> EIGEN_DONT_INLINE Packet4f pasin(Packet4f x)
+{
+  _EIGEN_DECLARE_CONST_Packet4f(half, 0.5);
+  _EIGEN_DECLARE_CONST_Packet4f(minus_half, -0.5);
+  _EIGEN_DECLARE_CONST_Packet4f(3half, 1.5);
+
+  _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(sign_mask, 0x80000000);
+
+  _EIGEN_DECLARE_CONST_Packet4f(pi, 3.141592654);
+  _EIGEN_DECLARE_CONST_Packet4f(pi_over_2, 3.141592654*0.5);
+
+  _EIGEN_DECLARE_CONST_Packet4f(asin1, 4.2163199048E-2);
+  _EIGEN_DECLARE_CONST_Packet4f(asin2, 2.4181311049E-2);
+  _EIGEN_DECLARE_CONST_Packet4f(asin3, 4.5470025998E-2);
+  _EIGEN_DECLARE_CONST_Packet4f(asin4, 7.4953002686E-2);
+  _EIGEN_DECLARE_CONST_Packet4f(asin5, 1.6666752422E-1);
+
+  Packet4f a = pabs(x);//got the absolute value
+
+  Packet4f sign_bit= _mm_and_ps(x, p4f_sign_mask);//extracted the sign bit
+
+  Packet4f z1,z2;//will need them during computation    
+
+
+//will compute the two branches for asin
+//so first compare with half
+
+  Packet4f branch_mask= _mm_cmpgt_ps(a, p4f_half);//this is to select which branch to take
+//both will be taken, and finally results will be merged
+//the branch for values >0.5
+
+    {
+//the core series expansion 
+    z1=pmadd(p4f_minus_half,a,p4f_half);
+    Packet4f x1=psqrt(z1);
+    Packet4f s1=pmadd(p4f_asin1, z1, p4f_asin2);
+    Packet4f s2=pmadd(s1, z1, p4f_asin3);
+    Packet4f s3=pmadd(s2,z1, p4f_asin4);
+    Packet4f s4=pmadd(s3,z1, p4f_asin5);
+    Packet4f temp=pmul(s4,z1);//not really a madd but a mul by z so that the next term can be a madd
+    z1=pmadd(temp,x1,x1);
+    z1=padd(z1,z1);
+    z1=psub(p4f_pi_over_2,z1);
+    }
+
+    {
+//the core series expansion 
+    Packet4f x2=a;
+    z2=pmul(x2,x2);
+    Packet4f s1=pmadd(p4f_asin1, z2, p4f_asin2);
+    Packet4f s2=pmadd(s1, z2, p4f_asin3);
+    Packet4f s3=pmadd(s2,z2, p4f_asin4);
+    Packet4f s4=pmadd(s3,z2, p4f_asin5);
+    Packet4f temp=pmul(s4,z2);//not really a madd but a mul by z so that the next term can be a madd
+    z2=pmadd(temp,x2,x2);
+    }
+
+/* select the correct result from the two branch evaluations */
+  z1  = _mm_and_ps(branch_mask, z1);
+  z2  = _mm_andnot_ps(branch_mask, z2);
+  Packet4f z  = _mm_or_ps(z1,z2);
+
+/* update the sign */
+  return _mm_xor_ps(z, sign_bit);
+}
+
+#endif // EIGEN_VECTORIZE_SSE
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MOREVECTORIZATION_MATHFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
new file mode 100644
index 0000000..07c5ef0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
@@ -0,0 +1,601 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HYBRIDNONLINEARSOLVER_H
+#define EIGEN_HYBRIDNONLINEARSOLVER_H
+
+namespace Eigen { 
+
+namespace HybridNonLinearSolverSpace { 
+    enum Status {
+        Running = -1,
+        ImproperInputParameters = 0,
+        RelativeErrorTooSmall = 1,
+        TooManyFunctionEvaluation = 2,
+        TolTooSmall = 3,
+        NotMakingProgressJacobian = 4,
+        NotMakingProgressIterations = 5,
+        UserAsked = 6
+    };
+}
+
+/**
+  * \ingroup NonLinearOptimization_Module
+  * \brief Finds a zero of a system of n
+  * nonlinear functions in n variables by a modification of the Powell
+  * hybrid method ("dogleg").
+  *
+  * The user must provide a subroutine which calculates the
+  * functions. The Jacobian is either provided by the user, or approximated
+  * using a forward-difference method.
+  *
+  */
+template<typename FunctorType, typename Scalar=double>
+class HybridNonLinearSolver
+{
+public:
+    typedef DenseIndex Index;
+
+    HybridNonLinearSolver(FunctorType &_functor)
+        : functor(_functor) { nfev=njev=iter = 0;  fnorm= 0.; useExternalScaling=false;}
+
+    struct Parameters {
+        Parameters()
+            : factor(Scalar(100.))
+            , maxfev(1000)
+            , xtol(numext::sqrt(NumTraits<Scalar>::epsilon()))
+            , nb_of_subdiagonals(-1)
+            , nb_of_superdiagonals(-1)
+            , epsfcn(Scalar(0.)) {}
+        Scalar factor;
+        Index maxfev;   // maximum number of function evaluation
+        Scalar xtol;
+        Index nb_of_subdiagonals;
+        Index nb_of_superdiagonals;
+        Scalar epsfcn;
+    };
+    typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
+    typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
+    /* TODO: if eigen provides a triangular storage, use it here */
+    typedef Matrix< Scalar, Dynamic, Dynamic > UpperTriangularType;
+
+    HybridNonLinearSolverSpace::Status hybrj1(
+            FVectorType  &x,
+            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
+            );
+
+    HybridNonLinearSolverSpace::Status solveInit(FVectorType  &x);
+    HybridNonLinearSolverSpace::Status solveOneStep(FVectorType  &x);
+    HybridNonLinearSolverSpace::Status solve(FVectorType  &x);
+
+    HybridNonLinearSolverSpace::Status hybrd1(
+            FVectorType  &x,
+            const Scalar tol = numext::sqrt(NumTraits<Scalar>::epsilon())
+            );
+
+    HybridNonLinearSolverSpace::Status solveNumericalDiffInit(FVectorType  &x);
+    HybridNonLinearSolverSpace::Status solveNumericalDiffOneStep(FVectorType  &x);
+    HybridNonLinearSolverSpace::Status solveNumericalDiff(FVectorType  &x);
+
+    void resetParameters(void) { parameters = Parameters(); }
+    Parameters parameters;
+    FVectorType  fvec, qtf, diag;
+    JacobianType fjac;
+    UpperTriangularType R;
+    Index nfev;
+    Index njev;
+    Index iter;
+    Scalar fnorm;
+    bool useExternalScaling; 
+private:
+    FunctorType &functor;
+    Index n;
+    Scalar sum;
+    bool sing;
+    Scalar temp;
+    Scalar delta;
+    bool jeval;
+    Index ncsuc;
+    Scalar ratio;
+    Scalar pnorm, xnorm, fnorm1;
+    Index nslow1, nslow2;
+    Index ncfail;
+    Scalar actred, prered;
+    FVectorType wa1, wa2, wa3, wa4;
+
+    HybridNonLinearSolver& operator=(const HybridNonLinearSolver&);
+};
+
+
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::hybrj1(
+        FVectorType  &x,
+        const Scalar tol
+        )
+{
+    n = x.size();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || tol < 0.)
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+    resetParameters();
+    parameters.maxfev = 100*(n+1);
+    parameters.xtol = tol;
+    diag.setConstant(n, 1.);
+    useExternalScaling = true;
+    return solve(x);
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveInit(FVectorType  &x)
+{
+    n = x.size();
+
+    wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
+    fvec.resize(n);
+    qtf.resize(n);
+    fjac.resize(n, n);
+    if (!useExternalScaling)
+        diag.resize(n);
+    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
+
+    /* Function Body */
+    nfev = 0;
+    njev = 0;
+
+    /*     check the input parameters for errors. */
+    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. )
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
+    if (useExternalScaling)
+        for (Index j = 0; j < n; ++j)
+            if (diag[j] <= 0.)
+                return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+    /*     evaluate the function at the starting point */
+    /*     and calculate its norm. */
+    nfev = 1;
+    if ( functor(x, fvec) < 0)
+        return HybridNonLinearSolverSpace::UserAsked;
+    fnorm = fvec.stableNorm();
+
+    /*     initialize iteration counter and monitors. */
+    iter = 1;
+    ncsuc = 0;
+    ncfail = 0;
+    nslow1 = 0;
+    nslow2 = 0;
+
+    return HybridNonLinearSolverSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType  &x)
+{
+    using std::abs;
+    
+    eigen_assert(x.size()==n); // check the caller is not cheating us
+
+    Index j;
+    std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
+
+    jeval = true;
+
+    /* calculate the jacobian matrix. */
+    if ( functor.df(x, fjac) < 0)
+        return HybridNonLinearSolverSpace::UserAsked;
+    ++njev;
+
+    wa2 = fjac.colwise().blueNorm();
+
+    /* on the first iteration and if external scaling is not used, scale according */
+    /* to the norms of the columns of the initial jacobian. */
+    if (iter == 1) {
+        if (!useExternalScaling)
+            for (j = 0; j < n; ++j)
+                diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
+
+        /* on the first iteration, calculate the norm of the scaled x */
+        /* and initialize the step bound delta. */
+        xnorm = diag.cwiseProduct(x).stableNorm();
+        delta = parameters.factor * xnorm;
+        if (delta == 0.)
+            delta = parameters.factor;
+    }
+
+    /* compute the qr factorization of the jacobian. */
+    HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
+
+    /* copy the triangular factor of the qr factorization into r. */
+    R = qrfac.matrixQR();
+
+    /* accumulate the orthogonal factor in fjac. */
+    fjac = qrfac.householderQ();
+
+    /* form (q transpose)*fvec and store in qtf. */
+    qtf = fjac.transpose() * fvec;
+
+    /* rescale if necessary. */
+    if (!useExternalScaling)
+        diag = diag.cwiseMax(wa2);
+
+    while (true) {
+        /* determine the direction p. */
+        internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
+
+        /* store the direction p and x + p. calculate the norm of p. */
+        wa1 = -wa1;
+        wa2 = x + wa1;
+        pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+        /* on the first iteration, adjust the initial step bound. */
+        if (iter == 1)
+            delta = (std::min)(delta,pnorm);
+
+        /* evaluate the function at x + p and calculate its norm. */
+        if ( functor(wa2, wa4) < 0)
+            return HybridNonLinearSolverSpace::UserAsked;
+        ++nfev;
+        fnorm1 = wa4.stableNorm();
+
+        /* compute the scaled actual reduction. */
+        actred = -1.;
+        if (fnorm1 < fnorm) /* Computing 2nd power */
+            actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+        /* compute the scaled predicted reduction. */
+        wa3 = R.template triangularView<Upper>()*wa1 + qtf;
+        temp = wa3.stableNorm();
+        prered = 0.;
+        if (temp < fnorm) /* Computing 2nd power */
+            prered = 1. - numext::abs2(temp / fnorm);
+
+        /* compute the ratio of the actual to the predicted reduction. */
+        ratio = 0.;
+        if (prered > 0.)
+            ratio = actred / prered;
+
+        /* update the step bound. */
+        if (ratio < Scalar(.1)) {
+            ncsuc = 0;
+            ++ncfail;
+            delta = Scalar(.5) * delta;
+        } else {
+            ncfail = 0;
+            ++ncsuc;
+            if (ratio >= Scalar(.5) || ncsuc > 1)
+                delta = (std::max)(delta, pnorm / Scalar(.5));
+            if (abs(ratio - 1.) <= Scalar(.1)) {
+                delta = pnorm / Scalar(.5);
+            }
+        }
+
+        /* test for successful iteration. */
+        if (ratio >= Scalar(1e-4)) {
+            /* successful iteration. update x, fvec, and their norms. */
+            x = wa2;
+            wa2 = diag.cwiseProduct(x);
+            fvec = wa4;
+            xnorm = wa2.stableNorm();
+            fnorm = fnorm1;
+            ++iter;
+        }
+
+        /* determine the progress of the iteration. */
+        ++nslow1;
+        if (actred >= Scalar(.001))
+            nslow1 = 0;
+        if (jeval)
+            ++nslow2;
+        if (actred >= Scalar(.1))
+            nslow2 = 0;
+
+        /* test for convergence. */
+        if (delta <= parameters.xtol * xnorm || fnorm == 0.)
+            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
+
+        /* tests for termination and stringent tolerances. */
+        if (nfev >= parameters.maxfev)
+            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+            return HybridNonLinearSolverSpace::TolTooSmall;
+        if (nslow2 == 5)
+            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
+        if (nslow1 == 10)
+            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
+
+        /* criterion for recalculating jacobian. */
+        if (ncfail == 2)
+            break; // leave inner loop and go for the next outer loop iteration
+
+        /* calculate the rank one modification to the jacobian */
+        /* and update qtf if necessary. */
+        wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
+        wa2 = fjac.transpose() * wa4;
+        if (ratio >= Scalar(1e-4))
+            qtf = wa2;
+        wa2 = (wa2-wa3)/pnorm;
+
+        /* compute the qr factorization of the updated jacobian. */
+        internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
+        internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
+        internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
+
+        jeval = false;
+    }
+    return HybridNonLinearSolverSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solve(FVectorType  &x)
+{
+    HybridNonLinearSolverSpace::Status status = solveInit(x);
+    if (status==HybridNonLinearSolverSpace::ImproperInputParameters)
+        return status;
+    while (status==HybridNonLinearSolverSpace::Running)
+        status = solveOneStep(x);
+    return status;
+}
+
+
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::hybrd1(
+        FVectorType  &x,
+        const Scalar tol
+        )
+{
+    n = x.size();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || tol < 0.)
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+    resetParameters();
+    parameters.maxfev = 200*(n+1);
+    parameters.xtol = tol;
+
+    diag.setConstant(n, 1.);
+    useExternalScaling = true;
+    return solveNumericalDiff(x);
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(FVectorType  &x)
+{
+    n = x.size();
+
+    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
+    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
+
+    wa1.resize(n); wa2.resize(n); wa3.resize(n); wa4.resize(n);
+    qtf.resize(n);
+    fjac.resize(n, n);
+    fvec.resize(n);
+    if (!useExternalScaling)
+        diag.resize(n);
+    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
+
+    /* Function Body */
+    nfev = 0;
+    njev = 0;
+
+    /*     check the input parameters for errors. */
+    if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. )
+        return HybridNonLinearSolverSpace::ImproperInputParameters;
+    if (useExternalScaling)
+        for (Index j = 0; j < n; ++j)
+            if (diag[j] <= 0.)
+                return HybridNonLinearSolverSpace::ImproperInputParameters;
+
+    /*     evaluate the function at the starting point */
+    /*     and calculate its norm. */
+    nfev = 1;
+    if ( functor(x, fvec) < 0)
+        return HybridNonLinearSolverSpace::UserAsked;
+    fnorm = fvec.stableNorm();
+
+    /*     initialize iteration counter and monitors. */
+    iter = 1;
+    ncsuc = 0;
+    ncfail = 0;
+    nslow1 = 0;
+    nslow2 = 0;
+
+    return HybridNonLinearSolverSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType  &x)
+{
+    using std::sqrt;
+    using std::abs;
+    
+    assert(x.size()==n); // check the caller is not cheating us
+
+    Index j;
+    std::vector<JacobiRotation<Scalar> > v_givens(n), w_givens(n);
+
+    jeval = true;
+    if (parameters.nb_of_subdiagonals<0) parameters.nb_of_subdiagonals= n-1;
+    if (parameters.nb_of_superdiagonals<0) parameters.nb_of_superdiagonals= n-1;
+
+    /* calculate the jacobian matrix. */
+    if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
+        return HybridNonLinearSolverSpace::UserAsked;
+    nfev += (std::min)(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
+
+    wa2 = fjac.colwise().blueNorm();
+
+    /* on the first iteration and if external scaling is not used, scale according */
+    /* to the norms of the columns of the initial jacobian. */
+    if (iter == 1) {
+        if (!useExternalScaling)
+            for (j = 0; j < n; ++j)
+                diag[j] = (wa2[j]==0.) ? 1. : wa2[j];
+
+        /* on the first iteration, calculate the norm of the scaled x */
+        /* and initialize the step bound delta. */
+        xnorm = diag.cwiseProduct(x).stableNorm();
+        delta = parameters.factor * xnorm;
+        if (delta == 0.)
+            delta = parameters.factor;
+    }
+
+    /* compute the qr factorization of the jacobian. */
+    HouseholderQR<JacobianType> qrfac(fjac); // no pivoting:
+
+    /* copy the triangular factor of the qr factorization into r. */
+    R = qrfac.matrixQR();
+
+    /* accumulate the orthogonal factor in fjac. */
+    fjac = qrfac.householderQ();
+
+    /* form (q transpose)*fvec and store in qtf. */
+    qtf = fjac.transpose() * fvec;
+
+    /* rescale if necessary. */
+    if (!useExternalScaling)
+        diag = diag.cwiseMax(wa2);
+
+    while (true) {
+        /* determine the direction p. */
+        internal::dogleg<Scalar>(R, diag, qtf, delta, wa1);
+
+        /* store the direction p and x + p. calculate the norm of p. */
+        wa1 = -wa1;
+        wa2 = x + wa1;
+        pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+        /* on the first iteration, adjust the initial step bound. */
+        if (iter == 1)
+            delta = (std::min)(delta,pnorm);
+
+        /* evaluate the function at x + p and calculate its norm. */
+        if ( functor(wa2, wa4) < 0)
+            return HybridNonLinearSolverSpace::UserAsked;
+        ++nfev;
+        fnorm1 = wa4.stableNorm();
+
+        /* compute the scaled actual reduction. */
+        actred = -1.;
+        if (fnorm1 < fnorm) /* Computing 2nd power */
+            actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+        /* compute the scaled predicted reduction. */
+        wa3 = R.template triangularView<Upper>()*wa1 + qtf;
+        temp = wa3.stableNorm();
+        prered = 0.;
+        if (temp < fnorm) /* Computing 2nd power */
+            prered = 1. - numext::abs2(temp / fnorm);
+
+        /* compute the ratio of the actual to the predicted reduction. */
+        ratio = 0.;
+        if (prered > 0.)
+            ratio = actred / prered;
+
+        /* update the step bound. */
+        if (ratio < Scalar(.1)) {
+            ncsuc = 0;
+            ++ncfail;
+            delta = Scalar(.5) * delta;
+        } else {
+            ncfail = 0;
+            ++ncsuc;
+            if (ratio >= Scalar(.5) || ncsuc > 1)
+                delta = (std::max)(delta, pnorm / Scalar(.5));
+            if (abs(ratio - 1.) <= Scalar(.1)) {
+                delta = pnorm / Scalar(.5);
+            }
+        }
+
+        /* test for successful iteration. */
+        if (ratio >= Scalar(1e-4)) {
+            /* successful iteration. update x, fvec, and their norms. */
+            x = wa2;
+            wa2 = diag.cwiseProduct(x);
+            fvec = wa4;
+            xnorm = wa2.stableNorm();
+            fnorm = fnorm1;
+            ++iter;
+        }
+
+        /* determine the progress of the iteration. */
+        ++nslow1;
+        if (actred >= Scalar(.001))
+            nslow1 = 0;
+        if (jeval)
+            ++nslow2;
+        if (actred >= Scalar(.1))
+            nslow2 = 0;
+
+        /* test for convergence. */
+        if (delta <= parameters.xtol * xnorm || fnorm == 0.)
+            return HybridNonLinearSolverSpace::RelativeErrorTooSmall;
+
+        /* tests for termination and stringent tolerances. */
+        if (nfev >= parameters.maxfev)
+            return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
+        if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
+            return HybridNonLinearSolverSpace::TolTooSmall;
+        if (nslow2 == 5)
+            return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
+        if (nslow1 == 10)
+            return HybridNonLinearSolverSpace::NotMakingProgressIterations;
+
+        /* criterion for recalculating jacobian. */
+        if (ncfail == 2)
+            break; // leave inner loop and go for the next outer loop iteration
+
+        /* calculate the rank one modification to the jacobian */
+        /* and update qtf if necessary. */
+        wa1 = diag.cwiseProduct( diag.cwiseProduct(wa1)/pnorm );
+        wa2 = fjac.transpose() * wa4;
+        if (ratio >= Scalar(1e-4))
+            qtf = wa2;
+        wa2 = (wa2-wa3)/pnorm;
+
+        /* compute the qr factorization of the updated jacobian. */
+        internal::r1updt<Scalar>(R, wa1, v_givens, w_givens, wa2, wa3, &sing);
+        internal::r1mpyq<Scalar>(n, n, fjac.data(), v_givens, w_givens);
+        internal::r1mpyq<Scalar>(1, n, qtf.data(), v_givens, w_givens);
+
+        jeval = false;
+    }
+    return HybridNonLinearSolverSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+HybridNonLinearSolverSpace::Status
+HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiff(FVectorType  &x)
+{
+    HybridNonLinearSolverSpace::Status status = solveNumericalDiffInit(x);
+    if (status==HybridNonLinearSolverSpace::ImproperInputParameters)
+        return status;
+    while (status==HybridNonLinearSolverSpace::Running)
+        status = solveNumericalDiffOneStep(x);
+    return status;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_HYBRIDNONLINEARSOLVER_H
+
+//vim: ai ts=4 sts=4 et sw=4
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
new file mode 100644
index 0000000..fe3b79c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
@@ -0,0 +1,657 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEVENBERGMARQUARDT__H
+#define EIGEN_LEVENBERGMARQUARDT__H
+
+namespace Eigen { 
+
+namespace LevenbergMarquardtSpace {
+    enum Status {
+        NotStarted = -2,
+        Running = -1,
+        ImproperInputParameters = 0,
+        RelativeReductionTooSmall = 1,
+        RelativeErrorTooSmall = 2,
+        RelativeErrorAndReductionTooSmall = 3,
+        CosinusTooSmall = 4,
+        TooManyFunctionEvaluation = 5,
+        FtolTooSmall = 6,
+        XtolTooSmall = 7,
+        GtolTooSmall = 8,
+        UserAsked = 9
+    };
+}
+
+
+
+/**
+  * \ingroup NonLinearOptimization_Module
+  * \brief Performs non linear optimization over a non-linear function,
+  * using a variant of the Levenberg Marquardt algorithm.
+  *
+  * Check wikipedia for more information.
+  * http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
+  */
+template<typename FunctorType, typename Scalar=double>
+class LevenbergMarquardt
+{
+    static Scalar sqrt_epsilon()
+    {
+      using std::sqrt;
+      return sqrt(NumTraits<Scalar>::epsilon());
+    }
+    
+public:
+    LevenbergMarquardt(FunctorType &_functor)
+        : functor(_functor) { nfev = njev = iter = 0;  fnorm = gnorm = 0.; useExternalScaling=false; }
+
+    typedef DenseIndex Index;
+    
+    struct Parameters {
+        Parameters()
+            : factor(Scalar(100.))
+            , maxfev(400)
+            , ftol(sqrt_epsilon())
+            , xtol(sqrt_epsilon())
+            , gtol(Scalar(0.))
+            , epsfcn(Scalar(0.)) {}
+        Scalar factor;
+        Index maxfev;   // maximum number of function evaluation
+        Scalar ftol;
+        Scalar xtol;
+        Scalar gtol;
+        Scalar epsfcn;
+    };
+
+    typedef Matrix< Scalar, Dynamic, 1 > FVectorType;
+    typedef Matrix< Scalar, Dynamic, Dynamic > JacobianType;
+
+    LevenbergMarquardtSpace::Status lmder1(
+            FVectorType &x,
+            const Scalar tol = sqrt_epsilon()
+            );
+
+    LevenbergMarquardtSpace::Status minimize(FVectorType &x);
+    LevenbergMarquardtSpace::Status minimizeInit(FVectorType &x);
+    LevenbergMarquardtSpace::Status minimizeOneStep(FVectorType &x);
+
+    static LevenbergMarquardtSpace::Status lmdif1(
+            FunctorType &functor,
+            FVectorType &x,
+            Index *nfev,
+            const Scalar tol = sqrt_epsilon()
+            );
+
+    LevenbergMarquardtSpace::Status lmstr1(
+            FVectorType  &x,
+            const Scalar tol = sqrt_epsilon()
+            );
+
+    LevenbergMarquardtSpace::Status minimizeOptimumStorage(FVectorType  &x);
+    LevenbergMarquardtSpace::Status minimizeOptimumStorageInit(FVectorType  &x);
+    LevenbergMarquardtSpace::Status minimizeOptimumStorageOneStep(FVectorType  &x);
+
+    void resetParameters(void) { parameters = Parameters(); }
+
+    Parameters parameters;
+    FVectorType  fvec, qtf, diag;
+    JacobianType fjac;
+    PermutationMatrix<Dynamic,Dynamic> permutation;
+    Index nfev;
+    Index njev;
+    Index iter;
+    Scalar fnorm, gnorm;
+    bool useExternalScaling; 
+
+    Scalar lm_param(void) { return par; }
+private:
+    
+    FunctorType &functor;
+    Index n;
+    Index m;
+    FVectorType wa1, wa2, wa3, wa4;
+
+    Scalar par, sum;
+    Scalar temp, temp1, temp2;
+    Scalar delta;
+    Scalar ratio;
+    Scalar pnorm, xnorm, fnorm1, actred, dirder, prered;
+
+    LevenbergMarquardt& operator=(const LevenbergMarquardt&);
+};
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::lmder1(
+        FVectorType  &x,
+        const Scalar tol
+        )
+{
+    n = x.size();
+    m = functor.values();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || m < n || tol < 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    resetParameters();
+    parameters.ftol = tol;
+    parameters.xtol = tol;
+    parameters.maxfev = 100*(n+1);
+
+    return minimize(x);
+}
+
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType  &x)
+{
+    LevenbergMarquardtSpace::Status status = minimizeInit(x);
+    if (status==LevenbergMarquardtSpace::ImproperInputParameters)
+        return status;
+    do {
+        status = minimizeOneStep(x);
+    } while (status==LevenbergMarquardtSpace::Running);
+    return status;
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(FVectorType  &x)
+{
+    n = x.size();
+    m = functor.values();
+
+    wa1.resize(n); wa2.resize(n); wa3.resize(n);
+    wa4.resize(m);
+    fvec.resize(m);
+    fjac.resize(m, n);
+    if (!useExternalScaling)
+        diag.resize(n);
+    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    qtf.resize(n);
+
+    /* Function Body */
+    nfev = 0;
+    njev = 0;
+
+    /*     check the input parameters for errors. */
+    if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    if (useExternalScaling)
+        for (Index j = 0; j < n; ++j)
+            if (diag[j] <= 0.)
+                return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    /*     evaluate the function at the starting point */
+    /*     and calculate its norm. */
+    nfev = 1;
+    if ( functor(x, fvec) < 0)
+        return LevenbergMarquardtSpace::UserAsked;
+    fnorm = fvec.stableNorm();
+
+    /*     initialize levenberg-marquardt parameter and iteration counter. */
+    par = 0.;
+    iter = 1;
+
+    return LevenbergMarquardtSpace::NotStarted;
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType  &x)
+{
+    using std::abs;
+    using std::sqrt;
+
+    eigen_assert(x.size()==n); // check the caller is not cheating us
+
+    /* calculate the jacobian matrix. */
+    Index df_ret = functor.df(x, fjac);
+    if (df_ret<0)
+        return LevenbergMarquardtSpace::UserAsked;
+    if (df_ret>0)
+        // numerical diff, we evaluated the function df_ret times
+        nfev += df_ret;
+    else njev++;
+
+    /* compute the qr factorization of the jacobian. */
+    wa2 = fjac.colwise().blueNorm();
+    ColPivHouseholderQR<JacobianType> qrfac(fjac);
+    fjac = qrfac.matrixQR();
+    permutation = qrfac.colsPermutation();
+
+    /* on the first iteration and if external scaling is not used, scale according */
+    /* to the norms of the columns of the initial jacobian. */
+    if (iter == 1) {
+        if (!useExternalScaling)
+            for (Index j = 0; j < n; ++j)
+                diag[j] = (wa2[j]==0.)? 1. : wa2[j];
+
+        /* on the first iteration, calculate the norm of the scaled x */
+        /* and initialize the step bound delta. */
+        xnorm = diag.cwiseProduct(x).stableNorm();
+        delta = parameters.factor * xnorm;
+        if (delta == 0.)
+            delta = parameters.factor;
+    }
+
+    /* form (q transpose)*fvec and store the first n components in */
+    /* qtf. */
+    wa4 = fvec;
+    wa4.applyOnTheLeft(qrfac.householderQ().adjoint());
+    qtf = wa4.head(n);
+
+    /* compute the norm of the scaled gradient. */
+    gnorm = 0.;
+    if (fnorm != 0.)
+        for (Index j = 0; j < n; ++j)
+            if (wa2[permutation.indices()[j]] != 0.)
+                gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+
+    /* test for convergence of the gradient norm. */
+    if (gnorm <= parameters.gtol)
+        return LevenbergMarquardtSpace::CosinusTooSmall;
+
+    /* rescale if necessary. */
+    if (!useExternalScaling)
+        diag = diag.cwiseMax(wa2);
+
+    do {
+
+        /* determine the levenberg-marquardt parameter. */
+        internal::lmpar2<Scalar>(qrfac, diag, qtf, delta, par, wa1);
+
+        /* store the direction p and x + p. calculate the norm of p. */
+        wa1 = -wa1;
+        wa2 = x + wa1;
+        pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+        /* on the first iteration, adjust the initial step bound. */
+        if (iter == 1)
+            delta = (std::min)(delta,pnorm);
+
+        /* evaluate the function at x + p and calculate its norm. */
+        if ( functor(wa2, wa4) < 0)
+            return LevenbergMarquardtSpace::UserAsked;
+        ++nfev;
+        fnorm1 = wa4.stableNorm();
+
+        /* compute the scaled actual reduction. */
+        actred = -1.;
+        if (Scalar(.1) * fnorm1 < fnorm)
+            actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+        /* compute the scaled predicted reduction and */
+        /* the scaled directional derivative. */
+        wa3 = fjac.template triangularView<Upper>() * (qrfac.colsPermutation().inverse() *wa1);
+        temp1 = numext::abs2(wa3.stableNorm() / fnorm);
+        temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
+        prered = temp1 + temp2 / Scalar(.5);
+        dirder = -(temp1 + temp2);
+
+        /* compute the ratio of the actual to the predicted */
+        /* reduction. */
+        ratio = 0.;
+        if (prered != 0.)
+            ratio = actred / prered;
+
+        /* update the step bound. */
+        if (ratio <= Scalar(.25)) {
+            if (actred >= 0.)
+                temp = Scalar(.5);
+            if (actred < 0.)
+                temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
+            if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
+                temp = Scalar(.1);
+            /* Computing MIN */
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
+            par /= temp;
+        } else if (!(par != 0. && ratio < Scalar(.75))) {
+            delta = pnorm / Scalar(.5);
+            par = Scalar(.5) * par;
+        }
+
+        /* test for successful iteration. */
+        if (ratio >= Scalar(1e-4)) {
+            /* successful iteration. update x, fvec, and their norms. */
+            x = wa2;
+            wa2 = diag.cwiseProduct(x);
+            fvec = wa4;
+            xnorm = wa2.stableNorm();
+            fnorm = fnorm1;
+            ++iter;
+        }
+
+        /* tests for convergence. */
+        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
+            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+        if (delta <= parameters.xtol * xnorm)
+            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+
+        /* tests for termination and stringent tolerances. */
+        if (nfev >= parameters.maxfev)
+            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+        if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
+            return LevenbergMarquardtSpace::FtolTooSmall;
+        if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
+            return LevenbergMarquardtSpace::XtolTooSmall;
+        if (gnorm <= NumTraits<Scalar>::epsilon())
+            return LevenbergMarquardtSpace::GtolTooSmall;
+
+    } while (ratio < Scalar(1e-4));
+
+    return LevenbergMarquardtSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::lmstr1(
+        FVectorType  &x,
+        const Scalar tol
+        )
+{
+    n = x.size();
+    m = functor.values();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || m < n || tol < 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    resetParameters();
+    parameters.ftol = tol;
+    parameters.xtol = tol;
+    parameters.maxfev = 100*(n+1);
+
+    return minimizeOptimumStorage(x);
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(FVectorType  &x)
+{
+    n = x.size();
+    m = functor.values();
+
+    wa1.resize(n); wa2.resize(n); wa3.resize(n);
+    wa4.resize(m);
+    fvec.resize(m);
+    // Only R is stored in fjac. Q is only used to compute 'qtf', which is
+    // Q.transpose()*rhs. qtf will be updated using givens rotation,
+    // instead of storing them in Q.
+    // The purpose it to only use a nxn matrix, instead of mxn here, so
+    // that we can handle cases where m>>n :
+    fjac.resize(n, n);
+    if (!useExternalScaling)
+        diag.resize(n);
+    eigen_assert( (!useExternalScaling || diag.size()==n) && "When useExternalScaling is set, the caller must provide a valid 'diag'");
+    qtf.resize(n);
+
+    /* Function Body */
+    nfev = 0;
+    njev = 0;
+
+    /*     check the input parameters for errors. */
+    if (n <= 0 || m < n || parameters.ftol < 0. || parameters.xtol < 0. || parameters.gtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    if (useExternalScaling)
+        for (Index j = 0; j < n; ++j)
+            if (diag[j] <= 0.)
+                return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    /*     evaluate the function at the starting point */
+    /*     and calculate its norm. */
+    nfev = 1;
+    if ( functor(x, fvec) < 0)
+        return LevenbergMarquardtSpace::UserAsked;
+    fnorm = fvec.stableNorm();
+
+    /*     initialize levenberg-marquardt parameter and iteration counter. */
+    par = 0.;
+    iter = 1;
+
+    return LevenbergMarquardtSpace::NotStarted;
+}
+
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorType  &x)
+{
+    using std::abs;
+    using std::sqrt;
+    
+    eigen_assert(x.size()==n); // check the caller is not cheating us
+
+    Index i, j;
+    bool sing;
+
+    /* compute the qr factorization of the jacobian matrix */
+    /* calculated one row at a time, while simultaneously */
+    /* forming (q transpose)*fvec and storing the first */
+    /* n components in qtf. */
+    qtf.fill(0.);
+    fjac.fill(0.);
+    Index rownb = 2;
+    for (i = 0; i < m; ++i) {
+        if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked;
+        internal::rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]);
+        ++rownb;
+    }
+    ++njev;
+
+    /* if the jacobian is rank deficient, call qrfac to */
+    /* reorder its columns and update the components of qtf. */
+    sing = false;
+    for (j = 0; j < n; ++j) {
+        if (fjac(j,j) == 0.)
+            sing = true;
+        wa2[j] = fjac.col(j).head(j).stableNorm();
+    }
+    permutation.setIdentity(n);
+    if (sing) {
+        wa2 = fjac.colwise().blueNorm();
+        // TODO We have no unit test covering this code path, do not modify
+        // until it is carefully tested
+        ColPivHouseholderQR<JacobianType> qrfac(fjac);
+        fjac = qrfac.matrixQR();
+        wa1 = fjac.diagonal();
+        fjac.diagonal() = qrfac.hCoeffs();
+        permutation = qrfac.colsPermutation();
+        // TODO : avoid this:
+        for(Index ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors
+
+        for (j = 0; j < n; ++j) {
+            if (fjac(j,j) != 0.) {
+                sum = 0.;
+                for (i = j; i < n; ++i)
+                    sum += fjac(i,j) * qtf[i];
+                temp = -sum / fjac(j,j);
+                for (i = j; i < n; ++i)
+                    qtf[i] += fjac(i,j) * temp;
+            }
+            fjac(j,j) = wa1[j];
+        }
+    }
+
+    /* on the first iteration and if external scaling is not used, scale according */
+    /* to the norms of the columns of the initial jacobian. */
+    if (iter == 1) {
+        if (!useExternalScaling)
+            for (j = 0; j < n; ++j)
+                diag[j] = (wa2[j]==0.)? 1. : wa2[j];
+
+        /* on the first iteration, calculate the norm of the scaled x */
+        /* and initialize the step bound delta. */
+        xnorm = diag.cwiseProduct(x).stableNorm();
+        delta = parameters.factor * xnorm;
+        if (delta == 0.)
+            delta = parameters.factor;
+    }
+
+    /* compute the norm of the scaled gradient. */
+    gnorm = 0.;
+    if (fnorm != 0.)
+        for (j = 0; j < n; ++j)
+            if (wa2[permutation.indices()[j]] != 0.)
+                gnorm = (std::max)(gnorm, abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
+
+    /* test for convergence of the gradient norm. */
+    if (gnorm <= parameters.gtol)
+        return LevenbergMarquardtSpace::CosinusTooSmall;
+
+    /* rescale if necessary. */
+    if (!useExternalScaling)
+        diag = diag.cwiseMax(wa2);
+
+    do {
+
+        /* determine the levenberg-marquardt parameter. */
+        internal::lmpar<Scalar>(fjac, permutation.indices(), diag, qtf, delta, par, wa1);
+
+        /* store the direction p and x + p. calculate the norm of p. */
+        wa1 = -wa1;
+        wa2 = x + wa1;
+        pnorm = diag.cwiseProduct(wa1).stableNorm();
+
+        /* on the first iteration, adjust the initial step bound. */
+        if (iter == 1)
+            delta = (std::min)(delta,pnorm);
+
+        /* evaluate the function at x + p and calculate its norm. */
+        if ( functor(wa2, wa4) < 0)
+            return LevenbergMarquardtSpace::UserAsked;
+        ++nfev;
+        fnorm1 = wa4.stableNorm();
+
+        /* compute the scaled actual reduction. */
+        actred = -1.;
+        if (Scalar(.1) * fnorm1 < fnorm)
+            actred = 1. - numext::abs2(fnorm1 / fnorm);
+
+        /* compute the scaled predicted reduction and */
+        /* the scaled directional derivative. */
+        wa3 = fjac.topLeftCorner(n,n).template triangularView<Upper>() * (permutation.inverse() * wa1);
+        temp1 = numext::abs2(wa3.stableNorm() / fnorm);
+        temp2 = numext::abs2(sqrt(par) * pnorm / fnorm);
+        prered = temp1 + temp2 / Scalar(.5);
+        dirder = -(temp1 + temp2);
+
+        /* compute the ratio of the actual to the predicted */
+        /* reduction. */
+        ratio = 0.;
+        if (prered != 0.)
+            ratio = actred / prered;
+
+        /* update the step bound. */
+        if (ratio <= Scalar(.25)) {
+            if (actred >= 0.)
+                temp = Scalar(.5);
+            if (actred < 0.)
+                temp = Scalar(.5) * dirder / (dirder + Scalar(.5) * actred);
+            if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
+                temp = Scalar(.1);
+            /* Computing MIN */
+            delta = temp * (std::min)(delta, pnorm / Scalar(.1));
+            par /= temp;
+        } else if (!(par != 0. && ratio < Scalar(.75))) {
+            delta = pnorm / Scalar(.5);
+            par = Scalar(.5) * par;
+        }
+
+        /* test for successful iteration. */
+        if (ratio >= Scalar(1e-4)) {
+            /* successful iteration. update x, fvec, and their norms. */
+            x = wa2;
+            wa2 = diag.cwiseProduct(x);
+            fvec = wa4;
+            xnorm = wa2.stableNorm();
+            fnorm = fnorm1;
+            ++iter;
+        }
+
+        /* tests for convergence. */
+        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1. && delta <= parameters.xtol * xnorm)
+            return LevenbergMarquardtSpace::RelativeErrorAndReductionTooSmall;
+        if (abs(actred) <= parameters.ftol && prered <= parameters.ftol && Scalar(.5) * ratio <= 1.)
+            return LevenbergMarquardtSpace::RelativeReductionTooSmall;
+        if (delta <= parameters.xtol * xnorm)
+            return LevenbergMarquardtSpace::RelativeErrorTooSmall;
+
+        /* tests for termination and stringent tolerances. */
+        if (nfev >= parameters.maxfev)
+            return LevenbergMarquardtSpace::TooManyFunctionEvaluation;
+        if (abs(actred) <= NumTraits<Scalar>::epsilon() && prered <= NumTraits<Scalar>::epsilon() && Scalar(.5) * ratio <= 1.)
+            return LevenbergMarquardtSpace::FtolTooSmall;
+        if (delta <= NumTraits<Scalar>::epsilon() * xnorm)
+            return LevenbergMarquardtSpace::XtolTooSmall;
+        if (gnorm <= NumTraits<Scalar>::epsilon())
+            return LevenbergMarquardtSpace::GtolTooSmall;
+
+    } while (ratio < Scalar(1e-4));
+
+    return LevenbergMarquardtSpace::Running;
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorage(FVectorType  &x)
+{
+    LevenbergMarquardtSpace::Status status = minimizeOptimumStorageInit(x);
+    if (status==LevenbergMarquardtSpace::ImproperInputParameters)
+        return status;
+    do {
+        status = minimizeOptimumStorageOneStep(x);
+    } while (status==LevenbergMarquardtSpace::Running);
+    return status;
+}
+
+template<typename FunctorType, typename Scalar>
+LevenbergMarquardtSpace::Status
+LevenbergMarquardt<FunctorType,Scalar>::lmdif1(
+        FunctorType &functor,
+        FVectorType  &x,
+        Index *nfev,
+        const Scalar tol
+        )
+{
+    Index n = x.size();
+    Index m = functor.values();
+
+    /* check the input parameters for errors. */
+    if (n <= 0 || m < n || tol < 0.)
+        return LevenbergMarquardtSpace::ImproperInputParameters;
+
+    NumericalDiff<FunctorType> numDiff(functor);
+    // embedded LevenbergMarquardt
+    LevenbergMarquardt<NumericalDiff<FunctorType>, Scalar > lm(numDiff);
+    lm.parameters.ftol = tol;
+    lm.parameters.xtol = tol;
+    lm.parameters.maxfev = 200*(n+1);
+
+    LevenbergMarquardtSpace::Status info = LevenbergMarquardtSpace::Status(lm.minimize(x));
+    if (nfev)
+        * nfev = lm.nfev;
+    return info;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LEVENBERGMARQUARDT__H
+
+//vim: ai ts=4 sts=4 et sw=4
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
new file mode 100644
index 0000000..db8ff7d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/chkder.h
@@ -0,0 +1,66 @@
+#define chkder_log10e 0.43429448190325182765
+#define chkder_factor 100.
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Scalar>
+void chkder(
+        const Matrix< Scalar, Dynamic, 1 >  &x,
+        const Matrix< Scalar, Dynamic, 1 >  &fvec,
+        const Matrix< Scalar, Dynamic, Dynamic > &fjac,
+        Matrix< Scalar, Dynamic, 1 >  &xp,
+        const Matrix< Scalar, Dynamic, 1 >  &fvecp,
+        int mode,
+        Matrix< Scalar, Dynamic, 1 >  &err
+        )
+{
+    using std::sqrt;
+    using std::abs;
+    using std::log;
+    
+    typedef DenseIndex Index;
+
+    const Scalar eps = sqrt(NumTraits<Scalar>::epsilon());
+    const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon();
+    const Scalar epslog = chkder_log10e * log(eps);
+    Scalar temp;
+
+    const Index m = fvec.size(), n = x.size();
+
+    if (mode != 2) {
+        /* mode = 1. */
+        xp.resize(n);
+        for (Index j = 0; j < n; ++j) {
+            temp = eps * abs(x[j]);
+            if (temp == 0.)
+                temp = eps;
+            xp[j] = x[j] + temp;
+        }
+    }
+    else {
+        /* mode = 2. */
+        err.setZero(m); 
+        for (Index j = 0; j < n; ++j) {
+            temp = abs(x[j]);
+            if (temp == 0.)
+                temp = 1.;
+            err += temp * fjac.col(j);
+        }
+        for (Index i = 0; i < m; ++i) {
+            temp = 1.;
+            if (fvec[i] != 0. && fvecp[i] != 0. && abs(fvecp[i] - fvec[i]) >= epsf * abs(fvec[i]))
+                temp = eps * abs((fvecp[i] - fvec[i]) / eps - err[i]) / (abs(fvec[i]) + abs(fvecp[i]));
+            err[i] = 1.;
+            if (temp > NumTraits<Scalar>::epsilon() && temp < eps)
+                err[i] = (chkder_log10e * log(temp) - epslog) / epslog;
+            if (temp >= eps)
+                err[i] = 0.;
+        }
+    }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
new file mode 100644
index 0000000..68260d1
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/covar.h
@@ -0,0 +1,70 @@
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void covar(
+        Matrix< Scalar, Dynamic, Dynamic > &r,
+        const VectorXi &ipvt,
+        Scalar tol = std::sqrt(NumTraits<Scalar>::epsilon()) )
+{
+    using std::abs;
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Index i, j, k, l, ii, jj;
+    bool sing;
+    Scalar temp;
+
+    /* Function Body */
+    const Index n = r.cols();
+    const Scalar tolr = tol * abs(r(0,0));
+    Matrix< Scalar, Dynamic, 1 > wa(n);
+    eigen_assert(ipvt.size()==n);
+
+    /* form the inverse of r in the full upper triangle of r. */
+    l = -1;
+    for (k = 0; k < n; ++k)
+        if (abs(r(k,k)) > tolr) {
+            r(k,k) = 1. / r(k,k);
+            for (j = 0; j <= k-1; ++j) {
+                temp = r(k,k) * r(j,k);
+                r(j,k) = 0.;
+                r.col(k).head(j+1) -= r.col(j).head(j+1) * temp;
+            }
+            l = k;
+        }
+
+    /* form the full upper triangle of the inverse of (r transpose)*r */
+    /* in the full upper triangle of r. */
+    for (k = 0; k <= l; ++k) {
+        for (j = 0; j <= k-1; ++j)
+            r.col(j).head(j+1) += r.col(k).head(j+1) * r(j,k);
+        r.col(k).head(k+1) *= r(k,k);
+    }
+
+    /* form the full lower triangle of the covariance matrix */
+    /* in the strict lower triangle of r and in wa. */
+    for (j = 0; j < n; ++j) {
+        jj = ipvt[j];
+        sing = j > l;
+        for (i = 0; i <= j; ++i) {
+            if (sing)
+                r(i,j) = 0.;
+            ii = ipvt[i];
+            if (ii > jj)
+                r(ii,jj) = r(i,j);
+            if (ii < jj)
+                r(jj,ii) = r(i,j);
+        }
+        wa[jj] = r(j,j);
+    }
+
+    /* symmetrize the covariance matrix in r. */
+    r.topLeftCorner(n,n).template triangularView<StrictlyUpper>() = r.topLeftCorner(n,n).transpose();
+    r.diagonal() = wa;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
new file mode 100644
index 0000000..80c5d27
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/dogleg.h
@@ -0,0 +1,107 @@
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void dogleg(
+        const Matrix< Scalar, Dynamic, Dynamic >  &qrfac,
+        const Matrix< Scalar, Dynamic, 1 >  &diag,
+        const Matrix< Scalar, Dynamic, 1 >  &qtb,
+        Scalar delta,
+        Matrix< Scalar, Dynamic, 1 >  &x)
+{
+    using std::abs;
+    using std::sqrt;
+    
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Index i, j;
+    Scalar sum, temp, alpha, bnorm;
+    Scalar gnorm, qnorm;
+    Scalar sgnorm;
+
+    /* Function Body */
+    const Scalar epsmch = NumTraits<Scalar>::epsilon();
+    const Index n = qrfac.cols();
+    eigen_assert(n==qtb.size());
+    eigen_assert(n==x.size());
+    eigen_assert(n==diag.size());
+    Matrix< Scalar, Dynamic, 1 >  wa1(n), wa2(n);
+
+    /* first, calculate the gauss-newton direction. */
+    for (j = n-1; j >=0; --j) {
+        temp = qrfac(j,j);
+        if (temp == 0.) {
+            temp = epsmch * qrfac.col(j).head(j+1).maxCoeff();
+            if (temp == 0.)
+                temp = epsmch;
+        }
+        if (j==n-1)
+            x[j] = qtb[j] / temp;
+        else
+            x[j] = (qtb[j] - qrfac.row(j).tail(n-j-1).dot(x.tail(n-j-1))) / temp;
+    }
+
+    /* test whether the gauss-newton direction is acceptable. */
+    qnorm = diag.cwiseProduct(x).stableNorm();
+    if (qnorm <= delta)
+        return;
+
+    // TODO : this path is not tested by Eigen unit tests
+
+    /* the gauss-newton direction is not acceptable. */
+    /* next, calculate the scaled gradient direction. */
+
+    wa1.fill(0.);
+    for (j = 0; j < n; ++j) {
+        wa1.tail(n-j) += qrfac.row(j).tail(n-j) * qtb[j];
+        wa1[j] /= diag[j];
+    }
+
+    /* calculate the norm of the scaled gradient and test for */
+    /* the special case in which the scaled gradient is zero. */
+    gnorm = wa1.stableNorm();
+    sgnorm = 0.;
+    alpha = delta / qnorm;
+    if (gnorm == 0.)
+        goto algo_end;
+
+    /* calculate the point along the scaled gradient */
+    /* at which the quadratic is minimized. */
+    wa1.array() /= (diag*gnorm).array();
+    // TODO : once unit tests cover this part,:
+    // wa2 = qrfac.template triangularView<Upper>() * wa1;
+    for (j = 0; j < n; ++j) {
+        sum = 0.;
+        for (i = j; i < n; ++i) {
+            sum += qrfac(j,i) * wa1[i];
+        }
+        wa2[j] = sum;
+    }
+    temp = wa2.stableNorm();
+    sgnorm = gnorm / temp / temp;
+
+    /* test whether the scaled gradient direction is acceptable. */
+    alpha = 0.;
+    if (sgnorm >= delta)
+        goto algo_end;
+
+    /* the scaled gradient direction is not acceptable. */
+    /* finally, calculate the point along the dogleg */
+    /* at which the quadratic is minimized. */
+    bnorm = qtb.stableNorm();
+    temp = bnorm / gnorm * (bnorm / qnorm) * (sgnorm / delta);
+    temp = temp - delta / qnorm * numext::abs2(sgnorm / delta) + sqrt(numext::abs2(temp - delta / qnorm) + (1.-numext::abs2(delta / qnorm)) * (1.-numext::abs2(sgnorm / delta)));
+    alpha = delta / qnorm * (1. - numext::abs2(sgnorm / delta)) / temp;
+algo_end:
+
+    /* form appropriate convex combination of the gauss-newton */
+    /* direction and the scaled gradient direction. */
+    temp = (1.-alpha) * (std::min)(sgnorm,delta);
+    x = temp * wa1 + alpha * x;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
new file mode 100644
index 0000000..bb7cf26
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
@@ -0,0 +1,79 @@
+namespace Eigen { 
+
+namespace internal {
+
+template<typename FunctorType, typename Scalar>
+DenseIndex fdjac1(
+        const FunctorType &Functor,
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        Matrix< Scalar, Dynamic, 1 >  &fvec,
+        Matrix< Scalar, Dynamic, Dynamic > &fjac,
+        DenseIndex ml, DenseIndex mu,
+        Scalar epsfcn)
+{
+    using std::sqrt;
+    using std::abs;
+    
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Scalar h;
+    Index j, k;
+    Scalar eps, temp;
+    Index msum;
+    int iflag;
+    Index start, length;
+
+    /* Function Body */
+    const Scalar epsmch = NumTraits<Scalar>::epsilon();
+    const Index n = x.size();
+    eigen_assert(fvec.size()==n);
+    Matrix< Scalar, Dynamic, 1 >  wa1(n);
+    Matrix< Scalar, Dynamic, 1 >  wa2(n);
+
+    eps = sqrt((std::max)(epsfcn,epsmch));
+    msum = ml + mu + 1;
+    if (msum >= n) {
+        /* computation of dense approximate jacobian. */
+        for (j = 0; j < n; ++j) {
+            temp = x[j];
+            h = eps * abs(temp);
+            if (h == 0.)
+                h = eps;
+            x[j] = temp + h;
+            iflag = Functor(x, wa1);
+            if (iflag < 0)
+                return iflag;
+            x[j] = temp;
+            fjac.col(j) = (wa1-fvec)/h;
+        }
+
+    }else {
+        /* computation of banded approximate jacobian. */
+        for (k = 0; k < msum; ++k) {
+            for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
+                wa2[j] = x[j];
+                h = eps * abs(wa2[j]);
+                if (h == 0.) h = eps;
+                x[j] = wa2[j] + h;
+            }
+            iflag = Functor(x, wa1);
+            if (iflag < 0)
+                return iflag;
+            for (j = k; (msum<0) ? (j>n): (j<n); j += msum) {
+                x[j] = wa2[j];
+                h = eps * abs(wa2[j]);
+                if (h == 0.) h = eps;
+                fjac.col(j).setZero();
+                start = std::max<Index>(0,j-mu);
+                length = (std::min)(n-1, j+ml) - start + 1;
+                fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
+            }
+        }
+    }
+    return 0;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
new file mode 100644
index 0000000..4c17d4c
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/lmpar.h
@@ -0,0 +1,298 @@
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void lmpar(
+        Matrix< Scalar, Dynamic, Dynamic > &r,
+        const VectorXi &ipvt,
+        const Matrix< Scalar, Dynamic, 1 >  &diag,
+        const Matrix< Scalar, Dynamic, 1 >  &qtb,
+        Scalar delta,
+        Scalar &par,
+        Matrix< Scalar, Dynamic, 1 >  &x)
+{
+    using std::abs;
+    using std::sqrt;
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Index i, j, l;
+    Scalar fp;
+    Scalar parc, parl;
+    Index iter;
+    Scalar temp, paru;
+    Scalar gnorm;
+    Scalar dxnorm;
+
+
+    /* Function Body */
+    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+    const Index n = r.cols();
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
+    eigen_assert(n==x.size());
+
+    Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
+
+    /* compute and store in x the gauss-newton direction. if the */
+    /* jacobian is rank-deficient, obtain a least squares solution. */
+    Index nsing = n-1;
+    wa1 = qtb;
+    for (j = 0; j < n; ++j) {
+        if (r(j,j) == 0. && nsing == n-1)
+            nsing = j - 1;
+        if (nsing < n-1)
+            wa1[j] = 0.;
+    }
+    for (j = nsing; j>=0; --j) {
+        wa1[j] /= r(j,j);
+        temp = wa1[j];
+        for (i = 0; i < j ; ++i)
+            wa1[i] -= r(i,j) * temp;
+    }
+
+    for (j = 0; j < n; ++j)
+        x[ipvt[j]] = wa1[j];
+
+    /* initialize the iteration counter. */
+    /* evaluate the function at the origin, and test */
+    /* for acceptance of the gauss-newton direction. */
+    iter = 0;
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    fp = dxnorm - delta;
+    if (fp <= Scalar(0.1) * delta) {
+        par = 0;
+        return;
+    }
+
+    /* if the jacobian is not rank deficient, the newton */
+    /* step provides a lower bound, parl, for the zero of */
+    /* the function. otherwise set this bound to zero. */
+    parl = 0.;
+    if (nsing >= n-1) {
+        for (j = 0; j < n; ++j) {
+            l = ipvt[j];
+            wa1[j] = diag[l] * (wa2[l] / dxnorm);
+        }
+        // it's actually a triangularView.solveInplace(), though in a weird
+        // way:
+        for (j = 0; j < n; ++j) {
+            Scalar sum = 0.;
+            for (i = 0; i < j; ++i)
+                sum += r(i,j) * wa1[i];
+            wa1[j] = (wa1[j] - sum) / r(j,j);
+        }
+        temp = wa1.blueNorm();
+        parl = fp / delta / temp / temp;
+    }
+
+    /* calculate an upper bound, paru, for the zero of the function. */
+    for (j = 0; j < n; ++j)
+        wa1[j] = r.col(j).head(j+1).dot(qtb.head(j+1)) / diag[ipvt[j]];
+
+    gnorm = wa1.stableNorm();
+    paru = gnorm / delta;
+    if (paru == 0.)
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
+
+    /* if the input par lies outside of the interval (parl,paru), */
+    /* set par to the closer endpoint. */
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
+    if (par == 0.)
+        par = gnorm / dxnorm;
+
+    /* beginning of an iteration. */
+    while (true) {
+        ++iter;
+
+        /* evaluate the function at the current value of par. */
+        if (par == 0.)
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
+        wa1 = sqrt(par)* diag;
+
+        Matrix< Scalar, Dynamic, 1 > sdiag(n);
+        qrsolv<Scalar>(r, ipvt, wa1, qtb, x, sdiag);
+
+        wa2 = diag.cwiseProduct(x);
+        dxnorm = wa2.blueNorm();
+        temp = fp;
+        fp = dxnorm - delta;
+
+        /* if the function is small enough, accept the current value */
+        /* of par. also test for the exceptional cases where parl */
+        /* is zero or the number of iterations has reached 10. */
+        if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
+            break;
+
+        /* compute the newton correction. */
+        for (j = 0; j < n; ++j) {
+            l = ipvt[j];
+            wa1[j] = diag[l] * (wa2[l] / dxnorm);
+        }
+        for (j = 0; j < n; ++j) {
+            wa1[j] /= sdiag[j];
+            temp = wa1[j];
+            for (i = j+1; i < n; ++i)
+                wa1[i] -= r(i,j) * temp;
+        }
+        temp = wa1.blueNorm();
+        parc = fp / delta / temp / temp;
+
+        /* depending on the sign of the function, update parl or paru. */
+        if (fp > 0.)
+            parl = (std::max)(parl,par);
+        if (fp < 0.)
+            paru = (std::min)(paru,par);
+
+        /* compute an improved estimate for par. */
+        /* Computing MAX */
+        par = (std::max)(parl,par+parc);
+
+        /* end of an iteration. */
+    }
+
+    /* termination. */
+    if (iter == 0)
+        par = 0.;
+    return;
+}
+
+template <typename Scalar>
+void lmpar2(
+        const ColPivHouseholderQR<Matrix< Scalar, Dynamic, Dynamic> > &qr,
+        const Matrix< Scalar, Dynamic, 1 >  &diag,
+        const Matrix< Scalar, Dynamic, 1 >  &qtb,
+        Scalar delta,
+        Scalar &par,
+        Matrix< Scalar, Dynamic, 1 >  &x)
+
+{
+    using std::sqrt;
+    using std::abs;
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Index j;
+    Scalar fp;
+    Scalar parc, parl;
+    Index iter;
+    Scalar temp, paru;
+    Scalar gnorm;
+    Scalar dxnorm;
+
+
+    /* Function Body */
+    const Scalar dwarf = (std::numeric_limits<Scalar>::min)();
+    const Index n = qr.matrixQR().cols();
+    eigen_assert(n==diag.size());
+    eigen_assert(n==qtb.size());
+
+    Matrix< Scalar, Dynamic, 1 >  wa1, wa2;
+
+    /* compute and store in x the gauss-newton direction. if the */
+    /* jacobian is rank-deficient, obtain a least squares solution. */
+
+//    const Index rank = qr.nonzeroPivots(); // exactly double(0.)
+    const Index rank = qr.rank(); // use a threshold
+    wa1 = qtb;
+    wa1.tail(n-rank).setZero();
+    qr.matrixQR().topLeftCorner(rank, rank).template triangularView<Upper>().solveInPlace(wa1.head(rank));
+
+    x = qr.colsPermutation()*wa1;
+
+    /* initialize the iteration counter. */
+    /* evaluate the function at the origin, and test */
+    /* for acceptance of the gauss-newton direction. */
+    iter = 0;
+    wa2 = diag.cwiseProduct(x);
+    dxnorm = wa2.blueNorm();
+    fp = dxnorm - delta;
+    if (fp <= Scalar(0.1) * delta) {
+        par = 0;
+        return;
+    }
+
+    /* if the jacobian is not rank deficient, the newton */
+    /* step provides a lower bound, parl, for the zero of */
+    /* the function. otherwise set this bound to zero. */
+    parl = 0.;
+    if (rank==n) {
+        wa1 = qr.colsPermutation().inverse() *  diag.cwiseProduct(wa2)/dxnorm;
+        qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+        temp = wa1.blueNorm();
+        parl = fp / delta / temp / temp;
+    }
+
+    /* calculate an upper bound, paru, for the zero of the function. */
+    for (j = 0; j < n; ++j)
+        wa1[j] = qr.matrixQR().col(j).head(j+1).dot(qtb.head(j+1)) / diag[qr.colsPermutation().indices()(j)];
+
+    gnorm = wa1.stableNorm();
+    paru = gnorm / delta;
+    if (paru == 0.)
+        paru = dwarf / (std::min)(delta,Scalar(0.1));
+
+    /* if the input par lies outside of the interval (parl,paru), */
+    /* set par to the closer endpoint. */
+    par = (std::max)(par,parl);
+    par = (std::min)(par,paru);
+    if (par == 0.)
+        par = gnorm / dxnorm;
+
+    /* beginning of an iteration. */
+    Matrix< Scalar, Dynamic, Dynamic > s = qr.matrixQR();
+    while (true) {
+        ++iter;
+
+        /* evaluate the function at the current value of par. */
+        if (par == 0.)
+            par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
+        wa1 = sqrt(par)* diag;
+
+        Matrix< Scalar, Dynamic, 1 > sdiag(n);
+        qrsolv<Scalar>(s, qr.colsPermutation().indices(), wa1, qtb, x, sdiag);
+
+        wa2 = diag.cwiseProduct(x);
+        dxnorm = wa2.blueNorm();
+        temp = fp;
+        fp = dxnorm - delta;
+
+        /* if the function is small enough, accept the current value */
+        /* of par. also test for the exceptional cases where parl */
+        /* is zero or the number of iterations has reached 10. */
+        if (abs(fp) <= Scalar(0.1) * delta || (parl == 0. && fp <= temp && temp < 0.) || iter == 10)
+            break;
+
+        /* compute the newton correction. */
+        wa1 = qr.colsPermutation().inverse() * diag.cwiseProduct(wa2/dxnorm);
+        // we could almost use this here, but the diagonal is outside qr, in sdiag[]
+        // qr.matrixQR().topLeftCorner(n, n).transpose().template triangularView<Lower>().solveInPlace(wa1);
+        for (j = 0; j < n; ++j) {
+            wa1[j] /= sdiag[j];
+            temp = wa1[j];
+            for (Index i = j+1; i < n; ++i)
+                wa1[i] -= s(i,j) * temp;
+        }
+        temp = wa1.blueNorm();
+        parc = fp / delta / temp / temp;
+
+        /* depending on the sign of the function, update parl or paru. */
+        if (fp > 0.)
+            parl = (std::max)(parl,par);
+        if (fp < 0.)
+            paru = (std::min)(paru,par);
+
+        /* compute an improved estimate for par. */
+        par = (std::max)(parl,par+parc);
+    }
+    if (iter == 0)
+        par = 0.;
+    return;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
new file mode 100644
index 0000000..4f2f560
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h
@@ -0,0 +1,91 @@
+namespace Eigen { 
+
+namespace internal {
+
+// TODO : once qrsolv2 is removed, use ColPivHouseholderQR or PermutationMatrix instead of ipvt
+template <typename Scalar>
+void qrsolv(
+        Matrix< Scalar, Dynamic, Dynamic > &s,
+        // TODO : use a PermutationMatrix once lmpar is no more:
+        const VectorXi &ipvt,
+        const Matrix< Scalar, Dynamic, 1 >  &diag,
+        const Matrix< Scalar, Dynamic, 1 >  &qtb,
+        Matrix< Scalar, Dynamic, 1 >  &x,
+        Matrix< Scalar, Dynamic, 1 >  &sdiag)
+
+{
+    typedef DenseIndex Index;
+
+    /* Local variables */
+    Index i, j, k, l;
+    Scalar temp;
+    Index n = s.cols();
+    Matrix< Scalar, Dynamic, 1 >  wa(n);
+    JacobiRotation<Scalar> givens;
+
+    /* Function Body */
+    // the following will only change the lower triangular part of s, including
+    // the diagonal, though the diagonal is restored afterward
+
+    /*     copy r and (q transpose)*b to preserve input and initialize s. */
+    /*     in particular, save the diagonal elements of r in x. */
+    x = s.diagonal();
+    wa = qtb;
+
+    s.topLeftCorner(n,n).template triangularView<StrictlyLower>() = s.topLeftCorner(n,n).transpose();
+
+    /*     eliminate the diagonal matrix d using a givens rotation. */
+    for (j = 0; j < n; ++j) {
+
+        /*        prepare the row of d to be eliminated, locating the */
+        /*        diagonal element using p from the qr factorization. */
+        l = ipvt[j];
+        if (diag[l] == 0.)
+            break;
+        sdiag.tail(n-j).setZero();
+        sdiag[j] = diag[l];
+
+        /*        the transformations to eliminate the row of d */
+        /*        modify only a single element of (q transpose)*b */
+        /*        beyond the first n, which is initially zero. */
+        Scalar qtbpj = 0.;
+        for (k = j; k < n; ++k) {
+            /*           determine a givens rotation which eliminates the */
+            /*           appropriate element in the current row of d. */
+            givens.makeGivens(-s(k,k), sdiag[k]);
+
+            /*           compute the modified diagonal element of r and */
+            /*           the modified element of ((q transpose)*b,0). */
+            s(k,k) = givens.c() * s(k,k) + givens.s() * sdiag[k];
+            temp = givens.c() * wa[k] + givens.s() * qtbpj;
+            qtbpj = -givens.s() * wa[k] + givens.c() * qtbpj;
+            wa[k] = temp;
+
+            /*           accumulate the transformation in the row of s. */
+            for (i = k+1; i<n; ++i) {
+                temp = givens.c() * s(i,k) + givens.s() * sdiag[i];
+                sdiag[i] = -givens.s() * s(i,k) + givens.c() * sdiag[i];
+                s(i,k) = temp;
+            }
+        }
+    }
+
+    /*     solve the triangular system for z. if the system is */
+    /*     singular, then obtain a least squares solution. */
+    Index nsing;
+    for(nsing=0; nsing<n && sdiag[nsing]!=0; nsing++) {}
+
+    wa.tail(n-nsing).setZero();
+    s.topLeftCorner(nsing, nsing).transpose().template triangularView<Upper>().solveInPlace(wa.head(nsing));
+
+    // restore
+    sdiag = s.diagonal();
+    s.diagonal() = x;
+
+    /*     permute the components of z back to components of x. */
+    for (j = 0; j < n; ++j) x[ipvt[j]] = wa[j];
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
new file mode 100644
index 0000000..36ff700
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h
@@ -0,0 +1,30 @@
+namespace Eigen { 
+
+namespace internal {
+
+// TODO : move this to GivensQR once there's such a thing in Eigen
+
+template <typename Scalar>
+void r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<JacobiRotation<Scalar> > &v_givens, const std::vector<JacobiRotation<Scalar> > &w_givens)
+{
+    typedef DenseIndex Index;
+
+    /*     apply the first set of givens rotations to a. */
+    for (Index j = n-2; j>=0; --j)
+        for (Index i = 0; i<m; ++i) {
+            Scalar temp = v_givens[j].c() * a[i+m*j] - v_givens[j].s() * a[i+m*(n-1)];
+            a[i+m*(n-1)] = v_givens[j].s() * a[i+m*j] + v_givens[j].c() * a[i+m*(n-1)];
+            a[i+m*j] = temp;
+        }
+    /*     apply the second set of givens rotations to a. */
+    for (Index j = 0; j<n-1; ++j)
+        for (Index i = 0; i<m; ++i) {
+            Scalar temp = w_givens[j].c() * a[i+m*j] + w_givens[j].s() * a[i+m*(n-1)];
+            a[i+m*(n-1)] = -w_givens[j].s() * a[i+m*j] + w_givens[j].c() * a[i+m*(n-1)];
+            a[i+m*j] = temp;
+        }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
new file mode 100644
index 0000000..09fc652
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/r1updt.h
@@ -0,0 +1,99 @@
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void r1updt(
+        Matrix< Scalar, Dynamic, Dynamic > &s,
+        const Matrix< Scalar, Dynamic, 1> &u,
+        std::vector<JacobiRotation<Scalar> > &v_givens,
+        std::vector<JacobiRotation<Scalar> > &w_givens,
+        Matrix< Scalar, Dynamic, 1> &v,
+        Matrix< Scalar, Dynamic, 1> &w,
+        bool *sing)
+{
+    typedef DenseIndex Index;
+    const JacobiRotation<Scalar> IdentityRotation = JacobiRotation<Scalar>(1,0);
+
+    /* Local variables */
+    const Index m = s.rows();
+    const Index n = s.cols();
+    Index i, j=1;
+    Scalar temp;
+    JacobiRotation<Scalar> givens;
+
+    // r1updt had a broader usecase, but we don't use it here. And, more
+    // importantly, we can not test it.
+    eigen_assert(m==n);
+    eigen_assert(u.size()==m);
+    eigen_assert(v.size()==n);
+    eigen_assert(w.size()==n);
+
+    /* move the nontrivial part of the last column of s into w. */
+    w[n-1] = s(n-1,n-1);
+
+    /* rotate the vector v into a multiple of the n-th unit vector */
+    /* in such a way that a spike is introduced into w. */
+    for (j=n-2; j>=0; --j) {
+        w[j] = 0.;
+        if (v[j] != 0.) {
+            /* determine a givens rotation which eliminates the */
+            /* j-th element of v. */
+            givens.makeGivens(-v[n-1], v[j]);
+
+            /* apply the transformation to v and store the information */
+            /* necessary to recover the givens rotation. */
+            v[n-1] = givens.s() * v[j] + givens.c() * v[n-1];
+            v_givens[j] = givens;
+
+            /* apply the transformation to s and extend the spike in w. */
+            for (i = j; i < m; ++i) {
+                temp = givens.c() * s(j,i) - givens.s() * w[i];
+                w[i] = givens.s() * s(j,i) + givens.c() * w[i];
+                s(j,i) = temp;
+            }
+        } else
+            v_givens[j] = IdentityRotation;
+    }
+
+    /* add the spike from the rank 1 update to w. */
+    w += v[n-1] * u;
+
+    /* eliminate the spike. */
+    *sing = false;
+    for (j = 0; j < n-1; ++j) {
+        if (w[j] != 0.) {
+            /* determine a givens rotation which eliminates the */
+            /* j-th element of the spike. */
+            givens.makeGivens(-s(j,j), w[j]);
+
+            /* apply the transformation to s and reduce the spike in w. */
+            for (i = j; i < m; ++i) {
+                temp = givens.c() * s(j,i) + givens.s() * w[i];
+                w[i] = -givens.s() * s(j,i) + givens.c() * w[i];
+                s(j,i) = temp;
+            }
+
+            /* store the information necessary to recover the */
+            /* givens rotation. */
+            w_givens[j] = givens;
+        } else
+            v_givens[j] = IdentityRotation;
+
+        /* test for zero diagonal elements in the output s. */
+        if (s(j,j) == 0.) {
+            *sing = true;
+        }
+    }
+    /* move w back into the last column of the output s. */
+    s(n-1,n-1) = w[n-1];
+
+    if (s(j,j) == 0.) {
+        *sing = true;
+    }
+    return;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
new file mode 100644
index 0000000..6ebf856
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h
@@ -0,0 +1,49 @@
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Scalar>
+void rwupdt(
+        Matrix< Scalar, Dynamic, Dynamic >  &r,
+        const Matrix< Scalar, Dynamic, 1>  &w,
+        Matrix< Scalar, Dynamic, 1>  &b,
+        Scalar alpha)
+{
+    typedef DenseIndex Index;
+
+    const Index n = r.cols();
+    eigen_assert(r.rows()>=n);
+    std::vector<JacobiRotation<Scalar> > givens(n);
+
+    /* Local variables */
+    Scalar temp, rowj;
+
+    /* Function Body */
+    for (Index j = 0; j < n; ++j) {
+        rowj = w[j];
+
+        /* apply the previous transformations to */
+        /* r(i,j), i=0,1,...,j-1, and to w(j). */
+        for (Index i = 0; i < j; ++i) {
+            temp = givens[i].c() * r(i,j) + givens[i].s() * rowj;
+            rowj = -givens[i].s() * r(i,j) + givens[i].c() * rowj;
+            r(i,j) = temp;
+        }
+
+        /* determine a givens rotation which eliminates w(j). */
+        givens[j].makeGivens(-r(j,j), rowj);
+
+        if (rowj == 0.)
+            continue; // givens[j] is identity
+
+        /* apply the current transformation to r(j,j), b(j), and alpha. */
+        r(j,j) = givens[j].c() * r(j,j) + givens[j].s() * rowj;
+        temp = givens[j].c() * b[j] + givens[j].s() * alpha;
+        alpha = -givens[j].s() * b[j] + givens[j].c() * alpha;
+        b[j] = temp;
+    }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
new file mode 100644
index 0000000..ea5d8bc
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -0,0 +1,130 @@
+// -*- coding: utf-8
+// vim: set fileencoding=utf-8
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMERICAL_DIFF_H
+#define EIGEN_NUMERICAL_DIFF_H
+
+namespace Eigen { 
+
+enum NumericalDiffMode {
+    Forward,
+    Central
+};
+
+
+/**
+  * This class allows you to add a method df() to your functor, which will 
+  * use numerical differentiation to compute an approximate of the
+  * derivative for the functor. Of course, if you have an analytical form
+  * for the derivative, you should rather implement df() by yourself.
+  *
+  * More information on
+  * http://en.wikipedia.org/wiki/Numerical_differentiation
+  *
+  * Currently only "Forward" and "Central" scheme are implemented.
+  */
+template<typename _Functor, NumericalDiffMode mode=Forward>
+class NumericalDiff : public _Functor
+{
+public:
+    typedef _Functor Functor;
+    typedef typename Functor::Scalar Scalar;
+    typedef typename Functor::InputType InputType;
+    typedef typename Functor::ValueType ValueType;
+    typedef typename Functor::JacobianType JacobianType;
+
+    NumericalDiff(Scalar _epsfcn=0.) : Functor(), epsfcn(_epsfcn) {}
+    NumericalDiff(const Functor& f, Scalar _epsfcn=0.) : Functor(f), epsfcn(_epsfcn) {}
+
+    // forward constructors
+    template<typename T0>
+        NumericalDiff(const T0& a0) : Functor(a0), epsfcn(0) {}
+    template<typename T0, typename T1>
+        NumericalDiff(const T0& a0, const T1& a1) : Functor(a0, a1), epsfcn(0) {}
+    template<typename T0, typename T1, typename T2>
+        NumericalDiff(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2), epsfcn(0) {}
+
+    enum {
+        InputsAtCompileTime = Functor::InputsAtCompileTime,
+        ValuesAtCompileTime = Functor::ValuesAtCompileTime
+    };
+
+    /**
+      * return the number of evaluation of functor
+     */
+    int df(const InputType& _x, JacobianType &jac) const
+    {
+        using std::sqrt;
+        using std::abs;
+        /* Local variables */
+        Scalar h;
+        int nfev=0;
+        const typename InputType::Index n = _x.size();
+        const Scalar eps = sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() )));
+        ValueType val1, val2;
+        InputType x = _x;
+        // TODO : we should do this only if the size is not already known
+        val1.resize(Functor::values());
+        val2.resize(Functor::values());
+
+        // initialization
+        switch(mode) {
+            case Forward:
+                // compute f(x)
+                Functor::operator()(x, val1); nfev++;
+                break;
+            case Central:
+                // do nothing
+                break;
+            default:
+                eigen_assert(false);
+        };
+
+        // Function Body
+        for (int j = 0; j < n; ++j) {
+            h = eps * abs(x[j]);
+            if (h == 0.) {
+                h = eps;
+            }
+            switch(mode) {
+                case Forward:
+                    x[j] += h;
+                    Functor::operator()(x, val2);
+                    nfev++;
+                    x[j] = _x[j];
+                    jac.col(j) = (val2-val1)/h;
+                    break;
+                case Central:
+                    x[j] += h;
+                    Functor::operator()(x, val2); nfev++;
+                    x[j] -= 2*h;
+                    Functor::operator()(x, val1); nfev++;
+                    x[j] = _x[j];
+                    jac.col(j) = (val2-val1)/(2*h);
+                    break;
+                default:
+                    eigen_assert(false);
+            };
+        }
+        return nfev;
+    }
+private:
+    Scalar epsfcn;
+
+    NumericalDiff& operator=(const NumericalDiff&);
+};
+
+} // end namespace Eigen
+
+//vim: ai ts=4 sts=4 et sw=4
+#endif // EIGEN_NUMERICAL_DIFF_H
+
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
new file mode 100644
index 0000000..59a15b0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/Companion.h
@@ -0,0 +1,280 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPANION_H
+#define EIGEN_COMPANION_H
+
+// This file requires the user to include
+// * Eigen/Core
+// * Eigen/src/PolynomialSolver.h
+
+namespace Eigen { 
+
+namespace internal {
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template<int Size>
+struct decrement_if_fixed_size
+{
+  enum {
+    ret = (Size == Dynamic) ? Dynamic : Size-1 };
+};
+
+#endif
+
+template< typename _Scalar, int _Deg >
+class companion
+{
+  public:
+    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
+
+    enum {
+      Deg = _Deg,
+      Deg_1=decrement_if_fixed_size<Deg>::ret
+    };
+
+    typedef _Scalar                                Scalar;
+    typedef typename NumTraits<Scalar>::Real       RealScalar;
+    typedef Matrix<Scalar, Deg, 1>                 RightColumn;
+    //typedef DiagonalMatrix< Scalar, Deg_1, Deg_1 > BottomLeftDiagonal;
+    typedef Matrix<Scalar, Deg_1, 1>               BottomLeftDiagonal;
+
+    typedef Matrix<Scalar, Deg, Deg>               DenseCompanionMatrixType;
+    typedef Matrix< Scalar, _Deg, Deg_1 >          LeftBlock;
+    typedef Matrix< Scalar, Deg_1, Deg_1 >         BottomLeftBlock;
+    typedef Matrix< Scalar, 1, Deg_1 >             LeftBlockFirstRow;
+
+    typedef DenseIndex Index;
+
+  public:
+    EIGEN_STRONG_INLINE const _Scalar operator()(Index row, Index col ) const
+    {
+      if( m_bl_diag.rows() > col )
+      {
+        if( 0 < row ){ return m_bl_diag[col]; }
+        else{ return 0; }
+      }
+      else{ return m_monic[row]; }
+    }
+
+  public:
+    template<typename VectorType>
+    void setPolynomial( const VectorType& poly )
+    {
+      const Index deg = poly.size()-1;
+      m_monic = -poly.head(deg)/poly[deg];
+      m_bl_diag.setOnes(deg-1);
+    }
+
+    template<typename VectorType>
+    companion( const VectorType& poly ){
+      setPolynomial( poly ); }
+
+  public:
+    DenseCompanionMatrixType denseMatrix() const
+    {
+      const Index deg   = m_monic.size();
+      const Index deg_1 = deg-1;
+      DenseCompanionMatrixType companMat(deg,deg);
+      companMat <<
+        ( LeftBlock(deg,deg_1)
+          << LeftBlockFirstRow::Zero(1,deg_1),
+          BottomLeftBlock::Identity(deg-1,deg-1)*m_bl_diag.asDiagonal() ).finished()
+        , m_monic;
+      return companMat;
+    }
+
+
+
+  protected:
+    /** Helper function for the balancing algorithm.
+     * \returns true if the row and the column, having colNorm and rowNorm
+     * as norms, are balanced, false otherwise.
+     * colB and rowB are respectively the multipliers for
+     * the column and the row in order to balance them.
+     * */
+    bool balanced( RealScalar colNorm, RealScalar rowNorm,
+        bool& isBalanced, RealScalar& colB, RealScalar& rowB );
+
+    /** Helper function for the balancing algorithm.
+     * \returns true if the row and the column, having colNorm and rowNorm
+     * as norms, are balanced, false otherwise.
+     * colB and rowB are respectively the multipliers for
+     * the column and the row in order to balance them.
+     * */
+    bool balancedR( RealScalar colNorm, RealScalar rowNorm,
+        bool& isBalanced, RealScalar& colB, RealScalar& rowB );
+
+  public:
+    /**
+     * Balancing algorithm from B. N. PARLETT and C. REINSCH (1969)
+     * "Balancing a matrix for calculation of eigenvalues and eigenvectors"
+     * adapted to the case of companion matrices.
+     * A matrix with non zero row and non zero column is balanced
+     * for a certain norm if the i-th row and the i-th column
+     * have same norm for all i.
+     */
+    void balance();
+
+  protected:
+      RightColumn                m_monic;
+      BottomLeftDiagonal         m_bl_diag;
+};
+
+
+
+template< typename _Scalar, int _Deg >
+inline
+bool companion<_Scalar,_Deg>::balanced( RealScalar colNorm, RealScalar rowNorm,
+    bool& isBalanced, RealScalar& colB, RealScalar& rowB )
+{
+  if( RealScalar(0) == colNorm || RealScalar(0) == rowNorm 
+      || !(numext::isfinite)(colNorm) || !(numext::isfinite)(rowNorm)){
+    return true;
+  }
+  else
+  {
+    //To find the balancing coefficients, if the radix is 2,
+    //one finds \f$ \sigma \f$ such that
+    // \f$ 2^{2\sigma-1} < rowNorm / colNorm \le 2^{2\sigma+1} \f$
+    // then the balancing coefficient for the row is \f$ 1/2^{\sigma} \f$
+    // and the balancing coefficient for the column is \f$ 2^{\sigma} \f$
+    const RealScalar radix = RealScalar(2);
+    const RealScalar radix2 = RealScalar(4);
+    
+    rowB = rowNorm / radix;
+    colB = RealScalar(1);
+    const RealScalar s = colNorm + rowNorm;
+
+    // Find sigma s.t. rowNorm / 2 <= 2^(2*sigma) * colNorm
+    RealScalar scout = colNorm;
+    while (scout < rowB)
+    {
+      colB *= radix;
+      scout *= radix2;
+    }
+    
+    // We now have an upper-bound for sigma, try to lower it.
+    // Find sigma s.t. 2^(2*sigma) * colNorm / 2 < rowNorm
+    scout = colNorm * (colB / radix) * colB;  // Avoid overflow.
+    while (scout >= rowNorm)
+    {
+      colB /= radix;
+      scout /= radix2;
+    }
+
+    // This line is used to avoid insubstantial balancing.
+    if ((rowNorm + radix * scout) < RealScalar(0.95) * s * colB)
+    {
+      isBalanced = false;
+      rowB = RealScalar(1) / colB;
+      return false;
+    }
+    else
+    {
+      return true;
+    }
+  }
+}
+
+template< typename _Scalar, int _Deg >
+inline
+bool companion<_Scalar,_Deg>::balancedR( RealScalar colNorm, RealScalar rowNorm,
+    bool& isBalanced, RealScalar& colB, RealScalar& rowB )
+{
+  if( RealScalar(0) == colNorm || RealScalar(0) == rowNorm ){ return true; }
+  else
+  {
+    /**
+     * Set the norm of the column and the row to the geometric mean
+     * of the row and column norm
+     */
+    const RealScalar q = colNorm/rowNorm;
+    if( !isApprox( q, _Scalar(1) ) )
+    {
+      rowB = sqrt( colNorm/rowNorm );
+      colB = RealScalar(1)/rowB;
+
+      isBalanced = false;
+      return false;
+    }
+    else{
+      return true; }
+  }
+}
+
+
+template< typename _Scalar, int _Deg >
+void companion<_Scalar,_Deg>::balance()
+{
+  using std::abs;
+  EIGEN_STATIC_ASSERT( Deg == Dynamic || 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE );
+  const Index deg   = m_monic.size();
+  const Index deg_1 = deg-1;
+
+  bool hasConverged=false;
+  while( !hasConverged )
+  {
+    hasConverged = true;
+    RealScalar colNorm,rowNorm;
+    RealScalar colB,rowB;
+
+    //First row, first column excluding the diagonal
+    //==============================================
+    colNorm = abs(m_bl_diag[0]);
+    rowNorm = abs(m_monic[0]);
+
+    //Compute balancing of the row and the column
+    if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
+    {
+      m_bl_diag[0] *= colB;
+      m_monic[0] *= rowB;
+    }
+
+    //Middle rows and columns excluding the diagonal
+    //==============================================
+    for( Index i=1; i<deg_1; ++i )
+    {
+      // column norm, excluding the diagonal
+      colNorm = abs(m_bl_diag[i]);
+
+      // row norm, excluding the diagonal
+      rowNorm = abs(m_bl_diag[i-1]) + abs(m_monic[i]);
+
+      //Compute balancing of the row and the column
+      if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
+      {
+        m_bl_diag[i]   *= colB;
+        m_bl_diag[i-1] *= rowB;
+        m_monic[i]     *= rowB;
+      }
+    }
+
+    //Last row, last column excluding the diagonal
+    //============================================
+    const Index ebl = m_bl_diag.size()-1;
+    VectorBlock<RightColumn,Deg_1> headMonic( m_monic, 0, deg_1 );
+    colNorm = headMonic.array().abs().sum();
+    rowNorm = abs( m_bl_diag[ebl] );
+
+    //Compute balancing of the row and the column
+    if( !balanced( colNorm, rowNorm, hasConverged, colB, rowB ) )
+    {
+      headMonic      *= colB;
+      m_bl_diag[ebl] *= rowB;
+    }
+  }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPANION_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
new file mode 100644
index 0000000..5e0ecbb
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialSolver.h
@@ -0,0 +1,428 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIAL_SOLVER_H
+#define EIGEN_POLYNOMIAL_SOLVER_H
+
+namespace Eigen { 
+
+/** \ingroup Polynomials_Module
+ *  \class PolynomialSolverBase.
+ *
+ * \brief Defined to be inherited by polynomial solvers: it provides
+ * convenient methods such as
+ *  - real roots,
+ *  - greatest, smallest complex roots,
+ *  - real roots with greatest, smallest absolute real value,
+ *  - greatest, smallest real roots.
+ *
+ * It stores the set of roots as a vector of complexes.
+ *
+ */
+template< typename _Scalar, int _Deg >
+class PolynomialSolverBase
+{
+  public:
+    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
+
+    typedef _Scalar                             Scalar;
+    typedef typename NumTraits<Scalar>::Real    RealScalar;
+    typedef std::complex<RealScalar>            RootType;
+    typedef Matrix<RootType,_Deg,1>             RootsType;
+
+    typedef DenseIndex Index;
+
+  protected:
+    template< typename OtherPolynomial >
+    inline void setPolynomial( const OtherPolynomial& poly ){
+      m_roots.resize(poly.size()-1); }
+
+  public:
+    template< typename OtherPolynomial >
+    inline PolynomialSolverBase( const OtherPolynomial& poly ){
+      setPolynomial( poly() ); }
+
+    inline PolynomialSolverBase(){}
+
+  public:
+    /** \returns the complex roots of the polynomial */
+    inline const RootsType& roots() const { return m_roots; }
+
+  public:
+    /** Clear and fills the back insertion sequence with the real roots of the polynomial
+     * i.e. the real part of the complex roots that have an imaginary part which
+     * absolute value is smaller than absImaginaryThreshold.
+     * absImaginaryThreshold takes the dummy_precision associated
+     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
+     *
+     * \param[out] bi_seq : the back insertion sequence (stl concept)
+     * \param[in]  absImaginaryThreshold : the maximum bound of the imaginary part of a complex
+     *  number that is considered as real.
+     * */
+    template<typename Stl_back_insertion_sequence>
+    inline void realRoots( Stl_back_insertion_sequence& bi_seq,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      using std::abs;
+      bi_seq.clear();
+      for(Index i=0; i<m_roots.size(); ++i )
+      {
+        if( abs( m_roots[i].imag() ) < absImaginaryThreshold ){
+          bi_seq.push_back( m_roots[i].real() ); }
+      }
+    }
+
+  protected:
+    template<typename squaredNormBinaryPredicate>
+    inline const RootType& selectComplexRoot_withRespectToNorm( squaredNormBinaryPredicate& pred ) const
+    {
+      Index res=0;
+      RealScalar norm2 = numext::abs2( m_roots[0] );
+      for( Index i=1; i<m_roots.size(); ++i )
+      {
+        const RealScalar currNorm2 = numext::abs2( m_roots[i] );
+        if( pred( currNorm2, norm2 ) ){
+          res=i; norm2=currNorm2; }
+      }
+      return m_roots[res];
+    }
+
+  public:
+    /**
+     * \returns the complex root with greatest norm.
+     */
+    inline const RootType& greatestRoot() const
+    {
+      std::greater<RealScalar> greater;
+      return selectComplexRoot_withRespectToNorm( greater );
+    }
+
+    /**
+     * \returns the complex root with smallest norm.
+     */
+    inline const RootType& smallestRoot() const
+    {
+      std::less<RealScalar> less;
+      return selectComplexRoot_withRespectToNorm( less );
+    }
+
+  protected:
+    template<typename squaredRealPartBinaryPredicate>
+    inline const RealScalar& selectRealRoot_withRespectToAbsRealPart(
+        squaredRealPartBinaryPredicate& pred,
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      using std::abs;
+      hasArealRoot = false;
+      Index res=0;
+      RealScalar abs2(0);
+
+      for( Index i=0; i<m_roots.size(); ++i )
+      {
+        if( abs( m_roots[i].imag() ) <= absImaginaryThreshold )
+        {
+          if( !hasArealRoot )
+          {
+            hasArealRoot = true;
+            res = i;
+            abs2 = m_roots[i].real() * m_roots[i].real();
+          }
+          else
+          {
+            const RealScalar currAbs2 = m_roots[i].real() * m_roots[i].real();
+            if( pred( currAbs2, abs2 ) )
+            {
+              abs2 = currAbs2;
+              res = i;
+            }
+          }
+        }
+        else if(!hasArealRoot)
+        {
+          if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){
+            res = i;}
+        }
+      }
+      return numext::real_ref(m_roots[res]);
+    }
+
+
+    template<typename RealPartBinaryPredicate>
+    inline const RealScalar& selectRealRoot_withRespectToRealPart(
+        RealPartBinaryPredicate& pred,
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      using std::abs;
+      hasArealRoot = false;
+      Index res=0;
+      RealScalar val(0);
+
+      for( Index i=0; i<m_roots.size(); ++i )
+      {
+        if( abs( m_roots[i].imag() ) <= absImaginaryThreshold )
+        {
+          if( !hasArealRoot )
+          {
+            hasArealRoot = true;
+            res = i;
+            val = m_roots[i].real();
+          }
+          else
+          {
+            const RealScalar curr = m_roots[i].real();
+            if( pred( curr, val ) )
+            {
+              val = curr;
+              res = i;
+            }
+          }
+        }
+        else
+        {
+          if( abs( m_roots[i].imag() ) < abs( m_roots[res].imag() ) ){
+            res = i; }
+        }
+      }
+      return numext::real_ref(m_roots[res]);
+    }
+
+  public:
+    /**
+     * \returns a real root with greatest absolute magnitude.
+     * A real root is defined as the real part of a complex root with absolute imaginary
+     * part smallest than absImaginaryThreshold.
+     * absImaginaryThreshold takes the dummy_precision associated
+     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
+     * If no real root is found the boolean hasArealRoot is set to false and the real part of
+     * the root with smallest absolute imaginary part is returned instead.
+     *
+     * \param[out] hasArealRoot : boolean true if a real root is found according to the
+     *  absImaginaryThreshold criterion, false otherwise.
+     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+     *  whether or not a root is real.
+     */
+    inline const RealScalar& absGreatestRealRoot(
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      std::greater<RealScalar> greater;
+      return selectRealRoot_withRespectToAbsRealPart( greater, hasArealRoot, absImaginaryThreshold );
+    }
+
+
+    /**
+     * \returns a real root with smallest absolute magnitude.
+     * A real root is defined as the real part of a complex root with absolute imaginary
+     * part smallest than absImaginaryThreshold.
+     * absImaginaryThreshold takes the dummy_precision associated
+     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
+     * If no real root is found the boolean hasArealRoot is set to false and the real part of
+     * the root with smallest absolute imaginary part is returned instead.
+     *
+     * \param[out] hasArealRoot : boolean true if a real root is found according to the
+     *  absImaginaryThreshold criterion, false otherwise.
+     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+     *  whether or not a root is real.
+     */
+    inline const RealScalar& absSmallestRealRoot(
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      std::less<RealScalar> less;
+      return selectRealRoot_withRespectToAbsRealPart( less, hasArealRoot, absImaginaryThreshold );
+    }
+
+
+    /**
+     * \returns the real root with greatest value.
+     * A real root is defined as the real part of a complex root with absolute imaginary
+     * part smallest than absImaginaryThreshold.
+     * absImaginaryThreshold takes the dummy_precision associated
+     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
+     * If no real root is found the boolean hasArealRoot is set to false and the real part of
+     * the root with smallest absolute imaginary part is returned instead.
+     *
+     * \param[out] hasArealRoot : boolean true if a real root is found according to the
+     *  absImaginaryThreshold criterion, false otherwise.
+     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+     *  whether or not a root is real.
+     */
+    inline const RealScalar& greatestRealRoot(
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      std::greater<RealScalar> greater;
+      return selectRealRoot_withRespectToRealPart( greater, hasArealRoot, absImaginaryThreshold );
+    }
+
+
+    /**
+     * \returns the real root with smallest value.
+     * A real root is defined as the real part of a complex root with absolute imaginary
+     * part smallest than absImaginaryThreshold.
+     * absImaginaryThreshold takes the dummy_precision associated
+     * with the _Scalar template parameter of the PolynomialSolver class as the default value.
+     * If no real root is found the boolean hasArealRoot is set to false and the real part of
+     * the root with smallest absolute imaginary part is returned instead.
+     *
+     * \param[out] hasArealRoot : boolean true if a real root is found according to the
+     *  absImaginaryThreshold criterion, false otherwise.
+     * \param[in] absImaginaryThreshold : threshold on the absolute imaginary part to decide
+     *  whether or not a root is real.
+     */
+    inline const RealScalar& smallestRealRoot(
+        bool& hasArealRoot,
+        const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const
+    {
+      std::less<RealScalar> less;
+      return selectRealRoot_withRespectToRealPart( less, hasArealRoot, absImaginaryThreshold );
+    }
+
+  protected:
+    RootsType               m_roots;
+};
+
+#define EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( BASE )  \
+  typedef typename BASE::Scalar                 Scalar;       \
+  typedef typename BASE::RealScalar             RealScalar;   \
+  typedef typename BASE::RootType               RootType;     \
+  typedef typename BASE::RootsType              RootsType;
+
+
+
+/** \ingroup Polynomials_Module
+  *
+  * \class PolynomialSolver
+  *
+  * \brief A polynomial solver
+  *
+  * Computes the complex roots of a real polynomial.
+  *
+  * \param _Scalar the scalar type, i.e., the type of the polynomial coefficients
+  * \param _Deg the degree of the polynomial, can be a compile time value or Dynamic.
+  *             Notice that the number of polynomial coefficients is _Deg+1.
+  *
+  * This class implements a polynomial solver and provides convenient methods such as
+  * - real roots,
+  * - greatest, smallest complex roots,
+  * - real roots with greatest, smallest absolute real value.
+  * - greatest, smallest real roots.
+  *
+  * WARNING: this polynomial solver is experimental, part of the unsupported Eigen modules.
+  *
+  *
+  * Currently a QR algorithm is used to compute the eigenvalues of the companion matrix of
+  * the polynomial to compute its roots.
+  * This supposes that the complex moduli of the roots are all distinct: e.g. there should
+  * be no multiple roots or conjugate roots for instance.
+  * With 32bit (float) floating types this problem shows up frequently.
+  * However, almost always, correct accuracy is reached even in these cases for 64bit
+  * (double) floating types and small polynomial degree (<20).
+  */
+template<typename _Scalar, int _Deg>
+class PolynomialSolver : public PolynomialSolverBase<_Scalar,_Deg>
+{
+  public:
+    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Deg==Dynamic ? Dynamic : _Deg)
+
+    typedef PolynomialSolverBase<_Scalar,_Deg>    PS_Base;
+    EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( PS_Base )
+
+    typedef Matrix<Scalar,_Deg,_Deg>                 CompanionMatrixType;
+    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+                                          ComplexEigenSolver<CompanionMatrixType>,
+                                          EigenSolver<CompanionMatrixType> >::type EigenSolverType;
+    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, Scalar, std::complex<Scalar> >::type ComplexScalar;
+
+  public:
+    /** Computes the complex roots of a new polynomial. */
+    template< typename OtherPolynomial >
+    void compute( const OtherPolynomial& poly )
+    {
+      eigen_assert( Scalar(0) != poly[poly.size()-1] );
+      eigen_assert( poly.size() > 1 );
+      if(poly.size() >  2 )
+      {
+        internal::companion<Scalar,_Deg> companion( poly );
+        companion.balance();
+        m_eigenSolver.compute( companion.denseMatrix() );
+        m_roots = m_eigenSolver.eigenvalues();
+        // cleanup noise in imaginary part of real roots:
+        // if the imaginary part is rather small compared to the real part
+        // and that cancelling the imaginary part yield a smaller evaluation,
+        // then it's safe to keep the real part only.
+        RealScalar coarse_prec = RealScalar(std::pow(4,poly.size()+1))*NumTraits<RealScalar>::epsilon();
+        for(Index i = 0; i<m_roots.size(); ++i)
+        {
+          if( internal::isMuchSmallerThan(numext::abs(numext::imag(m_roots[i])),
+                                          numext::abs(numext::real(m_roots[i])),
+                                          coarse_prec) )
+          {
+            ComplexScalar as_real_root = ComplexScalar(numext::real(m_roots[i]));
+            if(    numext::abs(poly_eval(poly, as_real_root))
+                <= numext::abs(poly_eval(poly, m_roots[i])))
+            {
+              m_roots[i] = as_real_root;
+            }
+          }
+        }
+      }
+      else if(poly.size () == 2)
+      {
+        m_roots.resize(1);
+        m_roots[0] = -poly[0]/poly[1];
+      }
+    }
+
+  public:
+    template< typename OtherPolynomial >
+    inline PolynomialSolver( const OtherPolynomial& poly ){
+      compute( poly ); }
+
+    inline PolynomialSolver(){}
+
+  protected:
+    using                   PS_Base::m_roots;
+    EigenSolverType         m_eigenSolver;
+};
+
+
+template< typename _Scalar >
+class PolynomialSolver<_Scalar,1> : public PolynomialSolverBase<_Scalar,1>
+{
+  public:
+    typedef PolynomialSolverBase<_Scalar,1>    PS_Base;
+    EIGEN_POLYNOMIAL_SOLVER_BASE_INHERITED_TYPES( PS_Base )
+
+  public:
+    /** Computes the complex roots of a new polynomial. */
+    template< typename OtherPolynomial >
+    void compute( const OtherPolynomial& poly )
+    {
+      eigen_assert( poly.size() == 2 );
+      eigen_assert( Scalar(0) != poly[1] );
+      m_roots[0] = -poly[0]/poly[1];
+    }
+
+  public:
+    template< typename OtherPolynomial >
+    inline PolynomialSolver( const OtherPolynomial& poly ){
+      compute( poly ); }
+
+    inline PolynomialSolver(){}
+
+  protected:
+    using                   PS_Base::m_roots;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_POLYNOMIAL_SOLVER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
new file mode 100644
index 0000000..394e857
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Polynomials/PolynomialUtils.h
@@ -0,0 +1,143 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Manuel Yguel <manuel.yguel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_POLYNOMIAL_UTILS_H
+#define EIGEN_POLYNOMIAL_UTILS_H
+
+namespace Eigen { 
+
+/** \ingroup Polynomials_Module
+ * \returns the evaluation of the polynomial at x using Horner algorithm.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ * \param[in] x : the value to evaluate the polynomial at.
+ *
+ * \note for stability:
+ *   \f$ |x| \le 1 \f$
+ */
+template <typename Polynomials, typename T>
+inline
+T poly_eval_horner( const Polynomials& poly, const T& x )
+{
+  T val=poly[poly.size()-1];
+  for(DenseIndex i=poly.size()-2; i>=0; --i ){
+    val = val*x + poly[i]; }
+  return val;
+}
+
+/** \ingroup Polynomials_Module
+ * \returns the evaluation of the polynomial at x using stabilized Horner algorithm.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ * \param[in] x : the value to evaluate the polynomial at.
+ */
+template <typename Polynomials, typename T>
+inline
+T poly_eval( const Polynomials& poly, const T& x )
+{
+  typedef typename NumTraits<T>::Real Real;
+
+  if( numext::abs2( x ) <= Real(1) ){
+    return poly_eval_horner( poly, x ); }
+  else
+  {
+    T val=poly[0];
+    T inv_x = T(1)/x;
+    for( DenseIndex i=1; i<poly.size(); ++i ){
+      val = val*inv_x + poly[i]; }
+
+    return numext::pow(x,(T)(poly.size()-1)) * val;
+  }
+}
+
+/** \ingroup Polynomials_Module
+ * \returns a maximum bound for the absolute value of any root of the polynomial.
+ *
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ *
+ *  \pre
+ *   the leading coefficient of the input polynomial poly must be non zero
+ */
+template <typename Polynomial>
+inline
+typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound( const Polynomial& poly )
+{
+  using std::abs;
+  typedef typename Polynomial::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  eigen_assert( Scalar(0) != poly[poly.size()-1] );
+  const Scalar inv_leading_coeff = Scalar(1)/poly[poly.size()-1];
+  Real cb(0);
+
+  for( DenseIndex i=0; i<poly.size()-1; ++i ){
+    cb += abs(poly[i]*inv_leading_coeff); }
+  return cb + Real(1);
+}
+
+/** \ingroup Polynomials_Module
+ * \returns a minimum bound for the absolute value of any non zero root of the polynomial.
+ * \param[in] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$.
+ */
+template <typename Polynomial>
+inline
+typename NumTraits<typename Polynomial::Scalar>::Real cauchy_min_bound( const Polynomial& poly )
+{
+  using std::abs;
+  typedef typename Polynomial::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real Real;
+
+  DenseIndex i=0;
+  while( i<poly.size()-1 && Scalar(0) == poly(i) ){ ++i; }
+  if( poly.size()-1 == i ){
+    return Real(1); }
+
+  const Scalar inv_min_coeff = Scalar(1)/poly[i];
+  Real cb(1);
+  for( DenseIndex j=i+1; j<poly.size(); ++j ){
+    cb += abs(poly[j]*inv_min_coeff); }
+  return Real(1)/cb;
+}
+
+/** \ingroup Polynomials_Module
+ * Given the roots of a polynomial compute the coefficients in the
+ * monomial basis of the monic polynomial with same roots and minimal degree.
+ * If RootVector is a vector of complexes, Polynomial should also be a vector
+ * of complexes.
+ * \param[in] rv : a vector containing the roots of a polynomial.
+ * \param[out] poly : the vector of coefficients of the polynomial ordered
+ *  by degrees i.e. poly[i] is the coefficient of degree i of the polynomial
+ *  e.g. \f$ 3 + x^2 \f$ is stored as a vector \f$ [ 3, 0, 1 ] \f$.
+ */
+template <typename RootVector, typename Polynomial>
+void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly )
+{
+
+  typedef typename Polynomial::Scalar Scalar;
+
+  poly.setZero( rv.size()+1 );
+  poly[0] = -rv[0]; poly[1] = Scalar(1);
+  for( DenseIndex i=1; i< rv.size(); ++i )
+  {
+    for( DenseIndex j=i+1; j>0; --j ){ poly[j] = poly[j-1] - rv[i]*poly[j]; }
+    poly[0] = -rv[i]*poly[0];
+  }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_POLYNOMIAL_UTILS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
new file mode 100644
index 0000000..6d0370d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
@@ -0,0 +1,352 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINEINPLACELU_H
+#define EIGEN_SKYLINEINPLACELU_H
+
+namespace Eigen { 
+
+/** \ingroup Skyline_Module
+ *
+ * \class SkylineInplaceLU
+ *
+ * \brief Inplace LU decomposition of a skyline matrix and associated features
+ *
+ * \param MatrixType the type of the matrix of which we are computing the LU factorization
+ *
+ */
+template<typename MatrixType>
+class SkylineInplaceLU {
+protected:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    
+    typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+
+public:
+
+    /** Creates a LU object and compute the respective factorization of \a matrix using
+     * flags \a flags. */
+    SkylineInplaceLU(MatrixType& matrix, int flags = 0)
+    : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0), m_lu(matrix) {
+        m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar > ();
+        m_lu.IsRowMajor ? computeRowMajor() : compute();
+    }
+
+    /** Sets the relative threshold value used to prune zero coefficients during the decomposition.
+     *
+     * Setting a value greater than zero speeds up computation, and yields to an incomplete
+     * factorization with fewer non zero coefficients. Such approximate factors are especially
+     * useful to initialize an iterative solver.
+     *
+     * Note that the exact meaning of this parameter might depends on the actual
+     * backend. Moreover, not all backends support this feature.
+     *
+     * \sa precision() */
+    void setPrecision(RealScalar v) {
+        m_precision = v;
+    }
+
+    /** \returns the current precision.
+     *
+     * \sa setPrecision() */
+    RealScalar precision() const {
+        return m_precision;
+    }
+
+    /** Sets the flags. Possible values are:
+     *  - CompleteFactorization
+     *  - IncompleteFactorization
+     *  - MemoryEfficient
+     *  - one of the ordering methods
+     *  - etc...
+     *
+     * \sa flags() */
+    void setFlags(int f) {
+        m_flags = f;
+    }
+
+    /** \returns the current flags */
+    int flags() const {
+        return m_flags;
+    }
+
+    void setOrderingMethod(int m) {
+        m_flags = m;
+    }
+
+    int orderingMethod() const {
+        return m_flags;
+    }
+
+    /** Computes/re-computes the LU factorization */
+    void compute();
+    void computeRowMajor();
+
+    /** \returns the lower triangular matrix L */
+    //inline const MatrixType& matrixL() const { return m_matrixL; }
+
+    /** \returns the upper triangular matrix U */
+    //inline const MatrixType& matrixU() const { return m_matrixU; }
+
+    template<typename BDerived, typename XDerived>
+    bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x,
+            const int transposed = 0) const;
+
+    /** \returns true if the factorization succeeded */
+    inline bool succeeded(void) const {
+        return m_succeeded;
+    }
+
+protected:
+    RealScalar m_precision;
+    int m_flags;
+    mutable int m_status;
+    bool m_succeeded;
+    MatrixType& m_lu;
+};
+
+/** Computes / recomputes the in place LU decomposition of the SkylineInplaceLU.
+ * using the default algorithm.
+ */
+template<typename MatrixType>
+//template<typename _Scalar>
+void SkylineInplaceLU<MatrixType>::compute() {
+    const size_t rows = m_lu.rows();
+    const size_t cols = m_lu.cols();
+
+    eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
+    eigen_assert(!m_lu.IsRowMajor && "LU decomposition does not work with rowMajor Storage");
+
+    for (Index row = 0; row < rows; row++) {
+        const double pivot = m_lu.coeffDiag(row);
+
+        //Lower matrix Columns update
+        const Index& col = row;
+        for (typename MatrixType::InnerLowerIterator lIt(m_lu, col); lIt; ++lIt) {
+            lIt.valueRef() /= pivot;
+        }
+
+        //Upper matrix update -> contiguous memory access
+        typename MatrixType::InnerLowerIterator lIt(m_lu, col);
+        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
+            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
+            typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
+            const double coef = lIt.value();
+
+            uItPivot += (rrow - row - 1);
+
+            //update upper part  -> contiguous memory access
+            for (++uItPivot; uIt && uItPivot;) {
+                uIt.valueRef() -= uItPivot.value() * coef;
+
+                ++uIt;
+                ++uItPivot;
+            }
+            ++lIt;
+        }
+
+        //Upper matrix update -> non contiguous memory access
+        typename MatrixType::InnerLowerIterator lIt3(m_lu, col);
+        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
+            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
+            const double coef = lIt3.value();
+
+            //update lower part ->  non contiguous memory access
+            for (Index i = 0; i < rrow - row - 1; i++) {
+                m_lu.coeffRefLower(rrow, row + i + 1) -= uItPivot.value() * coef;
+                ++uItPivot;
+            }
+            ++lIt3;
+        }
+        //update diag -> contiguous
+        typename MatrixType::InnerLowerIterator lIt2(m_lu, col);
+        for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
+
+            typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
+            typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
+            const double coef = lIt2.value();
+
+            uItPivot += (rrow - row - 1);
+            m_lu.coeffRefDiag(rrow) -= uItPivot.value() * coef;
+            ++lIt2;
+        }
+    }
+}
+
+template<typename MatrixType>
+void SkylineInplaceLU<MatrixType>::computeRowMajor() {
+    const size_t rows = m_lu.rows();
+    const size_t cols = m_lu.cols();
+
+    eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
+    eigen_assert(m_lu.IsRowMajor && "You're trying to apply rowMajor decomposition on a ColMajor matrix !");
+
+    for (Index row = 0; row < rows; row++) {
+        typename MatrixType::InnerLowerIterator llIt(m_lu, row);
+
+
+        for (Index col = llIt.col(); col < row; col++) {
+            if (m_lu.coeffExistLower(row, col)) {
+                const double diag = m_lu.coeffDiag(col);
+
+                typename MatrixType::InnerLowerIterator lIt(m_lu, row);
+                typename MatrixType::InnerUpperIterator uIt(m_lu, col);
+
+
+                const Index offset = lIt.col() - uIt.row();
+
+
+                Index stop = offset > 0 ? col - lIt.col() : col - uIt.row();
+
+                //#define VECTORIZE
+#ifdef VECTORIZE
+                Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
+                Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
+
+
+                Scalar newCoeff = m_lu.coeffLower(row, col) - rowVal.dot(colVal);
+#else
+                if (offset > 0) //Skip zero value of lIt
+                    uIt += offset;
+                else //Skip zero values of uIt
+                    lIt += -offset;
+                Scalar newCoeff = m_lu.coeffLower(row, col);
+
+                for (Index k = 0; k < stop; ++k) {
+                    const Scalar tmp = newCoeff;
+                    newCoeff = tmp - lIt.value() * uIt.value();
+                    ++lIt;
+                    ++uIt;
+                }
+#endif
+
+                m_lu.coeffRefLower(row, col) = newCoeff / diag;
+            }
+        }
+
+        //Upper matrix update
+        const Index col = row;
+        typename MatrixType::InnerUpperIterator uuIt(m_lu, col);
+        for (Index rrow = uuIt.row(); rrow < col; rrow++) {
+
+            typename MatrixType::InnerLowerIterator lIt(m_lu, rrow);
+            typename MatrixType::InnerUpperIterator uIt(m_lu, col);
+            const Index offset = lIt.col() - uIt.row();
+
+            Index stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row();
+
+#ifdef VECTORIZE
+            Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
+            Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
+
+            Scalar newCoeff = m_lu.coeffUpper(rrow, col) - rowVal.dot(colVal);
+#else
+            if (offset > 0) //Skip zero value of lIt
+                uIt += offset;
+            else //Skip zero values of uIt
+                lIt += -offset;
+            Scalar newCoeff = m_lu.coeffUpper(rrow, col);
+            for (Index k = 0; k < stop; ++k) {
+                const Scalar tmp = newCoeff;
+                newCoeff = tmp - lIt.value() * uIt.value();
+
+                ++lIt;
+                ++uIt;
+            }
+#endif
+            m_lu.coeffRefUpper(rrow, col) = newCoeff;
+        }
+
+
+        //Diag matrix update
+        typename MatrixType::InnerLowerIterator lIt(m_lu, row);
+        typename MatrixType::InnerUpperIterator uIt(m_lu, row);
+
+        const Index offset = lIt.col() - uIt.row();
+
+
+        Index stop = offset > 0 ? lIt.size() : uIt.size();
+#ifdef VECTORIZE
+        Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
+        Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
+        Scalar newCoeff = m_lu.coeffDiag(row) - rowVal.dot(colVal);
+#else
+        if (offset > 0) //Skip zero value of lIt
+            uIt += offset;
+        else //Skip zero values of uIt
+            lIt += -offset;
+        Scalar newCoeff = m_lu.coeffDiag(row);
+        for (Index k = 0; k < stop; ++k) {
+            const Scalar tmp = newCoeff;
+            newCoeff = tmp - lIt.value() * uIt.value();
+            ++lIt;
+            ++uIt;
+        }
+#endif
+        m_lu.coeffRefDiag(row) = newCoeff;
+    }
+}
+
+/** Computes *x = U^-1 L^-1 b
+ *
+ * If \a transpose is set to SvTranspose or SvAdjoint, the solution
+ * of the transposed/adjoint system is computed instead.
+ *
+ * Not all backends implement the solution of the transposed or
+ * adjoint system.
+ */
+template<typename MatrixType>
+template<typename BDerived, typename XDerived>
+bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, const int transposed) const {
+    const size_t rows = m_lu.rows();
+    const size_t cols = m_lu.cols();
+
+
+    for (Index row = 0; row < rows; row++) {
+        x->coeffRef(row) = b.coeff(row);
+        Scalar newVal = x->coeff(row);
+        typename MatrixType::InnerLowerIterator lIt(m_lu, row);
+
+        Index col = lIt.col();
+        while (lIt.col() < row) {
+
+            newVal -= x->coeff(col++) * lIt.value();
+            ++lIt;
+        }
+
+        x->coeffRef(row) = newVal;
+    }
+
+
+    for (Index col = rows - 1; col > 0; col--) {
+        x->coeffRef(col) = x->coeff(col) / m_lu.coeffDiag(col);
+
+        const Scalar x_col = x->coeff(col);
+
+        typename MatrixType::InnerUpperIterator uIt(m_lu, col);
+        uIt += uIt.size()-1;
+
+
+        while (uIt) {
+            x->coeffRef(uIt.row()) -= x_col * uIt.value();
+            //TODO : introduce --operator
+            uIt += -1;
+        }
+
+
+    }
+    x->coeffRef(0) = x->coeff(0) / m_lu.coeffDiag(0);
+
+    return true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINEINPLACELU_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
new file mode 100644
index 0000000..7c7eace
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrix.h
@@ -0,0 +1,862 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINEMATRIX_H
+#define EIGEN_SKYLINEMATRIX_H
+
+#include "SkylineStorage.h"
+#include "SkylineMatrixBase.h"
+
+namespace Eigen { 
+
+/** \ingroup Skyline_Module
+ *
+ * \class SkylineMatrix
+ *
+ * \brief The main skyline matrix class
+ *
+ * This class implements a skyline matrix using the very uncommon storage
+ * scheme.
+ *
+ * \param _Scalar the scalar type, i.e. the type of the coefficients
+ * \param _Options Union of bit flags controlling the storage scheme. Currently the only possibility
+ *                 is RowMajor. The default is 0 which means column-major.
+ *
+ *
+ */
+namespace internal {
+template<typename _Scalar, int _Options>
+struct traits<SkylineMatrix<_Scalar, _Options> > {
+    typedef _Scalar Scalar;
+    typedef Sparse StorageKind;
+
+    enum {
+        RowsAtCompileTime = Dynamic,
+        ColsAtCompileTime = Dynamic,
+        MaxRowsAtCompileTime = Dynamic,
+        MaxColsAtCompileTime = Dynamic,
+        Flags = SkylineBit | _Options,
+        CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    };
+};
+}
+
+template<typename _Scalar, int _Options>
+class SkylineMatrix
+: public SkylineMatrixBase<SkylineMatrix<_Scalar, _Options> > {
+public:
+    EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
+    EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
+    EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
+
+    using Base::IsRowMajor;
+
+protected:
+
+    typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
+
+    Index m_outerSize;
+    Index m_innerSize;
+
+public:
+    Index* m_colStartIndex;
+    Index* m_rowStartIndex;
+    SkylineStorage<Scalar> m_data;
+
+public:
+
+    inline Index rows() const {
+        return IsRowMajor ? m_outerSize : m_innerSize;
+    }
+
+    inline Index cols() const {
+        return IsRowMajor ? m_innerSize : m_outerSize;
+    }
+
+    inline Index innerSize() const {
+        return m_innerSize;
+    }
+
+    inline Index outerSize() const {
+        return m_outerSize;
+    }
+
+    inline Index upperNonZeros() const {
+        return m_data.upperSize();
+    }
+
+    inline Index lowerNonZeros() const {
+        return m_data.lowerSize();
+    }
+
+    inline Index upperNonZeros(Index j) const {
+        return m_colStartIndex[j + 1] - m_colStartIndex[j];
+    }
+
+    inline Index lowerNonZeros(Index j) const {
+        return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
+    }
+
+    inline const Scalar* _diagPtr() const {
+        return &m_data.diag(0);
+    }
+
+    inline Scalar* _diagPtr() {
+        return &m_data.diag(0);
+    }
+
+    inline const Scalar* _upperPtr() const {
+        return &m_data.upper(0);
+    }
+
+    inline Scalar* _upperPtr() {
+        return &m_data.upper(0);
+    }
+
+    inline const Scalar* _lowerPtr() const {
+        return &m_data.lower(0);
+    }
+
+    inline Scalar* _lowerPtr() {
+        return &m_data.lower(0);
+    }
+
+    inline const Index* _upperProfilePtr() const {
+        return &m_data.upperProfile(0);
+    }
+
+    inline Index* _upperProfilePtr() {
+        return &m_data.upperProfile(0);
+    }
+
+    inline const Index* _lowerProfilePtr() const {
+        return &m_data.lowerProfile(0);
+    }
+
+    inline Index* _lowerProfilePtr() {
+        return &m_data.lowerProfile(0);
+    }
+
+    inline Scalar coeff(Index row, Index col) const {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+
+        if (outer == inner)
+            return this->m_data.diag(outer);
+
+        if (IsRowMajor) {
+            if (inner > outer) //upper matrix
+            {
+                const Index minOuterIndex = inner - m_data.upperProfile(inner);
+                if (outer >= minOuterIndex)
+                    return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
+                else
+                    return Scalar(0);
+            }
+            if (inner < outer) //lower matrix
+            {
+                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+                if (inner >= minInnerIndex)
+                    return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
+                else
+                    return Scalar(0);
+            }
+            return m_data.upper(m_colStartIndex[inner] + outer - inner);
+        } else {
+            if (outer > inner) //upper matrix
+            {
+                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+                if (outer <= maxOuterIndex)
+                    return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
+                else
+                    return Scalar(0);
+            }
+            if (outer < inner) //lower matrix
+            {
+                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+
+                if (inner <= maxInnerIndex)
+                    return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
+                else
+                    return Scalar(0);
+            }
+        }
+    }
+
+    inline Scalar& coeffRef(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+
+        if (outer == inner)
+            return this->m_data.diag(outer);
+
+        if (IsRowMajor) {
+            if (col > row) //upper matrix
+            {
+                const Index minOuterIndex = inner - m_data.upperProfile(inner);
+                eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
+                return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
+            }
+            if (col < row) //lower matrix
+            {
+                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+                eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
+                return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
+            }
+        } else {
+            if (outer > inner) //upper matrix
+            {
+                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+                eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
+                return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
+            }
+            if (outer < inner) //lower matrix
+            {
+                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+                eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
+                return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
+            }
+        }
+    }
+
+    inline Scalar coeffDiag(Index idx) const {
+        eigen_assert(idx < outerSize());
+        eigen_assert(idx < innerSize());
+        return this->m_data.diag(idx);
+    }
+
+    inline Scalar coeffLower(Index row, Index col) const {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+            if (inner >= minInnerIndex)
+                return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
+            else
+                return Scalar(0);
+
+        } else {
+            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+            if (inner <= maxInnerIndex)
+                return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
+            else
+                return Scalar(0);
+        }
+    }
+
+    inline Scalar coeffUpper(Index row, Index col) const {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minOuterIndex = inner - m_data.upperProfile(inner);
+            if (outer >= minOuterIndex)
+                return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
+            else
+                return Scalar(0);
+        } else {
+            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+            if (outer <= maxOuterIndex)
+                return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
+            else
+                return Scalar(0);
+        }
+    }
+
+    inline Scalar& coeffRefDiag(Index idx) {
+        eigen_assert(idx < outerSize());
+        eigen_assert(idx < innerSize());
+        return this->m_data.diag(idx);
+    }
+
+    inline Scalar& coeffRefLower(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+            eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
+            return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
+        } else {
+            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+            eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
+            return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
+        }
+    }
+
+    inline bool coeffExistLower(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+            return inner >= minInnerIndex;
+        } else {
+            const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+            return inner <= maxInnerIndex;
+        }
+    }
+
+    inline Scalar& coeffRefUpper(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minOuterIndex = inner - m_data.upperProfile(inner);
+            eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
+            return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
+        } else {
+            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+            eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
+            return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
+        }
+    }
+
+    inline bool coeffExistUpper(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+        eigen_assert(inner != outer);
+
+        if (IsRowMajor) {
+            const Index minOuterIndex = inner - m_data.upperProfile(inner);
+            return outer >= minOuterIndex;
+        } else {
+            const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+            return outer <= maxOuterIndex;
+        }
+    }
+
+
+protected:
+
+public:
+    class InnerUpperIterator;
+    class InnerLowerIterator;
+
+    class OuterUpperIterator;
+    class OuterLowerIterator;
+
+    /** Removes all non zeros */
+    inline void setZero() {
+        m_data.clear();
+        memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
+        memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
+    }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const {
+        return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
+    }
+
+    /** Preallocates \a reserveSize non zeros */
+    inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
+        m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
+    }
+
+    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
+
+     *
+     * \warning This function can be extremely slow if the non zero coefficients
+     * are not inserted in a coherent order.
+     *
+     * After an insertion session, you should call the finalize() function.
+     */
+    EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
+        const Index outer = IsRowMajor ? row : col;
+        const Index inner = IsRowMajor ? col : row;
+
+        eigen_assert(outer < outerSize());
+        eigen_assert(inner < innerSize());
+
+        if (outer == inner)
+            return m_data.diag(col);
+
+        if (IsRowMajor) {
+            if (outer < inner) //upper matrix
+            {
+                Index minOuterIndex = 0;
+                minOuterIndex = inner - m_data.upperProfile(inner);
+
+                if (outer < minOuterIndex) //The value does not yet exist
+                {
+                    const Index previousProfile = m_data.upperProfile(inner);
+
+                    m_data.upperProfile(inner) = inner - outer;
+
+
+                    const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
+                    //shift data stored after this new one
+                    const Index stop = m_colStartIndex[cols()];
+                    const Index start = m_colStartIndex[inner];
+
+
+                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
+                        m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
+                    }
+
+                    for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
+                        m_colStartIndex[innerIdx] += bandIncrement;
+                    }
+
+                    //zeros new data
+                    memset(this->_upperPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
+
+                    return m_data.upper(m_colStartIndex[inner]);
+                } else {
+                    return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
+                }
+            }
+
+            if (outer > inner) //lower matrix
+            {
+                const Index minInnerIndex = outer - m_data.lowerProfile(outer);
+                if (inner < minInnerIndex) //The value does not yet exist
+                {
+                    const Index previousProfile = m_data.lowerProfile(outer);
+                    m_data.lowerProfile(outer) = outer - inner;
+
+                    const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
+                    //shift data stored after this new one
+                    const Index stop = m_rowStartIndex[rows()];
+                    const Index start = m_rowStartIndex[outer];
+
+
+                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
+                        m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
+                    }
+
+                    for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
+                        m_rowStartIndex[innerIdx] += bandIncrement;
+                    }
+
+                    //zeros new data
+                    memset(this->_lowerPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
+                    return m_data.lower(m_rowStartIndex[outer]);
+                } else {
+                    return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
+                }
+            }
+        } else {
+            if (outer > inner) //upper matrix
+            {
+                const Index maxOuterIndex = inner + m_data.upperProfile(inner);
+                if (outer > maxOuterIndex) //The value does not yet exist
+                {
+                    const Index previousProfile = m_data.upperProfile(inner);
+                    m_data.upperProfile(inner) = outer - inner;
+
+                    const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
+                    //shift data stored after this new one
+                    const Index stop = m_rowStartIndex[rows()];
+                    const Index start = m_rowStartIndex[inner + 1];
+
+                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
+                        m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
+                    }
+
+                    for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
+                        m_rowStartIndex[innerIdx] += bandIncrement;
+                    }
+                    memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
+                    return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
+                } else {
+                    return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
+                }
+            }
+
+            if (outer < inner) //lower matrix
+            {
+                const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
+                if (inner > maxInnerIndex) //The value does not yet exist
+                {
+                    const Index previousProfile = m_data.lowerProfile(outer);
+                    m_data.lowerProfile(outer) = inner - outer;
+
+                    const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
+                    //shift data stored after this new one
+                    const Index stop = m_colStartIndex[cols()];
+                    const Index start = m_colStartIndex[outer + 1];
+
+                    for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
+                        m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
+                    }
+
+                    for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
+                        m_colStartIndex[innerIdx] += bandIncrement;
+                    }
+                    memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
+                    return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
+                } else {
+                    return m_data.lower(m_colStartIndex[outer] + (inner - outer));
+                }
+            }
+        }
+    }
+
+    /** Must be called after inserting a set of non zero entries.
+     */
+    inline void finalize() {
+        if (IsRowMajor) {
+            if (rows() > cols())
+                m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
+            else
+                m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
+
+            //            eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
+            //
+            //            Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
+            //            Index dataIdx = 0;
+            //            for (Index row = 0; row < rows(); row++) {
+            //
+            //                const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
+            //                //                std::cout << "nbLowerElts" << nbLowerElts << std::endl;
+            //                memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
+            //                m_rowStartIndex[row] = dataIdx;
+            //                dataIdx += nbLowerElts;
+            //
+            //                const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
+            //                memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
+            //                m_colStartIndex[row] = dataIdx;
+            //                dataIdx += nbUpperElts;
+            //
+            //
+            //            }
+            //            //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
+            //            m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
+            //            m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
+            //
+            //            delete[] m_data.m_lower;
+            //            delete[] m_data.m_upper;
+            //
+            //            m_data.m_lower = newArray;
+            //            m_data.m_upper = newArray;
+        } else {
+            if (rows() > cols())
+                m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
+            else
+                m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
+        }
+    }
+
+    inline void squeeze() {
+        finalize();
+        m_data.squeeze();
+    }
+
+    void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
+        //TODO
+    }
+
+    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
+     * \sa resizeNonZeros(Index), reserve(), setZero()
+     */
+    void resize(size_t rows, size_t cols) {
+        const Index diagSize = rows > cols ? cols : rows;
+        m_innerSize = IsRowMajor ? cols : rows;
+
+        eigen_assert(rows == cols && "Skyline matrix must be square matrix");
+
+        if (diagSize % 2) { // diagSize is odd
+            const Index k = (diagSize - 1) / 2;
+
+            m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
+                    2 * k * k + k + 1,
+                    2 * k * k + k + 1);
+
+        } else // diagSize is even
+        {
+            const Index k = diagSize / 2;
+            m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
+                    2 * k * k - k + 1,
+                    2 * k * k - k + 1);
+        }
+
+        if (m_colStartIndex && m_rowStartIndex) {
+            delete[] m_colStartIndex;
+            delete[] m_rowStartIndex;
+        }
+        m_colStartIndex = new Index [cols + 1];
+        m_rowStartIndex = new Index [rows + 1];
+        m_outerSize = diagSize;
+
+        m_data.reset();
+        m_data.clear();
+
+        m_outerSize = diagSize;
+        memset(m_colStartIndex, 0, (cols + 1) * sizeof (Index));
+        memset(m_rowStartIndex, 0, (rows + 1) * sizeof (Index));
+    }
+
+    void resizeNonZeros(Index size) {
+        m_data.resize(size);
+    }
+
+    inline SkylineMatrix()
+    : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
+        resize(0, 0);
+    }
+
+    inline SkylineMatrix(size_t rows, size_t cols)
+    : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
+        resize(rows, cols);
+    }
+
+    template<typename OtherDerived>
+    inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
+    : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
+        *this = other.derived();
+    }
+
+    inline SkylineMatrix(const SkylineMatrix & other)
+    : Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
+        *this = other.derived();
+    }
+
+    inline void swap(SkylineMatrix & other) {
+        //EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
+        std::swap(m_colStartIndex, other.m_colStartIndex);
+        std::swap(m_rowStartIndex, other.m_rowStartIndex);
+        std::swap(m_innerSize, other.m_innerSize);
+        std::swap(m_outerSize, other.m_outerSize);
+        m_data.swap(other.m_data);
+    }
+
+    inline SkylineMatrix & operator=(const SkylineMatrix & other) {
+        std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
+        if (other.isRValue()) {
+            swap(other.const_cast_derived());
+        } else {
+            resize(other.rows(), other.cols());
+            memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
+            memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
+            m_data = other.m_data;
+        }
+        return *this;
+    }
+
+    template<typename OtherDerived>
+            inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
+        const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+        if (needToTranspose) {
+            //         TODO
+            //            return *this;
+        } else {
+            // there is no special optimization
+            return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
+        }
+    }
+
+    friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
+
+        EIGEN_DBG_SKYLINE(
+        std::cout << "upper elements : " << std::endl;
+        for (Index i = 0; i < m.m_data.upperSize(); i++)
+            std::cout << m.m_data.upper(i) << "\t";
+        std::cout << std::endl;
+        std::cout << "upper profile : " << std::endl;
+        for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
+            std::cout << m.m_data.upperProfile(i) << "\t";
+        std::cout << std::endl;
+        std::cout << "lower startIdx : " << std::endl;
+        for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
+            std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
+        std::cout << std::endl;
+
+
+        std::cout << "lower elements : " << std::endl;
+        for (Index i = 0; i < m.m_data.lowerSize(); i++)
+            std::cout << m.m_data.lower(i) << "\t";
+        std::cout << std::endl;
+        std::cout << "lower profile : " << std::endl;
+        for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
+            std::cout << m.m_data.lowerProfile(i) << "\t";
+        std::cout << std::endl;
+        std::cout << "lower startIdx : " << std::endl;
+        for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
+            std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
+        std::cout << std::endl;
+        );
+        for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
+            for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
+                s << m.coeff(rowIdx, colIdx) << "\t";
+            }
+            s << std::endl;
+        }
+        return s;
+    }
+
+    /** Destructor */
+    inline ~SkylineMatrix() {
+        delete[] m_colStartIndex;
+        delete[] m_rowStartIndex;
+    }
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+};
+
+template<typename Scalar, int _Options>
+class SkylineMatrix<Scalar, _Options>::InnerUpperIterator {
+public:
+
+    InnerUpperIterator(const SkylineMatrix& mat, Index outer)
+    : m_matrix(mat), m_outer(outer),
+    m_id(_Options == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
+    m_start(m_id),
+    m_end(_Options == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
+    }
+
+    inline InnerUpperIterator & operator++() {
+        m_id++;
+        return *this;
+    }
+
+    inline InnerUpperIterator & operator+=(Index shift) {
+        m_id += shift;
+        return *this;
+    }
+
+    inline Scalar value() const {
+        return m_matrix.m_data.upper(m_id);
+    }
+
+    inline Scalar* valuePtr() {
+        return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
+    }
+
+    inline Scalar& valueRef() {
+        return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
+    }
+
+    inline Index index() const {
+        return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
+                m_outer + (m_id - m_start) + 1;
+    }
+
+    inline Index row() const {
+        return IsRowMajor ? index() : m_outer;
+    }
+
+    inline Index col() const {
+        return IsRowMajor ? m_outer : index();
+    }
+
+    inline size_t size() const {
+        return m_matrix.m_data.upperProfile(m_outer);
+    }
+
+    inline operator bool() const {
+        return (m_id < m_end) && (m_id >= m_start);
+    }
+
+protected:
+    const SkylineMatrix& m_matrix;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+    const Index m_end;
+};
+
+template<typename Scalar, int _Options>
+class SkylineMatrix<Scalar, _Options>::InnerLowerIterator {
+public:
+
+    InnerLowerIterator(const SkylineMatrix& mat, Index outer)
+    : m_matrix(mat),
+    m_outer(outer),
+    m_id(_Options == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
+    m_start(m_id),
+    m_end(_Options == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
+    }
+
+    inline InnerLowerIterator & operator++() {
+        m_id++;
+        return *this;
+    }
+
+    inline InnerLowerIterator & operator+=(Index shift) {
+        m_id += shift;
+        return *this;
+    }
+
+    inline Scalar value() const {
+        return m_matrix.m_data.lower(m_id);
+    }
+
+    inline Scalar* valuePtr() {
+        return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
+    }
+
+    inline Scalar& valueRef() {
+        return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
+    }
+
+    inline Index index() const {
+        return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
+                m_outer + (m_id - m_start) + 1;
+        ;
+    }
+
+    inline Index row() const {
+        return IsRowMajor ? m_outer : index();
+    }
+
+    inline Index col() const {
+        return IsRowMajor ? index() : m_outer;
+    }
+
+    inline size_t size() const {
+        return m_matrix.m_data.lowerProfile(m_outer);
+    }
+
+    inline operator bool() const {
+        return (m_id < m_end) && (m_id >= m_start);
+    }
+
+protected:
+    const SkylineMatrix& m_matrix;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+    const Index m_end;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINEMATRIX_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
new file mode 100644
index 0000000..b0d5e10
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
@@ -0,0 +1,212 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINEMATRIXBASE_H
+#define EIGEN_SKYLINEMATRIXBASE_H
+
+#include "SkylineUtil.h"
+
+namespace Eigen {
+
+/** \ingroup Skyline_Module
+ *
+ * \class SkylineMatrixBase
+ *
+ * \brief Base class of any skyline matrices or skyline expressions
+ *
+ * \param Derived
+ *
+ */
+template<typename Derived> class SkylineMatrixBase : public EigenBase<Derived> {
+public:
+
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::index<StorageKind>::type Index;
+
+    enum {
+        RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+        /**< The number of rows at compile-time. This is just a copy of the value provided
+         * by the \a Derived type. If a value is not known at compile-time,
+         * it is set to the \a Dynamic constant.
+         * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+        ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+        /**< The number of columns at compile-time. This is just a copy of the value provided
+         * by the \a Derived type. If a value is not known at compile-time,
+         * it is set to the \a Dynamic constant.
+         * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+
+        SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
+        internal::traits<Derived>::ColsAtCompileTime>::ret),
+        /**< This is equal to the number of coefficients, i.e. the number of
+         * rows times the number of columns, or to \a Dynamic if this is not
+         * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+        MaxRowsAtCompileTime = RowsAtCompileTime,
+        MaxColsAtCompileTime = ColsAtCompileTime,
+
+        MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
+        MaxColsAtCompileTime>::ret),
+
+        IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
+        /**< This is set to true if either the number of rows or the number of
+         * columns is known at compile-time to be equal to 1. Indeed, in that case,
+         * we are dealing with a column-vector (if there is only one column) or with
+         * a row-vector (if there is only one row). */
+
+        Flags = internal::traits<Derived>::Flags,
+        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+         * constructed from this one. See the \ref flags "list of flags".
+         */
+
+        CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+        /**< This is a rough measure of how expensive it is to read one coefficient from
+         * this expression.
+         */
+
+        IsRowMajor = Flags & RowMajorBit ? 1 : 0
+    };
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
+     * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
+     * \a Scalar is \a std::complex<T> then RealScalar is \a T.
+     *
+     * \sa class NumTraits
+     */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** type of the equivalent square matrix */
+    typedef Matrix<Scalar, EIGEN_SIZE_MAX(RowsAtCompileTime, ColsAtCompileTime),
+                           EIGEN_SIZE_MAX(RowsAtCompileTime, ColsAtCompileTime) > SquareMatrixType;
+
+    inline const Derived& derived() const {
+        return *static_cast<const Derived*> (this);
+    }
+
+    inline Derived& derived() {
+        return *static_cast<Derived*> (this);
+    }
+
+    inline Derived& const_cast_derived() const {
+        return *static_cast<Derived*> (const_cast<SkylineMatrixBase*> (this));
+    }
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+    /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
+    inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT {
+        return derived().rows();
+    }
+
+    /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
+    inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT {
+        return derived().cols();
+    }
+
+    /** \returns the number of coefficients, which is \a rows()*cols().
+     * \sa rows(), cols(), SizeAtCompileTime. */
+    inline EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT {
+        return rows() * cols();
+    }
+
+    /** \returns the number of nonzero coefficients which is in practice the number
+     * of stored coefficients. */
+    inline Index nonZeros() const {
+        return derived().nonZeros();
+    }
+
+    /** \returns the size of the storage major dimension,
+     * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
+    Index outerSize() const {
+        return (int(Flags) & RowMajorBit) ? this->rows() : this->cols();
+    }
+
+    /** \returns the size of the inner dimension according to the storage order,
+     * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
+    Index innerSize() const {
+        return (int(Flags) & RowMajorBit) ? this->cols() : this->rows();
+    }
+
+    bool isRValue() const {
+        return m_isRValue;
+    }
+
+    Derived& markAsRValue() {
+        m_isRValue = true;
+        return derived();
+    }
+
+    SkylineMatrixBase() : m_isRValue(false) {
+        /* TODO check flags */
+    }
+
+    inline Derived & operator=(const Derived& other) {
+        this->operator=<Derived > (other);
+        return derived();
+    }
+
+    template<typename OtherDerived>
+    inline void assignGeneric(const OtherDerived& other) {
+        derived().resize(other.rows(), other.cols());
+        for (Index row = 0; row < rows(); row++)
+            for (Index col = 0; col < cols(); col++) {
+                if (other.coeff(row, col) != Scalar(0))
+                    derived().insert(row, col) = other.coeff(row, col);
+            }
+        derived().finalize();
+    }
+
+    template<typename OtherDerived>
+            inline Derived & operator=(const SkylineMatrixBase<OtherDerived>& other) {
+        //TODO
+    }
+
+    template<typename Lhs, typename Rhs>
+            inline Derived & operator=(const SkylineProduct<Lhs, Rhs, SkylineTimeSkylineProduct>& product);
+
+    friend std::ostream & operator <<(std::ostream & s, const SkylineMatrixBase& m) {
+        s << m.derived();
+        return s;
+    }
+
+    template<typename OtherDerived>
+    const typename SkylineProductReturnType<Derived, OtherDerived>::Type
+    operator*(const MatrixBase<OtherDerived> &other) const;
+
+    /** \internal use operator= */
+    template<typename DenseDerived>
+    void evalTo(MatrixBase<DenseDerived>& dst) const {
+        dst.setZero();
+        for (Index i = 0; i < rows(); i++)
+            for (Index j = 0; j < rows(); j++)
+                dst(i, j) = derived().coeff(i, j);
+    }
+
+    Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> toDense() const {
+        return derived();
+    }
+
+    /** \returns the matrix or vector obtained by evaluating this expression.
+     *
+     * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+     * a const reference, in order to avoid a useless copy.
+     */
+    EIGEN_STRONG_INLINE const typename internal::eval<Derived, IsSkyline>::type eval() const {
+        return typename internal::eval<Derived>::type(derived());
+    }
+
+protected:
+    bool m_isRValue;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINEMATRIXBASE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
new file mode 100644
index 0000000..d9eb814
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineProduct.h
@@ -0,0 +1,295 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINEPRODUCT_H
+#define EIGEN_SKYLINEPRODUCT_H
+
+namespace Eigen { 
+
+template<typename Lhs, typename Rhs, int ProductMode>
+struct SkylineProductReturnType {
+    typedef const typename internal::nested_eval<Lhs, Rhs::RowsAtCompileTime>::type LhsNested;
+    typedef const typename internal::nested_eval<Rhs, Lhs::RowsAtCompileTime>::type RhsNested;
+
+    typedef SkylineProduct<LhsNested, RhsNested, ProductMode> Type;
+};
+
+template<typename LhsNested, typename RhsNested, int ProductMode>
+struct internal::traits<SkylineProduct<LhsNested, RhsNested, ProductMode> > {
+    // clean the nested types:
+    typedef typename internal::remove_all<LhsNested>::type _LhsNested;
+    typedef typename internal::remove_all<RhsNested>::type _RhsNested;
+    typedef typename _LhsNested::Scalar Scalar;
+
+    enum {
+        LhsCoeffReadCost = _LhsNested::CoeffReadCost,
+        RhsCoeffReadCost = _RhsNested::CoeffReadCost,
+        LhsFlags = _LhsNested::Flags,
+        RhsFlags = _RhsNested::Flags,
+
+        RowsAtCompileTime = _LhsNested::RowsAtCompileTime,
+        ColsAtCompileTime = _RhsNested::ColsAtCompileTime,
+        InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
+
+        MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
+        MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
+
+        EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
+        ResultIsSkyline = ProductMode == SkylineTimeSkylineProduct,
+
+        RemovedBits = ~((EvalToRowMajor ? 0 : RowMajorBit) | (ResultIsSkyline ? 0 : SkylineBit)),
+
+        Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
+        | EvalBeforeAssigningBit
+        | EvalBeforeNestingBit,
+
+        CoeffReadCost = HugeCost
+    };
+
+    typedef typename internal::conditional<ResultIsSkyline,
+            SkylineMatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> >,
+            MatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> > >::type Base;
+};
+
+namespace internal {
+template<typename LhsNested, typename RhsNested, int ProductMode>
+class SkylineProduct : no_assignment_operator,
+public traits<SkylineProduct<LhsNested, RhsNested, ProductMode> >::Base {
+public:
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(SkylineProduct)
+
+private:
+
+    typedef typename traits<SkylineProduct>::_LhsNested _LhsNested;
+    typedef typename traits<SkylineProduct>::_RhsNested _RhsNested;
+
+public:
+
+    template<typename Lhs, typename Rhs>
+    EIGEN_STRONG_INLINE SkylineProduct(const Lhs& lhs, const Rhs& rhs)
+    : m_lhs(lhs), m_rhs(rhs) {
+        eigen_assert(lhs.cols() == rhs.rows());
+
+        enum {
+            ProductIsValid = _LhsNested::ColsAtCompileTime == Dynamic
+            || _RhsNested::RowsAtCompileTime == Dynamic
+            || int(_LhsNested::ColsAtCompileTime) == int(_RhsNested::RowsAtCompileTime),
+            AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime,
+            SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested, _RhsNested)
+        };
+        // note to the lost user:
+        //    * for a dot product use: v1.dot(v2)
+        //    * for a coeff-wise product use: v1.cwise()*v2
+        EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+                INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+                EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+                INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+                EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const {
+        return m_lhs.rows();
+    }
+
+    EIGEN_STRONG_INLINE Index cols() const {
+        return m_rhs.cols();
+    }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const {
+        return m_lhs;
+    }
+
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const {
+        return m_rhs;
+    }
+
+protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+// dense = skyline * dense
+// Note that here we force no inlining and separate the setZero() because GCC messes up otherwise
+
+template<typename Lhs, typename Rhs, typename Dest>
+EIGEN_DONT_INLINE void skyline_row_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
+    typedef typename remove_all<Lhs>::type _Lhs;
+    typedef typename remove_all<Rhs>::type _Rhs;
+    typedef typename traits<Lhs>::Scalar Scalar;
+
+    enum {
+        LhsIsRowMajor = (_Lhs::Flags & RowMajorBit) == RowMajorBit,
+        LhsIsSelfAdjoint = (_Lhs::Flags & SelfAdjointBit) == SelfAdjointBit,
+        ProcessFirstHalf = LhsIsSelfAdjoint
+        && (((_Lhs::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0)
+        || ((_Lhs::Flags & UpperTriangularBit) && !LhsIsRowMajor)
+        || ((_Lhs::Flags & LowerTriangularBit) && LhsIsRowMajor)),
+        ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
+    };
+
+    //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
+    for (Index col = 0; col < rhs.cols(); col++) {
+        for (Index row = 0; row < lhs.rows(); row++) {
+            dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
+        }
+    }
+    //Use matrix lower triangular part
+    for (Index row = 0; row < lhs.rows(); row++) {
+        typename _Lhs::InnerLowerIterator lIt(lhs, row);
+        const Index stop = lIt.col() + lIt.size();
+        for (Index col = 0; col < rhs.cols(); col++) {
+
+            Index k = lIt.col();
+            Scalar tmp = 0;
+            while (k < stop) {
+                tmp +=
+                        lIt.value() *
+                        rhs(k++, col);
+                ++lIt;
+            }
+            dst(row, col) += tmp;
+            lIt += -lIt.size();
+        }
+
+    }
+
+    //Use matrix upper triangular part
+    for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
+        typename _Lhs::InnerUpperIterator uIt(lhs, lhscol);
+        const Index stop = uIt.size() + uIt.row();
+        for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
+
+
+            const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
+            Index k = uIt.row();
+            while (k < stop) {
+                dst(k++, rhscol) +=
+                        uIt.value() *
+                        rhsCoeff;
+                ++uIt;
+            }
+            uIt += -uIt.size();
+        }
+    }
+
+}
+
+template<typename Lhs, typename Rhs, typename Dest>
+EIGEN_DONT_INLINE void skyline_col_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
+    typedef typename remove_all<Lhs>::type _Lhs;
+    typedef typename remove_all<Rhs>::type _Rhs;
+    typedef typename traits<Lhs>::Scalar Scalar;
+
+    enum {
+        LhsIsRowMajor = (_Lhs::Flags & RowMajorBit) == RowMajorBit,
+        LhsIsSelfAdjoint = (_Lhs::Flags & SelfAdjointBit) == SelfAdjointBit,
+        ProcessFirstHalf = LhsIsSelfAdjoint
+        && (((_Lhs::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0)
+        || ((_Lhs::Flags & UpperTriangularBit) && !LhsIsRowMajor)
+        || ((_Lhs::Flags & LowerTriangularBit) && LhsIsRowMajor)),
+        ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
+    };
+
+    //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
+    for (Index col = 0; col < rhs.cols(); col++) {
+        for (Index row = 0; row < lhs.rows(); row++) {
+            dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
+        }
+    }
+
+    //Use matrix upper triangular part
+    for (Index row = 0; row < lhs.rows(); row++) {
+        typename _Lhs::InnerUpperIterator uIt(lhs, row);
+        const Index stop = uIt.col() + uIt.size();
+        for (Index col = 0; col < rhs.cols(); col++) {
+
+            Index k = uIt.col();
+            Scalar tmp = 0;
+            while (k < stop) {
+                tmp +=
+                        uIt.value() *
+                        rhs(k++, col);
+                ++uIt;
+            }
+
+
+            dst(row, col) += tmp;
+            uIt += -uIt.size();
+        }
+    }
+
+    //Use matrix lower triangular part
+    for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
+        typename _Lhs::InnerLowerIterator lIt(lhs, lhscol);
+        const Index stop = lIt.size() + lIt.row();
+        for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
+
+            const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
+            Index k = lIt.row();
+            while (k < stop) {
+                dst(k++, rhscol) +=
+                        lIt.value() *
+                        rhsCoeff;
+                ++lIt;
+            }
+            lIt += -lIt.size();
+        }
+    }
+
+}
+
+template<typename Lhs, typename Rhs, typename ResultType,
+        int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit>
+        struct skyline_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct skyline_product_selector<Lhs, Rhs, ResultType, RowMajor> {
+    typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+    static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
+        skyline_row_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
+    }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct skyline_product_selector<Lhs, Rhs, ResultType, ColMajor> {
+    typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+    static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
+        skyline_col_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
+    }
+};
+
+} // end namespace internal
+
+// template<typename Derived>
+// template<typename Lhs, typename Rhs >
+// Derived & MatrixBase<Derived>::lazyAssign(const SkylineProduct<Lhs, Rhs, SkylineTimeDenseProduct>& product) {
+//     typedef typename internal::remove_all<Lhs>::type _Lhs;
+//     internal::skyline_product_selector<typename internal::remove_all<Lhs>::type,
+//             typename internal::remove_all<Rhs>::type,
+//             Derived>::run(product.lhs(), product.rhs(), derived());
+// 
+//     return derived();
+// }
+
+// skyline * dense
+
+template<typename Derived>
+template<typename OtherDerived >
+EIGEN_STRONG_INLINE const typename SkylineProductReturnType<Derived, OtherDerived>::Type
+SkylineMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const {
+
+    return typename SkylineProductReturnType<Derived, OtherDerived>::Type(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINEPRODUCT_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
new file mode 100644
index 0000000..cc7514f
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineStorage.h
@@ -0,0 +1,259 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINE_STORAGE_H
+#define EIGEN_SKYLINE_STORAGE_H
+
+namespace Eigen { 
+
+/** Stores a skyline set of values in three structures :
+ * The diagonal elements
+ * The upper elements
+ * The lower elements
+ *
+ */
+template<typename Scalar>
+class SkylineStorage {
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef SparseIndex Index;
+public:
+
+    SkylineStorage()
+    : m_diag(0),
+    m_lower(0),
+    m_upper(0),
+    m_lowerProfile(0),
+    m_upperProfile(0),
+    m_diagSize(0),
+    m_upperSize(0),
+    m_lowerSize(0),
+    m_upperProfileSize(0),
+    m_lowerProfileSize(0),
+    m_allocatedSize(0) {
+    }
+
+    SkylineStorage(const SkylineStorage& other)
+    : m_diag(0),
+    m_lower(0),
+    m_upper(0),
+    m_lowerProfile(0),
+    m_upperProfile(0),
+    m_diagSize(0),
+    m_upperSize(0),
+    m_lowerSize(0),
+    m_upperProfileSize(0),
+    m_lowerProfileSize(0),
+    m_allocatedSize(0) {
+        *this = other;
+    }
+
+    SkylineStorage & operator=(const SkylineStorage& other) {
+        resize(other.diagSize(), other.m_upperProfileSize, other.m_lowerProfileSize, other.upperSize(), other.lowerSize());
+        memcpy(m_diag, other.m_diag, m_diagSize * sizeof (Scalar));
+        memcpy(m_upper, other.m_upper, other.upperSize() * sizeof (Scalar));
+        memcpy(m_lower, other.m_lower, other.lowerSize() * sizeof (Scalar));
+        memcpy(m_upperProfile, other.m_upperProfile, m_upperProfileSize * sizeof (Index));
+        memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof (Index));
+        return *this;
+    }
+
+    void swap(SkylineStorage& other) {
+        std::swap(m_diag, other.m_diag);
+        std::swap(m_upper, other.m_upper);
+        std::swap(m_lower, other.m_lower);
+        std::swap(m_upperProfile, other.m_upperProfile);
+        std::swap(m_lowerProfile, other.m_lowerProfile);
+        std::swap(m_diagSize, other.m_diagSize);
+        std::swap(m_upperSize, other.m_upperSize);
+        std::swap(m_lowerSize, other.m_lowerSize);
+        std::swap(m_allocatedSize, other.m_allocatedSize);
+    }
+
+    ~SkylineStorage() {
+        delete[] m_diag;
+        delete[] m_upper;
+        if (m_upper != m_lower)
+            delete[] m_lower;
+        delete[] m_upperProfile;
+        delete[] m_lowerProfile;
+    }
+
+    void reserve(Index size, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) {
+        Index newAllocatedSize = size + upperSize + lowerSize;
+        if (newAllocatedSize > m_allocatedSize)
+            reallocate(size, upperProfileSize, lowerProfileSize, upperSize, lowerSize);
+    }
+
+    void squeeze() {
+        if (m_allocatedSize > m_diagSize + m_upperSize + m_lowerSize)
+            reallocate(m_diagSize, m_upperProfileSize, m_lowerProfileSize, m_upperSize, m_lowerSize);
+    }
+
+    void resize(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize, float reserveSizeFactor = 0) {
+        if (m_allocatedSize < diagSize + upperSize + lowerSize)
+            reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + Index(reserveSizeFactor * upperSize), lowerSize + Index(reserveSizeFactor * lowerSize));
+        m_diagSize = diagSize;
+        m_upperSize = upperSize;
+        m_lowerSize = lowerSize;
+        m_upperProfileSize = upperProfileSize;
+        m_lowerProfileSize = lowerProfileSize;
+    }
+
+    inline Index diagSize() const {
+        return m_diagSize;
+    }
+
+    inline Index upperSize() const {
+        return m_upperSize;
+    }
+
+    inline Index lowerSize() const {
+        return m_lowerSize;
+    }
+
+    inline Index upperProfileSize() const {
+        return m_upperProfileSize;
+    }
+
+    inline Index lowerProfileSize() const {
+        return m_lowerProfileSize;
+    }
+
+    inline Index allocatedSize() const {
+        return m_allocatedSize;
+    }
+
+    inline void clear() {
+        m_diagSize = 0;
+    }
+
+    inline Scalar& diag(Index i) {
+        return m_diag[i];
+    }
+
+    inline const Scalar& diag(Index i) const {
+        return m_diag[i];
+    }
+
+    inline Scalar& upper(Index i) {
+        return m_upper[i];
+    }
+
+    inline const Scalar& upper(Index i) const {
+        return m_upper[i];
+    }
+
+    inline Scalar& lower(Index i) {
+        return m_lower[i];
+    }
+
+    inline const Scalar& lower(Index i) const {
+        return m_lower[i];
+    }
+
+    inline Index& upperProfile(Index i) {
+        return m_upperProfile[i];
+    }
+
+    inline const Index& upperProfile(Index i) const {
+        return m_upperProfile[i];
+    }
+
+    inline Index& lowerProfile(Index i) {
+        return m_lowerProfile[i];
+    }
+
+    inline const Index& lowerProfile(Index i) const {
+        return m_lowerProfile[i];
+    }
+
+    static SkylineStorage Map(Index* upperProfile, Index* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower, Index size, Index upperSize, Index lowerSize) {
+        SkylineStorage res;
+        res.m_upperProfile = upperProfile;
+        res.m_lowerProfile = lowerProfile;
+        res.m_diag = diag;
+        res.m_upper = upper;
+        res.m_lower = lower;
+        res.m_allocatedSize = res.m_diagSize = size;
+        res.m_upperSize = upperSize;
+        res.m_lowerSize = lowerSize;
+        return res;
+    }
+
+    inline void reset() {
+        memset(m_diag, 0, m_diagSize * sizeof (Scalar));
+        memset(m_upper, 0, m_upperSize * sizeof (Scalar));
+        memset(m_lower, 0, m_lowerSize * sizeof (Scalar));
+        memset(m_upperProfile, 0, m_diagSize * sizeof (Index));
+        memset(m_lowerProfile, 0, m_diagSize * sizeof (Index));
+    }
+
+    void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>()) {
+        //TODO
+    }
+
+protected:
+
+    inline void reallocate(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) {
+
+        Scalar* diag = new Scalar[diagSize];
+        Scalar* upper = new Scalar[upperSize];
+        Scalar* lower = new Scalar[lowerSize];
+        Index* upperProfile = new Index[upperProfileSize];
+        Index* lowerProfile = new Index[lowerProfileSize];
+
+        Index copyDiagSize = (std::min)(diagSize, m_diagSize);
+        Index copyUpperSize = (std::min)(upperSize, m_upperSize);
+        Index copyLowerSize = (std::min)(lowerSize, m_lowerSize);
+        Index copyUpperProfileSize = (std::min)(upperProfileSize, m_upperProfileSize);
+        Index copyLowerProfileSize = (std::min)(lowerProfileSize, m_lowerProfileSize);
+
+        // copy
+        memcpy(diag, m_diag, copyDiagSize * sizeof (Scalar));
+        memcpy(upper, m_upper, copyUpperSize * sizeof (Scalar));
+        memcpy(lower, m_lower, copyLowerSize * sizeof (Scalar));
+        memcpy(upperProfile, m_upperProfile, copyUpperProfileSize * sizeof (Index));
+        memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof (Index));
+
+
+
+        // delete old stuff
+        delete[] m_diag;
+        delete[] m_upper;
+        delete[] m_lower;
+        delete[] m_upperProfile;
+        delete[] m_lowerProfile;
+        m_diag = diag;
+        m_upper = upper;
+        m_lower = lower;
+        m_upperProfile = upperProfile;
+        m_lowerProfile = lowerProfile;
+        m_allocatedSize = diagSize + upperSize + lowerSize;
+        m_upperSize = upperSize;
+        m_lowerSize = lowerSize;
+    }
+
+public:
+    Scalar* m_diag;
+    Scalar* m_upper;
+    Scalar* m_lower;
+    Index* m_upperProfile;
+    Index* m_lowerProfile;
+    Index m_diagSize;
+    Index m_upperSize;
+    Index m_lowerSize;
+    Index m_upperProfileSize;
+    Index m_lowerProfileSize;
+    Index m_allocatedSize;
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINE_STORAGE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
new file mode 100644
index 0000000..75eb612
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Skyline/SkylineUtil.h
@@ -0,0 +1,89 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Guillaume Saupin <guillaume.saupin@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKYLINEUTIL_H
+#define EIGEN_SKYLINEUTIL_H
+
+namespace Eigen { 
+
+#ifdef NDEBUG
+#define EIGEN_DBG_SKYLINE(X)
+#else
+#define EIGEN_DBG_SKYLINE(X) X
+#endif
+
+const unsigned int SkylineBit = 0x1200;
+template<typename Lhs, typename Rhs, int ProductMode> class SkylineProduct;
+enum AdditionalProductEvaluationMode {SkylineTimeDenseProduct, SkylineTimeSkylineProduct, DenseTimeSkylineProduct};
+enum {IsSkyline = SkylineBit};
+
+
+#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename OtherDerived> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SkylineMatrixBase<OtherDerived>& other) \
+{ \
+  return Base::operator Op(other.derived()); \
+} \
+EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
+{ \
+  return Base::operator Op(other); \
+}
+
+#define EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename Other> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
+{ \
+  return Base::operator Op(scalar); \
+}
+
+#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \
+  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \
+  EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \
+  EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \
+  EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=)
+
+#define _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, BaseClass) \
+  typedef BaseClass Base; \
+  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; \
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
+  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
+  typedef typename Eigen::internal::index<StorageKind>::type Index; \
+  enum {  Flags = Eigen::internal::traits<Derived>::Flags, };
+
+#define EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived) \
+  _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, Eigen::SkylineMatrixBase<Derived>)
+
+template<typename Derived> class SkylineMatrixBase;
+template<typename _Scalar, int _Flags = 0> class SkylineMatrix;
+template<typename _Scalar, int _Flags = 0> class DynamicSkylineMatrix;
+template<typename _Scalar, int _Flags = 0> class SkylineVector;
+template<typename _Scalar, int _Flags = 0> class MappedSkylineMatrix;
+
+namespace internal {
+
+template<typename Lhs, typename Rhs> struct skyline_product_mode;
+template<typename Lhs, typename Rhs, int ProductMode = skyline_product_mode<Lhs,Rhs>::value> struct SkylineProductReturnType;
+
+template<typename T> class eval<T,IsSkyline>
+{
+    typedef typename traits<T>::Scalar _Scalar;
+    enum {
+          _Flags = traits<T>::Flags
+    };
+
+  public:
+    typedef SkylineMatrix<_Scalar, _Flags> type;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SKYLINEUTIL_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
new file mode 100644
index 0000000..e9ec746
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h
@@ -0,0 +1,122 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
+#define EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
+
+namespace Eigen { 
+
+#if 0
+
+// NOTE Have to be reimplemented as a specialization of BlockImpl< DynamicSparseMatrix<_Scalar, _Options, _Index>, ... >
+// See SparseBlock.h for an example
+
+
+/***************************************************************************
+* specialisation for DynamicSparseMatrix
+***************************************************************************/
+
+template<typename _Scalar, int _Options, typename _Index, int Size>
+class SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options, _Index>, Size>
+  : public SparseMatrixBase<SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options, _Index>, Size> >
+{
+    typedef DynamicSparseMatrix<_Scalar, _Options, _Index> MatrixType;
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size!=Dynamic);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+    template<typename OtherDerived>
+    inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      if (IsRowMajor != ((OtherDerived::Flags&RowMajorBit)==RowMajorBit))
+      {
+        // need to transpose => perform a block evaluation followed by a big swap
+        DynamicSparseMatrix<Scalar,IsRowMajor?RowMajorBit:0> aux(other);
+        *this = aux.markAsRValue();
+      }
+      else
+      {
+        // evaluate/copy vector per vector
+        for (Index j=0; j<m_outerSize.value(); ++j)
+        {
+          SparseVector<Scalar,IsRowMajor ? RowMajorBit : 0> aux(other.innerVector(j));
+          m_matrix.const_cast_derived()._data()[m_outerStart+j].swap(aux._data());
+        }
+      }
+      return *this;
+    }
+
+    inline SparseInnerVectorSet& operator=(const SparseInnerVectorSet& other)
+    {
+      return operator=<SparseInnerVectorSet>(other);
+    }
+
+    Index nonZeros() const
+    {
+      Index count = 0;
+      for (Index j=0; j<m_outerSize.value(); ++j)
+        count += m_matrix._data()[m_outerStart+j].size();
+      return count;
+    }
+
+    const Scalar& lastCoeff() const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(SparseInnerVectorSet);
+      eigen_assert(m_matrix.data()[m_outerStart].size()>0);
+      return m_matrix.data()[m_outerStart].vale(m_matrix.data()[m_outerStart].size()-1);
+    }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+
+};
+
+#endif
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_BLOCKFORDYNAMICMATRIX_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
new file mode 100644
index 0000000..536a0c3
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h
@@ -0,0 +1,1079 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2013 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEBLOCKMATRIX_H
+#define EIGEN_SPARSEBLOCKMATRIX_H
+
+namespace Eigen { 
+/** \ingroup SparseCore_Module
+  *
+  * \class BlockSparseMatrix
+  *
+  * \brief A versatile sparse matrix representation where each element is a block
+  *
+  * This class provides routines to manipulate block sparse matrices stored in a
+  * BSR-like representation. There are two main types :
+  *
+  * 1. All blocks have the same number of rows and columns, called block size
+  * in the following. In this case, if this block size is known at compile time,
+  * it can be given as a template parameter like
+  * \code
+  * BlockSparseMatrix<Scalar, 3, ColMajor> bmat(b_rows, b_cols);
+  * \endcode
+  * Here, bmat is a b_rows x b_cols block sparse matrix
+  * where each coefficient is a 3x3 dense matrix.
+  * If the block size is fixed but will be given at runtime,
+  * \code
+  * BlockSparseMatrix<Scalar, Dynamic, ColMajor> bmat(b_rows, b_cols);
+  * bmat.setBlockSize(block_size);
+  * \endcode
+  *
+  * 2. The second case is for variable-block sparse matrices.
+  * Here each block has its own dimensions. The only restriction is that all the blocks
+  * in a row (resp. a column) should have the same number of rows (resp. of columns).
+  * It is thus required in this case to describe the layout of the matrix by calling
+  * setBlockLayout(rowBlocks, colBlocks).
+  *
+  * In any of the previous case, the matrix can be filled by calling setFromTriplets().
+  * A regular sparse matrix can be converted to a block sparse matrix and vice versa.
+  * It is obviously required to describe the block layout beforehand by calling either
+  * setBlockSize() for fixed-size blocks or setBlockLayout for variable-size blocks.
+  *
+  * \tparam _Scalar The Scalar type
+  * \tparam _BlockAtCompileTime The block layout option. It takes the following values
+  * Dynamic : block size known at runtime
+  * a numeric number : fixed-size block known at compile time
+  */
+template<typename _Scalar, int _BlockAtCompileTime=Dynamic, int _Options=ColMajor, typename _StorageIndex=int> class BlockSparseMatrix;
+
+template<typename BlockSparseMatrixT> class BlockSparseMatrixView;
+
+namespace internal {
+template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _Index>
+struct traits<BlockSparseMatrix<_Scalar,_BlockAtCompileTime,_Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind; // FIXME Where is it used ??
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    BlockSize = _BlockAtCompileTime,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+template<typename BlockSparseMatrixT>
+struct traits<BlockSparseMatrixView<BlockSparseMatrixT> >
+{
+  typedef Ref<Matrix<typename BlockSparseMatrixT::Scalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> > Scalar;
+  typedef Ref<Matrix<typename BlockSparseMatrixT::RealScalar, BlockSparseMatrixT::BlockSize, BlockSparseMatrixT::BlockSize> > RealScalar;
+
+};
+
+// Function object to sort a triplet list
+template<typename Iterator, bool IsColMajor>
+struct TripletComp
+{
+  typedef typename Iterator::value_type Triplet;
+  bool operator()(const Triplet& a, const Triplet& b)
+  { if(IsColMajor)
+      return ((a.col() == b.col() && a.row() < b.row()) || (a.col() < b.col()));
+    else
+      return ((a.row() == b.row() && a.col() < b.col()) || (a.row() < b.row()));
+  }
+};
+} // end namespace internal
+
+
+/* Proxy to view the block sparse matrix as a regular sparse matrix */
+template<typename BlockSparseMatrixT>
+class BlockSparseMatrixView : public SparseMatrixBase<BlockSparseMatrixT>
+{
+  public:
+    typedef Ref<typename BlockSparseMatrixT::BlockScalar> Scalar;
+    typedef Ref<typename BlockSparseMatrixT::BlockRealScalar> RealScalar;
+    typedef typename BlockSparseMatrixT::Index Index;
+    typedef  BlockSparseMatrixT Nested;
+    enum {
+      Flags = BlockSparseMatrixT::Options,
+      Options = BlockSparseMatrixT::Options,
+      RowsAtCompileTime = BlockSparseMatrixT::RowsAtCompileTime,
+      ColsAtCompileTime = BlockSparseMatrixT::ColsAtCompileTime,
+      MaxColsAtCompileTime = BlockSparseMatrixT::MaxColsAtCompileTime,
+      MaxRowsAtCompileTime = BlockSparseMatrixT::MaxRowsAtCompileTime
+    };
+  public:
+    BlockSparseMatrixView(const BlockSparseMatrixT& spblockmat)
+     : m_spblockmat(spblockmat)
+    {}
+
+    Index outerSize() const
+    {
+      return (Flags&RowMajorBit) == 1 ? this->rows() : this->cols();
+    }
+    Index cols() const
+    {
+      return m_spblockmat.blockCols();
+    }
+    Index rows() const
+    {
+      return m_spblockmat.blockRows();
+    }
+    Scalar coeff(Index row, Index col)
+    {
+      return m_spblockmat.coeff(row, col);
+    }
+    Scalar coeffRef(Index row, Index col)
+    {
+      return m_spblockmat.coeffRef(row, col);
+    }
+    // Wrapper to iterate over all blocks
+    class InnerIterator : public BlockSparseMatrixT::BlockInnerIterator
+    {
+      public:
+      InnerIterator(const BlockSparseMatrixView& mat, Index outer)
+          : BlockSparseMatrixT::BlockInnerIterator(mat.m_spblockmat, outer)
+      {}
+
+    };
+
+  protected:
+    const BlockSparseMatrixT& m_spblockmat;
+};
+
+// Proxy to view a regular vector as a block vector
+template<typename BlockSparseMatrixT, typename VectorType>
+class BlockVectorView
+{
+  public:
+    enum {
+      BlockSize = BlockSparseMatrixT::BlockSize,
+      ColsAtCompileTime = VectorType::ColsAtCompileTime,
+      RowsAtCompileTime = VectorType::RowsAtCompileTime,
+      Flags = VectorType::Flags
+    };
+    typedef Ref<const Matrix<typename BlockSparseMatrixT::Scalar, (RowsAtCompileTime==1)? 1 : BlockSize, (ColsAtCompileTime==1)? 1 : BlockSize> >Scalar;
+    typedef typename BlockSparseMatrixT::Index Index;
+  public:
+    BlockVectorView(const BlockSparseMatrixT& spblockmat, const VectorType& vec)
+    : m_spblockmat(spblockmat),m_vec(vec)
+    { }
+    inline Index cols() const
+    {
+      return m_vec.cols();
+    }
+    inline Index size() const
+    {
+      return m_spblockmat.blockRows();
+    }
+    inline Scalar coeff(Index bi) const
+    {
+      Index startRow = m_spblockmat.blockRowsIndex(bi);
+      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
+      return m_vec.middleRows(startRow, rowSize);
+    }
+    inline Scalar coeff(Index bi, Index j) const
+    {
+      Index startRow = m_spblockmat.blockRowsIndex(bi);
+      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
+      return m_vec.block(startRow, j, rowSize, 1);
+    }
+  protected:
+    const BlockSparseMatrixT& m_spblockmat;
+    const VectorType& m_vec;
+};
+
+template<typename VectorType, typename Index> class BlockVectorReturn;
+
+
+// Proxy to view a regular vector as a block vector
+template<typename BlockSparseMatrixT, typename VectorType>
+class BlockVectorReturn
+{
+  public:
+    enum {
+      ColsAtCompileTime = VectorType::ColsAtCompileTime,
+      RowsAtCompileTime = VectorType::RowsAtCompileTime,
+      Flags = VectorType::Flags
+    };
+    typedef Ref<Matrix<typename VectorType::Scalar, RowsAtCompileTime, ColsAtCompileTime> > Scalar;
+    typedef typename BlockSparseMatrixT::Index Index;
+  public:
+    BlockVectorReturn(const BlockSparseMatrixT& spblockmat, VectorType& vec)
+    : m_spblockmat(spblockmat),m_vec(vec)
+    { }
+    inline Index size() const
+    {
+      return m_spblockmat.blockRows();
+    }
+    inline Scalar coeffRef(Index bi)
+    {
+      Index startRow = m_spblockmat.blockRowsIndex(bi);
+      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
+      return m_vec.middleRows(startRow, rowSize);
+    }
+    inline Scalar coeffRef(Index bi, Index j)
+    {
+      Index startRow = m_spblockmat.blockRowsIndex(bi);
+      Index rowSize = m_spblockmat.blockRowsIndex(bi+1) - startRow;
+      return m_vec.block(startRow, j, rowSize, 1);
+    }
+
+  protected:
+    const BlockSparseMatrixT& m_spblockmat;
+    VectorType& m_vec;
+};
+
+// Block version of the sparse dense product
+template<typename Lhs, typename Rhs>
+class BlockSparseTimeDenseProduct;
+
+namespace internal {
+
+template<typename BlockSparseMatrixT, typename VecType>
+struct traits<BlockSparseTimeDenseProduct<BlockSparseMatrixT, VecType> >
+{
+  typedef Dense StorageKind;
+  typedef MatrixXpr XprKind;
+  typedef typename BlockSparseMatrixT::Scalar Scalar;
+  typedef typename BlockSparseMatrixT::Index Index;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = 0,
+    CoeffReadCost = internal::traits<BlockSparseMatrixT>::CoeffReadCost
+  };
+};
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class BlockSparseTimeDenseProduct
+  : public ProductBase<BlockSparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(BlockSparseTimeDenseProduct)
+
+    BlockSparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, const typename Rhs::Scalar& alpha) const
+    {
+      BlockVectorReturn<Lhs,Dest> tmpDest(m_lhs, dest);
+      internal::sparse_time_dense_product( BlockSparseMatrixView<Lhs>(m_lhs),  BlockVectorView<Lhs, Rhs>(m_lhs, m_rhs), tmpDest, alpha);
+    }
+
+  private:
+    BlockSparseTimeDenseProduct& operator=(const BlockSparseTimeDenseProduct&);
+};
+
+template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
+class BlockSparseMatrix : public SparseMatrixBase<BlockSparseMatrix<_Scalar,_BlockAtCompileTime, _Options,_StorageIndex> >
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef _StorageIndex StorageIndex;
+    typedef typename internal::ref_selector<BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex> >::type Nested;
+
+    enum {
+      Options = _Options,
+      Flags = Options,
+      BlockSize=_BlockAtCompileTime,
+      RowsAtCompileTime = Dynamic,
+      ColsAtCompileTime = Dynamic,
+      MaxRowsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic,
+      IsVectorAtCompileTime = 0,
+      IsColMajor = Flags&RowMajorBit ? 0 : 1
+    };
+    typedef Matrix<Scalar, _BlockAtCompileTime, _BlockAtCompileTime,IsColMajor ? ColMajor : RowMajor> BlockScalar;
+    typedef Matrix<RealScalar, _BlockAtCompileTime, _BlockAtCompileTime,IsColMajor ? ColMajor : RowMajor> BlockRealScalar;
+    typedef typename internal::conditional<_BlockAtCompileTime==Dynamic, Scalar, BlockScalar>::type BlockScalarReturnType;
+    typedef BlockSparseMatrix<Scalar, BlockSize, IsColMajor ? ColMajor : RowMajor, StorageIndex> PlainObject;
+  public:
+    // Default constructor
+    BlockSparseMatrix()
+    : m_innerBSize(0),m_outerBSize(0),m_innerOffset(0),m_outerOffset(0),
+      m_nonzerosblocks(0),m_values(0),m_blockPtr(0),m_indices(0),
+      m_outerIndex(0),m_blockSize(BlockSize)
+    { }
+
+
+    /**
+     * \brief Construct and resize
+     *
+     */
+    BlockSparseMatrix(Index brow, Index bcol)
+      : m_innerBSize(IsColMajor ? brow : bcol),
+        m_outerBSize(IsColMajor ? bcol : brow),
+        m_innerOffset(0),m_outerOffset(0),m_nonzerosblocks(0),
+        m_values(0),m_blockPtr(0),m_indices(0),
+        m_outerIndex(0),m_blockSize(BlockSize)
+    { }
+
+    /**
+     * \brief Copy-constructor
+     */
+    BlockSparseMatrix(const BlockSparseMatrix& other)
+      : m_innerBSize(other.m_innerBSize),m_outerBSize(other.m_outerBSize),
+        m_nonzerosblocks(other.m_nonzerosblocks),m_nonzeros(other.m_nonzeros),
+        m_blockPtr(0),m_blockSize(other.m_blockSize)
+    {
+      // should we allow copying between variable-size blocks and fixed-size blocks ??
+      eigen_assert(m_blockSize == BlockSize && " CAN NOT COPY BETWEEN FIXED-SIZE AND VARIABLE-SIZE BLOCKS");
+
+      std::copy(other.m_innerOffset, other.m_innerOffset+m_innerBSize+1, m_innerOffset);
+      std::copy(other.m_outerOffset, other.m_outerOffset+m_outerBSize+1, m_outerOffset);
+      std::copy(other.m_values, other.m_values+m_nonzeros, m_values);
+
+      if(m_blockSize != Dynamic)
+        std::copy(other.m_blockPtr, other.m_blockPtr+m_nonzerosblocks, m_blockPtr);
+
+      std::copy(other.m_indices, other.m_indices+m_nonzerosblocks, m_indices);
+      std::copy(other.m_outerIndex, other.m_outerIndex+m_outerBSize, m_outerIndex);
+    }
+
+    friend void swap(BlockSparseMatrix& first, BlockSparseMatrix& second)
+    {
+      std::swap(first.m_innerBSize, second.m_innerBSize);
+      std::swap(first.m_outerBSize, second.m_outerBSize);
+      std::swap(first.m_innerOffset, second.m_innerOffset);
+      std::swap(first.m_outerOffset, second.m_outerOffset);
+      std::swap(first.m_nonzerosblocks, second.m_nonzerosblocks);
+      std::swap(first.m_nonzeros, second.m_nonzeros);
+      std::swap(first.m_values, second.m_values);
+      std::swap(first.m_blockPtr, second.m_blockPtr);
+      std::swap(first.m_indices, second.m_indices);
+      std::swap(first.m_outerIndex, second.m_outerIndex);
+      std::swap(first.m_BlockSize, second.m_blockSize);
+    }
+
+    BlockSparseMatrix& operator=(BlockSparseMatrix other)
+    {
+      //Copy-and-swap paradigm ... avoid leaked data if thrown
+      swap(*this, other);
+      return *this;
+    }
+
+    // Destructor
+    ~BlockSparseMatrix()
+    {
+      delete[] m_outerIndex;
+      delete[] m_innerOffset;
+      delete[] m_outerOffset;
+      delete[] m_indices;
+      delete[] m_blockPtr;
+      delete[] m_values;
+    }
+
+
+    /**
+      * \brief Constructor from a sparse matrix
+      *
+      */
+    template<typename MatrixType>
+    inline BlockSparseMatrix(const MatrixType& spmat) : m_blockSize(BlockSize)
+    {
+      EIGEN_STATIC_ASSERT((m_blockSize != Dynamic), THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE);
+
+      *this = spmat;
+    }
+
+    /**
+      * \brief Assignment from a sparse matrix with the same storage order
+      *
+      * Convert from a sparse matrix to block sparse matrix.
+      * \warning Before calling this function, tt is necessary to call
+      * either setBlockLayout() (matrices with variable-size blocks)
+      * or setBlockSize() (for fixed-size blocks).
+      */
+    template<typename MatrixType>
+    inline BlockSparseMatrix& operator=(const MatrixType& spmat)
+    {
+      eigen_assert((m_innerBSize != 0 && m_outerBSize != 0)
+                   && "Trying to assign to a zero-size matrix, call resize() first");
+      eigen_assert(((MatrixType::Options&RowMajorBit) != IsColMajor) && "Wrong storage order");
+      typedef SparseMatrix<bool,MatrixType::Options,typename MatrixType::Index> MatrixPatternType;
+      MatrixPatternType  blockPattern(blockRows(), blockCols());
+      m_nonzeros = 0;
+
+      // First, compute the number of nonzero blocks and their locations
+      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+      {
+        // Browse each outer block and compute the structure
+        std::vector<bool> nzblocksFlag(m_innerBSize,false);  // Record the existing blocks
+        blockPattern.startVec(bj);
+        for(StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj+1); ++j)
+        {
+          typename MatrixType::InnerIterator it_spmat(spmat, j);
+          for(; it_spmat; ++it_spmat)
+          {
+            StorageIndex bi = innerToBlock(it_spmat.index()); // Index of the current nonzero block
+            if(!nzblocksFlag[bi])
+            {
+              // Save the index of this nonzero block
+              nzblocksFlag[bi] = true;
+              blockPattern.insertBackByOuterInnerUnordered(bj, bi) = true;
+              // Compute the total number of nonzeros (including explicit zeros in blocks)
+              m_nonzeros += blockOuterSize(bj) * blockInnerSize(bi);
+            }
+          }
+        } // end current outer block
+      }
+      blockPattern.finalize();
+
+      // Allocate the internal arrays
+      setBlockStructure(blockPattern);
+
+      for(StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
+      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+      {
+        // Now copy the values
+        for(StorageIndex j = blockOuterIndex(bj); j < blockOuterIndex(bj+1); ++j)
+        {
+          // Browse the outer block column by column (for column-major matrices)
+          typename MatrixType::InnerIterator it_spmat(spmat, j);
+          for(; it_spmat; ++it_spmat)
+          {
+            StorageIndex idx = 0; // Position of this block in the column block
+            StorageIndex bi = innerToBlock(it_spmat.index()); // Index of the current nonzero block
+            // Go to the inner block where this element belongs to
+            while(bi > m_indices[m_outerIndex[bj]+idx]) ++idx; // Not expensive for ordered blocks
+            StorageIndex idxVal;// Get the right position in the array of values for this element
+            if(m_blockSize == Dynamic)
+            {
+              // Offset from all blocks before ...
+              idxVal =  m_blockPtr[m_outerIndex[bj]+idx];
+              // ... and offset inside the block
+              idxVal += (j - blockOuterIndex(bj)) * blockOuterSize(bj) + it_spmat.index() - m_innerOffset[bi];
+            }
+            else
+            {
+              // All blocks before
+              idxVal = (m_outerIndex[bj] + idx) * m_blockSize * m_blockSize;
+              // inside the block
+              idxVal += (j - blockOuterIndex(bj)) * m_blockSize + (it_spmat.index()%m_blockSize);
+            }
+            // Insert the value
+            m_values[idxVal] = it_spmat.value();
+          } // end of this column
+        } // end of this block
+      } // end of this outer block
+
+      return *this;
+    }
+
+    /**
+      * \brief Set the nonzero block pattern of the matrix
+      *
+      * Given a sparse matrix describing the nonzero block pattern,
+      * this function prepares the internal pointers for values.
+      * After calling this function, any *nonzero* block (bi, bj) can be set
+      * with a simple call to coeffRef(bi,bj).
+      *
+      *
+      * \warning Before calling this function, tt is necessary to call
+      * either setBlockLayout() (matrices with variable-size blocks)
+      * or setBlockSize() (for fixed-size blocks).
+      *
+      * \param blockPattern Sparse matrix of boolean elements describing the block structure
+      *
+      * \sa setBlockLayout() \sa setBlockSize()
+      */
+    template<typename MatrixType>
+    void setBlockStructure(const MatrixType& blockPattern)
+    {
+      resize(blockPattern.rows(), blockPattern.cols());
+      reserve(blockPattern.nonZeros());
+
+      // Browse the block pattern and set up the various pointers
+      m_outerIndex[0] = 0;
+      if(m_blockSize == Dynamic) m_blockPtr[0] = 0;
+      for(StorageIndex nz = 0; nz < m_nonzeros; ++nz) m_values[nz] = Scalar(0);
+      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+      {
+        //Browse each outer block
+
+        //First, copy and save the indices of nonzero blocks
+        //FIXME : find a way to avoid this ...
+        std::vector<int> nzBlockIdx;
+        typename MatrixType::InnerIterator it(blockPattern, bj);
+        for(; it; ++it)
+        {
+          nzBlockIdx.push_back(it.index());
+        }
+        std::sort(nzBlockIdx.begin(), nzBlockIdx.end());
+
+        // Now, fill block indices and (eventually) pointers to blocks
+        for(StorageIndex idx = 0; idx < nzBlockIdx.size(); ++idx)
+        {
+          StorageIndex offset = m_outerIndex[bj]+idx; // offset in m_indices
+          m_indices[offset] = nzBlockIdx[idx];
+          if(m_blockSize == Dynamic)
+            m_blockPtr[offset] = m_blockPtr[offset-1] + blockInnerSize(nzBlockIdx[idx]) * blockOuterSize(bj);
+          // There is no blockPtr for fixed-size blocks... not needed !???
+        }
+        // Save the pointer to the next outer block
+        m_outerIndex[bj+1] = m_outerIndex[bj] + nzBlockIdx.size();
+      }
+    }
+
+    /**
+      * \brief Set the number of rows and columns blocks
+      */
+    inline void resize(Index brow, Index bcol)
+    {
+      m_innerBSize = IsColMajor ? brow : bcol;
+      m_outerBSize = IsColMajor ? bcol : brow;
+    }
+
+    /**
+      * \brief set the block size at runtime for fixed-size block layout
+      *
+      * Call this only for fixed-size blocks
+      */
+    inline void setBlockSize(Index blockSize)
+    {
+      m_blockSize = blockSize;
+    }
+
+    /**
+      * \brief Set the row and column block layouts,
+      *
+      * This function set the size of each row and column block.
+      * So this function should be used only for blocks with variable size.
+      * \param rowBlocks : Number of rows per row block
+      * \param colBlocks : Number of columns per column block
+      * \sa resize(), setBlockSize()
+      */
+    inline void setBlockLayout(const VectorXi& rowBlocks, const VectorXi& colBlocks)
+    {
+      const VectorXi& innerBlocks = IsColMajor ? rowBlocks : colBlocks;
+      const VectorXi& outerBlocks = IsColMajor ? colBlocks : rowBlocks;
+      eigen_assert(m_innerBSize == innerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
+      eigen_assert(m_outerBSize == outerBlocks.size() && "CHECK THE NUMBER OF ROW OR COLUMN BLOCKS");
+      m_outerBSize = outerBlocks.size();
+      //  starting index of blocks... cumulative sums
+      m_innerOffset = new StorageIndex[m_innerBSize+1];
+      m_outerOffset = new StorageIndex[m_outerBSize+1];
+      m_innerOffset[0] = 0;
+      m_outerOffset[0] = 0;
+      std::partial_sum(&innerBlocks[0], &innerBlocks[m_innerBSize-1]+1, &m_innerOffset[1]);
+      std::partial_sum(&outerBlocks[0], &outerBlocks[m_outerBSize-1]+1, &m_outerOffset[1]);
+
+      // Compute the total number of nonzeros
+      m_nonzeros = 0;
+      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+        for(StorageIndex bi = 0; bi < m_innerBSize; ++bi)
+          m_nonzeros += outerBlocks[bj] * innerBlocks[bi];
+
+    }
+
+    /**
+      * \brief Allocate the internal array of pointers to blocks and their inner indices
+      *
+      * \note For fixed-size blocks, call setBlockSize() to set the block.
+      * And For variable-size blocks, call setBlockLayout() before using this function
+      *
+      * \param nonzerosblocks Number of nonzero blocks. The total number of nonzeros is
+      * is computed in setBlockLayout() for variable-size blocks
+      * \sa setBlockSize()
+      */
+    inline void reserve(const Index nonzerosblocks)
+    {
+      eigen_assert((m_innerBSize != 0 && m_outerBSize != 0) &&
+          "TRYING TO RESERVE ZERO-SIZE MATRICES, CALL resize() first");
+
+      //FIXME Should free if already allocated
+      m_outerIndex = new StorageIndex[m_outerBSize+1];
+
+      m_nonzerosblocks = nonzerosblocks;
+      if(m_blockSize != Dynamic)
+      {
+        m_nonzeros = nonzerosblocks * (m_blockSize * m_blockSize);
+        m_blockPtr = 0;
+      }
+      else
+      {
+        // m_nonzeros  is already computed in setBlockLayout()
+        m_blockPtr = new StorageIndex[m_nonzerosblocks+1];
+      }
+      m_indices = new StorageIndex[m_nonzerosblocks+1];
+      m_values = new Scalar[m_nonzeros];
+    }
+
+
+    /**
+      * \brief Fill values in a matrix  from a triplet list.
+      *
+      * Each triplet item has a block stored in an Eigen dense matrix.
+      * The InputIterator class should provide the functions row(), col() and value()
+      *
+      * \note For fixed-size blocks, call setBlockSize() before this function.
+      *
+      * FIXME Do not accept duplicates
+      */
+    template<typename InputIterator>
+    void setFromTriplets(const InputIterator& begin, const InputIterator& end)
+    {
+      eigen_assert((m_innerBSize!=0 && m_outerBSize !=0) && "ZERO BLOCKS, PLEASE CALL resize() before");
+
+      /* First, sort the triplet list
+        * FIXME This can be unnecessarily expensive since only the inner indices have to be sorted
+        * The best approach is like in SparseMatrix::setFromTriplets()
+        */
+      internal::TripletComp<InputIterator, IsColMajor> tripletcomp;
+      std::sort(begin, end, tripletcomp);
+
+      /* Count the number of rows and column blocks,
+       * and the number of nonzero blocks per outer dimension
+       */
+      VectorXi rowBlocks(m_innerBSize); // Size of each block row
+      VectorXi colBlocks(m_outerBSize); // Size of each block column
+      rowBlocks.setZero(); colBlocks.setZero();
+      VectorXi nzblock_outer(m_outerBSize); // Number of nz blocks per outer vector
+      VectorXi nz_outer(m_outerBSize); // Number of nz per outer vector...for variable-size blocks
+      nzblock_outer.setZero();
+      nz_outer.setZero();
+      for(InputIterator it(begin); it !=end; ++it)
+      {
+        eigen_assert(it->row() >= 0 && it->row() < this->blockRows() && it->col() >= 0 && it->col() < this->blockCols());
+        eigen_assert((it->value().rows() == it->value().cols() && (it->value().rows() == m_blockSize))
+                     || (m_blockSize == Dynamic));
+
+        if(m_blockSize == Dynamic)
+        {
+          eigen_assert((rowBlocks[it->row()] == 0 || rowBlocks[it->row()] == it->value().rows()) &&
+              "NON CORRESPONDING SIZES FOR ROW BLOCKS");
+          eigen_assert((colBlocks[it->col()] == 0 || colBlocks[it->col()] == it->value().cols()) &&
+              "NON CORRESPONDING SIZES FOR COLUMN BLOCKS");
+          rowBlocks[it->row()] =it->value().rows();
+          colBlocks[it->col()] = it->value().cols();
+        }
+        nz_outer(IsColMajor ? it->col() : it->row()) += it->value().rows() * it->value().cols();
+        nzblock_outer(IsColMajor ? it->col() : it->row())++;
+      }
+      // Allocate member arrays
+      if(m_blockSize == Dynamic) setBlockLayout(rowBlocks, colBlocks);
+      StorageIndex nzblocks = nzblock_outer.sum();
+      reserve(nzblocks);
+
+       // Temporary markers
+      VectorXi block_id(m_outerBSize); // To be used as a block marker during insertion
+
+      // Setup outer index pointers and markers
+      m_outerIndex[0] = 0;
+      if (m_blockSize == Dynamic)  m_blockPtr[0] =  0;
+      for(StorageIndex bj = 0; bj < m_outerBSize; ++bj)
+      {
+        m_outerIndex[bj+1] = m_outerIndex[bj] + nzblock_outer(bj);
+        block_id(bj) = m_outerIndex[bj];
+        if(m_blockSize==Dynamic)
+        {
+          m_blockPtr[m_outerIndex[bj+1]] = m_blockPtr[m_outerIndex[bj]] + nz_outer(bj);
+        }
+      }
+
+      // Fill the matrix
+      for(InputIterator it(begin); it!=end; ++it)
+      {
+        StorageIndex outer = IsColMajor ? it->col() : it->row();
+        StorageIndex inner = IsColMajor ? it->row() : it->col();
+        m_indices[block_id(outer)] = inner;
+        StorageIndex block_size = it->value().rows()*it->value().cols();
+        StorageIndex nz_marker = blockPtr(block_id[outer]);
+        memcpy(&(m_values[nz_marker]), it->value().data(), block_size * sizeof(Scalar));
+        if(m_blockSize == Dynamic)
+        {
+          m_blockPtr[block_id(outer)+1] = m_blockPtr[block_id(outer)] + block_size;
+        }
+        block_id(outer)++;
+      }
+
+      // An alternative when the outer indices are sorted...no need to use an array of markers
+//      for(Index bcol = 0; bcol < m_outerBSize; ++bcol)
+//      {
+//      Index id = 0, id_nz = 0, id_nzblock = 0;
+//      for(InputIterator it(begin); it!=end; ++it)
+//      {
+//        while (id<bcol) // one pass should do the job unless there are empty columns
+//        {
+//          id++;
+//          m_outerIndex[id+1]=m_outerIndex[id];
+//        }
+//        m_outerIndex[id+1] += 1;
+//        m_indices[id_nzblock]=brow;
+//        Index block_size = it->value().rows()*it->value().cols();
+//        m_blockPtr[id_nzblock+1] = m_blockPtr[id_nzblock] + block_size;
+//        id_nzblock++;
+//        memcpy(&(m_values[id_nz]),it->value().data(), block_size*sizeof(Scalar));
+//        id_nz += block_size;
+//      }
+//      while(id < m_outerBSize-1) // Empty columns at the end
+//      {
+//        id++;
+//        m_outerIndex[id+1]=m_outerIndex[id];
+//      }
+//      }
+    }
+
+
+    /**
+      * \returns the number of rows
+      */
+    inline Index rows() const
+    {
+//      return blockRows();
+      return (IsColMajor ? innerSize() : outerSize());
+    }
+
+    /**
+      * \returns the number of cols
+      */
+    inline Index cols() const
+    {
+//      return blockCols();
+      return (IsColMajor ? outerSize() : innerSize());
+    }
+
+    inline Index innerSize() const
+    {
+      if(m_blockSize == Dynamic) return m_innerOffset[m_innerBSize];
+      else return  (m_innerBSize * m_blockSize) ;
+    }
+
+    inline Index outerSize() const
+    {
+      if(m_blockSize == Dynamic) return m_outerOffset[m_outerBSize];
+      else return  (m_outerBSize * m_blockSize) ;
+    }
+    /** \returns the number of rows grouped by blocks */
+    inline Index blockRows() const
+    {
+      return (IsColMajor ? m_innerBSize : m_outerBSize);
+    }
+    /** \returns the number of columns grouped by blocks */
+    inline Index blockCols() const
+    {
+      return (IsColMajor ? m_outerBSize : m_innerBSize);
+    }
+
+    inline Index outerBlocks() const { return m_outerBSize; }
+    inline Index innerBlocks() const { return m_innerBSize; }
+
+    /** \returns the block index where outer belongs to */
+    inline Index outerToBlock(Index outer) const
+    {
+      eigen_assert(outer < outerSize() && "OUTER INDEX OUT OF BOUNDS");
+
+      if(m_blockSize != Dynamic)
+        return (outer / m_blockSize); // Integer division
+
+      StorageIndex b_outer = 0;
+      while(m_outerOffset[b_outer] <= outer) ++b_outer;
+      return b_outer - 1;
+    }
+    /** \returns  the block index where inner belongs to */
+    inline Index innerToBlock(Index inner) const
+    {
+      eigen_assert(inner < innerSize() && "OUTER INDEX OUT OF BOUNDS");
+
+      if(m_blockSize != Dynamic)
+        return (inner / m_blockSize); // Integer division
+
+      StorageIndex b_inner = 0;
+      while(m_innerOffset[b_inner] <= inner) ++b_inner;
+      return b_inner - 1;
+    }
+
+    /**
+      *\returns a reference to the (i,j) block as an Eigen Dense Matrix
+      */
+    Ref<BlockScalar> coeffRef(Index brow, Index bcol)
+    {
+      eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
+      eigen_assert(bcol < blockCols() && "BLOCK nzblocksFlagCOLUMN OUT OF BOUNDS");
+
+      StorageIndex rsize = IsColMajor ? blockInnerSize(brow): blockOuterSize(bcol);
+      StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
+      StorageIndex inner = IsColMajor ? brow : bcol;
+      StorageIndex outer = IsColMajor ? bcol : brow;
+      StorageIndex offset = m_outerIndex[outer];
+      while(offset < m_outerIndex[outer+1] && m_indices[offset] != inner)
+        offset++;
+      if(m_indices[offset] == inner)
+      {
+        return Map<BlockScalar>(&(m_values[blockPtr(offset)]), rsize, csize);
+      }
+      else
+      {
+        //FIXME the block does not exist, Insert it !!!!!!!!!
+        eigen_assert("DYNAMIC INSERTION IS NOT YET SUPPORTED");
+      }
+    }
+
+    /**
+      * \returns the value of the (i,j) block as an Eigen Dense Matrix
+      */
+    Map<const BlockScalar> coeff(Index brow, Index bcol) const
+    {
+      eigen_assert(brow < blockRows() && "BLOCK ROW INDEX OUT OF BOUNDS");
+      eigen_assert(bcol < blockCols() && "BLOCK COLUMN OUT OF BOUNDS");
+
+      StorageIndex rsize = IsColMajor ? blockInnerSize(brow): blockOuterSize(bcol);
+      StorageIndex csize = IsColMajor ? blockOuterSize(bcol) : blockInnerSize(brow);
+      StorageIndex inner = IsColMajor ? brow : bcol;
+      StorageIndex outer = IsColMajor ? bcol : brow;
+      StorageIndex offset = m_outerIndex[outer];
+      while(offset < m_outerIndex[outer+1] && m_indices[offset] != inner) offset++;
+      if(m_indices[offset] == inner)
+      {
+        return Map<const BlockScalar> (&(m_values[blockPtr(offset)]), rsize, csize);
+      }
+      else
+//        return BlockScalar::Zero(rsize, csize);
+        eigen_assert("NOT YET SUPPORTED");
+    }
+
+    // Block Matrix times vector product
+    template<typename VecType>
+    BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType> operator*(const VecType& lhs) const
+    {
+      return BlockSparseTimeDenseProduct<BlockSparseMatrix, VecType>(*this, lhs);
+    }
+
+    /** \returns the number of nonzero blocks */
+    inline Index nonZerosBlocks() const { return m_nonzerosblocks; }
+    /** \returns the total number of nonzero elements, including eventual explicit zeros in blocks */
+    inline Index nonZeros() const { return m_nonzeros; }
+
+    inline BlockScalarReturnType *valuePtr() {return static_cast<BlockScalarReturnType *>(m_values);}
+//    inline Scalar *valuePtr(){ return m_values; }
+    inline StorageIndex *innerIndexPtr() {return m_indices; }
+    inline const StorageIndex *innerIndexPtr() const {return m_indices; }
+    inline StorageIndex *outerIndexPtr() {return m_outerIndex; }
+    inline const StorageIndex* outerIndexPtr() const {return m_outerIndex; }
+
+    /** \brief for compatibility purposes with the SparseMatrix class */
+    inline bool isCompressed() const {return true;}
+    /**
+      * \returns the starting index of the bi row block
+      */
+    inline Index blockRowsIndex(Index bi) const
+    {
+      return IsColMajor ? blockInnerIndex(bi) : blockOuterIndex(bi);
+    }
+
+    /**
+      * \returns the starting index of the bj col block
+      */
+    inline Index blockColsIndex(Index bj) const
+    {
+      return IsColMajor ? blockOuterIndex(bj) : blockInnerIndex(bj);
+    }
+
+    inline Index blockOuterIndex(Index bj) const
+    {
+      return (m_blockSize == Dynamic) ? m_outerOffset[bj] : (bj * m_blockSize);
+    }
+    inline Index blockInnerIndex(Index bi) const
+    {
+      return (m_blockSize == Dynamic) ? m_innerOffset[bi] : (bi * m_blockSize);
+    }
+
+    // Not needed ???
+    inline Index blockInnerSize(Index bi) const
+    {
+      return (m_blockSize == Dynamic) ? (m_innerOffset[bi+1] - m_innerOffset[bi]) : m_blockSize;
+    }
+    inline Index blockOuterSize(Index bj) const
+    {
+      return (m_blockSize == Dynamic) ? (m_outerOffset[bj+1]- m_outerOffset[bj]) : m_blockSize;
+    }
+
+    /**
+      * \brief Browse the matrix by outer index
+      */
+    class InnerIterator; // Browse column by column
+
+    /**
+      * \brief Browse the matrix by block outer index
+      */
+    class BlockInnerIterator; // Browse block by block
+
+    friend std::ostream & operator << (std::ostream & s, const BlockSparseMatrix& m)
+    {
+      for (StorageIndex j = 0; j < m.outerBlocks(); ++j)
+      {
+        BlockInnerIterator itb(m, j);
+        for(; itb; ++itb)
+        {
+          s << "("<<itb.row() << ", " << itb.col() << ")\n";
+          s << itb.value() <<"\n";
+        }
+      }
+      s << std::endl;
+      return s;
+    }
+
+    /**
+      * \returns the starting position of the block \p id in the array of values
+      */
+    Index blockPtr(Index id) const
+    {
+      if(m_blockSize == Dynamic) return m_blockPtr[id];
+      else return id * m_blockSize * m_blockSize;
+      //return blockDynIdx(id, typename internal::conditional<(BlockSize==Dynamic), internal::true_type, internal::false_type>::type());
+    }
+
+
+  protected:
+//    inline Index blockDynIdx(Index id, internal::true_type) const
+//    {
+//      return m_blockPtr[id];
+//    }
+//    inline Index blockDynIdx(Index id, internal::false_type) const
+//    {
+//      return id * BlockSize * BlockSize;
+//    }
+
+    // To be implemented
+    // Insert a block at a particular location... need to make a room for that
+    Map<BlockScalar> insert(Index brow, Index bcol);
+
+    Index m_innerBSize; // Number of block rows
+    Index m_outerBSize; // Number of block columns
+    StorageIndex *m_innerOffset; // Starting index of each inner block (size m_innerBSize+1)
+    StorageIndex *m_outerOffset; // Starting index of each outer block (size m_outerBSize+1)
+    Index m_nonzerosblocks; // Total nonzeros blocks (lower than  m_innerBSize x m_outerBSize)
+    Index m_nonzeros; // Total nonzeros elements
+    Scalar *m_values; //Values stored block column after block column (size m_nonzeros)
+    StorageIndex *m_blockPtr; // Pointer to the beginning of each block in m_values, size m_nonzeroblocks ... null for fixed-size blocks
+    StorageIndex *m_indices; //Inner block indices, size m_nonzerosblocks ... OK
+    StorageIndex *m_outerIndex; // Starting pointer of each block column in m_indices (size m_outerBSize)... OK
+    Index m_blockSize; // Size of a block for fixed-size blocks, otherwise -1
+};
+
+template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
+class BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex>::BlockInnerIterator
+{
+  public:
+
+    enum{
+      Flags = _Options
+    };
+
+    BlockInnerIterator(const BlockSparseMatrix& mat, const Index outer)
+    : m_mat(mat),m_outer(outer),
+      m_id(mat.m_outerIndex[outer]),
+      m_end(mat.m_outerIndex[outer+1])
+    {
+    }
+
+    inline BlockInnerIterator& operator++() {m_id++; return *this; }
+
+    inline const Map<const BlockScalar> value() const
+    {
+      return Map<const BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]),
+          rows(),cols());
+    }
+    inline Map<BlockScalar> valueRef()
+    {
+      return Map<BlockScalar>(&(m_mat.m_values[m_mat.blockPtr(m_id)]),
+          rows(),cols());
+    }
+    // Block inner index
+    inline Index index() const {return m_mat.m_indices[m_id]; }
+    inline Index outer() const { return m_outer; }
+    // block row index
+    inline Index row() const  {return index(); }
+    // block column index
+    inline Index col() const {return outer(); }
+    // FIXME Number of rows in the current block
+    inline Index rows() const { return (m_mat.m_blockSize==Dynamic) ? (m_mat.m_innerOffset[index()+1] - m_mat.m_innerOffset[index()]) : m_mat.m_blockSize; }
+    // Number of columns in the current block ...
+    inline Index cols() const { return (m_mat.m_blockSize==Dynamic) ? (m_mat.m_outerOffset[m_outer+1]-m_mat.m_outerOffset[m_outer]) : m_mat.m_blockSize;}
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, StorageIndex>& m_mat;
+    const Index m_outer;
+    Index m_id;
+    Index m_end;
+};
+
+template<typename _Scalar, int _BlockAtCompileTime, int _Options, typename _StorageIndex>
+class BlockSparseMatrix<_Scalar, _BlockAtCompileTime, _Options, _StorageIndex>::InnerIterator
+{
+  public:
+    InnerIterator(const BlockSparseMatrix& mat, Index outer)
+    : m_mat(mat),m_outerB(mat.outerToBlock(outer)),m_outer(outer),
+      itb(mat, mat.outerToBlock(outer)),
+      m_offset(outer - mat.blockOuterIndex(m_outerB))
+     {
+        if (itb)
+        {
+          m_id = m_mat.blockInnerIndex(itb.index());
+          m_start = m_id;
+          m_end = m_mat.blockInnerIndex(itb.index()+1);
+        }
+     }
+    inline InnerIterator& operator++()
+    {
+      m_id++;
+      if (m_id >= m_end)
+      {
+        ++itb;
+        if (itb)
+        {
+          m_id = m_mat.blockInnerIndex(itb.index());
+          m_start = m_id;
+          m_end = m_mat.blockInnerIndex(itb.index()+1);
+        }
+      }
+      return *this;
+    }
+    inline const Scalar& value() const
+    {
+      return itb.value().coeff(m_id - m_start, m_offset);
+    }
+    inline Scalar& valueRef()
+    {
+      return itb.valueRef().coeff(m_id - m_start, m_offset);
+    }
+    inline Index index() const { return m_id; }
+    inline Index outer() const {return m_outer; }
+    inline Index col() const {return outer(); }
+    inline Index row() const { return index();}
+    inline operator bool() const
+    {
+      return itb;
+    }
+  protected:
+    const BlockSparseMatrix& m_mat;
+    const Index m_outer;
+    const Index m_outerB;
+    BlockInnerIterator itb; // Iterator through the blocks
+    const Index m_offset; // Position of this column in the block
+    Index m_start; // starting inner index of this block
+    Index m_id; // current inner index in the block
+    Index m_end; // starting inner index of the next block
+
+};
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEBLOCKMATRIX_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
new file mode 100644
index 0000000..42c99e4
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h
@@ -0,0 +1,404 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DYNAMIC_SPARSEMATRIX_H
+#define EIGEN_DYNAMIC_SPARSEMATRIX_H
+
+namespace Eigen { 
+
+/** \deprecated use a SparseMatrix in an uncompressed mode
+  *
+  * \class DynamicSparseMatrix
+  *
+  * \brief A sparse matrix class designed for matrix assembly purpose
+  *
+  * \param _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * Unlike SparseMatrix, this class provides a much higher degree of flexibility. In particular, it allows
+  * random read/write accesses in log(rho*outer_size) where \c rho is the probability that a coefficient is
+  * nonzero and outer_size is the number of columns if the matrix is column-major and the number of rows
+  * otherwise.
+  *
+  * Internally, the data are stored as a std::vector of compressed vector. The performances of random writes might
+  * decrease as the number of nonzeros per inner-vector increase. In practice, we observed very good performance
+  * till about 100 nonzeros/vector, and the performance remains relatively good till 500 nonzeros/vectors.
+  *
+  * \see SparseMatrix
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _StorageIndex>
+struct traits<DynamicSparseMatrix<_Scalar, _Options, _StorageIndex> >
+{
+  typedef _Scalar Scalar;
+  typedef _StorageIndex StorageIndex;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = OuterRandomAccessPattern
+  };
+};
+}
+
+template<typename _Scalar, int _Options, typename _StorageIndex>
+ class  DynamicSparseMatrix
+  : public SparseMatrixBase<DynamicSparseMatrix<_Scalar, _Options, _StorageIndex> >
+{
+    typedef SparseMatrixBase<DynamicSparseMatrix> Base;
+    using Base::convert_index;
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(DynamicSparseMatrix)
+    // FIXME: why are these operator already alvailable ???
+    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, +=)
+    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, -=)
+    typedef MappedSparseMatrix<Scalar,Flags> Map;
+    using Base::IsRowMajor;
+    using Base::operator=;
+    enum {
+      Options = _Options
+    };
+
+  protected:
+
+    typedef DynamicSparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0), StorageIndex> TransposedSparseMatrix;
+
+    Index m_innerSize;
+    std::vector<internal::CompressedStorage<Scalar,StorageIndex> > m_data;
+
+  public:
+
+    inline Index rows() const { return IsRowMajor ? outerSize() : m_innerSize; }
+    inline Index cols() const { return IsRowMajor ? m_innerSize : outerSize(); }
+    inline Index innerSize() const { return m_innerSize; }
+    inline Index outerSize() const { return convert_index(m_data.size()); }
+    inline Index innerNonZeros(Index j) const { return m_data[j].size(); }
+
+    std::vector<internal::CompressedStorage<Scalar,StorageIndex> >& _data() { return m_data; }
+    const std::vector<internal::CompressedStorage<Scalar,StorageIndex> >& _data() const { return m_data; }
+
+    /** \returns the coefficient value at given position \a row, \a col
+      * This operation involes a log(rho*outer_size) binary search.
+      */
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return m_data[outer].at(inner);
+    }
+
+    /** \returns a reference to the coefficient value at given position \a row, \a col
+      * This operation involes a log(rho*outer_size) binary search. If the coefficient does not
+      * exist yet, then a sorted insertion into a sequential buffer is performed.
+      */
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return m_data[outer].atWithInsertion(inner);
+    }
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    void setZero()
+    {
+      for (Index j=0; j<outerSize(); ++j)
+        m_data[j].clear();
+    }
+
+    /** \returns the number of non zero coefficients */
+    Index nonZeros() const
+    {
+      Index res = 0;
+      for (Index j=0; j<outerSize(); ++j)
+        res += m_data[j].size();
+      return res;
+    }
+
+
+
+    void reserve(Index reserveSize = 1000)
+    {
+      if (outerSize()>0)
+      {
+        Index reserveSizePerVector = (std::max)(reserveSize/outerSize(),Index(4));
+        for (Index j=0; j<outerSize(); ++j)
+        {
+          m_data[j].reserve(reserveSizePerVector);
+        }
+      }
+    }
+
+    /** Does nothing: provided for compatibility with SparseMatrix */
+    inline void startVec(Index /*outer*/) {}
+
+    /** \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+      * - the nonzero does not already exist
+      * - the new coefficient is the last one of the given inner vector.
+      *
+      * \sa insert, insertBackByOuterInner */
+    inline Scalar& insertBack(Index row, Index col)
+    {
+      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
+    }
+
+    /** \sa insertBack */
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(outer<Index(m_data.size()) && inner<m_innerSize && "out of range");
+      eigen_assert(((m_data[outer].size()==0) || (m_data[outer].index(m_data[outer].size()-1)<inner))
+                && "wrong sorted insertion");
+      m_data[outer].append(0, inner);
+      return m_data[outer].value(m_data[outer].size()-1);
+    }
+
+    inline Scalar& insert(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index startId = 0;
+      Index id = static_cast<Index>(m_data[outer].size()) - 1;
+      m_data[outer].resize(id+2,1);
+
+      while ( (id >= startId) && (m_data[outer].index(id) > inner) )
+      {
+        m_data[outer].index(id+1) = m_data[outer].index(id);
+        m_data[outer].value(id+1) = m_data[outer].value(id);
+        --id;
+      }
+      m_data[outer].index(id+1) = inner;
+      m_data[outer].value(id+1) = 0;
+      return m_data[outer].value(id+1);
+    }
+
+    /** Does nothing: provided for compatibility with SparseMatrix */
+    inline void finalize() {}
+
+    /** Suppress all nonzeros which are smaller than \a reference under the tolerance \a epsilon */
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      for (Index j=0; j<outerSize(); ++j)
+        m_data[j].prune(reference,epsilon);
+    }
+
+    /** Resize the matrix without preserving the data (the matrix is set to zero)
+      */
+    void resize(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      m_innerSize = convert_index(IsRowMajor ? cols : rows);
+      setZero();
+      if (Index(m_data.size()) != outerSize)
+      {
+        m_data.resize(outerSize);
+      }
+    }
+
+    void resizeAndKeepData(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      const Index innerSize = IsRowMajor ? cols : rows;
+      if (m_innerSize>innerSize)
+      {
+        // remove all coefficients with innerCoord>=innerSize
+        // TODO
+        //std::cerr << "not implemented yet\n";
+        exit(2);
+      }
+      if (m_data.size() != outerSize)
+      {
+        m_data.resize(outerSize);
+      }
+    }
+
+    /** The class DynamicSparseMatrix is deprecated */
+    EIGEN_DEPRECATED inline DynamicSparseMatrix()
+      : m_innerSize(0), m_data(0)
+    {
+      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      #endif
+      eigen_assert(innerSize()==0 && outerSize()==0);
+    }
+
+    /** The class DynamicSparseMatrix is deprecated */
+    EIGEN_DEPRECATED inline DynamicSparseMatrix(Index rows, Index cols)
+      : m_innerSize(0)
+    {
+      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      #endif
+      resize(rows, cols);
+    }
+
+    /** The class DynamicSparseMatrix is deprecated */
+    template<typename OtherDerived>
+    EIGEN_DEPRECATED explicit inline DynamicSparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_innerSize(0)
+    {
+      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      #endif
+      Base::operator=(other.derived());
+    }
+
+    inline DynamicSparseMatrix(const DynamicSparseMatrix& other)
+      : Base(), m_innerSize(0)
+    {
+      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      #endif
+      *this = other.derived();
+    }
+
+    inline void swap(DynamicSparseMatrix& other)
+    {
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      std::swap(m_innerSize, other.m_innerSize);
+      //std::swap(m_outerSize, other.m_outerSize);
+      m_data.swap(other.m_data);
+    }
+
+    inline DynamicSparseMatrix& operator=(const DynamicSparseMatrix& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.rows(), other.cols());
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    /** Destructor */
+    inline ~DynamicSparseMatrix() {}
+
+  public:
+
+    /** \deprecated
+      * Set the matrix to zero and reserve the memory for \a reserveSize nonzero coefficients. */
+    EIGEN_DEPRECATED void startFill(Index reserveSize = 1000)
+    {
+      setZero();
+      reserve(reserveSize);
+    }
+
+    /** \deprecated use insert()
+      * inserts a nonzero coefficient at given coordinates \a row, \a col and returns its reference assuming that:
+      *  1 - the coefficient does not exist yet
+      *  2 - this the coefficient with greater inner coordinate for the given outer coordinate.
+      * In other words, assuming \c *this is column-major, then there must not exists any nonzero coefficient of coordinates
+      * \c i \c x \a col such that \c i >= \a row. Otherwise the matrix is invalid.
+      *
+      * \see fillrand(), coeffRef()
+      */
+    EIGEN_DEPRECATED Scalar& fill(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return insertBack(outer,inner);
+    }
+
+    /** \deprecated use insert()
+      * Like fill() but with random inner coordinates.
+      * Compared to the generic coeffRef(), the unique limitation is that we assume
+      * the coefficient does not exist yet.
+      */
+    EIGEN_DEPRECATED Scalar& fillrand(Index row, Index col)
+    {
+      return insert(row,col);
+    }
+
+    /** \deprecated use finalize()
+      * Does nothing. Provided for compatibility with SparseMatrix. */
+    EIGEN_DEPRECATED void endFill() {}
+    
+#   ifdef EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
+#     include EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
+#   endif
+ };
+
+template<typename Scalar, int _Options, typename _StorageIndex>
+class DynamicSparseMatrix<Scalar,_Options,_StorageIndex>::InnerIterator : public SparseVector<Scalar,_Options,_StorageIndex>::InnerIterator
+{
+    typedef typename SparseVector<Scalar,_Options,_StorageIndex>::InnerIterator Base;
+  public:
+    InnerIterator(const DynamicSparseMatrix& mat, Index outer)
+      : Base(mat.m_data[outer]), m_outer(outer)
+    {}
+
+    inline Index row() const { return IsRowMajor ? m_outer : Base::index(); }
+    inline Index col() const { return IsRowMajor ? Base::index() : m_outer; }
+    inline Index outer() const { return m_outer; }
+
+  protected:
+    const Index m_outer;
+};
+
+template<typename Scalar, int _Options, typename _StorageIndex>
+class DynamicSparseMatrix<Scalar,_Options,_StorageIndex>::ReverseInnerIterator : public SparseVector<Scalar,_Options,_StorageIndex>::ReverseInnerIterator
+{
+    typedef typename SparseVector<Scalar,_Options,_StorageIndex>::ReverseInnerIterator Base;
+  public:
+    ReverseInnerIterator(const DynamicSparseMatrix& mat, Index outer)
+      : Base(mat.m_data[outer]), m_outer(outer)
+    {}
+
+    inline Index row() const { return IsRowMajor ? m_outer : Base::index(); }
+    inline Index col() const { return IsRowMajor ? Base::index() : m_outer; }
+    inline Index outer() const { return m_outer; }
+
+  protected:
+    const Index m_outer;
+};
+
+namespace internal {
+
+template<typename _Scalar, int _Options, typename _StorageIndex>
+struct evaluator<DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> >
+  : evaluator_base<DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> >
+{
+  typedef _Scalar Scalar;
+  typedef DynamicSparseMatrix<_Scalar,_Options,_StorageIndex> SparseMatrixType;
+  typedef typename SparseMatrixType::InnerIterator InnerIterator;
+  typedef typename SparseMatrixType::ReverseInnerIterator ReverseInnerIterator;
+  
+  enum {
+    CoeffReadCost = NumTraits<_Scalar>::ReadCost,
+    Flags = SparseMatrixType::Flags
+  };
+  
+  evaluator() : m_matrix(0) {}
+  evaluator(const SparseMatrixType &mat) : m_matrix(&mat) {}
+  
+  operator SparseMatrixType&() { return m_matrix->const_cast_derived(); }
+  operator const SparseMatrixType&() const { return *m_matrix; }
+  
+  Scalar coeff(Index row, Index col) const { return m_matrix->coeff(row,col); }
+  
+  Index nonZerosEstimate() const { return m_matrix->nonZeros(); }
+
+  const SparseMatrixType *m_matrix;
+};
+
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_DYNAMIC_SPARSEMATRIX_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
new file mode 100644
index 0000000..dd786d5
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MarketIO.h
@@ -0,0 +1,282 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MARKET_IO_H
+#define EIGEN_SPARSE_MARKET_IO_H
+
+#include <iostream>
+#include <vector>
+
+namespace Eigen { 
+
+namespace internal 
+{
+  template <typename Scalar, typename StorageIndex>
+  inline void GetMarketLine (const char* line, StorageIndex& i, StorageIndex& j, Scalar& value)
+  {
+    std::stringstream sline(line);
+    sline >> i >> j >> value;
+  }
+
+  template<> inline void GetMarketLine (const char* line, int& i, int& j, float& value)
+  { std::sscanf(line, "%d %d %g", &i, &j, &value); }
+
+  template<> inline void GetMarketLine (const char* line, int& i, int& j, double& value)
+  { std::sscanf(line, "%d %d %lg", &i, &j, &value); }
+
+  template<> inline void GetMarketLine (const char* line, int& i, int& j, std::complex<float>& value)
+  { std::sscanf(line, "%d %d %g %g", &i, &j, &numext::real_ref(value), &numext::imag_ref(value)); }
+
+  template<> inline void GetMarketLine (const char* line, int& i, int& j, std::complex<double>& value)
+  { std::sscanf(line, "%d %d %lg %lg", &i, &j, &numext::real_ref(value), &numext::imag_ref(value)); }
+
+  template <typename Scalar, typename StorageIndex>
+  inline void GetMarketLine (const char* line, StorageIndex& i, StorageIndex& j, std::complex<Scalar>& value)
+  {
+    std::stringstream sline(line);
+    Scalar valR, valI;
+    sline >> i >> j >> valR >> valI;
+    value = std::complex<Scalar>(valR,valI);
+  }
+
+  template <typename RealScalar>
+  inline void  GetVectorElt (const std::string& line, RealScalar& val)
+  {
+    std::istringstream newline(line);
+    newline >> val;  
+  }
+
+  template <typename RealScalar>
+  inline void GetVectorElt (const std::string& line, std::complex<RealScalar>& val)
+  {
+    RealScalar valR, valI; 
+    std::istringstream newline(line);
+    newline >> valR >> valI; 
+    val = std::complex<RealScalar>(valR, valI);
+  }
+  
+  template<typename Scalar>
+  inline void putMarketHeader(std::string& header,int sym)
+  {
+    header= "%%MatrixMarket matrix coordinate ";
+    if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
+    {
+      header += " complex"; 
+      if(sym == Symmetric) header += " symmetric";
+      else if (sym == SelfAdjoint) header += " Hermitian";
+      else header += " general";
+    }
+    else
+    {
+      header += " real"; 
+      if(sym == Symmetric) header += " symmetric";
+      else header += " general";
+    }
+  }
+
+  template<typename Scalar, typename StorageIndex>
+  inline void PutMatrixElt(Scalar value, StorageIndex row, StorageIndex col, std::ofstream& out)
+  {
+    out << row << " "<< col << " " << value << "\n";
+  }
+  template<typename Scalar, typename StorageIndex>
+  inline void PutMatrixElt(std::complex<Scalar> value, StorageIndex row, StorageIndex col, std::ofstream& out)
+  {
+    out << row << " " << col << " " << value.real() << " " << value.imag() << "\n";
+  }
+
+
+  template<typename Scalar>
+  inline void putVectorElt(Scalar value, std::ofstream& out)
+  {
+    out << value << "\n"; 
+  }
+  template<typename Scalar>
+  inline void putVectorElt(std::complex<Scalar> value, std::ofstream& out)
+  {
+    out << value.real() << " " << value.imag()<< "\n"; 
+  }
+
+} // end namespace internal
+
+inline bool getMarketHeader(const std::string& filename, int& sym, bool& iscomplex, bool& isvector)
+{
+  sym = 0; 
+  iscomplex = false;
+  isvector = false;
+  std::ifstream in(filename.c_str(),std::ios::in);
+  if(!in)
+    return false;
+  
+  std::string line; 
+  // The matrix header is always the first line in the file 
+  std::getline(in, line); eigen_assert(in.good());
+  
+  std::stringstream fmtline(line); 
+  std::string substr[5];
+  fmtline>> substr[0] >> substr[1] >> substr[2] >> substr[3] >> substr[4];
+  if(substr[2].compare("array") == 0) isvector = true;
+  if(substr[3].compare("complex") == 0) iscomplex = true;
+  if(substr[4].compare("symmetric") == 0) sym = Symmetric;
+  else if (substr[4].compare("Hermitian") == 0) sym = SelfAdjoint;
+  
+  return true;
+}
+  
+template<typename SparseMatrixType>
+bool loadMarket(SparseMatrixType& mat, const std::string& filename)
+{
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::StorageIndex StorageIndex;
+  std::ifstream input(filename.c_str(),std::ios::in);
+  if(!input)
+    return false;
+
+  char rdbuffer[4096];
+  input.rdbuf()->pubsetbuf(rdbuffer, 4096);
+  
+  const int maxBuffersize = 2048;
+  char buffer[maxBuffersize];
+  
+  bool readsizes = false;
+
+  typedef Triplet<Scalar,StorageIndex> T;
+  std::vector<T> elements;
+  
+  Index M(-1), N(-1), NNZ(-1);
+  Index count = 0;
+  while(input.getline(buffer, maxBuffersize))
+  {
+    // skip comments   
+    //NOTE An appropriate test should be done on the header to get the  symmetry
+    if(buffer[0]=='%')
+      continue;
+
+    if(!readsizes)
+    {
+      std::stringstream line(buffer);
+      line >> M >> N >> NNZ;
+      if(M > 0 && N > 0)
+      {
+        readsizes = true;
+        mat.resize(M,N);
+        mat.reserve(NNZ);
+      }
+    }
+    else
+    { 
+      StorageIndex i(-1), j(-1);
+      Scalar value; 
+      internal::GetMarketLine(buffer, i, j, value);
+
+      i--;
+      j--;
+      if(i>=0 && j>=0 && i<M && j<N)
+      {
+        ++count;
+        elements.push_back(T(i,j,value));
+      }
+      else
+        std::cerr << "Invalid read: " << i << "," << j << "\n";        
+    }
+  }
+
+  mat.setFromTriplets(elements.begin(), elements.end());
+  if(count!=NNZ)
+    std::cerr << count << "!=" << NNZ << "\n";
+  
+  input.close();
+  return true;
+}
+
+template<typename VectorType>
+bool loadMarketVector(VectorType& vec, const std::string& filename)
+{
+   typedef typename VectorType::Scalar Scalar;
+  std::ifstream in(filename.c_str(), std::ios::in);
+  if(!in)
+    return false;
+  
+  std::string line; 
+  int n(0), col(0); 
+  do 
+  { // Skip comments
+    std::getline(in, line); eigen_assert(in.good());
+  } while (line[0] == '%');
+  std::istringstream newline(line);
+  newline  >> n >> col; 
+  eigen_assert(n>0 && col>0);
+  vec.resize(n);
+  int i = 0; 
+  Scalar value; 
+  while ( std::getline(in, line) && (i < n) ){
+    internal::GetVectorElt(line, value); 
+    vec(i++) = value; 
+  }
+  in.close();
+  if (i!=n){
+    std::cerr<< "Unable to read all elements from file " << filename << "\n";
+    return false;
+  }
+  return true;
+}
+
+template<typename SparseMatrixType>
+bool saveMarket(const SparseMatrixType& mat, const std::string& filename, int sym = 0)
+{
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::RealScalar RealScalar;
+  std::ofstream out(filename.c_str(),std::ios::out);
+  if(!out)
+    return false;
+  
+  out.flags(std::ios_base::scientific);
+  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
+  std::string header; 
+  internal::putMarketHeader<Scalar>(header, sym); 
+  out << header << std::endl; 
+  out << mat.rows() << " " << mat.cols() << " " << mat.nonZeros() << "\n";
+  int count = 0;
+  for(int j=0; j<mat.outerSize(); ++j)
+    for(typename SparseMatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      ++ count;
+      internal::PutMatrixElt(it.value(), it.row()+1, it.col()+1, out);
+    }
+  out.close();
+  return true;
+}
+
+template<typename VectorType>
+bool saveMarketVector (const VectorType& vec, const std::string& filename)
+{
+ typedef typename VectorType::Scalar Scalar;
+ typedef typename VectorType::RealScalar RealScalar;
+ std::ofstream out(filename.c_str(),std::ios::out);
+  if(!out)
+    return false;
+  
+  out.flags(std::ios_base::scientific);
+  out.precision(std::numeric_limits<RealScalar>::digits10 + 2);
+  if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
+      out << "%%MatrixMarket matrix array complex general\n"; 
+  else
+    out << "%%MatrixMarket matrix array real general\n"; 
+  out << vec.size() << " "<< 1 << "\n";
+  for (int i=0; i < vec.size(); i++){
+    internal::putVectorElt(vec(i), out); 
+  }
+  out.close();
+  return true; 
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_MARKET_IO_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
new file mode 100644
index 0000000..02916ea
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/MatrixMarketIterator.h
@@ -0,0 +1,247 @@
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire NUENTSA WAKAM <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BROWSE_MATRICES_H
+#define EIGEN_BROWSE_MATRICES_H
+
+namespace Eigen {
+
+enum {
+  SPD = 0x100,
+  NonSymmetric = 0x0
+}; 
+
+/** 
+ * @brief Iterator to browse matrices from a specified folder
+ * 
+ * This is used to load all the matrices from a folder. 
+ * The matrices should be in Matrix Market format
+ * It is assumed that the matrices are named as matname.mtx
+ * and matname_SPD.mtx if the matrix is Symmetric and positive definite (or Hermitian)
+ * The right hand side vectors are loaded as well, if they exist.
+ * They should be named as matname_b.mtx. 
+ * Note that the right hand side for a SPD matrix is named as matname_SPD_b.mtx
+ * 
+ * Sometimes a reference solution is available. In this case, it should be named as matname_x.mtx
+ * 
+ * Sample code
+ * \code
+ * 
+ * \endcode
+ * 
+ * \tparam Scalar The scalar type 
+ */
+template <typename Scalar>
+class MatrixMarketIterator 
+{
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+  public:
+    typedef Matrix<Scalar,Dynamic,1> VectorType; 
+    typedef SparseMatrix<Scalar,ColMajor> MatrixType; 
+  
+  public:
+    MatrixMarketIterator(const std::string &folder)
+      : m_sym(0), m_isvalid(false), m_matIsLoaded(false), m_hasRhs(false), m_hasrefX(false), m_folder(folder)
+    {
+      m_folder_id = opendir(folder.c_str());
+      if(m_folder_id)
+        Getnextvalidmatrix();
+    }
+    
+    ~MatrixMarketIterator()
+    {
+      if (m_folder_id) closedir(m_folder_id); 
+    }
+    
+    inline MatrixMarketIterator& operator++()
+    {
+      m_matIsLoaded = false;
+      m_hasrefX = false;
+      m_hasRhs = false;
+      Getnextvalidmatrix();
+      return *this;
+    }
+    inline operator bool() const { return m_isvalid;}
+    
+    /** Return the sparse matrix corresponding to the current file */
+    inline MatrixType& matrix() 
+    { 
+      // Read the matrix
+      if (m_matIsLoaded) return m_mat;
+      
+      std::string matrix_file = m_folder + "/" + m_matname + ".mtx";
+      if ( !loadMarket(m_mat, matrix_file)) 
+      {
+        std::cerr << "Warning loadMarket failed when loading \"" << matrix_file << "\"" << std::endl;
+        m_matIsLoaded = false;
+        return m_mat;
+      }
+      m_matIsLoaded = true; 
+
+      if (m_sym != NonSymmetric) 
+      {
+        // Check whether we need to restore a full matrix:
+        RealScalar diag_norm  = m_mat.diagonal().norm();
+        RealScalar lower_norm = m_mat.template triangularView<Lower>().norm();
+        RealScalar upper_norm = m_mat.template triangularView<Upper>().norm();
+        if(lower_norm>diag_norm && upper_norm==diag_norm)
+        {
+          // only the lower part is stored
+          MatrixType tmp(m_mat);
+          m_mat = tmp.template selfadjointView<Lower>();
+        }
+        else if(upper_norm>diag_norm && lower_norm==diag_norm)
+        {
+          // only the upper part is stored
+          MatrixType tmp(m_mat);
+          m_mat = tmp.template selfadjointView<Upper>();
+        }
+      }
+      return m_mat; 
+    }
+    
+    /** Return the right hand side corresponding to the current matrix. 
+     * If the rhs file is not provided, a random rhs is generated
+     */
+    inline VectorType& rhs() 
+    { 
+       // Get the right hand side
+      if (m_hasRhs) return m_rhs;
+      
+      std::string rhs_file;
+      rhs_file = m_folder + "/" + m_matname + "_b.mtx"; // The pattern is matname_b.mtx
+      m_hasRhs = Fileexists(rhs_file);
+      if (m_hasRhs)
+      {
+        m_rhs.resize(m_mat.cols());
+        m_hasRhs = loadMarketVector(m_rhs, rhs_file);
+      }
+      if (!m_hasRhs)
+      {
+        // Generate a random right hand side
+        if (!m_matIsLoaded) this->matrix(); 
+        m_refX.resize(m_mat.cols());
+        m_refX.setRandom();
+        m_rhs = m_mat * m_refX;
+        m_hasrefX = true;
+        m_hasRhs = true;
+      }
+      return m_rhs; 
+    }
+    
+    /** Return a reference solution
+     * If it is not provided and if the right hand side is not available
+     * then refX is randomly generated such that A*refX = b 
+     * where A and b are the matrix and the rhs. 
+     * Note that when a rhs is provided, refX is not available 
+     */
+    inline VectorType& refX() 
+    { 
+      // Check if a reference solution is provided
+      if (m_hasrefX) return m_refX;
+      
+      std::string lhs_file;
+      lhs_file = m_folder + "/" + m_matname + "_x.mtx"; 
+      m_hasrefX = Fileexists(lhs_file);
+      if (m_hasrefX)
+      {
+        m_refX.resize(m_mat.cols());
+        m_hasrefX = loadMarketVector(m_refX, lhs_file);
+      }
+      else
+        m_refX.resize(0);
+      return m_refX; 
+    }
+    
+    inline std::string& matname() { return m_matname; }
+    
+    inline int sym() { return m_sym; }
+    
+    bool hasRhs() {return m_hasRhs; }
+    bool hasrefX() {return m_hasrefX; }
+    bool isFolderValid() { return bool(m_folder_id); }
+    
+  protected:
+    
+    inline bool Fileexists(std::string file)
+    {
+      std::ifstream file_id(file.c_str());
+      if (!file_id.good() ) 
+      {
+        return false;
+      }
+      else 
+      {
+        file_id.close();
+        return true;
+      }
+    }
+    
+    void Getnextvalidmatrix( )
+    {
+      m_isvalid = false;
+      // Here, we return with the next valid matrix in the folder
+      while ( (m_curs_id = readdir(m_folder_id)) != NULL) {
+        m_isvalid = false;
+        std::string curfile;
+        curfile = m_folder + "/" + m_curs_id->d_name;
+        // Discard if it is a folder
+        if (m_curs_id->d_type == DT_DIR) continue; //FIXME This may not be available on non BSD systems
+//         struct stat st_buf; 
+//         stat (curfile.c_str(), &st_buf);
+//         if (S_ISDIR(st_buf.st_mode)) continue;
+        
+        // Determine from the header if it is a matrix or a right hand side 
+        bool isvector,iscomplex=false;
+        if(!getMarketHeader(curfile,m_sym,iscomplex,isvector)) continue;
+        if(isvector) continue;
+        if (!iscomplex)
+        {
+          if(internal::is_same<Scalar, std::complex<float> >::value || internal::is_same<Scalar, std::complex<double> >::value)
+            continue; 
+        }
+        if (iscomplex)
+        {
+          if(internal::is_same<Scalar, float>::value || internal::is_same<Scalar, double>::value)
+            continue; 
+        }
+        
+        
+        // Get the matrix name
+        std::string filename = m_curs_id->d_name;
+        m_matname = filename.substr(0, filename.length()-4); 
+        
+        // Find if the matrix is SPD 
+        size_t found = m_matname.find("SPD");
+        if( (found!=std::string::npos) && (m_sym != NonSymmetric) )
+          m_sym = SPD;
+       
+        m_isvalid = true;
+        break; 
+      }
+    }
+    int m_sym; // Symmetry of the matrix
+    MatrixType m_mat; // Current matrix  
+    VectorType m_rhs;  // Current vector
+    VectorType m_refX; // The reference solution, if exists
+    std::string m_matname; // Matrix Name
+    bool m_isvalid; 
+    bool m_matIsLoaded; // Determine if the matrix has already been loaded from the file
+    bool m_hasRhs; // The right hand side exists
+    bool m_hasrefX; // A reference solution is provided
+    std::string m_folder;
+    DIR * m_folder_id;
+    struct dirent *m_curs_id; 
+    
+};
+
+} // end namespace Eigen
+
+#endif
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
new file mode 100644
index 0000000..985702b
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SparseExtra/RandomSetter.h
@@ -0,0 +1,349 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RANDOMSETTER_H
+#define EIGEN_RANDOMSETTER_H
+
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+// Ensure the ::google namespace exists, required for checking existence of 
+// ::google::dense_hash_map and ::google::sparse_hash_map.
+namespace google {}
+#endif
+
+namespace Eigen {
+
+/** Represents a std::map
+  *
+  * \see RandomSetter
+  */
+template<typename Scalar> struct StdMapTraits
+{
+  typedef int KeyType;
+  typedef std::map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 1
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+
+#ifdef EIGEN_UNORDERED_MAP_SUPPORT
+/** Represents a std::unordered_map
+  *
+  * To use it you need to both define EIGEN_UNORDERED_MAP_SUPPORT and include the unordered_map header file
+  * yourself making sure that unordered_map is defined in the std namespace.
+  *
+  * For instance, with current version of gcc you can either enable C++0x standard (-std=c++0x) or do:
+  * \code
+  * #include <tr1/unordered_map>
+  * #define EIGEN_UNORDERED_MAP_SUPPORT
+  * namespace std {
+  *   using std::tr1::unordered_map;
+  * }
+  * \endcode
+  *
+  * \see RandomSetter
+  */
+template<typename Scalar> struct StdUnorderedMapTraits
+{
+  typedef int KeyType;
+  typedef std::unordered_map<KeyType,Scalar> Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+#endif // EIGEN_UNORDERED_MAP_SUPPORT
+
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+
+namespace google {
+  
+// Namespace work-around, since sometimes dense_hash_map and sparse_hash_map
+// are in the global namespace, and other times they are under ::google.
+using namespace ::google;
+
+template<typename KeyType, typename Scalar>
+struct DenseHashMap {
+  typedef dense_hash_map<KeyType, Scalar> type;
+};
+
+template<typename KeyType, typename Scalar>
+struct SparseHashMap {
+  typedef sparse_hash_map<KeyType, Scalar> type;
+};
+
+} // namespace google
+
+/** Represents a google::dense_hash_map
+  *
+  * \see RandomSetter
+  */
+template<typename Scalar> struct GoogleDenseHashMapTraits
+{
+  typedef int KeyType;
+  typedef typename google::DenseHashMap<KeyType,Scalar>::type Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type& map, const KeyType& k)
+  { map.set_empty_key(k); }
+};
+
+/** Represents a google::sparse_hash_map
+  *
+  * \see RandomSetter
+  */
+template<typename Scalar> struct GoogleSparseHashMapTraits
+{
+  typedef int KeyType;
+  typedef typename google::SparseHashMap<KeyType,Scalar>::type Type;
+  enum {
+    IsSorted = 0
+  };
+
+  static void setInvalidKey(Type&, const KeyType&) {}
+};
+#endif
+
+/** \class RandomSetter
+  *
+  * \brief The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
+  *
+  * \tparam SparseMatrixType the type of the sparse matrix we are updating
+  * \tparam MapTraits a traits class representing the map implementation used for the temporary sparse storage.
+  *                  Its default value depends on the system.
+  * \tparam OuterPacketBits defines the number of rows (or columns) manage by a single map object
+  *                        as a power of two exponent.
+  *
+  * This class temporarily represents a sparse matrix object using a generic map implementation allowing for
+  * efficient random access. The conversion from the compressed representation to a hash_map object is performed
+  * in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy
+  * suggest the use of nested blocks as in this example:
+  *
+  * \code
+  * SparseMatrix<double> m(rows,cols);
+  * {
+  *   RandomSetter<SparseMatrix<double> > w(m);
+  *   // don't use m but w instead with read/write random access to the coefficients:
+  *   for(;;)
+  *     w(rand(),rand()) = rand;
+  * }
+  * // when w is deleted, the data are copied back to m
+  * // and m is ready to use.
+  * \endcode
+  *
+  * Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would
+  * involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter
+  * use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order.
+  * To reach optimal performance, this value should be adjusted according to the average number of nonzeros
+  * per rows/columns.
+  *
+  * The possible values for the template parameter MapTraits are:
+  *  - \b StdMapTraits: corresponds to std::map. (does not perform very well)
+  *  - \b GnuHashMapTraits: corresponds to __gnu_cxx::hash_map (available only with GCC)
+  *  - \b GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)
+  *  - \b GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)
+  *
+  * The default map implementation depends on the availability, and the preferred order is:
+  * GoogleSparseHashMapTraits, GnuHashMapTraits, and finally StdMapTraits.
+  *
+  * For performance and memory consumption reasons it is highly recommended to use one of
+  * Google's hash_map implementations. To enable the support for them, you must define
+  * EIGEN_GOOGLEHASH_SUPPORT. This will include both <google/dense_hash_map> and
+  * <google/sparse_hash_map> for you.
+  *
+  * \see https://github.com/sparsehash/sparsehash
+  */
+template<typename SparseMatrixType,
+         template <typename T> class MapTraits =
+#if defined(EIGEN_GOOGLEHASH_SUPPORT)
+          GoogleDenseHashMapTraits
+#elif defined(_HASH_MAP)
+          GnuHashMapTraits
+#else
+          StdMapTraits
+#endif
+         ,int OuterPacketBits = 6>
+class RandomSetter
+{
+    typedef typename SparseMatrixType::Scalar Scalar;
+    typedef typename SparseMatrixType::StorageIndex StorageIndex;
+
+    struct ScalarWrapper
+    {
+      ScalarWrapper() : value(0) {}
+      Scalar value;
+    };
+    typedef typename MapTraits<ScalarWrapper>::KeyType KeyType;
+    typedef typename MapTraits<ScalarWrapper>::Type HashMapType;
+    static const int OuterPacketMask = (1 << OuterPacketBits) - 1;
+    enum {
+      SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
+      TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
+      SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor
+    };
+
+  public:
+
+    /** Constructs a random setter object from the sparse matrix \a target
+      *
+      * Note that the initial value of \a target are imported. If you want to re-set
+      * a sparse matrix from scratch, then you must set it to zero first using the
+      * setZero() function.
+      */
+    inline RandomSetter(SparseMatrixType& target)
+      : mp_target(&target)
+    {
+      const Index outerSize = SwapStorage ? target.innerSize() : target.outerSize();
+      const Index innerSize = SwapStorage ? target.outerSize() : target.innerSize();
+      m_outerPackets = outerSize >> OuterPacketBits;
+      if (outerSize&OuterPacketMask)
+        m_outerPackets += 1;
+      m_hashmaps = new HashMapType[m_outerPackets];
+      // compute number of bits needed to store inner indices
+      Index aux = innerSize - 1;
+      m_keyBitsOffset = 0;
+      while (aux)
+      {
+        ++m_keyBitsOffset;
+        aux = aux >> 1;
+      }
+      KeyType ik = (1<<(OuterPacketBits+m_keyBitsOffset));
+      for (Index k=0; k<m_outerPackets; ++k)
+        MapTraits<ScalarWrapper>::setInvalidKey(m_hashmaps[k],ik);
+
+      // insert current coeffs
+      for (Index j=0; j<mp_target->outerSize(); ++j)
+        for (typename SparseMatrixType::InnerIterator it(*mp_target,j); it; ++it)
+          (*this)(TargetRowMajor?j:it.index(), TargetRowMajor?it.index():j) = it.value();
+    }
+
+    /** Destructor updating back the sparse matrix target */
+    ~RandomSetter()
+    {
+      KeyType keyBitsMask = (1<<m_keyBitsOffset)-1;
+      if (!SwapStorage) // also means the map is sorted
+      {
+        mp_target->setZero();
+        mp_target->makeCompressed();
+        mp_target->reserve(nonZeros());
+        Index prevOuter = -1;
+        for (Index k=0; k<m_outerPackets; ++k)
+        {
+          const Index outerOffset = (1<<OuterPacketBits) * k;
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const Index outer = (it->first >> m_keyBitsOffset) + outerOffset;
+            const Index inner = it->first & keyBitsMask;
+            if (prevOuter!=outer)
+            {
+              for (Index j=prevOuter+1;j<=outer;++j)
+                mp_target->startVec(j);
+              prevOuter = outer;
+            }
+            mp_target->insertBackByOuterInner(outer, inner) = it->second.value;
+          }
+        }
+        mp_target->finalize();
+      }
+      else
+      {
+        VectorXi positions(mp_target->outerSize());
+        positions.setZero();
+        // pass 1
+        for (Index k=0; k<m_outerPackets; ++k)
+        {
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const Index outer = it->first & keyBitsMask;
+            ++positions[outer];
+          }
+        }
+        // prefix sum
+        StorageIndex count = 0;
+        for (Index j=0; j<mp_target->outerSize(); ++j)
+        {
+          StorageIndex tmp = positions[j];
+          mp_target->outerIndexPtr()[j] = count;
+          positions[j] = count;
+          count += tmp;
+        }
+        mp_target->makeCompressed();
+        mp_target->outerIndexPtr()[mp_target->outerSize()] = count;
+        mp_target->resizeNonZeros(count);
+        // pass 2
+        for (Index k=0; k<m_outerPackets; ++k)
+        {
+          const Index outerOffset = (1<<OuterPacketBits) * k;
+          typename HashMapType::iterator end = m_hashmaps[k].end();
+          for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
+          {
+            const Index inner = (it->first >> m_keyBitsOffset) + outerOffset;
+            const Index outer = it->first & keyBitsMask;
+            // sorted insertion
+            // Note that we have to deal with at most 2^OuterPacketBits unsorted coefficients,
+            // moreover those 2^OuterPacketBits coeffs are likely to be sparse, an so only a
+            // small fraction of them have to be sorted, whence the following simple procedure:
+            Index posStart = mp_target->outerIndexPtr()[outer];
+            Index i = (positions[outer]++) - 1;
+            while ( (i >= posStart) && (mp_target->innerIndexPtr()[i] > inner) )
+            {
+              mp_target->valuePtr()[i+1] = mp_target->valuePtr()[i];
+              mp_target->innerIndexPtr()[i+1] = mp_target->innerIndexPtr()[i];
+              --i;
+            }
+            mp_target->innerIndexPtr()[i+1] = internal::convert_index<StorageIndex>(inner);
+            mp_target->valuePtr()[i+1] = it->second.value;
+          }
+        }
+      }
+      delete[] m_hashmaps;
+    }
+
+    /** \returns a reference to the coefficient at given coordinates \a row, \a col */
+    Scalar& operator() (Index row, Index col)
+    {
+      const Index outer = SetterRowMajor ? row : col;
+      const Index inner = SetterRowMajor ? col : row;
+      const Index outerMajor = outer >> OuterPacketBits; // index of the packet/map
+      const Index outerMinor = outer & OuterPacketMask;  // index of the inner vector in the packet
+      const KeyType key = internal::convert_index<KeyType>((outerMinor<<m_keyBitsOffset) | inner);
+      return m_hashmaps[outerMajor][key].value;
+    }
+
+    /** \returns the number of non zero coefficients
+      *
+      * \note According to the underlying map/hash_map implementation,
+      * this function might be quite expensive.
+      */
+    Index nonZeros() const
+    {
+      Index nz = 0;
+      for (Index k=0; k<m_outerPackets; ++k)
+        nz += static_cast<Index>(m_hashmaps[k].size());
+      return nz;
+    }
+
+
+  protected:
+
+    HashMapType* m_hashmaps;
+    SparseMatrixType* mp_target;
+    Index m_outerPackets;
+    unsigned char m_keyBitsOffset;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_RANDOMSETTER_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
new file mode 100644
index 0000000..41d2bf6
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsArrayAPI.h
@@ -0,0 +1,286 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
+#define EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
+
+namespace Eigen {
+
+/** \returns an expression of the coefficient-wise i0(\a x) to the given
+ * arrays.
+  *
+  * It returns the modified Bessel function of the first kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of i0(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_i0()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>, const Derived>
+bessel_i0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_i0_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i0e(\a x) to the given
+ * arrays.
+  *
+  * It returns the exponentially scaled modified Bessel
+  * function of the first kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of i0e(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_i0e()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>, const Derived>
+bessel_i0e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_i0e_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i1(\a x) to the given
+ * arrays.
+  *
+  * It returns the modified Bessel function of the first kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of i1(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_i1()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>, const Derived>
+bessel_i1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_i1_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise i1e(\a x) to the given
+ * arrays.
+  *
+  * It returns the exponentially scaled modified Bessel
+  * function of the first kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of i1e(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_i1e()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>, const Derived>
+bessel_i1e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_i1e_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k0(\a x) to the given
+ * arrays.
+  *
+  * It returns the modified Bessel function of the second kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of k0(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_k0()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>, const Derived>
+bessel_k0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_k0_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k0e(\a x) to the given
+ * arrays.
+  *
+  * It returns the exponentially scaled modified Bessel
+  * function of the second kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of k0e(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_k0e()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>, const Derived>
+bessel_k0e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_k0e_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k1(\a x) to the given
+ * arrays.
+  *
+  * It returns the modified Bessel function of the second kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of k1(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_k1()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>, const Derived>
+bessel_k1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_k1_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise k1e(\a x) to the given
+ * arrays.
+  *
+  * It returns the exponentially scaled modified Bessel
+  * function of the second kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of k1e(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_k1e()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>, const Derived>
+bessel_k1e(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_k1e_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise j0(\a x) to the given
+ * arrays.
+  *
+  * It returns the Bessel function of the first kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of j0(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_j0()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>, const Derived>
+bessel_j0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_j0_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise y0(\a x) to the given
+ * arrays.
+  *
+  * It returns the Bessel function of the second kind of order zero.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of y0(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_y0()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>, const Derived>
+bessel_y0(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_y0_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise j1(\a x) to the given
+ * arrays.
+  *
+  * It returns the modified Bessel function of the first kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of j1(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_j1()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>, const Derived>
+bessel_j1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_j1_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+/** \returns an expression of the coefficient-wise y1(\a x) to the given
+ * arrays.
+  *
+  * It returns the Bessel function of the second kind of order one.
+  *
+  * \param x is the argument
+  *
+  * \note This function supports only float and double scalar types. To support
+  * other scalar types, the user has to provide implementations of y1(T) for
+  * any scalar type T to be supported.
+  *
+  * \sa ArrayBase::bessel_y1()
+  */
+template <typename Derived>
+EIGEN_STRONG_INLINE const Eigen::CwiseUnaryOp<
+    Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>, const Derived>
+bessel_y1(const Eigen::ArrayBase<Derived>& x) {
+  return Eigen::CwiseUnaryOp<
+      Eigen::internal::scalar_bessel_y1_op<typename Derived::Scalar>,
+      const Derived>(x.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_BESSELFUNCTIONS_ARRAYAPI_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
new file mode 100644
index 0000000..6049cc2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsBFloat16.h
@@ -0,0 +1,68 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_BFLOAT16_H
+#define EIGEN_BESSELFUNCTIONS_BFLOAT16_H
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i0e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_i1e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_i1e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_j0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_j1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_j1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_y0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_y1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_y1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k0e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bessel_k1e(const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::bessel_k1e(static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_BFLOAT16_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
new file mode 100644
index 0000000..8606a9f
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsFunctors.h
@@ -0,0 +1,357 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_FUNCTORS_H
+#define EIGEN_BESSELFUNCTIONS_FUNCTORS_H
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the first
+ * kind of order zero.
+ * \sa class CwiseUnaryOp, Cwise::bessel_i0()
+ */
+template <typename Scalar>
+struct scalar_bessel_i0_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i0_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i0;
+    return bessel_i0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_i0(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i0_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions. We also add
+    // the cost of an additional exp over i0e.
+    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the first kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_i0e()
+ */
+template <typename Scalar>
+struct scalar_bessel_i0e_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i0e_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i0e;
+    return bessel_i0e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_i0e(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i0e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions.
+    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the first
+ * kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_i1()
+ */
+template <typename Scalar>
+struct scalar_bessel_i1_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i1_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i1;
+    return bessel_i1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_i1(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i1_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions. We also add
+    // the cost of an additional exp over i1e.
+    Cost = 28 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the first kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_i1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_i1e_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_i1e_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_i1e;
+    return bessel_i1e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_i1e(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_i1e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=20 is computed.
+    // The cost is N multiplications and 2N additions.
+    Cost = 20 * NumTraits<Scalar>::MulCost + 40 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_j0()
+ */
+template <typename Scalar>
+struct scalar_bessel_j0_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_j0_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_j0;
+    return bessel_j0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_j0(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_j0_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine and rsqrt cost.
+    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_y0()
+ */
+template <typename Scalar>
+struct scalar_bessel_y0_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_y0_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_y0;
+    return bessel_y0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_y0(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_y0_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine, rsqrt and j0 cost.
+    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the first kind of
+ * order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_j1()
+ */
+template <typename Scalar>
+struct scalar_bessel_j1_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_j1_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_j1;
+    return bessel_j1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_j1(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_j1_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine and rsqrt cost.
+    Cost = 63 * NumTraits<Scalar>::MulCost + 48 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Bessel function of the second kind of
+ * order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_j1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_y1_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_y1_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_y1;
+    return bessel_y1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_y1(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_y1_op<Scalar> > {
+  enum {
+    // 6 polynomial of order ~N=8 is computed.
+    // The cost is N multiplications and N additions each, along with a
+    // sine, cosine, rsqrt and j1 cost.
+    Cost = 126 * NumTraits<Scalar>::MulCost + 96 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the second
+ * kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_k0()
+ */
+template <typename Scalar>
+struct scalar_bessel_k0_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k0_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k0;
+    return bessel_k0(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_k0(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k0_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i0, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the second kind of order zero
+ * \sa class CwiseUnaryOp, Cwise::bessel_k0e()
+ */
+template <typename Scalar>
+struct scalar_bessel_k0e_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k0e_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k0e;
+    return bessel_k0e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_k0e(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k0e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i0, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the modified Bessel function of the
+ * second kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_k1()
+ */
+template <typename Scalar>
+struct scalar_bessel_k1_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k1_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k1;
+    return bessel_k1(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_k1(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k1_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i1, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the exponentially scaled modified Bessel
+ * function of the second kind of order one
+ * \sa class CwiseUnaryOp, Cwise::bessel_k1e()
+ */
+template <typename Scalar>
+struct scalar_bessel_k1e_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_bessel_k1e_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& x) const {
+    using numext::bessel_k1e;
+    return bessel_k1e(x);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return internal::pbessel_k1e(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bessel_k1e_op<Scalar> > {
+  enum {
+    // On average, a Chebyshev polynomial of order N=10 is computed.
+    // The cost is N multiplications and 2N additions. In addition we compute
+    // i1, a log, exp and prsqrt and sin and cos.
+    Cost = 68 * NumTraits<Scalar>::MulCost + 88 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBessel
+  };
+};
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BESSELFUNCTIONS_FUNCTORS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
new file mode 100644
index 0000000..8930d1a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsHalf.h
@@ -0,0 +1,66 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_HALF_H
+#define EIGEN_BESSELFUNCTIONS_HALF_H
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i0e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_i1e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_i1e(static_cast<float>(x)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_j0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_j1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_j1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_y0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_y1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_y1(static_cast<float>(x)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k0(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k0e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k0e(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k1(static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bessel_k1e(const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::bessel_k1e(static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSELFUNCTIONS_HALF_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
new file mode 100644
index 0000000..24812be
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsImpl.h
@@ -0,0 +1,1959 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSEL_FUNCTIONS_H
+#define EIGEN_BESSEL_FUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+//  Parts of this code are based on the Cephes Math Library.
+//
+//  Cephes Math Library Release 2.8:  June, 2000
+//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
+//
+//  Permission has been kindly provided by the original author
+//  to incorporate the Cephes software into the Eigen codebase:
+//
+//    From: Stephen Moshier
+//    To: Eugene Brevdo
+//    Subject: Re: Permission to wrap several cephes functions in Eigen
+//
+//    Hello Eugene,
+//
+//    Thank you for writing.
+//
+//    If your licensing is similar to BSD, the formal way that has been
+//    handled is simply to add a statement to the effect that you are incorporating
+//    the Cephes software by permission of the author.
+//
+//    Good luck with your project,
+//    Steve
+
+
+/****************************************************************************
+ * Implementation of Bessel function, based on Cephes                       *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct bessel_i0e_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i0e {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_i0e<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i0ef.c
+     *
+     *  Modified Bessel function of order zero,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, i0ef();
+     *
+     * y = i0ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order zero of the argument.
+     *
+     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        100000      3.7e-7      7.0e-8
+     * See i0f().
+     *
+     */
+
+    const float A[] = {-1.30002500998624804212E-8f, 6.04699502254191894932E-8f,
+                       -2.67079385394061173391E-7f, 1.11738753912010371815E-6f,
+                       -4.41673835845875056359E-6f, 1.64484480707288970893E-5f,
+                       -5.75419501008210370398E-5f, 1.88502885095841655729E-4f,
+                       -5.76375574538582365885E-4f, 1.63947561694133579842E-3f,
+                       -4.32430999505057594430E-3f, 1.05464603945949983183E-2f,
+                       -2.37374148058994688156E-2f, 4.93052842396707084878E-2f,
+                       -9.49010970480476444210E-2f, 1.71620901522208775349E-1f,
+                       -3.04682672343198398683E-1f, 6.76795274409476084995E-1f};
+
+    const float B[] = {3.39623202570838634515E-9f, 2.26666899049817806459E-8f,
+                       2.04891858946906374183E-7f, 2.89137052083475648297E-6f,
+                       6.88975834691682398426E-5f, 3.36911647825569408990E-3f,
+                       8.04490411014108831608E-1f};
+    T y = pabs(x);
+    T y_le_eight = internal::pchebevl<T, 18>::run(
+        pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A);
+    T y_gt_eight = pmul(
+        internal::pchebevl<T, 7>::run(
+            psub(pdiv(pset1<T>(32.0f), y), pset1<T>(2.0f)), B),
+        prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    return pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
+  }
+};
+
+template <typename T>
+struct generic_i0e<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i0e.c
+     *
+     *  Modified Bessel function of order zero,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, i0e();
+     *
+     * y = i0e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order zero of the argument.
+     *
+     * The function is defined as i0e(x) = exp(-|x|) j0( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       5.4e-16     1.2e-16
+     * See i0().
+     *
+     */
+
+    const double A[] = {-4.41534164647933937950E-18, 3.33079451882223809783E-17,
+                        -2.43127984654795469359E-16, 1.71539128555513303061E-15,
+                        -1.16853328779934516808E-14, 7.67618549860493561688E-14,
+                        -4.85644678311192946090E-13, 2.95505266312963983461E-12,
+                        -1.72682629144155570723E-11, 9.67580903537323691224E-11,
+                        -5.18979560163526290666E-10, 2.65982372468238665035E-9,
+                        -1.30002500998624804212E-8,  6.04699502254191894932E-8,
+                        -2.67079385394061173391E-7,  1.11738753912010371815E-6,
+                        -4.41673835845875056359E-6,  1.64484480707288970893E-5,
+                        -5.75419501008210370398E-5,  1.88502885095841655729E-4,
+                        -5.76375574538582365885E-4,  1.63947561694133579842E-3,
+                        -4.32430999505057594430E-3,  1.05464603945949983183E-2,
+                        -2.37374148058994688156E-2,  4.93052842396707084878E-2,
+                        -9.49010970480476444210E-2,  1.71620901522208775349E-1,
+                        -3.04682672343198398683E-1,  6.76795274409476084995E-1};
+    const double B[] = {
+        -7.23318048787475395456E-18, -4.83050448594418207126E-18,
+        4.46562142029675999901E-17,  3.46122286769746109310E-17,
+        -2.82762398051658348494E-16, -3.42548561967721913462E-16,
+        1.77256013305652638360E-15,  3.81168066935262242075E-15,
+        -9.55484669882830764870E-15, -4.15056934728722208663E-14,
+        1.54008621752140982691E-14,  3.85277838274214270114E-13,
+        7.18012445138366623367E-13,  -1.79417853150680611778E-12,
+        -1.32158118404477131188E-11, -3.14991652796324136454E-11,
+        1.18891471078464383424E-11,  4.94060238822496958910E-10,
+        3.39623202570838634515E-9,   2.26666899049817806459E-8,
+        2.04891858946906374183E-7,   2.89137052083475648297E-6,
+        6.88975834691682398426E-5,   3.36911647825569408990E-3,
+        8.04490411014108831608E-1};
+    T y = pabs(x);
+    T y_le_eight = internal::pchebevl<T, 30>::run(
+        pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A);
+    T y_gt_eight = pmul(
+        internal::pchebevl<T, 25>::run(
+            psub(pdiv(pset1<T>(32.0), y), pset1<T>(2.0)), B),
+        prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    return pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
+  }
+};
+
+template <typename T>
+struct bessel_i0e_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_i0e<T>::run(x);
+  }
+};
+
+template <typename Scalar>
+struct bessel_i0_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i0 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    return pmul(
+        pexp(pabs(x)),
+        generic_i0e<T, ScalarType>::run(x));
+  }
+};
+
+template <typename T>
+struct bessel_i0_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_i0<T>::run(x);
+  }
+};
+
+template <typename Scalar>
+struct bessel_i1e_retval {
+  typedef Scalar type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type >
+struct generic_i1e {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_i1e<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* i1ef.c
+     *
+     *  Modified Bessel function of order one,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, i1ef();
+     *
+     * y = i1ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order one of the argument.
+     *
+     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.5e-6      1.5e-7
+     * See i1().
+     *
+     */
+    const float A[] = {9.38153738649577178388E-9f, -4.44505912879632808065E-8f,
+                       2.00329475355213526229E-7f, -8.56872026469545474066E-7f,
+                       3.47025130813767847674E-6f, -1.32731636560394358279E-5f,
+                       4.78156510755005422638E-5f, -1.61760815825896745588E-4f,
+                       5.12285956168575772895E-4f, -1.51357245063125314899E-3f,
+                       4.15642294431288815669E-3f, -1.05640848946261981558E-2f,
+                       2.47264490306265168283E-2f, -5.29459812080949914269E-2f,
+                       1.02643658689847095384E-1f, -1.76416518357834055153E-1f,
+                       2.52587186443633654823E-1f};
+
+    const float B[] = {-3.83538038596423702205E-9f, -2.63146884688951950684E-8f,
+                       -2.51223623787020892529E-7f, -3.88256480887769039346E-6f,
+                       -1.10588938762623716291E-4f, -9.76109749136146840777E-3f,
+                       7.78576235018280120474E-1f};
+
+
+    T y = pabs(x);
+    T y_le_eight = pmul(y, internal::pchebevl<T, 17>::run(
+        pmadd(pset1<T>(0.5f), y, pset1<T>(-2.0f)), A));
+    T y_gt_eight = pmul(
+        internal::pchebevl<T, 7>::run(
+            psub(pdiv(pset1<T>(32.0f), y),
+                 pset1<T>(2.0f)), B),
+        prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    y = pselect(pcmp_le(y, pset1<T>(8.0f)), y_le_eight, y_gt_eight);
+    return pselect(pcmp_lt(x, pset1<T>(0.0f)), pnegate(y), y);
+  }
+};
+
+template <typename T>
+struct generic_i1e<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  i1e.c
+     *
+     *  Modified Bessel function of order one,
+     *  exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, i1e();
+     *
+     * y = i1e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of order one of the argument.
+     *
+     * The function is defined as i1(x) = -i exp(-|x|) j1( ix ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       2.0e-15     2.0e-16
+     * See i1().
+     *
+     */
+    const double A[] = {2.77791411276104639959E-18, -2.11142121435816608115E-17,
+                        1.55363195773620046921E-16, -1.10559694773538630805E-15,
+                        7.60068429473540693410E-15, -5.04218550472791168711E-14,
+                        3.22379336594557470981E-13, -1.98397439776494371520E-12,
+                        1.17361862988909016308E-11, -6.66348972350202774223E-11,
+                        3.62559028155211703701E-10, -1.88724975172282928790E-9,
+                        9.38153738649577178388E-9,  -4.44505912879632808065E-8,
+                        2.00329475355213526229E-7,  -8.56872026469545474066E-7,
+                        3.47025130813767847674E-6,  -1.32731636560394358279E-5,
+                        4.78156510755005422638E-5,  -1.61760815825896745588E-4,
+                        5.12285956168575772895E-4,  -1.51357245063125314899E-3,
+                        4.15642294431288815669E-3,  -1.05640848946261981558E-2,
+                        2.47264490306265168283E-2,  -5.29459812080949914269E-2,
+                        1.02643658689847095384E-1,  -1.76416518357834055153E-1,
+                        2.52587186443633654823E-1};
+    const double B[] = {
+        7.51729631084210481353E-18,  4.41434832307170791151E-18,
+        -4.65030536848935832153E-17, -3.20952592199342395980E-17,
+        2.96262899764595013876E-16,  3.30820231092092828324E-16,
+        -1.88035477551078244854E-15, -3.81440307243700780478E-15,
+        1.04202769841288027642E-14,  4.27244001671195135429E-14,
+        -2.10154184277266431302E-14, -4.08355111109219731823E-13,
+        -7.19855177624590851209E-13, 2.03562854414708950722E-12,
+        1.41258074366137813316E-11,  3.25260358301548823856E-11,
+        -1.89749581235054123450E-11, -5.58974346219658380687E-10,
+        -3.83538038596423702205E-9,  -2.63146884688951950684E-8,
+        -2.51223623787020892529E-7,  -3.88256480887769039346E-6,
+        -1.10588938762623716291E-4,  -9.76109749136146840777E-3,
+        7.78576235018280120474E-1};
+    T y = pabs(x);
+    T y_le_eight = pmul(y, internal::pchebevl<T, 29>::run(
+        pmadd(pset1<T>(0.5), y, pset1<T>(-2.0)), A));
+    T y_gt_eight = pmul(
+        internal::pchebevl<T, 25>::run(
+            psub(pdiv(pset1<T>(32.0), y),
+                 pset1<T>(2.0)), B),
+        prsqrt(y));
+    // TODO: Perhaps instead check whether all packet elements are in
+    // [-8, 8] and evaluate a branch based off of that. It's possible
+    // in practice most elements are in this region.
+    y = pselect(pcmp_le(y, pset1<T>(8.0)), y_le_eight, y_gt_eight);
+    return pselect(pcmp_lt(x, pset1<T>(0.0)), pnegate(y), y);
+  }
+};
+
+template <typename T>
+struct bessel_i1e_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_i1e<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_i1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_i1 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    return pmul(
+        pexp(pabs(x)),
+        generic_i1e<T, ScalarType>::run(x));
+  }
+};
+
+template <typename T>
+struct bessel_i1_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_i1<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_k0e_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k0e {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_k0e<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0ef.c
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k0ef();
+     *
+     * y = k0ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       8.1e-7      7.8e-8
+     * See k0().
+     *
+     */
+
+    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f,
+                       2.28621210311945178607E-5f, 1.26461541144692592338E-3f,
+                       3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
+                       -5.35327393233902768720E-1f};
+
+    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,
+                       -4.66048989768794782956E-8f, 2.76681363944501510342E-7f,
+                       -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
+                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,
+                       -3.14481013119645005427E-2f, 2.44030308206595545468E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 7>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(
+        generic_i0<T, float>::run(x), pnegate(
+            plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pmul(pexp(x), x_le_two);
+    T x_gt_two = pmul(
+            internal::pchebevl<T, 10>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B),
+            prsqrt(x));
+    return pselect(
+        pcmp_le(x, pset1<T>(0.0)),
+        MAXNUM,
+        pselect(pcmp_le(x, two), x_le_two, x_gt_two));
+  }
+};
+
+template <typename T>
+struct generic_k0e<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0e.c
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k0e();
+     *
+     * y = k0e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
+     * See k0().
+     *
+     */
+
+    const double A[] = {
+      1.37446543561352307156E-16,
+      4.25981614279661018399E-14,
+      1.03496952576338420167E-11,
+      1.90451637722020886025E-9,
+      2.53479107902614945675E-7,
+      2.28621210311945178607E-5,
+      1.26461541144692592338E-3,
+      3.59799365153615016266E-2,
+      3.44289899924628486886E-1,
+      -5.35327393233902768720E-1};
+    const double B[] = {
+       5.30043377268626276149E-18, -1.64758043015242134646E-17,
+       5.21039150503902756861E-17, -1.67823109680541210385E-16,
+       5.51205597852431940784E-16, -1.84859337734377901440E-15,
+       6.34007647740507060557E-15, -2.22751332699166985548E-14,
+       8.03289077536357521100E-14, -2.98009692317273043925E-13,
+       1.14034058820847496303E-12, -4.51459788337394416547E-12,
+       1.85594911495471785253E-11, -7.95748924447710747776E-11,
+       3.57739728140030116597E-10, -1.69753450938905987466E-9,
+       8.57403401741422608519E-9, -4.66048989768794782956E-8,
+       2.76681363944501510342E-7, -1.83175552271911948767E-6,
+       1.39498137188764993662E-5, -1.28495495816278026384E-4,
+       1.56988388573005337491E-3, -3.14481013119645005427E-2,
+       2.44030308206595545468E0
+    };
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 10>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(
+        generic_i0<T, double>::run(x), pmul(
+            pset1<T>(-1.0), plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pmul(pexp(x), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+            internal::pchebevl<T, 25>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B),
+            prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k0e_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_k0e<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_k0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k0 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_k0<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k0f.c
+     *	Modified Bessel function, third kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k0f();
+     *
+     * y = k0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns modified Bessel function of the third kind
+     * of order zero of the argument.
+     *
+     * The range is partitioned into the two intervals [0,8] and
+     * (8, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     * Tested at 2000 random points between 0 and 8.  Peak absolute
+     * error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15.
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       7.8e-7      8.5e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     *  K0 domain          x <= 0          MAXNUM
+     *
+     */
+
+    const float A[] = {1.90451637722020886025E-9f, 2.53479107902614945675E-7f,
+                       2.28621210311945178607E-5f, 1.26461541144692592338E-3f,
+                       3.59799365153615016266E-2f, 3.44289899924628486886E-1f,
+                       -5.35327393233902768720E-1f};
+
+    const float B[] = {-1.69753450938905987466E-9f, 8.57403401741422608519E-9f,
+                       -4.66048989768794782956E-8f, 2.76681363944501510342E-7f,
+                       -1.83175552271911948767E-6f, 1.39498137188764993662E-5f,
+                       -1.28495495816278026384E-4f, 1.56988388573005337491E-3f,
+                       -3.14481013119645005427E-2f, 2.44030308206595545468E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 7>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(
+        generic_i0<T, float>::run(x), pnegate(
+            plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        pmul(
+            pexp(pnegate(x)),
+            internal::pchebevl<T, 10>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B)),
+        prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k0<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*
+     *
+     *	Modified Bessel function, third kind, order zero,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k0();
+     *
+     * y = k0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order zero of the argument.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       1.4e-15     1.4e-16
+     * See k0().
+     *
+     */
+    const double A[] = {
+      1.37446543561352307156E-16,
+      4.25981614279661018399E-14,
+      1.03496952576338420167E-11,
+      1.90451637722020886025E-9,
+      2.53479107902614945675E-7,
+      2.28621210311945178607E-5,
+      1.26461541144692592338E-3,
+      3.59799365153615016266E-2,
+      3.44289899924628486886E-1,
+      -5.35327393233902768720E-1};
+    const double B[] = {
+       5.30043377268626276149E-18, -1.64758043015242134646E-17,
+       5.21039150503902756861E-17, -1.67823109680541210385E-16,
+       5.51205597852431940784E-16, -1.84859337734377901440E-15,
+       6.34007647740507060557E-15, -2.22751332699166985548E-14,
+       8.03289077536357521100E-14, -2.98009692317273043925E-13,
+       1.14034058820847496303E-12, -4.51459788337394416547E-12,
+       1.85594911495471785253E-11, -7.95748924447710747776E-11,
+       3.57739728140030116597E-10, -1.69753450938905987466E-9,
+       8.57403401741422608519E-9, -4.66048989768794782956E-8,
+       2.76681363944501510342E-7, -1.83175552271911948767E-6,
+       1.39498137188764993662E-5, -1.28495495816278026384E-4,
+       1.56988388573005337491E-3, -3.14481013119645005427E-2,
+       2.44030308206595545468E0
+    };
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = internal::pchebevl<T, 10>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A);
+    x_le_two = pmadd(
+        generic_i0<T, double>::run(x), pnegate(
+            plog(pmul(pset1<T>(0.5), x))), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        pmul(
+            pexp(-x),
+            internal::pchebevl<T, 25>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B)),
+        prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k0_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_k0<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_k1e_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k1e {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_k1e<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* k1ef.c
+     *
+     *	Modified Bessel function, third kind, order one,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1ef();
+     *
+     * y = k1ef( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order one of the argument:
+     *
+     *      k1e(x) = exp(x) * k1(x).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.9e-7      6.7e-8
+     * See k1().
+     *
+     */
+
+    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f,
+                        -1.73028895751305206302E-4f, -6.97572385963986435018E-3f,
+                        -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
+                        1.52530022733894777053E0f};
+    const float B[] = {2.01504975519703286596E-9f, -1.03457624656780970260E-8f,
+                       5.74108412545004946722E-8f, -3.50196060308781257119E-7f,
+                       2.40648494783721712015E-6f, -1.93619797416608296024E-5f,
+                       1.95215518471351631108E-4f, -2.85781685962277938680E-3f,
+                       1.03923736576817238437E-1f, 2.72062619048444266945E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(
+        generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pmul(x_le_two, pexp(x));
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        internal::pchebevl<T, 10>::run(
+            psub(pdiv(pset1<T>(8.0), x), two), B),
+        prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k1e<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k1e.c
+     *
+     *	Modified Bessel function, third kind, order one,
+     *	exponentially scaled
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, k1e();
+     *
+     * y = k1e( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns exponentially scaled modified Bessel function
+     * of the third kind of order one of the argument:
+     *
+     *      k1e(x) = exp(x) * k1(x).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       7.8e-16     1.2e-16
+     * See k1().
+     *
+     */
+    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15,
+                        -6.66690169419932900609E-13, -1.41148839263352776110E-10,
+                        -2.21338763073472585583E-8, -2.43340614156596823496E-6,
+                        -1.73028895751305206302E-4, -6.97572385963986435018E-3,
+                        -1.22611180822657148235E-1, -3.53155960776544875667E-1,
+                        1.52530022733894777053E0};
+    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,
+                        -5.68946255844285935196E-17, 1.83809354436663880070E-16,
+                        -6.05704724837331885336E-16, 2.03870316562433424052E-15,
+                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,
+                        -8.97670518232499435011E-14, 3.34841966607842919884E-13,
+                        -1.28917396095102890680E-12, 5.13963967348173025100E-12,
+                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,
+                        -4.19035475934189648750E-10, 2.01504975519703286596E-9,
+                        -1.03457624656780970260E-8, 5.74108412545004946722E-8,
+                        -3.50196060308781257119E-7, 2.40648494783721712015E-6,
+                        -1.93619797416608296024E-5, 1.95215518471351631108E-4,
+                        -2.85781685962277938680E-3, 1.03923736576817238437E-1,
+                        2.72062619048444266945E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(
+        generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pmul(x_le_two, pexp(x));
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        internal::pchebevl<T, 25>::run(
+            psub(pdiv(pset1<T>(8.0), x), two), B),
+        prsqrt(x));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k1e_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_k1e<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_k1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_k1 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_k1<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* k1f.c
+     *	Modified Bessel function, third kind, order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1f();
+     *
+     * y = k1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Computes the modified Bessel function of the third kind
+     * of order one of the argument.
+     *
+     * The range is partitioned into the two intervals [0,2] and
+     * (2, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     * k1 domain          x <= 0          MAXNUM
+     *
+     */
+
+    const float A[] = {-2.21338763073472585583E-8f, -2.43340614156596823496E-6f,
+                        -1.73028895751305206302E-4f, -6.97572385963986435018E-3f,
+                        -1.22611180822657148235E-1f, -3.53155960776544875667E-1f,
+                        1.52530022733894777053E0f};
+    const float B[] = {2.01504975519703286596E-9f, -1.03457624656780970260E-8f,
+                       5.74108412545004946722E-8f, -3.50196060308781257119E-7f,
+                       2.40648494783721712015E-6f, -1.93619797416608296024E-5f,
+                       1.95215518471351631108E-4f, -2.85781685962277938680E-3f,
+                       1.03923736576817238437E-1f, 2.72062619048444266945E0f};
+    const T MAXNUM = pset1<T>(NumTraits<float>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 7>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(
+        generic_i1<T, float>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        pexp(pnegate(x)),
+        pmul(
+            internal::pchebevl<T, 10>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B),
+            prsqrt(x)));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_k1<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  k1.c
+     *	Modified Bessel function, third kind, order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, k1f();
+     *
+     * y = k1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Computes the modified Bessel function of the third kind
+     * of order one of the argument.
+     *
+     * The range is partitioned into the two intervals [0,2] and
+     * (2, infinity).  Chebyshev polynomial expansions are employed
+     * in each interval.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 30       30000       4.6e-7      7.6e-8
+     *
+     * ERROR MESSAGES:
+     *
+     *   message         condition      value returned
+     * k1 domain          x <= 0          MAXNUM
+     *
+     */
+    const double A[] = {-7.02386347938628759343E-18, -2.42744985051936593393E-15,
+                        -6.66690169419932900609E-13, -1.41148839263352776110E-10,
+                        -2.21338763073472585583E-8, -2.43340614156596823496E-6,
+                        -1.73028895751305206302E-4, -6.97572385963986435018E-3,
+                        -1.22611180822657148235E-1, -3.53155960776544875667E-1,
+                        1.52530022733894777053E0};
+    const double B[] = {-5.75674448366501715755E-18, 1.79405087314755922667E-17,
+                        -5.68946255844285935196E-17, 1.83809354436663880070E-16,
+                        -6.05704724837331885336E-16, 2.03870316562433424052E-15,
+                        -7.01983709041831346144E-15, 2.47715442448130437068E-14,
+                        -8.97670518232499435011E-14, 3.34841966607842919884E-13,
+                        -1.28917396095102890680E-12, 5.13963967348173025100E-12,
+                        -2.12996783842756842877E-11, 9.21831518760500529508E-11,
+                        -4.19035475934189648750E-10, 2.01504975519703286596E-9,
+                        -1.03457624656780970260E-8, 5.74108412545004946722E-8,
+                        -3.50196060308781257119E-7, 2.40648494783721712015E-6,
+                        -1.93619797416608296024E-5, 1.95215518471351631108E-4,
+                        -2.85781685962277938680E-3, 1.03923736576817238437E-1,
+                        2.72062619048444266945E0};
+    const T MAXNUM = pset1<T>(NumTraits<double>::infinity());
+    const T two = pset1<T>(2.0);
+    T x_le_two = pdiv(internal::pchebevl<T, 11>::run(
+        pmadd(x, x, pset1<T>(-2.0)), A), x);
+    x_le_two = pmadd(
+        generic_i1<T, double>::run(x), plog(pmul(pset1<T>(0.5), x)), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), MAXNUM, x_le_two);
+    T x_gt_two = pmul(
+        pexp(-x),
+        pmul(
+            internal::pchebevl<T, 25>::run(
+                psub(pdiv(pset1<T>(8.0), x), two), B),
+            prsqrt(x)));
+    return pselect(pcmp_le(x, two), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct bessel_k1_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_k1<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_j0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_j0 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_j0<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j0f.c
+     *	Bessel function of order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, j0f();
+     *
+     * y = j0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order zero of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval the following polynomial
+     * approximation is used:
+     *
+     *
+     *        2         2         2
+     * (w - r  ) (w - r  ) (w - r  ) P(w)
+     *       1         2         3
+     *
+     *            2
+     * where w = x  and the three r's are zeros of the function.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x R(1/x^2) - pi/4.  The function is
+     *
+     *   j0(x) = Modulus(x) cos( Phase(x) ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0, 2        100000      1.3e-7      3.6e-8
+     *    IEEE      2, 32       100000      1.9e-7      5.4e-8
+     *
+     */
+
+    const float JP[] = {-6.068350350393235E-008f, 6.388945720783375E-006f,
+                        -3.969646342510940E-004f, 1.332913422519003E-002f,
+                        -1.729150680240724E-001f};
+    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,
+                        -2.145007480346739E-001f, 1.197549369473540E-001f,
+                        -3.560281861530129E-003f, -4.969382655296620E-002f,
+                        -3.355424622293709E-006f, 7.978845717621440E-001f};
+    const float PH[] = {3.242077816988247E+001f, -3.630592630518434E+001f,
+                        1.756221482109099E+001f, -4.974978466280903E+000f,
+                        1.001973420681837E+000f, -1.939906941791308E-001f,
+                        6.490598792654666E-002f, -1.249992184872738E-001f};
+    const T DR1 =  pset1<T>(5.78318596294678452118f);
+    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f); /* -pi / 4 */
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_two = pselect(
+        pcmp_lt(y, pset1<T>(1.0e-3f)),
+        pmadd(z, pset1<T>(-0.25f), pset1<T>(1.0f)),
+        pmul(psub(z, DR1), internal::ppolevl<T, 4>::run(z, JP)));
+    T q = pdiv(pset1<T>(1.0f), y);
+    T w = prsqrt(y);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
+    w = pmul(q, q);
+    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH), NEG_PIO4F);
+    T y_gt_two = pmul(p, pcos(padd(yn, y)));
+    return pselect(pcmp_le(y, pset1<T>(2.0)), y_le_two, y_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_j0<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j0.c
+     *	Bessel function of order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, j0();
+     *
+     * y = j0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order zero of the argument.
+     *
+     * The domain is divided into the intervals [0, 5] and
+     * (5, infinity). In the first interval the following rational
+     * approximation is used:
+     *
+     *
+     *        2         2
+     * (w - r  ) (w - r  ) P (w) / Q (w)
+     *       1         2    3       8
+     *
+     *            2
+     * where w = x  and the two r's are zeros of the function.
+     *
+     * In the second interval, the Hankel asymptotic expansion
+     * is employed with two rational functions of degree 6/6
+     * and 7/7.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain     # trials      peak         rms
+     *    DEC       0, 30       10000       4.4e-17     6.3e-18
+     *    IEEE      0, 30       60000       4.2e-16     1.1e-16
+     *
+     */
+    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2,
+                        1.23953371646414299388E0, 5.44725003058768775090E0,
+                        8.74716500199817011941E0, 5.30324038235394892183E0,
+                        9.99999999999999997821E-1};
+    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2,
+                         1.25352743901058953537E0, 5.47097740330417105182E0,
+                         8.76190883237069594232E0, 5.30605288235394617618E0,
+                         1.00000000000000000218E0};
+    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0,
+                         -1.95539544257735972385E1, -9.32060152123768231369E1,
+                         -1.77681167980488050595E2, -1.47077505154951170175E2,
+                         -5.14105326766599330220E1, -6.05014350600728481186E0};
+    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1,
+                         8.56430025976980587198E2, 3.88240183605401609683E3,
+                         7.24046774195652478189E3, 5.93072701187316984827E3,
+                         2.06209331660327847417E3, 2.42005740240291393179E2};
+    const double RP[] = {-4.79443220978201773821E9, 1.95617491946556577543E12,
+                         -2.49248344360967716204E14, 9.70862251047306323952E15};
+    const double RQ[] = {1.00000000000000000000E0, 4.99563147152651017219E2,
+                         1.73785401676374683123E5, 4.84409658339962045305E7,
+                         1.11855537045356834862E10, 2.11277520115489217587E12,
+                         3.10518229857422583814E14, 3.18121955943204943306E16,
+                         1.71086294081043136091E18};
+    const T DR1 = pset1<T>(5.78318596294678452118E0);
+    const T DR2 = pset1<T>(3.04712623436620863991E1);
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096); /* pi / 4 */
+
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_five = pselect(
+        pcmp_lt(y, pset1<T>(1.0e-5)),
+        pmadd(z, pset1<T>(-0.25), pset1<T>(1.0)),
+        pmul(pmul(psub(z, DR1), psub(z, DR2)),
+             pdiv(internal::ppolevl<T, 3>::run(z, RP),
+                  internal::ppolevl<T, 8>::run(z, RQ))));
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(
+        internal::ppolevl<T, 6>::run(s, PP),
+        internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(
+        internal::ppolevl<T, 7>::run(s, QP),
+        internal::ppolevl<T, 7>::run(s, QQ));
+    T yn = padd(y, NEG_PIO4);
+    T w = pdiv(pset1<T>(-5.0), y);
+    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
+    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
+    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_j0_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_j0<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_y0_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_y0 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_y0<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j0f.c
+     * 	Bessel function of the second kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, y0f();
+     *
+     * y = y0f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind, of order
+     * zero, of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *
+     *                  2         2         2
+     * y0(x)  =  (w - r  ) (w - r  ) (w - r  ) R(x)  +  2/pi ln(x) j0(x).
+     *                 1         2         3
+     *
+     * Thus a call to j0() is required.  The three zeros are removed
+     * from R(x) to improve its numerical stability.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x S(1/x^2) - pi/4.  Then the function is
+     *
+     *   y0(x) = Modulus(x) sin( Phase(x) ).
+     *
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *  Absolute error, when y0(x) < 1; else relative error:
+     *
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,  2       100000      2.4e-7      3.4e-8
+     *    IEEE      2, 32       100000      1.8e-7      5.3e-8
+     *
+     */
+
+    const float YP[] = {9.454583683980369E-008f, -9.413212653797057E-006f,
+                        5.344486707214273E-004f, -1.584289289821316E-002f,
+                        1.707584643733568E-001f};
+    const float MO[] = {-6.838999669318810E-002f, 1.864949361379502E-001f,
+                        -2.145007480346739E-001f, 1.197549369473540E-001f,
+                        -3.560281861530129E-003f, -4.969382655296620E-002f,
+                        -3.355424622293709E-006f, 7.978845717621440E-001f};
+    const float PH[] = {3.242077816988247E+001f, -3.630592630518434E+001f,
+                        1.756221482109099E+001f, -4.974978466280903E+000f,
+                        1.001973420681837E+000f, -1.939906941791308E-001f,
+                        6.490598792654666E-002f, -1.249992184872738E-001f};
+    const T YZ1 = pset1<T>(0.43221455686510834878f);
+    const T TWOOPI =  pset1<T>(0.636619772367581343075535f); /* 2 / pi */
+    const T NEG_PIO4F = pset1<T>(-0.7853981633974483096f); /* -pi / 4 */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
+    T z = pmul(x, x);
+    T x_le_two = pmul(TWOOPI, pmul(plog(x), generic_j0<T, float>::run(x)));
+    x_le_two = pmadd(
+        psub(z, YZ1), internal::ppolevl<T, 4>::run(z, YP), x_le_two);
+    x_le_two = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_two);
+    T q = pdiv(pset1<T>(1.0), x);
+    T w = prsqrt(x);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO));
+    T u = pmul(q, q);
+    T xn = pmadd(q, internal::ppolevl<T, 7>::run(u, PH), NEG_PIO4F);
+    T x_gt_two = pmul(p, psin(padd(xn, x)));
+    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_y0<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j0.c
+     *	Bessel function of the second kind, order zero
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y0();
+     *
+     * y = y0( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind, of order
+     * zero, of the argument.
+     *
+     * The domain is divided into the intervals [0, 5] and
+     * (5, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *   y0(x)  = R(x)  +   2 * log(x) * j0(x) / PI.
+     * Thus a call to j0() is required.
+     *
+     * In the second interval, the Hankel asymptotic expansion
+     * is employed with two rational functions of degree 6/6
+     * and 7/7.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *  Absolute error, when y0(x) < 1; else relative error:
+     *
+     * arithmetic   domain     # trials      peak         rms
+     *    DEC       0, 30        9400       7.0e-17     7.9e-18
+     *    IEEE      0, 30       30000       1.3e-15     1.6e-16
+     *
+     */
+    const double PP[] = {7.96936729297347051624E-4, 8.28352392107440799803E-2,
+                        1.23953371646414299388E0, 5.44725003058768775090E0,
+                        8.74716500199817011941E0, 5.30324038235394892183E0,
+                        9.99999999999999997821E-1};
+    const double PQ[] = {9.24408810558863637013E-4, 8.56288474354474431428E-2,
+                         1.25352743901058953537E0, 5.47097740330417105182E0,
+                         8.76190883237069594232E0, 5.30605288235394617618E0,
+                         1.00000000000000000218E0};
+    const double QP[] = {-1.13663838898469149931E-2, -1.28252718670509318512E0,
+                         -1.95539544257735972385E1, -9.32060152123768231369E1,
+                         -1.77681167980488050595E2, -1.47077505154951170175E2,
+                         -5.14105326766599330220E1, -6.05014350600728481186E0};
+    const double QQ[] = {1.00000000000000000000E0, 6.43178256118178023184E1,
+                         8.56430025976980587198E2, 3.88240183605401609683E3,
+                         7.24046774195652478189E3, 5.93072701187316984827E3,
+                         2.06209331660327847417E3, 2.42005740240291393179E2};
+    const double YP[] = {1.55924367855235737965E4, -1.46639295903971606143E7,
+                         5.43526477051876500413E9, -9.82136065717911466409E11,
+                         8.75906394395366999549E13, -3.46628303384729719441E15,
+                         4.42733268572569800351E16, -1.84950800436986690637E16};
+    const double YQ[] = {1.00000000000000000000E0,  1.04128353664259848412E3,
+                         6.26107330137134956842E5, 2.68919633393814121987E8,
+                         8.64002487103935000337E10, 2.02979612750105546709E13,
+                         3.17157752842975028269E15, 2.50596256172653059228E17};
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    const T TWOOPI =  pset1<T>(0.636619772367581343075535); /* 2 / pi */
+    const T NEG_PIO4 = pset1<T>(-0.7853981633974483096); /* -pi / 4 */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_five = pdiv(internal::ppolevl<T, 7>::run(z, YP),
+                       internal::ppolevl<T, 7>::run(z, YQ));
+    x_le_five = pmadd(
+        pmul(TWOOPI, plog(x)), generic_j0<T, double>::run(x), x_le_five);
+    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(
+        internal::ppolevl<T, 6>::run(s, PP),
+        internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(
+        internal::ppolevl<T, 7>::run(s, QP),
+        internal::ppolevl<T, 7>::run(s, QQ));
+    T xn = padd(x, NEG_PIO4);
+    T w = pdiv(pset1<T>(5.0), x);
+    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
+    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
+    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_y0_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_y0<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_j1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_j1 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_j1<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j1f.c
+     *	Bessel function of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float x, y, j1f();
+     *
+     * y = j1f( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order one of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a polynomial approximation
+     *        2
+     * (w - r  ) x P(w)
+     *       1
+     *                     2
+     * is used, where w = x  and r is the first zero of the function.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x R(1/x^2) - 3pi/4.  The function is
+     *
+     *   j0(x) = Modulus(x) cos( Phase(x) ).
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak       rms
+     *    IEEE      0,  2       100000       1.2e-7     2.5e-8
+     *    IEEE      2, 32       100000       2.0e-7     5.3e-8
+     *
+     *
+     */
+
+    const float JP[] = {-4.878788132172128E-009f, 6.009061827883699E-007f,
+                        -4.541343896997497E-005f, 1.937383947804541E-003f,
+                        -3.405537384615824E-002f};
+    const float MO1[] = {6.913942741265801E-002f, -2.284801500053359E-001f,
+                        3.138238455499697E-001f, -2.102302420403875E-001f,
+                        5.435364690523026E-003f, 1.493389585089498E-001f,
+                        4.976029650847191E-006f, 7.978845453073848E-001f};
+    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,
+                        -2.485774108720340E+001f, 7.222973196770240E+000f,
+                        -1.544842782180211E+000f, 3.503787691653334E-001f,
+                        -1.637986776941202E-001f, 3.749989509080821E-001f};
+    const T Z1 = pset1<T>(1.46819706421238932572E1f);
+    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f);    /* -3*pi/4 */
+
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_two = pmul(
+        psub(z, Z1),
+        pmul(x, internal::ppolevl<T, 4>::run(z, JP)));
+    T q = pdiv(pset1<T>(1.0f), y);
+    T w = prsqrt(y);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
+    w = pmul(q, q);
+    T yn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
+    T y_gt_two = pmul(p, pcos(padd(yn, y)));
+    // j1 is an odd function. This implementation differs from cephes to
+    // take this fact in to account. Cephes returns -j1(x) for y > 2 range.
+    y_gt_two = pselect(
+        pcmp_lt(x, pset1<T>(0.0f)), pnegate(y_gt_two), y_gt_two);
+    return pselect(pcmp_le(y, pset1<T>(2.0f)), y_le_two, y_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_j1<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j1.c
+     *	Bessel function of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, j1();
+     *
+     * y = j1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of order one of the argument.
+     *
+     * The domain is divided into the intervals [0, 8] and
+     * (8, infinity). In the first interval a 24 term Chebyshev
+     * expansion is used. In the second, the asymptotic
+     * trigonometric representation is employed using two
+     * rational functions of degree 5/5.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    DEC       0, 30       10000       4.0e-17     1.1e-17
+     *    IEEE      0, 30       30000       2.6e-16     1.1e-16
+     *
+     */
+    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2,
+                         1.12719608129684925192E0, 5.11207951146807644818E0,
+                         8.42404590141772420927E0, 5.21451598682361504063E0,
+                         1.00000000000000000254E0};
+    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2,
+                         1.10514232634061696926E0, 5.07386386128601488557E0,
+                         8.39985554327604159757E0, 5.20982848682361821619E0,
+                         9.99999999999999997461E-1};
+    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0,
+                         7.58238284132545283818E1, 3.66779609360150777800E2,
+                         7.10856304998926107277E2, 5.97489612400613639965E2,
+                         2.11688757100572135698E2, 2.52070205858023719784E1};
+    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1,
+                         1.05644886038262816351E3, 4.98641058337653607651E3,
+                         9.56231892404756170795E3, 7.99704160447350683650E3,
+                         2.82619278517639096600E3, 3.36093607810698293419E2};
+    const double RP[] = {-8.99971225705559398224E8, 4.52228297998194034323E11,
+                         -7.27494245221818276015E13, 3.68295732863852883286E15};
+    const double RQ[] = {1.00000000000000000000E0, 6.20836478118054335476E2,
+                         2.56987256757748830383E5, 8.35146791431949253037E7,
+                         2.21511595479792499675E10, 4.74914122079991414898E12,
+                         7.84369607876235854894E14, 8.95222336184627338078E16,
+                         5.32278620332680085395E18};
+    const T Z1 = pset1<T>(1.46819706421238932572E1);
+    const T Z2 = pset1<T>(4.92184563216946036703E1);
+    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885);    /* -3*pi/4 */
+    const T SQ2OPI = pset1<T>(7.9788456080286535587989E-1); /* sqrt(2 / pi) */
+    T y = pabs(x);
+    T z = pmul(y, y);
+    T y_le_five = pdiv(internal::ppolevl<T, 3>::run(z, RP),
+                       internal::ppolevl<T, 8>::run(z, RQ));
+    y_le_five = pmul(pmul(pmul(y_le_five, x), psub(z, Z1)), psub(z, Z2));
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(
+        internal::ppolevl<T, 6>::run(s, PP),
+        internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(
+        internal::ppolevl<T, 7>::run(s, QP),
+        internal::ppolevl<T, 7>::run(s, QQ));
+    T yn = padd(y, NEG_THPIO4);
+    T w = pdiv(pset1<T>(-5.0), y);
+    p = pmadd(p, pcos(yn), pmul(w, pmul(q, psin(yn))));
+    T y_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(y)));
+    // j1 is an odd function. This implementation differs from cephes to
+    // take this fact in to account. Cephes returns -j1(x) for y > 5 range.
+    y_gt_five = pselect(
+        pcmp_lt(x, pset1<T>(0.0)), pnegate(y_gt_five), y_gt_five);
+    return pselect(pcmp_le(y, pset1<T>(5.0)), y_le_five, y_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_j1_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_j1<T>::run(x);
+  }
+};
+
+template <typename T>
+struct bessel_y1_retval {
+  typedef T type;
+};
+
+template <typename T, typename ScalarType = typename unpacket_traits<T>::type>
+struct generic_y1 {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T&) {
+    EIGEN_STATIC_ASSERT((internal::is_same<T, T>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return ScalarType(0);
+  }
+};
+
+template <typename T>
+struct generic_y1<T, float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /* j1f.c
+     *	Bessel function of second kind of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y1();
+     *
+     * y = y1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind of order one
+     * of the argument.
+     *
+     * The domain is divided into the intervals [0, 2] and
+     * (2, infinity). In the first interval a rational approximation
+     * R(x) is employed to compute
+     *
+     *                  2
+     * y0(x)  =  (w - r  ) x R(x^2)  +  2/pi (ln(x) j1(x) - 1/x) .
+     *                 1
+     *
+     * Thus a call to j1() is required.
+     *
+     * In the second interval, the modulus and phase are approximated
+     * by polynomials of the form Modulus(x) = sqrt(1/x) Q(1/x)
+     * and Phase(x) = x + 1/x S(1/x^2) - 3pi/4.  Then the function is
+     *
+     *   y0(x) = Modulus(x) sin( Phase(x) ).
+     *
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    IEEE      0,  2       100000       2.2e-7     4.6e-8
+     *    IEEE      2, 32       100000       1.9e-7     5.3e-8
+     *
+     * (error criterion relative when |y1| > 1).
+     *
+     */
+
+    const float YP[] = {8.061978323326852E-009f, -9.496460629917016E-007f,
+                        6.719543806674249E-005f, -2.641785726447862E-003f,
+                        4.202369946500099E-002f};
+    const float MO1[] = {6.913942741265801E-002f, -2.284801500053359E-001f,
+                        3.138238455499697E-001f, -2.102302420403875E-001f,
+                        5.435364690523026E-003f, 1.493389585089498E-001f,
+                        4.976029650847191E-006f, 7.978845453073848E-001f};
+    const float PH1[] = {-4.497014141919556E+001f, 5.073465654089319E+001f,
+                        -2.485774108720340E+001f, 7.222973196770240E+000f,
+                        -1.544842782180211E+000f, 3.503787691653334E-001f,
+                        -1.637986776941202E-001f, 3.749989509080821E-001f};
+    const T YO1 = pset1<T>(4.66539330185668857532f);
+    const T NEG_THPIO4F = pset1<T>(-2.35619449019234492885f);    /* -3*pi/4 */
+    const T TWOOPI = pset1<T>(0.636619772367581343075535f); /* 2/pi */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<float>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_two = pmul(psub(z, YO1), internal::ppolevl<T, 4>::run(z, YP));
+    x_le_two = pmadd(
+       x_le_two, x,
+       pmul(TWOOPI, pmadd(
+           generic_j1<T, float>::run(x), plog(x),
+           pdiv(pset1<T>(-1.0f), x))));
+    x_le_two = pselect(pcmp_lt(x, pset1<T>(0.0f)), NEG_MAXNUM, x_le_two);
+
+    T q = pdiv(pset1<T>(1.0), x);
+    T w = prsqrt(x);
+    T p = pmul(w, internal::ppolevl<T, 7>::run(q, MO1));
+    w = pmul(q, q);
+    T xn = pmadd(q, internal::ppolevl<T, 7>::run(w, PH1), NEG_THPIO4F);
+    T x_gt_two = pmul(p, psin(padd(xn, x)));
+    return pselect(pcmp_le(x, pset1<T>(2.0)), x_le_two, x_gt_two);
+  }
+};
+
+template <typename T>
+struct generic_y1<T, double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    /*  j1.c
+     *	Bessel function of second kind of order one
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, y1();
+     *
+     * y = y1( x );
+     *
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns Bessel function of the second kind of order one
+     * of the argument.
+     *
+     * The domain is divided into the intervals [0, 8] and
+     * (8, infinity). In the first interval a 25 term Chebyshev
+     * expansion is used, and a call to j1() is required.
+     * In the second, the asymptotic trigonometric representation
+     * is employed using two rational functions of degree 5/5.
+     *
+     *
+     *
+     * ACCURACY:
+     *
+     *                      Absolute error:
+     * arithmetic   domain      # trials      peak         rms
+     *    DEC       0, 30       10000       8.6e-17     1.3e-17
+     *    IEEE      0, 30       30000       1.0e-15     1.3e-16
+     *
+     * (error criterion relative when |y1| > 1).
+     *
+     */
+    const double PP[] = {7.62125616208173112003E-4, 7.31397056940917570436E-2,
+                         1.12719608129684925192E0, 5.11207951146807644818E0,
+                         8.42404590141772420927E0, 5.21451598682361504063E0,
+                         1.00000000000000000254E0};
+    const double PQ[] = {5.71323128072548699714E-4, 6.88455908754495404082E-2,
+                         1.10514232634061696926E0, 5.07386386128601488557E0,
+                         8.39985554327604159757E0, 5.20982848682361821619E0,
+                         9.99999999999999997461E-1};
+    const double QP[] = {5.10862594750176621635E-2, 4.98213872951233449420E0,
+                         7.58238284132545283818E1, 3.66779609360150777800E2,
+                         7.10856304998926107277E2, 5.97489612400613639965E2,
+                         2.11688757100572135698E2, 2.52070205858023719784E1};
+    const double QQ[] = {1.00000000000000000000E0, 7.42373277035675149943E1,
+                         1.05644886038262816351E3, 4.98641058337653607651E3,
+                         9.56231892404756170795E3, 7.99704160447350683650E3,
+                         2.82619278517639096600E3, 3.36093607810698293419E2};
+    const double YP[] = {1.26320474790178026440E9, -6.47355876379160291031E11,
+                         1.14509511541823727583E14, -8.12770255501325109621E15,
+                         2.02439475713594898196E17, -7.78877196265950026825E17};
+    const double YQ[] = {1.00000000000000000000E0, 5.94301592346128195359E2,
+                         2.35564092943068577943E5, 7.34811944459721705660E7,
+                         1.87601316108706159478E10, 3.88231277496238566008E12,
+                         6.20557727146953693363E14, 6.87141087355300489866E16,
+                         3.97270608116560655612E18};
+    const T SQ2OPI = pset1<T>(.79788456080286535588);
+    const T NEG_THPIO4 = pset1<T>(-2.35619449019234492885);    /* -3*pi/4 */
+    const T TWOOPI = pset1<T>(0.636619772367581343075535); /* 2/pi */
+    const T NEG_MAXNUM = pset1<T>(-NumTraits<double>::infinity());
+
+    T z = pmul(x, x);
+    T x_le_five = pdiv(internal::ppolevl<T, 5>::run(z, YP),
+                   internal::ppolevl<T, 8>::run(z, YQ));
+    x_le_five = pmadd(
+        x_le_five, x, pmul(
+            TWOOPI, pmadd(generic_j1<T, double>::run(x), plog(x),
+                          pdiv(pset1<T>(-1.0), x))));
+
+    x_le_five = pselect(pcmp_le(x, pset1<T>(0.0)), NEG_MAXNUM, x_le_five);
+    T s = pdiv(pset1<T>(25.0), z);
+    T p = pdiv(
+        internal::ppolevl<T, 6>::run(s, PP),
+        internal::ppolevl<T, 6>::run(s, PQ));
+    T q = pdiv(
+        internal::ppolevl<T, 7>::run(s, QP),
+        internal::ppolevl<T, 7>::run(s, QQ));
+    T xn = padd(x, NEG_THPIO4);
+    T w = pdiv(pset1<T>(5.0), x);
+    p = pmadd(p, psin(xn), pmul(w, pmul(q, pcos(xn))));
+    T x_gt_five = pmul(p, pmul(SQ2OPI, prsqrt(x)));
+    return pselect(pcmp_le(x, pset1<T>(5.0)), x_le_five, x_gt_five);
+  }
+};
+
+template <typename T>
+struct bessel_y1_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T x) {
+    return generic_y1<T>::run(x);
+  }
+};
+
+}  // end namespace internal
+
+namespace numext {
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0, Scalar)
+    bessel_i0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i0e, Scalar)
+    bessel_i0e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i0e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1, Scalar)
+    bessel_i1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_i1e, Scalar)
+    bessel_i1e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_i1e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0, Scalar)
+    bessel_k0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k0e, Scalar)
+    bessel_k0e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k0e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1, Scalar)
+    bessel_k1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_k1e, Scalar)
+    bessel_k1e(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_k1e, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j0, Scalar)
+    bessel_j0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_j0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y0, Scalar)
+    bessel_y0(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_y0, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_j1, Scalar)
+    bessel_j1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_j1, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(bessel_y1, Scalar)
+    bessel_y1(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(bessel_y1, Scalar)::run(x);
+}
+
+}  // end namespace numext
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BESSEL_FUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
new file mode 100644
index 0000000..943d10f
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/BesselFunctionsPacketMath.h
@@ -0,0 +1,118 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BESSELFUNCTIONS_PACKETMATH_H
+#define EIGEN_BESSELFUNCTIONS_PACKETMATH_H
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero i0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_i0(const Packet& x) {
+  return numext::bessel_i0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero i0e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_i0e(const Packet& x) {
+  return numext::bessel_i0e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one i1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_i1(const Packet& x) {
+  return numext::bessel_i1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one i1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_i1e(const Packet& x) {
+  return numext::bessel_i1e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero j0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_j0(const Packet& x) {
+  return numext::bessel_j0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero j1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_j1(const Packet& x) {
+  return numext::bessel_j1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one y0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_y0(const Packet& x) {
+  return numext::bessel_y0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one y1(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_y1(const Packet& x) {
+  return numext::bessel_y1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero k0(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_k0(const Packet& x) {
+  return numext::bessel_k0(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order zero k0e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_k0e(const Packet& x) {
+  return numext::bessel_k0e(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one k1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_k1(const Packet& x) {
+  return numext::bessel_k1(x);
+}
+
+/** \internal \returns the exponentially scaled modified Bessel function of
+ * order one k1e(\a a) (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pbessel_k1e(const Packet& x) {
+  return numext::bessel_k1e(x);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BESSELFUNCTIONS_PACKETMATH_H
+
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
new file mode 100644
index 0000000..d7b231a
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/HipVectorCompatibility.h
@@ -0,0 +1,67 @@
+#ifndef HIP_VECTOR_COMPATIBILITY_H
+#define HIP_VECTOR_COMPATIBILITY_H
+
+namespace hip_impl {
+  template <typename, typename, unsigned int> struct Scalar_accessor;
+}   // end namespace hip_impl
+
+namespace Eigen {
+namespace internal {
+
+#define HIP_SCALAR_ACCESSOR_BUILDER(NAME) \
+template <typename T, typename U, unsigned int n> \
+struct NAME <hip_impl::Scalar_accessor<T, U, n>> : NAME <T> {};
+
+#define HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(NAME) \
+template <typename T, typename U, unsigned int n> \
+struct NAME##_impl <hip_impl::Scalar_accessor<T, U, n>> : NAME##_impl <T> {}; \
+template <typename T, typename U, unsigned int n> \
+struct NAME##_retval <hip_impl::Scalar_accessor<T, U, n>> : NAME##_retval <T> {};
+
+#define HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(NAME) \
+template <typename T, typename U, unsigned int n, IgammaComputationMode mode> \
+struct NAME <hip_impl::Scalar_accessor<T, U, n>, mode> : NAME <T, mode> {};
+
+#if EIGEN_HAS_C99_MATH
+HIP_SCALAR_ACCESSOR_BUILDER(betainc_helper)
+HIP_SCALAR_ACCESSOR_BUILDER(incbeta_cfe)
+
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erf)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(erfc)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igammac)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(lgamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(ndtri)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(polygamma)
+
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_generic_impl)
+#endif
+
+HIP_SCALAR_ACCESSOR_BUILDER(digamma_impl_maybe_poly)
+HIP_SCALAR_ACCESSOR_BUILDER(zeta_impl_series)
+
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i0e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_i1e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_j1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k0e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_k1e)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y0)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(bessel_y1)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(betainc)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(digamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(gamma_sample_der_alpha)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma_der_a)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(igamma)
+HIP_SCALAR_ACCESSOR_BUILDER_RETVAL(zeta)
+
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igamma_series_impl)
+HIP_SCALAR_ACCESSOR_BUILDER_IGAMMA(igammac_cf_impl)
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // HIP_VECTOR_COMPATIBILITY_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
new file mode 100644
index 0000000..691ff4d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsArrayAPI.h
@@ -0,0 +1,167 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
+#define EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
+
+namespace Eigen {
+
+/** \cpp11 \returns an expression of the coefficient-wise igamma(\a a, \a x) to the given arrays.
+  *
+  * This function computes the coefficient-wise incomplete gamma function.
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::igammac(), Eigen::lgamma()
+  */
+template<typename Derived,typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
+igamma(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
+{
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
+    a.derived(),
+    x.derived()
+  );
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise igamma_der_a(\a a, \a x) to the given arrays.
+  *
+  * This function computes the coefficient-wise derivative of the incomplete
+  * gamma function with respect to the parameter a.
+  *
+  * \note This function supports only float and double scalar types in c++11
+  * mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations
+  * of igamma_der_a(T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::igamma(), Eigen::lgamma()
+  */
+template <typename Derived, typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
+igamma_der_a(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_der_a_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
+    a.derived(),
+    x.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise gamma_sample_der_alpha(\a alpha, \a sample) to the given arrays.
+  *
+  * This function computes the coefficient-wise derivative of the sample
+  * of a Gamma(alpha, 1) random variable with respect to the parameter alpha.
+  *
+  * \note This function supports only float and double scalar types in c++11
+  * mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations
+  * of gamma_sample_der_alpha(T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::igamma(), Eigen::lgamma()
+  */
+template <typename AlphaDerived, typename SampleDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>, const AlphaDerived, const SampleDerived>
+gamma_sample_der_alpha(const Eigen::ArrayBase<AlphaDerived>& alpha, const Eigen::ArrayBase<SampleDerived>& sample) {
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_gamma_sample_der_alpha_op<typename AlphaDerived::Scalar>, const AlphaDerived, const SampleDerived>(
+      alpha.derived(),
+      sample.derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise igammac(\a a, \a x) to the given arrays.
+  *
+  * This function computes the coefficient-wise complementary incomplete gamma function.
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::igamma(), Eigen::lgamma()
+  */
+template<typename Derived,typename ExponentDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>
+igammac(const Eigen::ArrayBase<Derived>& a, const Eigen::ArrayBase<ExponentDerived>& x)
+{
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived>(
+    a.derived(),
+    x.derived()
+  );
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise polygamma(\a n, \a x) to the given arrays.
+  *
+  * It returns the \a n -th derivative of the digamma(psi) evaluated at \c x.
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of polygamma(T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::digamma()
+  */
+// * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
+// * \sa ArrayBase::polygamma()
+template<typename DerivedN,typename DerivedX>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN, const DerivedX>
+polygamma(const Eigen::ArrayBase<DerivedN>& n, const Eigen::ArrayBase<DerivedX>& x)
+{
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN, const DerivedX>(
+    n.derived(),
+    x.derived()
+  );
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise betainc(\a x, \a a, \a b) to the given arrays.
+  *
+  * This function computes the regularized incomplete beta function (integral).
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of betainc(T,T,T) for any scalar
+  * type T to be supported.
+  *
+  * \sa Eigen::betainc(), Eigen::lgamma()
+  */
+template<typename ArgADerived, typename ArgBDerived, typename ArgXDerived>
+EIGEN_STRONG_INLINE const Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived, const ArgBDerived, const ArgXDerived>
+betainc(const Eigen::ArrayBase<ArgADerived>& a, const Eigen::ArrayBase<ArgBDerived>& b, const Eigen::ArrayBase<ArgXDerived>& x)
+{
+  return Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived, const ArgBDerived, const ArgXDerived>(
+    a.derived(),
+    b.derived(),
+    x.derived()
+  );
+}
+
+
+/** \returns an expression of the coefficient-wise zeta(\a x, \a q) to the given arrays.
+  *
+  * It returns the Riemann zeta function of two arguments \a x and \a q:
+  *
+  * \param x is the exponent, it must be > 1
+  * \param q is the shift, it must be > 0
+  *
+  * \note This function supports only float and double scalar types. To support other scalar types, the user has
+  * to provide implementations of zeta(T,T) for any scalar type T to be supported.
+  *
+  * \sa ArrayBase::zeta()
+  */
+template<typename DerivedX,typename DerivedQ>
+EIGEN_STRONG_INLINE const Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ>
+zeta(const Eigen::ArrayBase<DerivedX>& x, const Eigen::ArrayBase<DerivedQ>& q)
+{
+  return Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ>(
+    x.derived(),
+    q.derived()
+  );
+}
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPECIALFUNCTIONS_ARRAYAPI_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
new file mode 100644
index 0000000..2d94231
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsBFloat16.h
@@ -0,0 +1,58 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
+#define EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 lgamma(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::lgamma(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 digamma(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::digamma(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 zeta(const Eigen::bfloat16& x, const Eigen::bfloat16& q) {
+  return Eigen::bfloat16(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 polygamma(const Eigen::bfloat16& n, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erf(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::erf(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 erfc(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::erfc(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 ndtri(const Eigen::bfloat16& a) {
+  return Eigen::bfloat16(Eigen::numext::ndtri(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igamma_der_a(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 gamma_sample_der_alpha(const Eigen::bfloat16& alpha, const Eigen::bfloat16& sample) {
+  return Eigen::bfloat16(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 igammac(const Eigen::bfloat16& a, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 betainc(const Eigen::bfloat16& a, const Eigen::bfloat16& b, const Eigen::bfloat16& x) {
+  return Eigen::bfloat16(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_BFLOAT16_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
new file mode 100644
index 0000000..abefe99
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsFunctors.h
@@ -0,0 +1,330 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
+#define EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
+
+namespace Eigen {
+
+namespace internal {
+
+
+/** \internal
+  * \brief Template functor to compute the incomplete gamma function igamma(a, x)
+  *
+  * \sa class CwiseBinaryOp, Cwise::igamma
+  */
+template<typename Scalar> struct scalar_igamma_op : binary_op_base<Scalar,Scalar>
+{
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
+    using numext::igamma; return igamma(a, x);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
+    return internal::pigamma(a, x);
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_igamma_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGamma
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the derivative of the incomplete gamma
+  * function igamma_der_a(a, x)
+  *
+  * \sa class CwiseBinaryOp, Cwise::igamma_der_a
+  */
+template <typename Scalar>
+struct scalar_igamma_der_a_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_igamma_der_a_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a, const Scalar& x) const {
+    using numext::igamma_der_a;
+    return igamma_der_a(a, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const {
+    return internal::pigamma_der_a(a, x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_igamma_der_a_op<Scalar> > {
+  enum {
+    // 2x the cost of igamma
+    Cost = 40 * NumTraits<Scalar>::MulCost + 20 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGammaDerA
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the derivative of the sample
+  * of a Gamma(alpha, 1) random variable with respect to the parameter alpha
+  * gamma_sample_der_alpha(alpha, sample)
+  *
+  * \sa class CwiseBinaryOp, Cwise::gamma_sample_der_alpha
+  */
+template <typename Scalar>
+struct scalar_gamma_sample_der_alpha_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_gamma_sample_der_alpha_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& alpha, const Scalar& sample) const {
+    using numext::gamma_sample_der_alpha;
+    return gamma_sample_der_alpha(alpha, sample);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& alpha, const Packet& sample) const {
+    return internal::pgamma_sample_der_alpha(alpha, sample);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_gamma_sample_der_alpha_op<Scalar> > {
+  enum {
+    // 2x the cost of igamma, minus the lgamma cost (the lgamma cancels out)
+    Cost = 30 * NumTraits<Scalar>::MulCost + 15 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasGammaSampleDerAlpha
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the complementary incomplete gamma function igammac(a, x)
+  *
+  * \sa class CwiseBinaryOp, Cwise::igammac
+  */
+template<typename Scalar> struct scalar_igammac_op : binary_op_base<Scalar,Scalar>
+{
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_igammac_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& x) const {
+    using numext::igammac; return igammac(a, x);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& x) const
+  {
+    return internal::pigammac(a, x);
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_igammac_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 20 * NumTraits<Scalar>::MulCost + 10 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasIGammac
+  };
+};
+
+
+/** \internal
+  * \brief Template functor to compute the incomplete beta integral betainc(a, b, x)
+  *
+  */
+template<typename Scalar> struct scalar_betainc_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_betainc_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& x, const Scalar& a, const Scalar& b) const {
+    using numext::betainc; return betainc(x, a, b);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& x, const Packet& a, const Packet& b) const
+  {
+    return internal::pbetainc(x, a, b);
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_betainc_op<Scalar> > {
+  enum {
+    // Guesstimate
+    Cost = 400 * NumTraits<Scalar>::MulCost + 400 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasBetaInc
+  };
+};
+
+
+/** \internal
+ * \brief Template functor to compute the natural log of the absolute
+ * value of Gamma of a scalar
+ * \sa class CwiseUnaryOp, Cwise::lgamma()
+ */
+template<typename Scalar> struct scalar_lgamma_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_lgamma_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
+    using numext::lgamma; return lgamma(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::plgamma(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_lgamma_op<Scalar> >
+{
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasLGamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute psi, the derivative of lgamma of a scalar.
+ * \sa class CwiseUnaryOp, Cwise::digamma()
+ */
+template<typename Scalar> struct scalar_digamma_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_digamma_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
+    using numext::digamma; return digamma(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pdigamma(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_digamma_op<Scalar> >
+{
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasDiGamma
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Riemann Zeta function of two arguments.
+ * \sa class CwiseUnaryOp, Cwise::zeta()
+ */
+template<typename Scalar> struct scalar_zeta_op {
+    EIGEN_EMPTY_STRUCT_CTOR(scalar_zeta_op)
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& x, const Scalar& q) const {
+        using numext::zeta; return zeta(x, q);
+    }
+    typedef typename packet_traits<Scalar>::type Packet;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x, const Packet& q) const { return internal::pzeta(x, q); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_zeta_op<Scalar> >
+{
+    enum {
+        // Guesstimate
+        Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+        PacketAccess = packet_traits<Scalar>::HasZeta
+    };
+};
+
+/** \internal
+ * \brief Template functor to compute the polygamma function.
+ * \sa class CwiseUnaryOp, Cwise::polygamma()
+ */
+template<typename Scalar> struct scalar_polygamma_op {
+    EIGEN_EMPTY_STRUCT_CTOR(scalar_polygamma_op)
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& n, const Scalar& x) const {
+        using numext::polygamma; return polygamma(n, x);
+    }
+    typedef typename packet_traits<Scalar>::type Packet;
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& n, const Packet& x) const { return internal::ppolygamma(n, x); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_polygamma_op<Scalar> >
+{
+    enum {
+        // Guesstimate
+        Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+        PacketAccess = packet_traits<Scalar>::HasPolygamma
+    };
+};
+
+/** \internal
+ * \brief Template functor to compute the error function of a scalar
+ * \sa class CwiseUnaryOp, ArrayBase::erf()
+ */
+template<typename Scalar> struct scalar_erf_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_erf_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
+  operator()(const Scalar& a) const {
+    return numext::erf(a);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& x) const {
+    return perf(x);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_erf_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasErf,
+    Cost =
+        (PacketAccess
+#ifdef EIGEN_VECTORIZE_FMA
+             // TODO(rmlarsen): Move the FMA cost model to a central location.
+             // Haswell can issue 2 add/mul/madd per cycle.
+             // 10 pmadd, 2 pmul, 1 div, 2 other
+             ? (2 * NumTraits<Scalar>::AddCost +
+                7 * NumTraits<Scalar>::MulCost +
+                scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#else
+             ? (12 * NumTraits<Scalar>::AddCost +
+                12 * NumTraits<Scalar>::MulCost +
+                scalar_div_cost<Scalar, packet_traits<Scalar>::HasDiv>::value)
+#endif
+             // Assume for simplicity that this is as expensive as an exp().
+             : (functor_traits<scalar_exp_op<Scalar> >::Cost))
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Complementary Error Function
+ * of a scalar
+ * \sa class CwiseUnaryOp, Cwise::erfc()
+ */
+template<typename Scalar> struct scalar_erfc_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_erfc_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
+    using numext::erfc; return erfc(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::perfc(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_erfc_op<Scalar> >
+{
+  enum {
+    // Guesstimate
+    Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasErfc
+  };
+};
+
+/** \internal
+ * \brief Template functor to compute the Inverse of the normal distribution
+ * function of a scalar
+ * \sa class CwiseUnaryOp, Cwise::ndtri()
+ */
+template<typename Scalar> struct scalar_ndtri_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_ndtri_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const {
+    using numext::ndtri; return ndtri(a);
+  }
+  typedef typename packet_traits<Scalar>::type Packet;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { return internal::pndtri(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_ndtri_op<Scalar> >
+{
+  enum {
+    // On average, We are evaluating rational functions with degree N=9 in the
+    // numerator and denominator. This results in 2*N additions and 2*N
+    // multiplications.
+    Cost = 18 * NumTraits<Scalar>::MulCost + 18 * NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasNdtri
+  };
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPECIALFUNCTIONS_FUNCTORS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
new file mode 100644
index 0000000..2a3a531
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsHalf.h
@@ -0,0 +1,58 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_HALF_H
+#define EIGEN_SPECIALFUNCTIONS_HALF_H
+
+namespace Eigen {
+namespace numext {
+
+#if EIGEN_HAS_C99_MATH
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half lgamma(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::lgamma(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half digamma(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::digamma(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half zeta(const Eigen::half& x, const Eigen::half& q) {
+  return Eigen::half(Eigen::numext::zeta(static_cast<float>(x), static_cast<float>(q)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half polygamma(const Eigen::half& n, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::polygamma(static_cast<float>(n), static_cast<float>(x)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erf(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::erf(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half erfc(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::erfc(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half ndtri(const Eigen::half& a) {
+  return Eigen::half(Eigen::numext::ndtri(static_cast<float>(a)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igamma(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igamma_der_a(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igamma_der_a(static_cast<float>(a), static_cast<float>(x)));
+}
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half gamma_sample_der_alpha(const Eigen::half& alpha, const Eigen::half& sample) {
+  return Eigen::half(Eigen::numext::gamma_sample_der_alpha(static_cast<float>(alpha), static_cast<float>(sample)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half igammac(const Eigen::half& a, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::igammac(static_cast<float>(a), static_cast<float>(x)));
+}
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half betainc(const Eigen::half& a, const Eigen::half& b, const Eigen::half& x) {
+  return Eigen::half(Eigen::numext::betainc(static_cast<float>(a), static_cast<float>(b), static_cast<float>(x)));
+}
+#endif
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIALFUNCTIONS_HALF_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
new file mode 100644
index 0000000..f1c260e
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsImpl.h
@@ -0,0 +1,2045 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIAL_FUNCTIONS_H
+#define EIGEN_SPECIAL_FUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+//  Parts of this code are based on the Cephes Math Library.
+//
+//  Cephes Math Library Release 2.8:  June, 2000
+//  Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier
+//
+//  Permission has been kindly provided by the original author
+//  to incorporate the Cephes software into the Eigen codebase:
+//
+//    From: Stephen Moshier
+//    To: Eugene Brevdo
+//    Subject: Re: Permission to wrap several cephes functions in Eigen
+//
+//    Hello Eugene,
+//
+//    Thank you for writing.
+//
+//    If your licensing is similar to BSD, the formal way that has been
+//    handled is simply to add a statement to the effect that you are incorporating
+//    the Cephes software by permission of the author.
+//
+//    Good luck with your project,
+//    Steve
+
+
+/****************************************************************************
+ * Implementation of lgamma, requires C++11/C99                             *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct lgamma_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+template <typename Scalar>
+struct lgamma_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+// Since glibc 2.19
+#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 19) || __GLIBC__>2) \
+ && (defined(_DEFAULT_SOURCE) || defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
+#define EIGEN_HAS_LGAMMA_R
+#endif
+
+// Glibc versions before 2.19
+#if defined(__GLIBC__) && ((__GLIBC__==2 && __GLIBC_MINOR__ < 19) || __GLIBC__<2) \
+ && (defined(_BSD_SOURCE) || defined(_SVID_SOURCE))
+#define EIGEN_HAS_LGAMMA_R
+#endif
+
+template <>
+struct lgamma_impl<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float run(float x) {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined (EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
+    int dummy;
+    return ::lgammaf_r(x, &dummy);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::lgamma(x);
+#else
+    return ::lgammaf(x);
+#endif
+  }
+};
+
+template <>
+struct lgamma_impl<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double run(double x) {
+#if !defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_HAS_LGAMMA_R) && !defined(__APPLE__)
+    int dummy;
+    return ::lgamma_r(x, &dummy);
+#elif defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::lgamma(x);
+#else
+    return ::lgamma(x);
+#endif
+  }
+};
+
+#undef EIGEN_HAS_LGAMMA_R
+#endif
+
+/****************************************************************************
+ * Implementation of digamma (psi), based on Cephes                         *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct digamma_retval {
+  typedef Scalar type;
+};
+
+/*
+ *
+ * Polynomial evaluation helper for the Psi (digamma) function.
+ *
+ * digamma_impl_maybe_poly::run(s) evaluates the asymptotic Psi expansion for
+ * input Scalar s, assuming s is above 10.0.
+ *
+ * If s is above a certain threshold for the given Scalar type, zero
+ * is returned.  Otherwise the polynomial is evaluated with enough
+ * coefficients for results matching Scalar machine precision.
+ *
+ *
+ */
+template <typename Scalar>
+struct digamma_impl_maybe_poly {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+
+template <>
+struct digamma_impl_maybe_poly<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float run(const float s) {
+    const float A[] = {
+      -4.16666666666666666667E-3f,
+      3.96825396825396825397E-3f,
+      -8.33333333333333333333E-3f,
+      8.33333333333333333333E-2f
+    };
+
+    float z;
+    if (s < 1.0e8f) {
+      z = 1.0f / (s * s);
+      return z * internal::ppolevl<float, 3>::run(z, A);
+    } else return 0.0f;
+  }
+};
+
+template <>
+struct digamma_impl_maybe_poly<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double run(const double s) {
+    const double A[] = {
+      8.33333333333333333333E-2,
+      -2.10927960927960927961E-2,
+      7.57575757575757575758E-3,
+      -4.16666666666666666667E-3,
+      3.96825396825396825397E-3,
+      -8.33333333333333333333E-3,
+      8.33333333333333333333E-2
+    };
+
+    double z;
+    if (s < 1.0e17) {
+      z = 1.0 / (s * s);
+      return z * internal::ppolevl<double, 6>::run(z, A);
+    }
+    else return 0.0;
+  }
+};
+
+template <typename Scalar>
+struct digamma_impl {
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar x) {
+    /*
+     *
+     *     Psi (digamma) function (modified for Eigen)
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double x, y, psi();
+     *
+     * y = psi( x );
+     *
+     *
+     * DESCRIPTION:
+     *
+     *              d      -
+     *   psi(x)  =  -- ln | (x)
+     *              dx
+     *
+     * is the logarithmic derivative of the gamma function.
+     * For integer x,
+     *                   n-1
+     *                    -
+     * psi(n) = -EUL  +   >  1/k.
+     *                    -
+     *                   k=1
+     *
+     * If x is negative, it is transformed to a positive argument by the
+     * reflection formula  psi(1-x) = psi(x) + pi cot(pi x).
+     * For general positive x, the argument is made greater than 10
+     * using the recurrence  psi(x+1) = psi(x) + 1/x.
+     * Then the following asymptotic expansion is applied:
+     *
+     *                           inf.   B
+     *                            -      2k
+     * psi(x) = log(x) - 1/2x -   >   -------
+     *                            -        2k
+     *                           k=1   2k x
+     *
+     * where the B2k are Bernoulli numbers.
+     *
+     * ACCURACY (float):
+     *    Relative error (except absolute when |psi| < 1):
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       1.3e-15     1.4e-16
+     *    IEEE      -30,0       40000       1.5e-15     2.2e-16
+     *
+     * ACCURACY (double):
+     *    Absolute error,  relative when |psi| > 1 :
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      -33,0        30000      8.2e-7      1.2e-7
+     *    IEEE      0,33        100000      7.3e-7      7.7e-8
+     *
+     * ERROR MESSAGES:
+     *     message         condition      value returned
+     * psi singularity    x integer <=0      INFINITY
+     */
+
+    Scalar p, q, nz, s, w, y;
+    bool negative = false;
+
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+    const Scalar m_pi = Scalar(EIGEN_PI);
+
+    const Scalar zero = Scalar(0);
+    const Scalar one = Scalar(1);
+    const Scalar half = Scalar(0.5);
+    nz = zero;
+
+    if (x <= zero) {
+      negative = true;
+      q = x;
+      p = numext::floor(q);
+      if (p == q) {
+        return nan;
+      }
+      /* Remove the zeros of tan(m_pi x)
+       * by subtracting the nearest integer from x
+       */
+      nz = q - p;
+      if (nz != half) {
+        if (nz > half) {
+          p += one;
+          nz = q - p;
+        }
+        nz = m_pi / numext::tan(m_pi * nz);
+      }
+      else {
+        nz = zero;
+      }
+      x = one - x;
+    }
+
+    /* use the recurrence psi(x+1) = psi(x) + 1/x. */
+    s = x;
+    w = zero;
+    while (s < Scalar(10)) {
+      w += one / s;
+      s += one;
+    }
+
+    y = digamma_impl_maybe_poly<Scalar>::run(s);
+
+    y = numext::log(s) - (half / s) - y - w;
+
+    return (negative) ? y - nz : y;
+  }
+};
+
+/****************************************************************************
+ * Implementation of erf, requires C++11/C99                                *
+ ****************************************************************************/
+
+/** \internal \returns the error function of \a a (coeff-wise)
+    Doesn't do anything fancy, just a 13/8-degree rational interpolant which
+    is accurate up to a couple of ulp in the range [-4, 4], outside of which
+    fl(erf(x)) = +/-1.
+
+    This implementation works on both scalars and Ts.
+*/
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_fast_erf_float(const T& a_x) {
+  // Clamp the inputs to the range [-4, 4] since anything outside
+  // this range is +/-1.0f in single-precision.
+  const T plus_4 = pset1<T>(4.f);
+  const T minus_4 = pset1<T>(-4.f);
+  const T x = pmax(pmin(a_x, plus_4), minus_4);
+  // The monomial coefficients of the numerator polynomial (odd).
+  const T alpha_1 = pset1<T>(-1.60960333262415e-02f);
+  const T alpha_3 = pset1<T>(-2.95459980854025e-03f);
+  const T alpha_5 = pset1<T>(-7.34990630326855e-04f);
+  const T alpha_7 = pset1<T>(-5.69250639462346e-05f);
+  const T alpha_9 = pset1<T>(-2.10102402082508e-06f);
+  const T alpha_11 = pset1<T>(2.77068142495902e-08f);
+  const T alpha_13 = pset1<T>(-2.72614225801306e-10f);
+
+  // The monomial coefficients of the denominator polynomial (even).
+  const T beta_0 = pset1<T>(-1.42647390514189e-02f);
+  const T beta_2 = pset1<T>(-7.37332916720468e-03f);
+  const T beta_4 = pset1<T>(-1.68282697438203e-03f);
+  const T beta_6 = pset1<T>(-2.13374055278905e-04f);
+  const T beta_8 = pset1<T>(-1.45660718464996e-05f);
+
+  // Since the polynomials are odd/even, we need x^2.
+  const T x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial p.
+  T p = pmadd(x2, alpha_13, alpha_11);
+  p = pmadd(x2, p, alpha_9);
+  p = pmadd(x2, p, alpha_7);
+  p = pmadd(x2, p, alpha_5);
+  p = pmadd(x2, p, alpha_3);
+  p = pmadd(x2, p, alpha_1);
+  p = pmul(x, p);
+
+  // Evaluate the denominator polynomial p.
+  T q = pmadd(x2, beta_8, beta_6);
+  q = pmadd(x2, q, beta_4);
+  q = pmadd(x2, q, beta_2);
+  q = pmadd(x2, q, beta_0);
+
+  // Divide the numerator by the denominator.
+  return pdiv(p, q);
+}
+
+template <typename T>
+struct erf_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE T run(const T& x) {
+    return generic_fast_erf_float(x);
+  }
+};
+
+template <typename Scalar>
+struct erf_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+template <>
+struct erf_impl<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float run(float x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erf(x);
+#else
+    return generic_fast_erf_float(x);
+#endif
+  }
+};
+
+template <>
+struct erf_impl<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double run(double x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erf(x);
+#else
+    return ::erf(x);
+#endif
+  }
+};
+#endif  // EIGEN_HAS_C99_MATH
+
+/***************************************************************************
+* Implementation of erfc, requires C++11/C99                               *
+****************************************************************************/
+
+template <typename Scalar>
+struct erfc_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+template <typename Scalar>
+struct erfc_retval {
+  typedef Scalar type;
+};
+
+#if EIGEN_HAS_C99_MATH
+template <>
+struct erfc_impl<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float run(const float x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erfc(x);
+#else
+    return ::erfcf(x);
+#endif
+  }
+};
+
+template <>
+struct erfc_impl<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double run(const double x) {
+#if defined(SYCL_DEVICE_ONLY)
+    return cl::sycl::erfc(x);
+#else
+    return ::erfc(x);
+#endif
+  }
+};
+#endif  // EIGEN_HAS_C99_MATH
+
+
+/***************************************************************************
+* Implementation of ndtri.                                                 *
+****************************************************************************/
+
+/* Inverse of Normal distribution function (modified for Eigen).
+ *
+ *
+ * SYNOPSIS:
+ *
+ * double x, y, ndtri();
+ *
+ * x = ndtri( y );
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Returns the argument, x, for which the area under the
+ * Gaussian probability density function (integrated from
+ * minus infinity to x) is equal to y.
+ *
+ *
+ * For small arguments 0 < y < exp(-2), the program computes
+ * z = sqrt( -2.0 * log(y) );  then the approximation is
+ * x = z - log(z)/z  - (1/z) P(1/z) / Q(1/z).
+ * There are two rational functions P/Q, one for 0 < y < exp(-32)
+ * and the other for y up to exp(-2).  For larger arguments,
+ * w = y - 0.5, and  x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)).
+ *
+ *
+ * ACCURACY:
+ *
+ *                      Relative error:
+ * arithmetic   domain        # trials      peak         rms
+ *    DEC      0.125, 1         5500       9.5e-17     2.1e-17
+ *    DEC      6e-39, 0.135     3500       5.7e-17     1.3e-17
+ *    IEEE     0.125, 1        20000       7.2e-16     1.3e-16
+ *    IEEE     3e-308, 0.135   50000       4.6e-16     9.8e-17
+ *
+ *
+ * ERROR MESSAGES:
+ *
+ *   message         condition    value returned
+ * ndtri domain       x <= 0        -MAXNUM
+ * ndtri domain       x >= 1         MAXNUM
+ *
+ */
+ /*
+   Cephes Math Library Release 2.2: June, 1992
+   Copyright 1985, 1987, 1992 by Stephen L. Moshier
+   Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+ */
+
+
+// TODO: Add a cheaper approximation for float.
+
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T flipsign(
+    const T& should_flipsign, const T& x) {
+  typedef typename unpacket_traits<T>::type Scalar;
+  const T sign_mask = pset1<T>(Scalar(-0.0));
+  T sign_bit = pand<T>(should_flipsign, sign_mask);
+  return pxor<T>(sign_bit, x);
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double flipsign<double>(
+    const double& should_flipsign, const double& x) {
+  return should_flipsign == 0 ? x : -x;
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float flipsign<float>(
+    const float& should_flipsign, const float& x) {
+  return should_flipsign == 0 ? x : -x;
+}
+
+// We split this computation in to two so that in the scalar path
+// only one branch is evaluated (due to our template specialization of pselect
+// being an if statement.)
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_gt_exp_neg_two(const T& b) {
+  const ScalarType p0[] = {
+    ScalarType(-5.99633501014107895267e1),
+    ScalarType(9.80010754185999661536e1),
+    ScalarType(-5.66762857469070293439e1),
+    ScalarType(1.39312609387279679503e1),
+    ScalarType(-1.23916583867381258016e0)
+  };
+  const ScalarType q0[] = {
+    ScalarType(1.0),
+    ScalarType(1.95448858338141759834e0),
+    ScalarType(4.67627912898881538453e0),
+    ScalarType(8.63602421390890590575e1),
+    ScalarType(-2.25462687854119370527e2),
+    ScalarType(2.00260212380060660359e2),
+    ScalarType(-8.20372256168333339912e1),
+    ScalarType(1.59056225126211695515e1),
+    ScalarType(-1.18331621121330003142e0)
+  };
+  const T sqrt2pi = pset1<T>(ScalarType(2.50662827463100050242e0));
+  const T half = pset1<T>(ScalarType(0.5));
+  T c, c2, ndtri_gt_exp_neg_two;
+
+  c = psub(b, half);
+  c2 = pmul(c, c);
+  ndtri_gt_exp_neg_two = pmadd(c, pmul(
+      c2, pdiv(
+          internal::ppolevl<T, 4>::run(c2, p0),
+          internal::ppolevl<T, 8>::run(c2, q0))), c);
+  return pmul(ndtri_gt_exp_neg_two, sqrt2pi);
+}
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T generic_ndtri_lt_exp_neg_two(
+    const T& b, const T& should_flipsign) {
+  /* Approximation for interval z = sqrt(-2 log a ) between 2 and 8
+   * i.e., a between exp(-2) = .135 and exp(-32) = 1.27e-14.
+   */
+  const ScalarType p1[] = {
+    ScalarType(4.05544892305962419923e0),
+    ScalarType(3.15251094599893866154e1),
+    ScalarType(5.71628192246421288162e1),
+    ScalarType(4.40805073893200834700e1),
+    ScalarType(1.46849561928858024014e1),
+    ScalarType(2.18663306850790267539e0),
+    ScalarType(-1.40256079171354495875e-1),
+    ScalarType(-3.50424626827848203418e-2),
+    ScalarType(-8.57456785154685413611e-4)
+  };
+  const ScalarType q1[] = {
+    ScalarType(1.0),
+    ScalarType(1.57799883256466749731e1),
+    ScalarType(4.53907635128879210584e1),
+    ScalarType(4.13172038254672030440e1),
+    ScalarType(1.50425385692907503408e1),
+    ScalarType(2.50464946208309415979e0),
+    ScalarType(-1.42182922854787788574e-1),
+    ScalarType(-3.80806407691578277194e-2),
+    ScalarType(-9.33259480895457427372e-4)
+  };
+  /* Approximation for interval z = sqrt(-2 log a ) between 8 and 64
+   * i.e., a between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.
+   */
+  const ScalarType p2[] = {
+    ScalarType(3.23774891776946035970e0),
+    ScalarType(6.91522889068984211695e0),
+    ScalarType(3.93881025292474443415e0),
+    ScalarType(1.33303460815807542389e0),
+    ScalarType(2.01485389549179081538e-1),
+    ScalarType(1.23716634817820021358e-2),
+    ScalarType(3.01581553508235416007e-4),
+    ScalarType(2.65806974686737550832e-6),
+    ScalarType(6.23974539184983293730e-9)
+  };
+  const ScalarType q2[] = {
+    ScalarType(1.0),
+    ScalarType(6.02427039364742014255e0),
+    ScalarType(3.67983563856160859403e0),
+    ScalarType(1.37702099489081330271e0),
+    ScalarType(2.16236993594496635890e-1),
+    ScalarType(1.34204006088543189037e-2),
+    ScalarType(3.28014464682127739104e-4),
+    ScalarType(2.89247864745380683936e-6),
+    ScalarType(6.79019408009981274425e-9)
+  };
+  const T eight = pset1<T>(ScalarType(8.0));
+  const T one = pset1<T>(ScalarType(1));
+  const T neg_two = pset1<T>(ScalarType(-2));
+  T x, x0, x1, z;
+
+  x = psqrt(pmul(neg_two, plog(b)));
+  x0 = psub(x, pdiv(plog(x), x));
+  z = pdiv(one, x);
+  x1 = pmul(
+      z, pselect(
+          pcmp_lt(x, eight),
+          pdiv(internal::ppolevl<T, 8>::run(z, p1),
+               internal::ppolevl<T, 8>::run(z, q1)),
+          pdiv(internal::ppolevl<T, 8>::run(z, p2),
+               internal::ppolevl<T, 8>::run(z, q2))));
+  return flipsign(should_flipsign, psub(x0, x1));
+}
+
+template <typename T, typename ScalarType>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T generic_ndtri(const T& a) {
+  const T maxnum = pset1<T>(NumTraits<ScalarType>::infinity());
+  const T neg_maxnum = pset1<T>(-NumTraits<ScalarType>::infinity());
+
+  const T zero = pset1<T>(ScalarType(0));
+  const T one = pset1<T>(ScalarType(1));
+  // exp(-2)
+  const T exp_neg_two = pset1<T>(ScalarType(0.13533528323661269189));
+  T b, ndtri, should_flipsign;
+
+  should_flipsign = pcmp_le(a, psub(one, exp_neg_two));
+  b = pselect(should_flipsign, a, psub(one, a));
+
+  ndtri = pselect(
+      pcmp_lt(exp_neg_two, b),
+      generic_ndtri_gt_exp_neg_two<T, ScalarType>(b),
+      generic_ndtri_lt_exp_neg_two<T, ScalarType>(b, should_flipsign));
+
+  return pselect(
+      pcmp_le(a, zero), neg_maxnum,
+      pselect(pcmp_le(one, a), maxnum, ndtri));
+}
+
+template <typename Scalar>
+struct ndtri_retval {
+  typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct ndtri_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+# else
+
+template <typename Scalar>
+struct ndtri_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar x) {
+    return generic_ndtri<Scalar, Scalar>(x);
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+
+/**************************************************************************************************************
+ * Implementation of igammac (complemented incomplete gamma integral), based on Cephes but requires C++11/C99 *
+ **************************************************************************************************************/
+
+template <typename Scalar>
+struct igammac_retval {
+  typedef Scalar type;
+};
+
+// NOTE: cephes_helper is also used to implement zeta
+template <typename Scalar>
+struct cephes_helper {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar machep() { assert(false && "machep not supported for this type"); return 0.0; }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar big() { assert(false && "big not supported for this type"); return 0.0; }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar biginv() { assert(false && "biginv not supported for this type"); return 0.0; }
+};
+
+template <>
+struct cephes_helper<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float machep() {
+    return NumTraits<float>::epsilon() / 2;  // 1.0 - machep == 1.0
+  }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float big() {
+    // use epsneg (1.0 - epsneg == 1.0)
+    return 1.0f / (NumTraits<float>::epsilon() / 2);
+  }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float biginv() {
+    // epsneg
+    return machep();
+  }
+};
+
+template <>
+struct cephes_helper<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double machep() {
+    return NumTraits<double>::epsilon() / 2;  // 1.0 - machep == 1.0
+  }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double big() {
+    return 1.0 / NumTraits<double>::epsilon();
+  }
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double biginv() {
+    // inverse of eps
+    return NumTraits<double>::epsilon();
+  }
+};
+
+enum IgammaComputationMode { VALUE, DERIVATIVE, SAMPLE_DERIVATIVE };
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC
+static EIGEN_STRONG_INLINE Scalar main_igamma_term(Scalar a, Scalar x) {
+    /* Compute  x**a * exp(-x) / gamma(a)  */
+    Scalar logax = a * numext::log(x) - x - lgamma_impl<Scalar>::run(a);
+    if (logax < -numext::log(NumTraits<Scalar>::highest()) ||
+        // Assuming x and a aren't Nan.
+        (numext::isnan)(logax)) {
+      return Scalar(0);
+    }
+    return numext::exp(logax);
+}
+
+template <typename Scalar, IgammaComputationMode mode>
+EIGEN_DEVICE_FUNC
+int igamma_num_iterations() {
+  /* Returns the maximum number of internal iterations for igamma computation.
+   */
+  if (mode == VALUE) {
+    return 2000;
+  }
+
+  if (internal::is_same<Scalar, float>::value) {
+    return 200;
+  } else if (internal::is_same<Scalar, double>::value) {
+    return 500;
+  } else {
+    return 2000;
+  }
+}
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igammac_cf_impl {
+  /* Computes igamc(a, x) or derivative (depending on the mode)
+   * using the continued fraction expansion of the complementary
+   * incomplete Gamma function.
+   *
+   * Preconditions:
+   *   a > 0
+   *   x >= 1
+   *   x >= a
+   */
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar a, Scalar x) {
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar two = 2;
+    const Scalar machep = cephes_helper<Scalar>::machep();
+    const Scalar big = cephes_helper<Scalar>::big();
+    const Scalar biginv = cephes_helper<Scalar>::biginv();
+
+    if ((numext::isinf)(x)) {
+      return zero;
+    }
+
+    Scalar ax = main_igamma_term<Scalar>(a, x);
+    // This is independent of mode. If this value is zero,
+    // then the function value is zero. If the function value is zero,
+    // then we are in a neighborhood where the function value evalutes to zero,
+    // so the derivative is zero.
+    if (ax == zero) {
+      return zero;
+    }
+
+    // continued fraction
+    Scalar y = one - a;
+    Scalar z = x + y + one;
+    Scalar c = zero;
+    Scalar pkm2 = one;
+    Scalar qkm2 = x;
+    Scalar pkm1 = x + one;
+    Scalar qkm1 = z * x;
+    Scalar ans = pkm1 / qkm1;
+
+    Scalar dpkm2_da = zero;
+    Scalar dqkm2_da = zero;
+    Scalar dpkm1_da = zero;
+    Scalar dqkm1_da = -x;
+    Scalar dans_da = (dpkm1_da - ans * dqkm1_da) / qkm1;
+
+    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
+      c += one;
+      y += one;
+      z += two;
+
+      Scalar yc = y * c;
+      Scalar pk = pkm1 * z - pkm2 * yc;
+      Scalar qk = qkm1 * z - qkm2 * yc;
+
+      Scalar dpk_da = dpkm1_da * z - pkm1 - dpkm2_da * yc + pkm2 * c;
+      Scalar dqk_da = dqkm1_da * z - qkm1 - dqkm2_da * yc + qkm2 * c;
+
+      if (qk != zero) {
+        Scalar ans_prev = ans;
+        ans = pk / qk;
+
+        Scalar dans_da_prev = dans_da;
+        dans_da = (dpk_da - ans * dqk_da) / qk;
+
+        if (mode == VALUE) {
+          if (numext::abs(ans_prev - ans) <= machep * numext::abs(ans)) {
+            break;
+          }
+        } else {
+          if (numext::abs(dans_da - dans_da_prev) <= machep) {
+            break;
+          }
+        }
+      }
+
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      dpkm2_da = dpkm1_da;
+      dpkm1_da = dpk_da;
+      dqkm2_da = dqkm1_da;
+      dqkm1_da = dqk_da;
+
+      if (numext::abs(pk) > big) {
+        pkm2 *= biginv;
+        pkm1 *= biginv;
+        qkm2 *= biginv;
+        qkm1 *= biginv;
+
+        dpkm2_da *= biginv;
+        dpkm1_da *= biginv;
+        dqkm2_da *= biginv;
+        dqkm1_da *= biginv;
+      }
+    }
+
+    /* Compute  x**a * exp(-x) / gamma(a)  */
+    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a);
+    Scalar dax_da = ax * dlogax_da;
+
+    switch (mode) {
+      case VALUE:
+        return ans * ax;
+      case DERIVATIVE:
+        return ans * dax_da + dans_da * ax;
+      case SAMPLE_DERIVATIVE:
+      default: // this is needed to suppress clang warning
+        return -(dans_da + ans * dlogax_da) * x;
+    }
+  }
+};
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_series_impl {
+  /* Computes igam(a, x) or its derivative (depending on the mode)
+   * using the series expansion of the incomplete Gamma function.
+   *
+   * Preconditions:
+   *   x > 0
+   *   a > 0
+   *   !(x > 1 && x > a)
+   */
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar a, Scalar x) {
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar machep = cephes_helper<Scalar>::machep();
+
+    Scalar ax = main_igamma_term<Scalar>(a, x);
+
+    // This is independent of mode. If this value is zero,
+    // then the function value is zero. If the function value is zero,
+    // then we are in a neighborhood where the function value evalutes to zero,
+    // so the derivative is zero.
+    if (ax == zero) {
+      return zero;
+    }
+
+    ax /= a;
+
+    /* power series */
+    Scalar r = a;
+    Scalar c = one;
+    Scalar ans = one;
+
+    Scalar dc_da = zero;
+    Scalar dans_da = zero;
+
+    for (int i = 0; i < igamma_num_iterations<Scalar, mode>(); i++) {
+      r += one;
+      Scalar term = x / r;
+      Scalar dterm_da = -x / (r * r);
+      dc_da = term * dc_da + dterm_da * c;
+      dans_da += dc_da;
+      c *= term;
+      ans += c;
+
+      if (mode == VALUE) {
+        if (c <= machep * ans) {
+          break;
+        }
+      } else {
+        if (numext::abs(dc_da) <= machep * numext::abs(dans_da)) {
+          break;
+        }
+      }
+    }
+
+    Scalar dlogax_da = numext::log(x) - digamma_impl<Scalar>::run(a + one);
+    Scalar dax_da = ax * dlogax_da;
+
+    switch (mode) {
+      case VALUE:
+        return ans * ax;
+      case DERIVATIVE:
+        return ans * dax_da + dans_da * ax;
+      case SAMPLE_DERIVATIVE:
+      default: // this is needed to suppress clang warning
+        return -(dans_da + ans * dlogax_da) * x / a;
+    }
+  }
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct igammac_impl {
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar a, Scalar x) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+#else
+
+template <typename Scalar>
+struct igammac_impl {
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar a, Scalar x) {
+    /*  igamc()
+     *
+     *	Incomplete gamma integral (modified for Eigen)
+     *
+     *
+     *
+     * SYNOPSIS:
+     *
+     * double a, x, y, igamc();
+     *
+     * y = igamc( a, x );
+     *
+     * DESCRIPTION:
+     *
+     * The function is defined by
+     *
+     *
+     *  igamc(a,x)   =   1 - igam(a,x)
+     *
+     *                            inf.
+     *                              -
+     *                     1       | |  -t  a-1
+     *               =   -----     |   e   t   dt.
+     *                    -      | |
+     *                   | (a)    -
+     *                             x
+     *
+     *
+     * In this implementation both arguments must be positive.
+     * The integral is evaluated by either a power series or
+     * continued fraction expansion, depending on the relative
+     * values of a and x.
+     *
+     * ACCURACY (float):
+     *
+     *                      Relative error:
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,30        30000       7.8e-6      5.9e-7
+     *
+     *
+     * ACCURACY (double):
+     *
+     * Tested at random a, x.
+     *                a         x                      Relative error:
+     * arithmetic   domain   domain     # trials      peak         rms
+     *    IEEE     0.5,100   0,100      200000       1.9e-14     1.7e-15
+     *    IEEE     0.01,0.5  0,100      200000       1.4e-13     1.6e-15
+     *
+     */
+    /*
+      Cephes Math Library Release 2.2: June, 1992
+      Copyright 1985, 1987, 1992 by Stephen L. Moshier
+      Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+    */
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    if ((x < zero) || (a <= zero)) {
+      // domain error
+      return nan;
+    }
+
+    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
+      return nan;
+    }
+
+    if ((x < one) || (x < a)) {
+      return (one - igamma_series_impl<Scalar, VALUE>::run(a, x));
+    }
+
+    return igammac_cf_impl<Scalar, VALUE>::run(a, x);
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+/************************************************************************************************
+ * Implementation of igamma (incomplete gamma integral), based on Cephes but requires C++11/C99 *
+ ************************************************************************************************/
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_generic_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar x) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+#else
+
+template <typename Scalar, IgammaComputationMode mode>
+struct igamma_generic_impl {
+  EIGEN_DEVICE_FUNC
+  static Scalar run(Scalar a, Scalar x) {
+    /* Depending on the mode, returns
+     * - VALUE: incomplete Gamma function igamma(a, x)
+     * - DERIVATIVE: derivative of incomplete Gamma function d/da igamma(a, x)
+     * - SAMPLE_DERIVATIVE: implicit derivative of a Gamma random variable
+     * x ~ Gamma(x | a, 1), dx/da = -1 / Gamma(x | a, 1) * d igamma(a, x) / dx
+     *
+     * Derivatives are implemented by forward-mode differentiation.
+     */
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+    if (x == zero) return zero;
+
+    if ((x < zero) || (a <= zero)) {  // domain error
+      return nan;
+    }
+
+    if ((numext::isnan)(a) || (numext::isnan)(x)) {  // propagate nans
+      return nan;
+    }
+
+    if ((x > one) && (x > a)) {
+      Scalar ret = igammac_cf_impl<Scalar, mode>::run(a, x);
+      if (mode == VALUE) {
+        return one - ret;
+      } else {
+        return -ret;
+      }
+    }
+
+    return igamma_series_impl<Scalar, mode>::run(a, x);
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct igamma_retval {
+  typedef Scalar type;
+};
+
+template <typename Scalar>
+struct igamma_impl : igamma_generic_impl<Scalar, VALUE> {
+  /* igam()
+   * Incomplete gamma integral.
+   *
+   * The CDF of Gamma(a, 1) random variable at the point x.
+   *
+   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
+   * The ground truth is computed by mpmath. Mean absolute error:
+   * float: 1.26713e-05
+   * double: 2.33606e-12
+   *
+   * Cephes documentation below.
+   *
+   * SYNOPSIS:
+   *
+   * double a, x, y, igam();
+   *
+   * y = igam( a, x );
+   *
+   * DESCRIPTION:
+   *
+   * The function is defined by
+   *
+   *                           x
+   *                            -
+   *                   1       | |  -t  a-1
+   *  igam(a,x)  =   -----     |   e   t   dt.
+   *                  -      | |
+   *                 | (a)    -
+   *                           0
+   *
+   *
+   * In this implementation both arguments must be positive.
+   * The integral is evaluated by either a power series or
+   * continued fraction expansion, depending on the relative
+   * values of a and x.
+   *
+   * ACCURACY (double):
+   *
+   *                      Relative error:
+   * arithmetic   domain     # trials      peak         rms
+   *    IEEE      0,30       200000       3.6e-14     2.9e-15
+   *    IEEE      0,100      300000       9.9e-14     1.5e-14
+   *
+   *
+   * ACCURACY (float):
+   *
+   *                      Relative error:
+   * arithmetic   domain     # trials      peak         rms
+   *    IEEE      0,30        20000       7.8e-6      5.9e-7
+   *
+   */
+  /*
+    Cephes Math Library Release 2.2: June, 1992
+    Copyright 1985, 1987, 1992 by Stephen L. Moshier
+    Direct inquiries to 30 Frost Street, Cambridge, MA 02140
+  */
+
+  /* left tail of incomplete gamma function:
+   *
+   *          inf.      k
+   *   a  -x   -       x
+   *  x  e     >   ----------
+   *           -     -
+   *          k=0   | (a+k+1)
+   *
+   */
+};
+
+template <typename Scalar>
+struct igamma_der_a_retval : igamma_retval<Scalar> {};
+
+template <typename Scalar>
+struct igamma_der_a_impl : igamma_generic_impl<Scalar, DERIVATIVE> {
+  /* Derivative of the incomplete Gamma function with respect to a.
+   *
+   * Computes d/da igamma(a, x) by forward differentiation of the igamma code.
+   *
+   * Accuracy estimation. For each a in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables x ~ Gamma(x | a, 1), a total of 300 points.
+   * The ground truth is computed by mpmath. Mean absolute error:
+   * float: 6.17992e-07
+   * double: 4.60453e-12
+   *
+   * Reference:
+   * R. Moore. "Algorithm AS 187: Derivatives of the incomplete gamma
+   * integral". Journal of the Royal Statistical Society. 1982
+   */
+};
+
+template <typename Scalar>
+struct gamma_sample_der_alpha_retval : igamma_retval<Scalar> {};
+
+template <typename Scalar>
+struct gamma_sample_der_alpha_impl
+    : igamma_generic_impl<Scalar, SAMPLE_DERIVATIVE> {
+  /* Derivative of a Gamma random variable sample with respect to alpha.
+   *
+   * Consider a sample of a Gamma random variable with the concentration
+   * parameter alpha: sample ~ Gamma(alpha, 1). The reparameterization
+   * derivative that we want to compute is dsample / dalpha =
+   * d igammainv(alpha, u) / dalpha, where u = igamma(alpha, sample).
+   * However, this formula is numerically unstable and expensive, so instead
+   * we use implicit differentiation:
+   *
+   * igamma(alpha, sample) = u, where u ~ Uniform(0, 1).
+   * Apply d / dalpha to both sides:
+   * d igamma(alpha, sample) / dalpha
+   *     + d igamma(alpha, sample) / dsample * dsample/dalpha  = 0
+   * d igamma(alpha, sample) / dalpha
+   *     + Gamma(sample | alpha, 1) dsample / dalpha = 0
+   * dsample/dalpha = - (d igamma(alpha, sample) / dalpha)
+   *                   / Gamma(sample | alpha, 1)
+   *
+   * Here Gamma(sample | alpha, 1) is the PDF of the Gamma distribution
+   * (note that the derivative of the CDF w.r.t. sample is the PDF).
+   * See the reference below for more details.
+   *
+   * The derivative of igamma(alpha, sample) is computed by forward
+   * differentiation of the igamma code. Division by the Gamma PDF is performed
+   * in the same code, increasing the accuracy and speed due to cancellation
+   * of some terms.
+   *
+   * Accuracy estimation. For each alpha in [10^-2, 10^-1...10^3] we sample
+   * 50 Gamma random variables sample ~ Gamma(sample | alpha, 1), a total of 300
+   * points. The ground truth is computed by mpmath. Mean absolute error:
+   * float: 2.1686e-06
+   * double: 1.4774e-12
+   *
+   * Reference:
+   * M. Figurnov, S. Mohamed, A. Mnih "Implicit Reparameterization Gradients".
+   * 2018
+   */
+};
+
+/*****************************************************************************
+ * Implementation of Riemann zeta function of two arguments, based on Cephes *
+ *****************************************************************************/
+
+template <typename Scalar>
+struct zeta_retval {
+    typedef Scalar type;
+};
+
+template <typename Scalar>
+struct zeta_impl_series {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Scalar) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+template <>
+struct zeta_impl_series<float> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE bool run(float& a, float& b, float& s, const float x, const float machep) {
+    int i = 0;
+    while(i < 9)
+    {
+        i += 1;
+        a += 1.0f;
+        b = numext::pow( a, -x );
+        s += b;
+        if( numext::abs(b/s) < machep )
+            return true;
+    }
+
+    //Return whether we are done
+    return false;
+  }
+};
+
+template <>
+struct zeta_impl_series<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE bool run(double& a, double& b, double& s, const double x, const double machep) {
+    int i = 0;
+    while( (i < 9) || (a <= 9.0) )
+    {
+        i += 1;
+        a += 1.0;
+        b = numext::pow( a, -x );
+        s += b;
+        if( numext::abs(b/s) < machep )
+            return true;
+    }
+
+    //Return whether we are done
+    return false;
+  }
+};
+
+template <typename Scalar>
+struct zeta_impl {
+    EIGEN_DEVICE_FUNC
+    static Scalar run(Scalar x, Scalar q) {
+        /*							zeta.c
+         *
+         *	Riemann zeta function of two arguments
+         *
+         *
+         *
+         * SYNOPSIS:
+         *
+         * double x, q, y, zeta();
+         *
+         * y = zeta( x, q );
+         *
+         *
+         *
+         * DESCRIPTION:
+         *
+         *
+         *
+         *                 inf.
+         *                  -        -x
+         *   zeta(x,q)  =   >   (k+q)
+         *                  -
+         *                 k=0
+         *
+         * where x > 1 and q is not a negative integer or zero.
+         * The Euler-Maclaurin summation formula is used to obtain
+         * the expansion
+         *
+         *                n
+         *                -       -x
+         * zeta(x,q)  =   >  (k+q)
+         *                -
+         *               k=1
+         *
+         *           1-x                 inf.  B   x(x+1)...(x+2j)
+         *      (n+q)           1         -     2j
+         *  +  ---------  -  -------  +   >    --------------------
+         *        x-1              x      -                   x+2j+1
+         *                   2(n+q)      j=1       (2j)! (n+q)
+         *
+         * where the B2j are Bernoulli numbers.  Note that (see zetac.c)
+         * zeta(x,1) = zetac(x) + 1.
+         *
+         *
+         *
+         * ACCURACY:
+         *
+         * Relative error for single precision:
+         * arithmetic   domain     # trials      peak         rms
+         *    IEEE      0,25        10000       6.9e-7      1.0e-7
+         *
+         * Large arguments may produce underflow in powf(), in which
+         * case the results are inaccurate.
+         *
+         * REFERENCE:
+         *
+         * Gradshteyn, I. S., and I. M. Ryzhik, Tables of Integrals,
+         * Series, and Products, p. 1073; Academic Press, 1980.
+         *
+         */
+
+        int i;
+        Scalar p, r, a, b, k, s, t, w;
+
+        const Scalar A[] = {
+            Scalar(12.0),
+            Scalar(-720.0),
+            Scalar(30240.0),
+            Scalar(-1209600.0),
+            Scalar(47900160.0),
+            Scalar(-1.8924375803183791606e9), /*1.307674368e12/691*/
+            Scalar(7.47242496e10),
+            Scalar(-2.950130727918164224e12), /*1.067062284288e16/3617*/
+            Scalar(1.1646782814350067249e14), /*5.109094217170944e18/43867*/
+            Scalar(-4.5979787224074726105e15), /*8.028576626982912e20/174611*/
+            Scalar(1.8152105401943546773e17), /*1.5511210043330985984e23/854513*/
+            Scalar(-7.1661652561756670113e18) /*1.6938241367317436694528e27/236364091*/
+            };
+
+        const Scalar maxnum = NumTraits<Scalar>::infinity();
+        const Scalar zero = 0.0, half = 0.5, one = 1.0;
+        const Scalar machep = cephes_helper<Scalar>::machep();
+        const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+        if( x == one )
+            return maxnum;
+
+        if( x < one )
+        {
+            return nan;
+        }
+
+        if( q <= zero )
+        {
+            if(q == numext::floor(q))
+            {
+                if (x == numext::floor(x) && long(x) % 2 == 0) {
+                    return maxnum;
+                }
+                else {
+                    return nan;
+                }
+            }
+            p = x;
+            r = numext::floor(p);
+            if (p != r)
+                return nan;
+        }
+
+        /* Permit negative q but continue sum until n+q > +9 .
+         * This case should be handled by a reflection formula.
+         * If q<0 and x is an integer, there is a relation to
+         * the polygamma function.
+         */
+        s = numext::pow( q, -x );
+        a = q;
+        b = zero;
+        // Run the summation in a helper function that is specific to the floating precision
+        if (zeta_impl_series<Scalar>::run(a, b, s, x, machep)) {
+            return s;
+        }
+
+        w = a;
+        s += b*w/(x-one);
+        s -= half * b;
+        a = one;
+        k = zero;
+        for( i=0; i<12; i++ )
+        {
+            a *= x + k;
+            b /= w;
+            t = a*b/A[i];
+            s = s + t;
+            t = numext::abs(t/s);
+            if( t < machep ) {
+              break;
+            }
+            k += one;
+            a *= x + k;
+            b /= w;
+            k += one;
+        }
+        return s;
+  }
+};
+
+/****************************************************************************
+ * Implementation of polygamma function, requires C++11/C99                 *
+ ****************************************************************************/
+
+template <typename Scalar>
+struct polygamma_retval {
+    typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct polygamma_impl {
+    EIGEN_DEVICE_FUNC
+    static EIGEN_STRONG_INLINE Scalar run(Scalar n, Scalar x) {
+        EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                            THIS_TYPE_IS_NOT_SUPPORTED);
+        return Scalar(0);
+    }
+};
+
+#else
+
+template <typename Scalar>
+struct polygamma_impl {
+    EIGEN_DEVICE_FUNC
+    static Scalar run(Scalar n, Scalar x) {
+        Scalar zero = 0.0, one = 1.0;
+        Scalar nplus = n + one;
+        const Scalar nan = NumTraits<Scalar>::quiet_NaN();
+
+        // Check that n is a non-negative integer
+        if (numext::floor(n) != n || n < zero) {
+            return nan;
+        }
+        // Just return the digamma function for n = 0
+        else if (n == zero) {
+            return digamma_impl<Scalar>::run(x);
+        }
+        // Use the same implementation as scipy
+        else {
+            Scalar factorial = numext::exp(lgamma_impl<Scalar>::run(nplus));
+            return numext::pow(-one, nplus) * factorial * zeta_impl<Scalar>::run(nplus, x);
+        }
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+/************************************************************************************************
+ * Implementation of betainc (incomplete beta integral), based on Cephes but requires C++11/C99 *
+ ************************************************************************************************/
+
+template <typename Scalar>
+struct betainc_retval {
+  typedef Scalar type;
+};
+
+#if !EIGEN_HAS_C99_MATH
+
+template <typename Scalar>
+struct betainc_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+#else
+
+template <typename Scalar>
+struct betainc_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(Scalar, Scalar, Scalar) {
+    /*	betaincf.c
+     *
+     *	Incomplete beta integral
+     *
+     *
+     * SYNOPSIS:
+     *
+     * float a, b, x, y, betaincf();
+     *
+     * y = betaincf( a, b, x );
+     *
+     *
+     * DESCRIPTION:
+     *
+     * Returns incomplete beta integral of the arguments, evaluated
+     * from zero to x.  The function is defined as
+     *
+     *                  x
+     *     -            -
+     *    | (a+b)      | |  a-1     b-1
+     *  -----------    |   t   (1-t)   dt.
+     *   -     -     | |
+     *  | (a) | (b)   -
+     *                 0
+     *
+     * The domain of definition is 0 <= x <= 1.  In this
+     * implementation a and b are restricted to positive values.
+     * The integral from x to 1 may be obtained by the symmetry
+     * relation
+     *
+     *    1 - betainc( a, b, x )  =  betainc( b, a, 1-x ).
+     *
+     * The integral is evaluated by a continued fraction expansion.
+     * If a < 1, the function calls itself recursively after a
+     * transformation to increase a to a+1.
+     *
+     * ACCURACY (float):
+     *
+     * Tested at random points (a,b,x) with a and b in the indicated
+     * interval and x between 0 and 1.
+     *
+     * arithmetic   domain     # trials      peak         rms
+     * Relative error:
+     *    IEEE       0,30       10000       3.7e-5      5.1e-6
+     *    IEEE       0,100      10000       1.7e-4      2.5e-5
+     * The useful domain for relative error is limited by underflow
+     * of the single precision exponential function.
+     * Absolute error:
+     *    IEEE       0,30      100000       2.2e-5      9.6e-7
+     *    IEEE       0,100      10000       6.5e-5      3.7e-6
+     *
+     * Larger errors may occur for extreme ratios of a and b.
+     *
+     * ACCURACY (double):
+     * arithmetic   domain     # trials      peak         rms
+     *    IEEE      0,5         10000       6.9e-15     4.5e-16
+     *    IEEE      0,85       250000       2.2e-13     1.7e-14
+     *    IEEE      0,1000      30000       5.3e-12     6.3e-13
+     *    IEEE      0,10000    250000       9.3e-11     7.1e-12
+     *    IEEE      0,100000    10000       8.7e-10     4.8e-11
+     * Outputs smaller than the IEEE gradual underflow threshold
+     * were excluded from these statistics.
+     *
+     * ERROR MESSAGES:
+     *   message         condition      value returned
+     * incbet domain      x<0, x>1          nan
+     * incbet underflow                     nan
+     */
+
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    return Scalar(0);
+  }
+};
+
+/* Continued fraction expansion #1 for incomplete beta integral (small_branch = True)
+ * Continued fraction expansion #2 for incomplete beta integral (small_branch = False)
+ */
+template <typename Scalar>
+struct incbeta_cfe {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(Scalar a, Scalar b, Scalar x, bool small_branch) {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar, float>::value ||
+                         internal::is_same<Scalar, double>::value),
+                        THIS_TYPE_IS_NOT_SUPPORTED);
+    const Scalar big = cephes_helper<Scalar>::big();
+    const Scalar machep = cephes_helper<Scalar>::machep();
+    const Scalar biginv = cephes_helper<Scalar>::biginv();
+
+    const Scalar zero = 0;
+    const Scalar one = 1;
+    const Scalar two = 2;
+
+    Scalar xk, pk, pkm1, pkm2, qk, qkm1, qkm2;
+    Scalar k1, k2, k3, k4, k5, k6, k7, k8, k26update;
+    Scalar ans;
+    int n;
+
+    const int num_iters = (internal::is_same<Scalar, float>::value) ? 100 : 300;
+    const Scalar thresh =
+        (internal::is_same<Scalar, float>::value) ? machep : Scalar(3) * machep;
+    Scalar r = (internal::is_same<Scalar, float>::value) ? zero : one;
+
+    if (small_branch) {
+      k1 = a;
+      k2 = a + b;
+      k3 = a;
+      k4 = a + one;
+      k5 = one;
+      k6 = b - one;
+      k7 = k4;
+      k8 = a + two;
+      k26update = one;
+    } else {
+      k1 = a;
+      k2 = b - one;
+      k3 = a;
+      k4 = a + one;
+      k5 = one;
+      k6 = a + b;
+      k7 = a + one;
+      k8 = a + two;
+      k26update = -one;
+      x = x / (one - x);
+    }
+
+    pkm2 = zero;
+    qkm2 = one;
+    pkm1 = one;
+    qkm1 = one;
+    ans = one;
+    n = 0;
+
+    do {
+      xk = -(x * k1 * k2) / (k3 * k4);
+      pk = pkm1 + pkm2 * xk;
+      qk = qkm1 + qkm2 * xk;
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      xk = (x * k5 * k6) / (k7 * k8);
+      pk = pkm1 + pkm2 * xk;
+      qk = qkm1 + qkm2 * xk;
+      pkm2 = pkm1;
+      pkm1 = pk;
+      qkm2 = qkm1;
+      qkm1 = qk;
+
+      if (qk != zero) {
+        r = pk / qk;
+        if (numext::abs(ans - r) < numext::abs(r) * thresh) {
+          return r;
+        }
+        ans = r;
+      }
+
+      k1 += one;
+      k2 += k26update;
+      k3 += two;
+      k4 += two;
+      k5 += one;
+      k6 -= k26update;
+      k7 += two;
+      k8 += two;
+
+      if ((numext::abs(qk) + numext::abs(pk)) > big) {
+        pkm2 *= biginv;
+        pkm1 *= biginv;
+        qkm2 *= biginv;
+        qkm1 *= biginv;
+      }
+      if ((numext::abs(qk) < biginv) || (numext::abs(pk) < biginv)) {
+        pkm2 *= big;
+        pkm1 *= big;
+        qkm2 *= big;
+        qkm1 *= big;
+      }
+    } while (++n < num_iters);
+
+    return ans;
+  }
+};
+
+/* Helper functions depending on the Scalar type */
+template <typename Scalar>
+struct betainc_helper {};
+
+template <>
+struct betainc_helper<float> {
+  /* Core implementation, assumes a large (> 1.0) */
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE float incbsa(float aa, float bb,
+                                                            float xx) {
+    float ans, a, b, t, x, onemx;
+    bool reversed_a_b = false;
+
+    onemx = 1.0f - xx;
+
+    /* see if x is greater than the mean */
+    if (xx > (aa / (aa + bb))) {
+      reversed_a_b = true;
+      a = bb;
+      b = aa;
+      t = xx;
+      x = onemx;
+    } else {
+      a = aa;
+      b = bb;
+      t = onemx;
+      x = xx;
+    }
+
+    /* Choose expansion for optimal convergence */
+    if (b > 10.0f) {
+      if (numext::abs(b * x / a) < 0.3f) {
+        t = betainc_helper<float>::incbps(a, b, x);
+        if (reversed_a_b) t = 1.0f - t;
+        return t;
+      }
+    }
+
+    ans = x * (a + b - 2.0f) / (a - 1.0f);
+    if (ans < 1.0f) {
+      ans = incbeta_cfe<float>::run(a, b, x, true /* small_branch */);
+      t = b * numext::log(t);
+    } else {
+      ans = incbeta_cfe<float>::run(a, b, x, false /* small_branch */);
+      t = (b - 1.0f) * numext::log(t);
+    }
+
+    t += a * numext::log(x) + lgamma_impl<float>::run(a + b) -
+         lgamma_impl<float>::run(a) - lgamma_impl<float>::run(b);
+    t += numext::log(ans / a);
+    t = numext::exp(t);
+
+    if (reversed_a_b) t = 1.0f - t;
+    return t;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE float incbps(float a, float b, float x) {
+    float t, u, y, s;
+    const float machep = cephes_helper<float>::machep();
+
+    y = a * numext::log(x) + (b - 1.0f) * numext::log1p(-x) - numext::log(a);
+    y -= lgamma_impl<float>::run(a) + lgamma_impl<float>::run(b);
+    y += lgamma_impl<float>::run(a + b);
+
+    t = x / (1.0f - x);
+    s = 0.0f;
+    u = 1.0f;
+    do {
+      b -= 1.0f;
+      if (b == 0.0f) {
+        break;
+      }
+      a += 1.0f;
+      u *= t * b / a;
+      s += u;
+    } while (numext::abs(u) > machep);
+
+    return numext::exp(y) * (1.0f + s);
+  }
+};
+
+template <>
+struct betainc_impl<float> {
+  EIGEN_DEVICE_FUNC
+  static float run(float a, float b, float x) {
+    const float nan = NumTraits<float>::quiet_NaN();
+    float ans, t;
+
+    if (a <= 0.0f) return nan;
+    if (b <= 0.0f) return nan;
+    if ((x <= 0.0f) || (x >= 1.0f)) {
+      if (x == 0.0f) return 0.0f;
+      if (x == 1.0f) return 1.0f;
+      // mtherr("betaincf", DOMAIN);
+      return nan;
+    }
+
+    /* transformation for small aa */
+    if (a <= 1.0f) {
+      ans = betainc_helper<float>::incbsa(a + 1.0f, b, x);
+      t = a * numext::log(x) + b * numext::log1p(-x) +
+          lgamma_impl<float>::run(a + b) - lgamma_impl<float>::run(a + 1.0f) -
+          lgamma_impl<float>::run(b);
+      return (ans + numext::exp(t));
+    } else {
+      return betainc_helper<float>::incbsa(a, b, x);
+    }
+  }
+};
+
+template <>
+struct betainc_helper<double> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE double incbps(double a, double b, double x) {
+    const double machep = cephes_helper<double>::machep();
+
+    double s, t, u, v, n, t1, z, ai;
+
+    ai = 1.0 / a;
+    u = (1.0 - b) * x;
+    v = u / (a + 1.0);
+    t1 = v;
+    t = u;
+    n = 2.0;
+    s = 0.0;
+    z = machep * ai;
+    while (numext::abs(v) > z) {
+      u = (n - b) * x / n;
+      t *= u;
+      v = t / (a + n);
+      s += v;
+      n += 1.0;
+    }
+    s += t1;
+    s += ai;
+
+    u = a * numext::log(x);
+    // TODO: gamma() is not directly implemented in Eigen.
+    /*
+    if ((a + b) < maxgam && numext::abs(u) < maxlog) {
+      t = gamma(a + b) / (gamma(a) * gamma(b));
+      s = s * t * pow(x, a);
+    }
+    */
+    t = lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) -
+        lgamma_impl<double>::run(b) + u + numext::log(s);
+    return s = numext::exp(t);
+  }
+};
+
+template <>
+struct betainc_impl<double> {
+  EIGEN_DEVICE_FUNC
+  static double run(double aa, double bb, double xx) {
+    const double nan = NumTraits<double>::quiet_NaN();
+    const double machep = cephes_helper<double>::machep();
+    // const double maxgam = 171.624376956302725;
+
+    double a, b, t, x, xc, w, y;
+    bool reversed_a_b = false;
+
+    if (aa <= 0.0 || bb <= 0.0) {
+      return nan;  // goto domerr;
+    }
+
+    if ((xx <= 0.0) || (xx >= 1.0)) {
+      if (xx == 0.0) return (0.0);
+      if (xx == 1.0) return (1.0);
+      // mtherr("incbet", DOMAIN);
+      return nan;
+    }
+
+    if ((bb * xx) <= 1.0 && xx <= 0.95) {
+      return betainc_helper<double>::incbps(aa, bb, xx);
+    }
+
+    w = 1.0 - xx;
+
+    /* Reverse a and b if x is greater than the mean. */
+    if (xx > (aa / (aa + bb))) {
+      reversed_a_b = true;
+      a = bb;
+      b = aa;
+      xc = xx;
+      x = w;
+    } else {
+      a = aa;
+      b = bb;
+      xc = w;
+      x = xx;
+    }
+
+    if (reversed_a_b && (b * x) <= 1.0 && x <= 0.95) {
+      t = betainc_helper<double>::incbps(a, b, x);
+      if (t <= machep) {
+        t = 1.0 - machep;
+      } else {
+        t = 1.0 - t;
+      }
+      return t;
+    }
+
+    /* Choose expansion for better convergence. */
+    y = x * (a + b - 2.0) - (a - 1.0);
+    if (y < 0.0) {
+      w = incbeta_cfe<double>::run(a, b, x, true /* small_branch */);
+    } else {
+      w = incbeta_cfe<double>::run(a, b, x, false /* small_branch */) / xc;
+    }
+
+    /* Multiply w by the factor
+         a      b   _             _     _
+        x  (1-x)   | (a+b) / ( a | (a) | (b) ) .   */
+
+    y = a * numext::log(x);
+    t = b * numext::log(xc);
+    // TODO: gamma is not directly implemented in Eigen.
+    /*
+    if ((a + b) < maxgam && numext::abs(y) < maxlog && numext::abs(t) < maxlog)
+    {
+      t = pow(xc, b);
+      t *= pow(x, a);
+      t /= a;
+      t *= w;
+      t *= gamma(a + b) / (gamma(a) * gamma(b));
+    } else {
+    */
+    /* Resort to logarithms.  */
+    y += t + lgamma_impl<double>::run(a + b) - lgamma_impl<double>::run(a) -
+         lgamma_impl<double>::run(b);
+    y += numext::log(w / a);
+    t = numext::exp(y);
+
+    /* } */
+    // done:
+
+    if (reversed_a_b) {
+      if (t <= machep) {
+        t = 1.0 - machep;
+      } else {
+        t = 1.0 - t;
+      }
+    }
+    return t;
+  }
+};
+
+#endif  // EIGEN_HAS_C99_MATH
+
+}  // end namespace internal
+
+namespace numext {
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar)
+    lgamma(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(digamma, Scalar)
+    digamma(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(digamma, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(zeta, Scalar)
+zeta(const Scalar& x, const Scalar& q) {
+    return EIGEN_MATHFUNC_IMPL(zeta, Scalar)::run(x, q);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(polygamma, Scalar)
+polygamma(const Scalar& n, const Scalar& x) {
+    return EIGEN_MATHFUNC_IMPL(polygamma, Scalar)::run(n, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erf, Scalar)
+    erf(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar)
+    erfc(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(ndtri, Scalar)
+    ndtri(const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(ndtri, Scalar)::run(x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma, Scalar)
+    igamma(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igamma, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igamma_der_a, Scalar)
+    igamma_der_a(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igamma_der_a, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(gamma_sample_der_alpha, Scalar)
+    gamma_sample_der_alpha(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(gamma_sample_der_alpha, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(igammac, Scalar)
+    igammac(const Scalar& a, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(igammac, Scalar)::run(a, x);
+}
+
+template <typename Scalar>
+EIGEN_DEVICE_FUNC inline EIGEN_MATHFUNC_RETVAL(betainc, Scalar)
+    betainc(const Scalar& a, const Scalar& b, const Scalar& x) {
+  return EIGEN_MATHFUNC_IMPL(betainc, Scalar)::run(a, b, x);
+}
+
+}  // end namespace numext
+}  // end namespace Eigen
+
+#endif  // EIGEN_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
new file mode 100644
index 0000000..2bb0179
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/SpecialFunctionsPacketMath.h
@@ -0,0 +1,79 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
+#define EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \returns the ln(|gamma(\a a)|) (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plgamma(const Packet& a) { using numext::lgamma; return lgamma(a); }
+
+/** \internal \returns the derivative of lgamma, psi(\a a) (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pdigamma(const Packet& a) { using numext::digamma; return digamma(a); }
+
+/** \internal \returns the zeta function of two arguments (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pzeta(const Packet& x, const Packet& q) { using numext::zeta; return zeta(x, q); }
+
+/** \internal \returns the polygamma function (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet ppolygamma(const Packet& n, const Packet& x) { using numext::polygamma; return polygamma(n, x); }
+
+/** \internal \returns the erf(\a a) (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet perf(const Packet& a) { using numext::erf; return erf(a); }
+
+/** \internal \returns the erfc(\a a) (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet perfc(const Packet& a) { using numext::erfc; return erfc(a); }
+
+/** \internal \returns the ndtri(\a a) (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pndtri(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  using internal::generic_ndtri; return generic_ndtri<Packet, ScalarType>(a);
+}
+
+/** \internal \returns the incomplete gamma function igamma(\a a, \a x) */
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Packet pigamma(const Packet& a, const Packet& x) { using numext::igamma; return igamma(a, x); }
+
+/** \internal \returns the derivative of the incomplete gamma function
+ * igamma_der_a(\a a, \a x) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pigamma_der_a(const Packet& a, const Packet& x) {
+  using numext::igamma_der_a; return igamma_der_a(a, x);
+}
+
+/** \internal \returns compute the derivative of the sample
+  * of Gamma(alpha, 1) random variable with respect to the parameter a
+  * gamma_sample_der_alpha(\a alpha, \a sample) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pgamma_sample_der_alpha(const Packet& alpha, const Packet& sample) {
+  using numext::gamma_sample_der_alpha; return gamma_sample_der_alpha(alpha, sample);
+}
+
+/** \internal \returns the complementary incomplete gamma function igammac(\a a, \a x) */
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Packet pigammac(const Packet& a, const Packet& x) { using numext::igammac; return igammac(a, x); }
+
+/** \internal \returns the complementary incomplete gamma function betainc(\a a, \a b, \a x) */
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Packet pbetainc(const Packet& a, const Packet& b,const Packet& x) { using numext::betainc; return betainc(a, b, x); }
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPECIALFUNCTIONS_PACKETMATH_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
new file mode 100644
index 0000000..2d76692
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/BesselFunctions.h
@@ -0,0 +1,46 @@
+#ifndef EIGEN_AVX_BESSELFUNCTIONS_H
+#define EIGEN_AVX_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i0)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i0)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i0e)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i0e)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i1)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i1)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_i1e)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_i1e)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_j0)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_j0)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_j1)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_j1)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k0)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k0)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k0e)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k0e)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k1)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k1)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_k1e)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_k1e)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_y0)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_y0)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pbessel_y1)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pbessel_y1)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX_BESSELFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
new file mode 100644
index 0000000..35e62a8
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX/SpecialFunctions.h
@@ -0,0 +1,16 @@
+#ifndef EIGEN_AVX_SPECIALFUNCTIONS_H
+#define EIGEN_AVX_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, perf)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, perf)
+
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pndtri)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pndtri)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
new file mode 100644
index 0000000..7dd3c3e
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/BesselFunctions.h
@@ -0,0 +1,46 @@
+#ifndef EIGEN_AVX512_BESSELFUNCTIONS_H
+#define EIGEN_AVX512_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i0)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i0)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i0e)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i0e)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i1)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i1)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_i1e)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_i1e)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_j0)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_j0)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_j1)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_j1)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k0)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k0)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k0e)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k0e)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k1)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k1)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_k1e)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_k1e)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_y0)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_y0)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pbessel_y1)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pbessel_y1)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX512_BESSELFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
new file mode 100644
index 0000000..79878f2
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/AVX512/SpecialFunctions.h
@@ -0,0 +1,16 @@
+#ifndef EIGEN_AVX512_SPECIALFUNCTIONS_H
+#define EIGEN_AVX512_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, perf)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, perf)
+
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pndtri)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pndtri)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_AVX512_SPECIAL_FUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
new file mode 100644
index 0000000..dd3bf4d
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/GPU/SpecialFunctions.h
@@ -0,0 +1,369 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GPU_SPECIALFUNCTIONS_H
+#define EIGEN_GPU_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 plgamma<float4>(const float4& a)
+{
+  return make_float4(lgammaf(a.x), lgammaf(a.y), lgammaf(a.z), lgammaf(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 plgamma<double2>(const double2& a)
+{
+  using numext::lgamma;
+  return make_double2(lgamma(a.x), lgamma(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pdigamma<float4>(const float4& a)
+{
+  using numext::digamma;
+  return make_float4(digamma(a.x), digamma(a.y), digamma(a.z), digamma(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pdigamma<double2>(const double2& a)
+{
+  using numext::digamma;
+  return make_double2(digamma(a.x), digamma(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pzeta<float4>(const float4& x, const float4& q)
+{
+    using numext::zeta;
+    return make_float4(zeta(x.x, q.x), zeta(x.y, q.y), zeta(x.z, q.z), zeta(x.w, q.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pzeta<double2>(const double2& x, const double2& q)
+{
+    using numext::zeta;
+    return make_double2(zeta(x.x, q.x), zeta(x.y, q.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 ppolygamma<float4>(const float4& n, const float4& x)
+{
+    using numext::polygamma;
+    return make_float4(polygamma(n.x, x.x), polygamma(n.y, x.y), polygamma(n.z, x.z), polygamma(n.w, x.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 ppolygamma<double2>(const double2& n, const double2& x)
+{
+    using numext::polygamma;
+    return make_double2(polygamma(n.x, x.x), polygamma(n.y, x.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 perf<float4>(const float4& a)
+{
+  return make_float4(erff(a.x), erff(a.y), erff(a.z), erff(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 perf<double2>(const double2& a)
+{
+  using numext::erf;
+  return make_double2(erf(a.x), erf(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 perfc<float4>(const float4& a)
+{
+  using numext::erfc;
+  return make_float4(erfc(a.x), erfc(a.y), erfc(a.z), erfc(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 perfc<double2>(const double2& a)
+{
+  using numext::erfc;
+  return make_double2(erfc(a.x), erfc(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pndtri<float4>(const float4& a)
+{
+  using numext::ndtri;
+  return make_float4(ndtri(a.x), ndtri(a.y), ndtri(a.z), ndtri(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pndtri<double2>(const double2& a)
+{
+  using numext::ndtri;
+  return make_double2(ndtri(a.x), ndtri(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pigamma<float4>(const float4& a, const float4& x)
+{
+  using numext::igamma;
+  return make_float4(
+      igamma(a.x, x.x),
+      igamma(a.y, x.y),
+      igamma(a.z, x.z),
+      igamma(a.w, x.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pigamma<double2>(const double2& a, const double2& x)
+{
+  using numext::igamma;
+  return make_double2(igamma(a.x, x.x), igamma(a.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pigamma_der_a<float4>(
+    const float4& a, const float4& x) {
+  using numext::igamma_der_a;
+  return make_float4(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y),
+                     igamma_der_a(a.z, x.z), igamma_der_a(a.w, x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pigamma_der_a<double2>(const double2& a, const double2& x) {
+  using numext::igamma_der_a;
+  return make_double2(igamma_der_a(a.x, x.x), igamma_der_a(a.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pgamma_sample_der_alpha<float4>(
+    const float4& alpha, const float4& sample) {
+  using numext::gamma_sample_der_alpha;
+  return make_float4(
+      gamma_sample_der_alpha(alpha.x, sample.x),
+      gamma_sample_der_alpha(alpha.y, sample.y),
+      gamma_sample_der_alpha(alpha.z, sample.z),
+      gamma_sample_der_alpha(alpha.w, sample.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pgamma_sample_der_alpha<double2>(const double2& alpha, const double2& sample) {
+  using numext::gamma_sample_der_alpha;
+  return make_double2(
+      gamma_sample_der_alpha(alpha.x, sample.x),
+      gamma_sample_der_alpha(alpha.y, sample.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pigammac<float4>(const float4& a, const float4& x)
+{
+  using numext::igammac;
+  return make_float4(
+      igammac(a.x, x.x),
+      igammac(a.y, x.y),
+      igammac(a.z, x.z),
+      igammac(a.w, x.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pigammac<double2>(const double2& a, const double2& x)
+{
+  using numext::igammac;
+  return make_double2(igammac(a.x, x.x), igammac(a.y, x.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pbetainc<float4>(const float4& a, const float4& b, const float4& x)
+{
+  using numext::betainc;
+  return make_float4(
+      betainc(a.x, b.x, x.x),
+      betainc(a.y, b.y, x.y),
+      betainc(a.z, b.z, x.z),
+      betainc(a.w, b.w, x.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pbetainc<double2>(const double2& a, const double2& b, const double2& x)
+{
+  using numext::betainc;
+  return make_double2(betainc(a.x, b.x, x.x), betainc(a.y, b.y, x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0e<float4>(const float4& x) {
+  using numext::bessel_i0e;
+  return make_float4(bessel_i0e(x.x), bessel_i0e(x.y), bessel_i0e(x.z), bessel_i0e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_i0e<double2>(const double2& x) {
+  using numext::bessel_i0e;
+  return make_double2(bessel_i0e(x.x), bessel_i0e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i0<float4>(const float4& x) {
+  using numext::bessel_i0;
+  return make_float4(bessel_i0(x.x), bessel_i0(x.y), bessel_i0(x.z), bessel_i0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_i0<double2>(const double2& x) {
+  using numext::bessel_i0;
+  return make_double2(bessel_i0(x.x), bessel_i0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1e<float4>(const float4& x) {
+  using numext::bessel_i1e;
+  return make_float4(bessel_i1e(x.x), bessel_i1e(x.y), bessel_i1e(x.z), bessel_i1e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_i1e<double2>(const double2& x) {
+  using numext::bessel_i1e;
+  return make_double2(bessel_i1e(x.x), bessel_i1e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_i1<float4>(const float4& x) {
+  using numext::bessel_i1;
+  return make_float4(bessel_i1(x.x), bessel_i1(x.y), bessel_i1(x.z), bessel_i1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_i1<double2>(const double2& x) {
+  using numext::bessel_i1;
+  return make_double2(bessel_i1(x.x), bessel_i1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0e<float4>(const float4& x) {
+  using numext::bessel_k0e;
+  return make_float4(bessel_k0e(x.x), bessel_k0e(x.y), bessel_k0e(x.z), bessel_k0e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_k0e<double2>(const double2& x) {
+  using numext::bessel_k0e;
+  return make_double2(bessel_k0e(x.x), bessel_k0e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k0<float4>(const float4& x) {
+  using numext::bessel_k0;
+  return make_float4(bessel_k0(x.x), bessel_k0(x.y), bessel_k0(x.z), bessel_k0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_k0<double2>(const double2& x) {
+  using numext::bessel_k0;
+  return make_double2(bessel_k0(x.x), bessel_k0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1e<float4>(const float4& x) {
+  using numext::bessel_k1e;
+  return make_float4(bessel_k1e(x.x), bessel_k1e(x.y), bessel_k1e(x.z), bessel_k1e(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_k1e<double2>(const double2& x) {
+  using numext::bessel_k1e;
+  return make_double2(bessel_k1e(x.x), bessel_k1e(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_k1<float4>(const float4& x) {
+  using numext::bessel_k1;
+  return make_float4(bessel_k1(x.x), bessel_k1(x.y), bessel_k1(x.z), bessel_k1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_k1<double2>(const double2& x) {
+  using numext::bessel_k1;
+  return make_double2(bessel_k1(x.x), bessel_k1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j0<float4>(const float4& x) {
+  using numext::bessel_j0;
+  return make_float4(bessel_j0(x.x), bessel_j0(x.y), bessel_j0(x.z), bessel_j0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_j0<double2>(const double2& x) {
+  using numext::bessel_j0;
+  return make_double2(bessel_j0(x.x), bessel_j0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_j1<float4>(const float4& x) {
+  using numext::bessel_j1;
+  return make_float4(bessel_j1(x.x), bessel_j1(x.y), bessel_j1(x.z), bessel_j1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_j1<double2>(const double2& x) {
+  using numext::bessel_j1;
+  return make_double2(bessel_j1(x.x), bessel_j1(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y0<float4>(const float4& x) {
+  using numext::bessel_y0;
+  return make_float4(bessel_y0(x.x), bessel_y0(x.y), bessel_y0(x.z), bessel_y0(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_y0<double2>(const double2& x) {
+  using numext::bessel_y0;
+  return make_double2(bessel_y0(x.x), bessel_y0(x.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pbessel_y1<float4>(const float4& x) {
+  using numext::bessel_y1;
+  return make_float4(bessel_y1(x.x), bessel_y1(x.y), bessel_y1(x.z), bessel_y1(x.w));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pbessel_y1<double2>(const double2& x) {
+  using numext::bessel_y1;
+  return make_double2(bessel_y1(x.x), bessel_y1(x.y));
+}
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GPU_SPECIALFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
new file mode 100644
index 0000000..67433b0
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/BesselFunctions.h
@@ -0,0 +1,54 @@
+#ifndef EIGEN_NEON_BESSELFUNCTIONS_H
+#define EIGEN_NEON_BESSELFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                            \
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
+Packet8hf METHOD<Packet8hf>(const Packet8hf& x) {                       \
+  const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));  \
+  const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x))); \
+  return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));              \
+}                                                                       \
+                                                                        \
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
+Packet4hf METHOD<Packet4hf>(const Packet4hf& x) {                       \
+  return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));               \
+}
+
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i0e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_i1e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_j1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k0e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_k1e)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y0)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pbessel_y1)
+
+#undef NEON_HALF_TO_FLOAT_FUNCTIONS
+#endif
+
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i0)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i0e)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i1)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_i1e)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_j0)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_j1)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k0)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k0e)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k1)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_k1e)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_y0)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pbessel_y1)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_NEON_BESSELFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
new file mode 100644
index 0000000..ec92951
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/SpecialFunctions/arch/NEON/SpecialFunctions.h
@@ -0,0 +1,34 @@
+#ifndef EIGEN_NEON_SPECIALFUNCTIONS_H
+#define EIGEN_NEON_SPECIALFUNCTIONS_H
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+#define NEON_HALF_TO_FLOAT_FUNCTIONS(METHOD)                            \
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
+Packet8hf METHOD<Packet8hf>(const Packet8hf& x) {                       \
+  const Packet4f lo = METHOD<Packet4f>(vcvt_f32_f16(vget_low_f16(x)));  \
+  const Packet4f hi = METHOD<Packet4f>(vcvt_f32_f16(vget_high_f16(x))); \
+  return vcombine_f16(vcvt_f16_f32(lo), vcvt_f16_f32(hi));              \
+}                                                                       \
+                                                                        \
+template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                       \
+Packet4hf METHOD<Packet4hf>(const Packet4hf& x) {                       \
+  return vcvt_f16_f32(METHOD<Packet4f>(vcvt_f32_f16(x)));               \
+}
+
+NEON_HALF_TO_FLOAT_FUNCTIONS(perf)
+NEON_HALF_TO_FLOAT_FUNCTIONS(pndtri)
+
+#undef NEON_HALF_TO_FLOAT_FUNCTIONS
+#endif
+
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, perf)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pndtri)
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_NEON_SPECIALFUNCTIONS_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/Spline.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
new file mode 100644
index 0000000..79edd52
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/Spline.h
@@ -0,0 +1,507 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINE_H
+#define EIGEN_SPLINE_H
+
+#include "SplineFwd.h"
+
+namespace Eigen
+{
+    /**
+     * \ingroup Splines_Module
+     * \class Spline
+     * \brief A class representing multi-dimensional spline curves.
+     *
+     * The class represents B-splines with non-uniform knot vectors. Each control
+     * point of the B-spline is associated with a basis function
+     * \f{align*}
+     *   C(u) & = \sum_{i=0}^{n}N_{i,p}(u)P_i
+     * \f}
+     *
+     * \tparam _Scalar The underlying data type (typically float or double)
+     * \tparam _Dim The curve dimension (e.g. 2 or 3)
+     * \tparam _Degree Per default set to Dynamic; could be set to the actual desired
+     *                degree for optimization purposes (would result in stack allocation
+     *                of several temporary variables).
+     **/
+  template <typename _Scalar, int _Dim, int _Degree>
+  class Spline
+  {
+  public:
+    typedef _Scalar Scalar; /*!< The spline curve's scalar type. */
+    enum { Dimension = _Dim /*!< The spline curve's dimension. */ };
+    enum { Degree = _Degree /*!< The spline curve's degree. */ };
+
+    /** \brief The point type the spline is representing. */
+    typedef typename SplineTraits<Spline>::PointType PointType;
+    
+    /** \brief The data type used to store knot vectors. */
+    typedef typename SplineTraits<Spline>::KnotVectorType KnotVectorType;
+
+    /** \brief The data type used to store parameter vectors. */
+    typedef typename SplineTraits<Spline>::ParameterVectorType ParameterVectorType;
+    
+    /** \brief The data type used to store non-zero basis functions. */
+    typedef typename SplineTraits<Spline>::BasisVectorType BasisVectorType;
+
+    /** \brief The data type used to store the values of the basis function derivatives. */
+    typedef typename SplineTraits<Spline>::BasisDerivativeType BasisDerivativeType;
+    
+    /** \brief The data type representing the spline's control points. */
+    typedef typename SplineTraits<Spline>::ControlPointVectorType ControlPointVectorType;
+    
+    /**
+    * \brief Creates a (constant) zero spline.
+    * For Splines with dynamic degree, the resulting degree will be 0.
+    **/
+    Spline() 
+    : m_knots(1, (Degree==Dynamic ? 2 : 2*Degree+2))
+    , m_ctrls(ControlPointVectorType::Zero(Dimension,(Degree==Dynamic ? 1 : Degree+1))) 
+    {
+      // in theory this code can go to the initializer list but it will get pretty
+      // much unreadable ...
+      enum { MinDegree = (Degree==Dynamic ? 0 : Degree) };
+      m_knots.template segment<MinDegree+1>(0) = Array<Scalar,1,MinDegree+1>::Zero();
+      m_knots.template segment<MinDegree+1>(MinDegree+1) = Array<Scalar,1,MinDegree+1>::Ones();
+    }
+
+    /**
+    * \brief Creates a spline from a knot vector and control points.
+    * \param knots The spline's knot vector.
+    * \param ctrls The spline's control point vector.
+    **/
+    template <typename OtherVectorType, typename OtherArrayType>
+    Spline(const OtherVectorType& knots, const OtherArrayType& ctrls) : m_knots(knots), m_ctrls(ctrls) {}
+
+    /**
+    * \brief Copy constructor for splines.
+    * \param spline The input spline.
+    **/
+    template <int OtherDegree>
+    Spline(const Spline<Scalar, Dimension, OtherDegree>& spline) : 
+    m_knots(spline.knots()), m_ctrls(spline.ctrls()) {}
+
+    /**
+     * \brief Returns the knots of the underlying spline.
+     **/
+    const KnotVectorType& knots() const { return m_knots; }
+    
+    /**
+     * \brief Returns the ctrls of the underlying spline.
+     **/    
+    const ControlPointVectorType& ctrls() const { return m_ctrls; }
+
+    /**
+     * \brief Returns the spline value at a given site \f$u\f$.
+     *
+     * The function returns
+     * \f{align*}
+     *   C(u) & = \sum_{i=0}^{n}N_{i,p}P_i
+     * \f}
+     *
+     * \param u Parameter \f$u \in [0;1]\f$ at which the spline is evaluated.
+     * \return The spline value at the given location \f$u\f$.
+     **/
+    PointType operator()(Scalar u) const;
+
+    /**
+     * \brief Evaluation of spline derivatives of up-to given order.
+     *
+     * The function returns
+     * \f{align*}
+     *   \frac{d^i}{du^i}C(u) & = \sum_{i=0}^{n} \frac{d^i}{du^i} N_{i,p}(u)P_i
+     * \f}
+     * for i ranging between 0 and order.
+     *
+     * \param u Parameter \f$u \in [0;1]\f$ at which the spline derivative is evaluated.
+     * \param order The order up to which the derivatives are computed.
+     **/
+    typename SplineTraits<Spline>::DerivativeType
+      derivatives(Scalar u, DenseIndex order) const;
+
+    /**
+     * \copydoc Spline::derivatives
+     * Using the template version of this function is more efficieent since
+     * temporary objects are allocated on the stack whenever this is possible.
+     **/    
+    template <int DerivativeOrder>
+    typename SplineTraits<Spline,DerivativeOrder>::DerivativeType
+      derivatives(Scalar u, DenseIndex order = DerivativeOrder) const;
+
+    /**
+     * \brief Computes the non-zero basis functions at the given site.
+     *
+     * Splines have local support and a point from their image is defined
+     * by exactly \f$p+1\f$ control points \f$P_i\f$ where \f$p\f$ is the
+     * spline degree.
+     *
+     * This function computes the \f$p+1\f$ non-zero basis function values
+     * for a given parameter value \f$u\f$. It returns
+     * \f{align*}{
+     *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
+     * \f}
+     *
+     * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis functions 
+     *          are computed.
+     **/
+    typename SplineTraits<Spline>::BasisVectorType
+      basisFunctions(Scalar u) const;
+
+    /**
+     * \brief Computes the non-zero spline basis function derivatives up to given order.
+     *
+     * The function computes
+     * \f{align*}{
+     *   \frac{d^i}{du^i} N_{i,p}(u), \hdots, \frac{d^i}{du^i} N_{i+p+1,p}(u)
+     * \f}
+     * with i ranging from 0 up to the specified order.
+     *
+     * \param u Parameter \f$u \in [0;1]\f$ at which the non-zero basis function
+     *          derivatives are computed.
+     * \param order The order up to which the basis function derivatives are computes.
+     **/
+    typename SplineTraits<Spline>::BasisDerivativeType
+      basisFunctionDerivatives(Scalar u, DenseIndex order) const;
+
+    /**
+     * \copydoc Spline::basisFunctionDerivatives
+     * Using the template version of this function is more efficieent since
+     * temporary objects are allocated on the stack whenever this is possible.
+     **/    
+    template <int DerivativeOrder>
+    typename SplineTraits<Spline,DerivativeOrder>::BasisDerivativeType
+      basisFunctionDerivatives(Scalar u, DenseIndex order = DerivativeOrder) const;
+
+    /**
+     * \brief Returns the spline degree.
+     **/ 
+    DenseIndex degree() const;
+
+    /** 
+     * \brief Returns the span within the knot vector in which u is falling.
+     * \param u The site for which the span is determined.
+     **/
+    DenseIndex span(Scalar u) const;
+
+    /**
+     * \brief Computes the span within the provided knot vector in which u is falling.
+     **/
+    static DenseIndex Span(typename SplineTraits<Spline>::Scalar u, DenseIndex degree, const typename SplineTraits<Spline>::KnotVectorType& knots);
+    
+    /**
+     * \brief Returns the spline's non-zero basis functions.
+     *
+     * The function computes and returns
+     * \f{align*}{
+     *   N_{i,p}(u), \hdots, N_{i+p+1,p}(u)
+     * \f}
+     *
+     * \param u The site at which the basis functions are computed.
+     * \param degree The degree of the underlying spline.
+     * \param knots The underlying spline's knot vector.
+     **/
+    static BasisVectorType BasisFunctions(Scalar u, DenseIndex degree, const KnotVectorType& knots);
+
+    /**
+     * \copydoc Spline::basisFunctionDerivatives
+     * \param degree The degree of the underlying spline
+     * \param knots The underlying spline's knot vector.
+     **/    
+    static BasisDerivativeType BasisFunctionDerivatives(
+      const Scalar u, const DenseIndex order, const DenseIndex degree, const KnotVectorType& knots);
+
+  private:
+    KnotVectorType m_knots; /*!< Knot vector. */
+    ControlPointVectorType  m_ctrls; /*!< Control points. */
+
+    template <typename DerivativeType>
+    static void BasisFunctionDerivativesImpl(
+      const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
+      const DenseIndex order,
+      const DenseIndex p, 
+      const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& U,
+      DerivativeType& N_);
+  };
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  DenseIndex Spline<_Scalar, _Dim, _Degree>::Span(
+    typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::Scalar u,
+    DenseIndex degree,
+    const typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::KnotVectorType& knots)
+  {
+    // Piegl & Tiller, "The NURBS Book", A2.1 (p. 68)
+    if (u <= knots(0)) return degree;
+    const Scalar* pos = std::upper_bound(knots.data()+degree-1, knots.data()+knots.size()-degree-1, u);
+    return static_cast<DenseIndex>( std::distance(knots.data(), pos) - 1 );
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename Spline<_Scalar, _Dim, _Degree>::BasisVectorType
+    Spline<_Scalar, _Dim, _Degree>::BasisFunctions(
+    typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
+    DenseIndex degree,
+    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& knots)
+  {
+    const DenseIndex p = degree;
+    const DenseIndex i = Spline::Span(u, degree, knots);
+
+    const KnotVectorType& U = knots;
+
+    BasisVectorType left(p+1); left(0) = Scalar(0);
+    BasisVectorType right(p+1); right(0) = Scalar(0);
+
+    VectorBlock<BasisVectorType,Degree>(left,1,p) = u - VectorBlock<const KnotVectorType,Degree>(U,i+1-p,p).reverse();
+    VectorBlock<BasisVectorType,Degree>(right,1,p) = VectorBlock<const KnotVectorType,Degree>(U,i+1,p) - u;
+
+    BasisVectorType N(1,p+1);
+    N(0) = Scalar(1);
+    for (DenseIndex j=1; j<=p; ++j)
+    {
+      Scalar saved = Scalar(0);
+      for (DenseIndex r=0; r<j; r++)
+      {
+        const Scalar tmp = N(r)/(right(r+1)+left(j-r));
+        N[r] = saved + right(r+1)*tmp;
+        saved = left(j-r)*tmp;
+      }
+      N(j) = saved;
+    }
+    return N;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  DenseIndex Spline<_Scalar, _Dim, _Degree>::degree() const
+  {
+    if (_Degree == Dynamic)
+      return m_knots.size() - m_ctrls.cols() - 1;
+    else
+      return _Degree;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  DenseIndex Spline<_Scalar, _Dim, _Degree>::span(Scalar u) const
+  {
+    return Spline::Span(u, degree(), knots());
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename Spline<_Scalar, _Dim, _Degree>::PointType Spline<_Scalar, _Dim, _Degree>::operator()(Scalar u) const
+  {
+    enum { Order = SplineTraits<Spline>::OrderAtCompileTime };
+
+    const DenseIndex span = this->span(u);
+    const DenseIndex p = degree();
+    const BasisVectorType basis_funcs = basisFunctions(u);
+
+    const Replicate<BasisVectorType,Dimension,1> ctrl_weights(basis_funcs);
+    const Block<const ControlPointVectorType,Dimension,Order> ctrl_pts(ctrls(),0,span-p,Dimension,p+1);
+    return (ctrl_weights * ctrl_pts).rowwise().sum();
+  }
+
+  /* --------------------------------------------------------------------------------------------- */
+
+  template <typename SplineType, typename DerivativeType>
+  void derivativesImpl(const SplineType& spline, typename SplineType::Scalar u, DenseIndex order, DerivativeType& der)
+  {    
+    enum { Dimension = SplineTraits<SplineType>::Dimension };
+    enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
+    enum { DerivativeOrder = DerivativeType::ColsAtCompileTime };
+
+    typedef typename SplineTraits<SplineType>::ControlPointVectorType ControlPointVectorType;
+    typedef typename SplineTraits<SplineType,DerivativeOrder>::BasisDerivativeType BasisDerivativeType;
+    typedef typename BasisDerivativeType::ConstRowXpr BasisDerivativeRowXpr;    
+
+    const DenseIndex p = spline.degree();
+    const DenseIndex span = spline.span(u);
+
+    const DenseIndex n = (std::min)(p, order);
+
+    der.resize(Dimension,n+1);
+
+    // Retrieve the basis function derivatives up to the desired order...    
+    const BasisDerivativeType basis_func_ders = spline.template basisFunctionDerivatives<DerivativeOrder>(u, n+1);
+
+    // ... and perform the linear combinations of the control points.
+    for (DenseIndex der_order=0; der_order<n+1; ++der_order)
+    {
+      const Replicate<BasisDerivativeRowXpr,Dimension,1> ctrl_weights( basis_func_ders.row(der_order) );
+      const Block<const ControlPointVectorType,Dimension,Order> ctrl_pts(spline.ctrls(),0,span-p,Dimension,p+1);
+      der.col(der_order) = (ctrl_weights * ctrl_pts).rowwise().sum();
+    }
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::DerivativeType
+    Spline<_Scalar, _Dim, _Degree>::derivatives(Scalar u, DenseIndex order) const
+  {
+    typename SplineTraits< Spline >::DerivativeType res;
+    derivativesImpl(*this, u, order, res);
+    return res;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  template <int DerivativeOrder>
+  typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::DerivativeType
+    Spline<_Scalar, _Dim, _Degree>::derivatives(Scalar u, DenseIndex order) const
+  {
+    typename SplineTraits< Spline, DerivativeOrder >::DerivativeType res;
+    derivativesImpl(*this, u, order, res);
+    return res;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::BasisVectorType
+    Spline<_Scalar, _Dim, _Degree>::basisFunctions(Scalar u) const
+  {
+    return Spline::BasisFunctions(u, degree(), knots());
+  }
+
+  /* --------------------------------------------------------------------------------------------- */
+  
+  
+  template <typename _Scalar, int _Dim, int _Degree>
+  template <typename DerivativeType>
+  void Spline<_Scalar, _Dim, _Degree>::BasisFunctionDerivativesImpl(
+    const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
+    const DenseIndex order,
+    const DenseIndex p, 
+    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& U,
+    DerivativeType& N_)
+  {
+    typedef Spline<_Scalar, _Dim, _Degree> SplineType;
+    enum { Order = SplineTraits<SplineType>::OrderAtCompileTime };
+
+    const DenseIndex span = SplineType::Span(u, p, U);
+
+    const DenseIndex n = (std::min)(p, order);
+
+    N_.resize(n+1, p+1);
+
+    BasisVectorType left = BasisVectorType::Zero(p+1);
+    BasisVectorType right = BasisVectorType::Zero(p+1);
+
+    Matrix<Scalar,Order,Order> ndu(p+1,p+1);
+
+    Scalar saved, temp; // FIXME These were double instead of Scalar. Was there a reason for that?
+
+    ndu(0,0) = 1.0;
+
+    DenseIndex j;
+    for (j=1; j<=p; ++j)
+    {
+      left[j] = u-U[span+1-j];
+      right[j] = U[span+j]-u;
+      saved = 0.0;
+
+      for (DenseIndex r=0; r<j; ++r)
+      {
+        /* Lower triangle */
+        ndu(j,r) = right[r+1]+left[j-r];
+        temp = ndu(r,j-1)/ndu(j,r);
+        /* Upper triangle */
+        ndu(r,j) = static_cast<Scalar>(saved+right[r+1] * temp);
+        saved = left[j-r] * temp;
+      }
+
+      ndu(j,j) = static_cast<Scalar>(saved);
+    }
+
+    for (j = p; j>=0; --j) 
+      N_(0,j) = ndu(j,p);
+
+    // Compute the derivatives
+    DerivativeType a(n+1,p+1);
+    DenseIndex r=0;
+    for (; r<=p; ++r)
+    {
+      DenseIndex s1,s2;
+      s1 = 0; s2 = 1; // alternate rows in array a
+      a(0,0) = 1.0;
+
+      // Compute the k-th derivative
+      for (DenseIndex k=1; k<=static_cast<DenseIndex>(n); ++k)
+      {
+        Scalar d = 0.0;
+        DenseIndex rk,pk,j1,j2;
+        rk = r-k; pk = p-k;
+
+        if (r>=k)
+        {
+          a(s2,0) = a(s1,0)/ndu(pk+1,rk);
+          d = a(s2,0)*ndu(rk,pk);
+        }
+
+        if (rk>=-1) j1 = 1;
+        else        j1 = -rk;
+
+        if (r-1 <= pk) j2 = k-1;
+        else           j2 = p-r;
+
+        for (j=j1; j<=j2; ++j)
+        {
+          a(s2,j) = (a(s1,j)-a(s1,j-1))/ndu(pk+1,rk+j);
+          d += a(s2,j)*ndu(rk+j,pk);
+        }
+
+        if (r<=pk)
+        {
+          a(s2,k) = -a(s1,k-1)/ndu(pk+1,r);
+          d += a(s2,k)*ndu(r,pk);
+        }
+
+        N_(k,r) = static_cast<Scalar>(d);
+        j = s1; s1 = s2; s2 = j; // Switch rows
+      }
+    }
+
+    /* Multiply through by the correct factors */
+    /* (Eq. [2.9])                             */
+    r = p;
+    for (DenseIndex k=1; k<=static_cast<DenseIndex>(n); ++k)
+    {
+      for (j=p; j>=0; --j) N_(k,j) *= r;
+      r *= p-k;
+    }
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename SplineTraits< Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType
+    Spline<_Scalar, _Dim, _Degree>::basisFunctionDerivatives(Scalar u, DenseIndex order) const
+  {
+    typename SplineTraits<Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType der;
+    BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
+    return der;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  template <int DerivativeOrder>
+  typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::BasisDerivativeType
+    Spline<_Scalar, _Dim, _Degree>::basisFunctionDerivatives(Scalar u, DenseIndex order) const
+  {
+    typename SplineTraits< Spline<_Scalar, _Dim, _Degree>, DerivativeOrder >::BasisDerivativeType der;
+    BasisFunctionDerivativesImpl(u, order, degree(), knots(), der);
+    return der;
+  }
+
+  template <typename _Scalar, int _Dim, int _Degree>
+  typename SplineTraits<Spline<_Scalar, _Dim, _Degree> >::BasisDerivativeType
+  Spline<_Scalar, _Dim, _Degree>::BasisFunctionDerivatives(
+    const typename Spline<_Scalar, _Dim, _Degree>::Scalar u,
+    const DenseIndex order,
+    const DenseIndex degree,
+    const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& knots)
+  {
+    typename SplineTraits<Spline>::BasisDerivativeType der;
+    BasisFunctionDerivativesImpl(u, order, degree, knots, der);
+    return der;
+  }
+}
+
+#endif // EIGEN_SPLINE_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
new file mode 100644
index 0000000..9f6e8af
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFitting.h
@@ -0,0 +1,431 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINE_FITTING_H
+#define EIGEN_SPLINE_FITTING_H
+
+#include <algorithm>
+#include <functional>
+#include <numeric>
+#include <vector>
+
+#include "SplineFwd.h"
+
+#include "../../../../Eigen/LU"
+#include "../../../../Eigen/QR"
+
+namespace Eigen
+{
+  /**
+   * \brief Computes knot averages.
+   * \ingroup Splines_Module
+   *
+   * The knots are computed as
+   * \f{align*}
+   *  u_0 & = \hdots = u_p = 0 \\
+   *  u_{m-p} & = \hdots = u_{m} = 1 \\
+   *  u_{j+p} & = \frac{1}{p}\sum_{i=j}^{j+p-1}\bar{u}_i \quad\quad j=1,\hdots,n-p
+   * \f}
+   * where \f$p\f$ is the degree and \f$m+1\f$ the number knots
+   * of the desired interpolating spline.
+   *
+   * \param[in] parameters The input parameters. During interpolation one for each data point.
+   * \param[in] degree The spline degree which is used during the interpolation.
+   * \param[out] knots The output knot vector.
+   *
+   * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
+   **/
+  template <typename KnotVectorType>
+  void KnotAveraging(const KnotVectorType& parameters, DenseIndex degree, KnotVectorType& knots)
+  {
+    knots.resize(parameters.size()+degree+1);      
+
+    for (DenseIndex j=1; j<parameters.size()-degree; ++j)
+      knots(j+degree) = parameters.segment(j,degree).mean();
+
+    knots.segment(0,degree+1) = KnotVectorType::Zero(degree+1);
+    knots.segment(knots.size()-degree-1,degree+1) = KnotVectorType::Ones(degree+1);
+  }
+
+  /**
+   * \brief Computes knot averages when derivative constraints are present.
+   * Note that this is a technical interpretation of the referenced article
+   * since the algorithm contained therein is incorrect as written.
+   * \ingroup Splines_Module
+   *
+   * \param[in] parameters The parameters at which the interpolation B-Spline
+   *            will intersect the given interpolation points. The parameters
+   *            are assumed to be a non-decreasing sequence.
+   * \param[in] degree The degree of the interpolating B-Spline. This must be
+   *            greater than zero.
+   * \param[in] derivativeIndices The indices corresponding to parameters at
+   *            which there are derivative constraints. The indices are assumed
+   *            to be a non-decreasing sequence.
+   * \param[out] knots The calculated knot vector. These will be returned as a
+   *             non-decreasing sequence
+   *
+   * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+   * Curve interpolation with directional constraints for engineering design. 
+   * Engineering with Computers
+   **/
+  template <typename KnotVectorType, typename ParameterVectorType, typename IndexArray>
+  void KnotAveragingWithDerivatives(const ParameterVectorType& parameters,
+                                    const unsigned int degree,
+                                    const IndexArray& derivativeIndices,
+                                    KnotVectorType& knots)
+  {
+    typedef typename ParameterVectorType::Scalar Scalar;
+
+    DenseIndex numParameters = parameters.size();
+    DenseIndex numDerivatives = derivativeIndices.size();
+
+    if (numDerivatives < 1)
+    {
+      KnotAveraging(parameters, degree, knots);
+      return;
+    }
+
+    DenseIndex startIndex;
+    DenseIndex endIndex;
+  
+    DenseIndex numInternalDerivatives = numDerivatives;
+    
+    if (derivativeIndices[0] == 0)
+    {
+      startIndex = 0;
+      --numInternalDerivatives;
+    }
+    else
+    {
+      startIndex = 1;
+    }
+    if (derivativeIndices[numDerivatives - 1] == numParameters - 1)
+    {
+      endIndex = numParameters - degree;
+      --numInternalDerivatives;
+    }
+    else
+    {
+      endIndex = numParameters - degree - 1;
+    }
+
+    // There are (endIndex - startIndex + 1) knots obtained from the averaging
+    // and 2 for the first and last parameters.
+    DenseIndex numAverageKnots = endIndex - startIndex + 3;
+    KnotVectorType averageKnots(numAverageKnots);
+    averageKnots[0] = parameters[0];
+
+    int newKnotIndex = 0;
+    for (DenseIndex i = startIndex; i <= endIndex; ++i)
+      averageKnots[++newKnotIndex] = parameters.segment(i, degree).mean();
+    averageKnots[++newKnotIndex] = parameters[numParameters - 1];
+
+    newKnotIndex = -1;
+  
+    ParameterVectorType temporaryParameters(numParameters + 1);
+    KnotVectorType derivativeKnots(numInternalDerivatives);
+    for (DenseIndex i = 0; i < numAverageKnots - 1; ++i)
+    {
+      temporaryParameters[0] = averageKnots[i];
+      ParameterVectorType parameterIndices(numParameters);
+      int temporaryParameterIndex = 1;
+      for (DenseIndex j = 0; j < numParameters; ++j)
+      {
+        Scalar parameter = parameters[j];
+        if (parameter >= averageKnots[i] && parameter < averageKnots[i + 1])
+        {
+          parameterIndices[temporaryParameterIndex] = j;
+          temporaryParameters[temporaryParameterIndex++] = parameter;
+        }
+      }
+      temporaryParameters[temporaryParameterIndex] = averageKnots[i + 1];
+
+      for (int j = 0; j <= temporaryParameterIndex - 2; ++j)
+      {
+        for (DenseIndex k = 0; k < derivativeIndices.size(); ++k)
+        {
+          if (parameterIndices[j + 1] == derivativeIndices[k]
+              && parameterIndices[j + 1] != 0
+              && parameterIndices[j + 1] != numParameters - 1)
+          {
+            derivativeKnots[++newKnotIndex] = temporaryParameters.segment(j, 3).mean();
+            break;
+          }
+        }
+      }
+    }
+    
+    KnotVectorType temporaryKnots(averageKnots.size() + derivativeKnots.size());
+
+    std::merge(averageKnots.data(), averageKnots.data() + averageKnots.size(),
+               derivativeKnots.data(), derivativeKnots.data() + derivativeKnots.size(),
+               temporaryKnots.data());
+
+    // Number of knots (one for each point and derivative) plus spline order.
+    DenseIndex numKnots = numParameters + numDerivatives + degree + 1;
+    knots.resize(numKnots);
+
+    knots.head(degree).fill(temporaryKnots[0]);
+    knots.tail(degree).fill(temporaryKnots.template tail<1>()[0]);
+    knots.segment(degree, temporaryKnots.size()) = temporaryKnots;
+  }
+
+  /**
+   * \brief Computes chord length parameters which are required for spline interpolation.
+   * \ingroup Splines_Module
+   *
+   * \param[in] pts The data points to which a spline should be fit.
+   * \param[out] chord_lengths The resulting chord length vector.
+   *
+   * \sa Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data
+   **/   
+  template <typename PointArrayType, typename KnotVectorType>
+  void ChordLengths(const PointArrayType& pts, KnotVectorType& chord_lengths)
+  {
+    typedef typename KnotVectorType::Scalar Scalar;
+
+    const DenseIndex n = pts.cols();
+
+    // 1. compute the column-wise norms
+    chord_lengths.resize(pts.cols());
+    chord_lengths[0] = 0;
+    chord_lengths.rightCols(n-1) = (pts.array().leftCols(n-1) - pts.array().rightCols(n-1)).matrix().colwise().norm();
+
+    // 2. compute the partial sums
+    std::partial_sum(chord_lengths.data(), chord_lengths.data()+n, chord_lengths.data());
+
+    // 3. normalize the data
+    chord_lengths /= chord_lengths(n-1);
+    chord_lengths(n-1) = Scalar(1);
+  }
+
+  /**
+   * \brief Spline fitting methods.
+   * \ingroup Splines_Module
+   **/     
+  template <typename SplineType>
+  struct SplineFitting
+  {
+    typedef typename SplineType::KnotVectorType KnotVectorType;
+    typedef typename SplineType::ParameterVectorType ParameterVectorType;
+
+    /**
+     * \brief Fits an interpolating Spline to the given data points.
+     *
+     * \param pts The points for which an interpolating spline will be computed.
+     * \param degree The degree of the interpolating spline.
+     *
+     * \returns A spline interpolating the initially provided points.
+     **/
+    template <typename PointArrayType>
+    static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree);
+
+    /**
+     * \brief Fits an interpolating Spline to the given data points.
+     *
+     * \param pts The points for which an interpolating spline will be computed.
+     * \param degree The degree of the interpolating spline.
+     * \param knot_parameters The knot parameters for the interpolation.
+     *
+     * \returns A spline interpolating the initially provided points.
+     **/
+    template <typename PointArrayType>
+    static SplineType Interpolate(const PointArrayType& pts, DenseIndex degree, const KnotVectorType& knot_parameters);
+
+    /**
+     * \brief Fits an interpolating spline to the given data points and
+     * derivatives.
+     * 
+     * \param points The points for which an interpolating spline will be computed.
+     * \param derivatives The desired derivatives of the interpolating spline at interpolation
+     *                    points.
+     * \param derivativeIndices An array indicating which point each derivative belongs to. This
+     *                          must be the same size as @a derivatives.
+     * \param degree The degree of the interpolating spline.
+     *
+     * \returns A spline interpolating @a points with @a derivatives at those points.
+     *
+     * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+     * Curve interpolation with directional constraints for engineering design. 
+     * Engineering with Computers
+     **/
+    template <typename PointArrayType, typename IndexArray>
+    static SplineType InterpolateWithDerivatives(const PointArrayType& points,
+                                                 const PointArrayType& derivatives,
+                                                 const IndexArray& derivativeIndices,
+                                                 const unsigned int degree);
+
+    /**
+     * \brief Fits an interpolating spline to the given data points and derivatives.
+     * 
+     * \param points The points for which an interpolating spline will be computed.
+     * \param derivatives The desired derivatives of the interpolating spline at interpolation points.
+     * \param derivativeIndices An array indicating which point each derivative belongs to. This
+     *                          must be the same size as @a derivatives.
+     * \param degree The degree of the interpolating spline.
+     * \param parameters The parameters corresponding to the interpolation points.
+     *
+     * \returns A spline interpolating @a points with @a derivatives at those points.
+     *
+     * \sa Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008.
+     * Curve interpolation with directional constraints for engineering design. 
+     * Engineering with Computers
+     */
+    template <typename PointArrayType, typename IndexArray>
+    static SplineType InterpolateWithDerivatives(const PointArrayType& points,
+                                                 const PointArrayType& derivatives,
+                                                 const IndexArray& derivativeIndices,
+                                                 const unsigned int degree,
+                                                 const ParameterVectorType& parameters);
+  };
+
+  template <typename SplineType>
+  template <typename PointArrayType>
+  SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree, const KnotVectorType& knot_parameters)
+  {
+    typedef typename SplineType::KnotVectorType::Scalar Scalar;      
+    typedef typename SplineType::ControlPointVectorType ControlPointVectorType;      
+
+    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
+
+    KnotVectorType knots;
+    KnotAveraging(knot_parameters, degree, knots);
+
+    DenseIndex n = pts.cols();
+    MatrixType A = MatrixType::Zero(n,n);
+    for (DenseIndex i=1; i<n-1; ++i)
+    {
+      const DenseIndex span = SplineType::Span(knot_parameters[i], degree, knots);
+
+      // The segment call should somehow be told the spline order at compile time.
+      A.row(i).segment(span-degree, degree+1) = SplineType::BasisFunctions(knot_parameters[i], degree, knots);
+    }
+    A(0,0) = 1.0;
+    A(n-1,n-1) = 1.0;
+
+    HouseholderQR<MatrixType> qr(A);
+
+    // Here, we are creating a temporary due to an Eigen issue.
+    ControlPointVectorType ctrls = qr.solve(MatrixType(pts.transpose())).transpose();
+
+    return SplineType(knots, ctrls);
+  }
+
+  template <typename SplineType>
+  template <typename PointArrayType>
+  SplineType SplineFitting<SplineType>::Interpolate(const PointArrayType& pts, DenseIndex degree)
+  {
+    KnotVectorType chord_lengths; // knot parameters
+    ChordLengths(pts, chord_lengths);
+    return Interpolate(pts, degree, chord_lengths);
+  }
+  
+  template <typename SplineType>
+  template <typename PointArrayType, typename IndexArray>
+  SplineType 
+  SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
+                                                        const PointArrayType& derivatives,
+                                                        const IndexArray& derivativeIndices,
+                                                        const unsigned int degree,
+                                                        const ParameterVectorType& parameters)
+  {
+    typedef typename SplineType::KnotVectorType::Scalar Scalar;      
+    typedef typename SplineType::ControlPointVectorType ControlPointVectorType;
+
+    typedef Matrix<Scalar, Dynamic, Dynamic> MatrixType;
+
+    const DenseIndex n = points.cols() + derivatives.cols();
+    
+    KnotVectorType knots;
+
+    KnotAveragingWithDerivatives(parameters, degree, derivativeIndices, knots);
+    
+    // fill matrix
+    MatrixType A = MatrixType::Zero(n, n);
+
+    // Use these dimensions for quicker populating, then transpose for solving.
+    MatrixType b(points.rows(), n);
+
+    DenseIndex startRow;
+    DenseIndex derivativeStart;
+
+    // End derivatives.
+    if (derivativeIndices[0] == 0)
+    {
+      A.template block<1, 2>(1, 0) << -1, 1;
+      
+      Scalar y = (knots(degree + 1) - knots(0)) / degree;
+      b.col(1) = y*derivatives.col(0);
+      
+      startRow = 2;
+      derivativeStart = 1;
+    }
+    else
+    {
+      startRow = 1;
+      derivativeStart = 0;
+    }
+    if (derivativeIndices[derivatives.cols() - 1] == points.cols() - 1)
+    {
+      A.template block<1, 2>(n - 2, n - 2) << -1, 1;
+
+      Scalar y = (knots(knots.size() - 1) - knots(knots.size() - (degree + 2))) / degree;
+      b.col(b.cols() - 2) = y*derivatives.col(derivatives.cols() - 1);
+    }
+    
+    DenseIndex row = startRow;
+    DenseIndex derivativeIndex = derivativeStart;
+    for (DenseIndex i = 1; i < parameters.size() - 1; ++i)
+    {
+      const DenseIndex span = SplineType::Span(parameters[i], degree, knots);
+
+      if (derivativeIndex < derivativeIndices.size() && derivativeIndices[derivativeIndex] == i)
+      {
+        A.block(row, span - degree, 2, degree + 1)
+          = SplineType::BasisFunctionDerivatives(parameters[i], 1, degree, knots);
+
+        b.col(row++) = points.col(i);
+        b.col(row++) = derivatives.col(derivativeIndex++);
+      }
+      else
+      {
+        A.row(row).segment(span - degree, degree + 1)
+          = SplineType::BasisFunctions(parameters[i], degree, knots);
+        b.col(row++) = points.col(i);
+      }
+    }
+    b.col(0) = points.col(0);
+    b.col(b.cols() - 1) = points.col(points.cols() - 1);
+    A(0,0) = 1;
+    A(n - 1, n - 1) = 1;
+    
+    // Solve
+    FullPivLU<MatrixType> lu(A);
+    ControlPointVectorType controlPoints = lu.solve(MatrixType(b.transpose())).transpose();
+
+    SplineType spline(knots, controlPoints);
+    
+    return spline;
+  }
+  
+  template <typename SplineType>
+  template <typename PointArrayType, typename IndexArray>
+  SplineType
+  SplineFitting<SplineType>::InterpolateWithDerivatives(const PointArrayType& points,
+                                                        const PointArrayType& derivatives,
+                                                        const IndexArray& derivativeIndices,
+                                                        const unsigned int degree)
+  {
+    ParameterVectorType parameters;
+    ChordLengths(points, parameters);
+    return InterpolateWithDerivatives(points, derivatives, derivativeIndices, degree, parameters);
+  }
+}
+
+#endif // EIGEN_SPLINE_FITTING_H
diff --git a/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
new file mode 100644
index 0000000..00d6b49
--- /dev/null
+++ b/3rdParty/ceres_2.2/include/eigen3/unsupported/Eigen/src/Splines/SplineFwd.h
@@ -0,0 +1,93 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 20010-2011 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPLINES_FWD_H
+#define EIGEN_SPLINES_FWD_H
+
+#include "../../../../Eigen/Core"
+
+namespace Eigen
+{
+    template <typename Scalar, int Dim, int Degree = Dynamic> class Spline;
+
+    template < typename SplineType, int DerivativeOrder = Dynamic > struct SplineTraits {};
+
+    /**
+     * \ingroup Splines_Module
+     * \brief Compile-time attributes of the Spline class for Dynamic degree.
+     **/
+    template <typename _Scalar, int _Dim, int _Degree>
+    struct SplineTraits< Spline<_Scalar, _Dim, _Degree>, Dynamic >
+    {
+      typedef _Scalar Scalar; /*!< The spline curve's scalar type. */
+      enum { Dimension = _Dim /*!< The spline curve's dimension. */ };
+      enum { Degree = _Degree /*!< The spline curve's degree. */ };
+
+      enum { OrderAtCompileTime = _Degree==Dynamic ? Dynamic : _Degree+1 /*!< The spline curve's order at compile-time. */ };
+      enum { NumOfDerivativesAtCompileTime = OrderAtCompileTime /*!< The number of derivatives defined for the current spline. */ };
+      
+      enum { DerivativeMemoryLayout = Dimension==1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
+
+      /** \brief The data type used to store non-zero basis functions. */
+      typedef Array<Scalar,1,OrderAtCompileTime> BasisVectorType;
+
+      /** \brief The data type used to store the values of the basis function derivatives. */
+      typedef Array<Scalar,Dynamic,Dynamic,RowMajor,NumOfDerivativesAtCompileTime,OrderAtCompileTime> BasisDerivativeType;
+      
+      /** \brief The data type used to store the spline's derivative values. */
+      typedef Array<Scalar,Dimension,Dynamic,DerivativeMemoryLayout,Dimension,NumOfDerivativesAtCompileTime> DerivativeType;
+
+      /** \brief The point type the spline is representing. */
+      typedef Array<Scalar,Dimension,1> PointType;
+      
+      /** \brief The data type used to store knot vectors. */
+      typedef Array<Scalar,1,Dynamic> KnotVectorType;
+
+      /** \brief The data type used to store parameter vectors. */
+      typedef Array<Scalar,1,Dynamic> ParameterVectorType;
+      
+      /** \brief The data type representing the spline's control points. */
+      typedef Array<Scalar,Dimension,Dynamic> ControlPointVectorType;
+    };
+
+    /**
+     * \ingroup Splines_Module
+     * \brief Compile-time attributes of the Spline class for fixed degree.
+     *
+     * The traits class inherits all attributes from the SplineTraits of Dynamic degree.
+     **/
+    template < typename _Scalar, int _Dim, int _Degree, int _DerivativeOrder >
+    struct SplineTraits< Spline<_Scalar, _Dim, _Degree>, _DerivativeOrder > : public SplineTraits< Spline<_Scalar, _Dim, _Degree> >
+    {
+      enum { OrderAtCompileTime = _Degree==Dynamic ? Dynamic : _Degree+1 /*!< The spline curve's order at compile-time. */ };
+      enum { NumOfDerivativesAtCompileTime = _DerivativeOrder==Dynamic ? Dynamic : _DerivativeOrder+1 /*!< The number of derivatives defined for the current spline. */ };
+      
+      enum { DerivativeMemoryLayout = _Dim==1 ? RowMajor : ColMajor /*!< The derivative type's memory layout. */ };
+
+      /** \brief The data type used to store the values of the basis function derivatives. */
+      typedef Array<_Scalar,Dynamic,Dynamic,RowMajor,NumOfDerivativesAtCompileTime,OrderAtCompileTime> BasisDerivativeType;
+      
+      /** \brief The data type used to store the spline's derivative values. */      
+      typedef Array<_Scalar,_Dim,Dynamic,DerivativeMemoryLayout,_Dim,NumOfDerivativesAtCompileTime> DerivativeType;
+    };
+
+    /** \brief 2D float B-spline with dynamic degree. */
+    typedef Spline<float,2> Spline2f;
+    
+    /** \brief 3D float B-spline with dynamic degree. */
+    typedef Spline<float,3> Spline3f;
+
+    /** \brief 2D double B-spline with dynamic degree. */
+    typedef Spline<double,2> Spline2d;
+    
+    /** \brief 3D double B-spline with dynamic degree. */
+    typedef Spline<double,3> Spline3d;
+}
+
+#endif // EIGEN_SPLINES_FWD_H
diff --git a/3rdParty/ceres_2.2/include/glog/export.h b/3rdParty/ceres_2.2/include/glog/export.h
deleted file mode 100644
index 465727f..0000000
--- a/3rdParty/ceres_2.2/include/glog/export.h
+++ /dev/null
@@ -1,43 +0,0 @@
-
-#ifndef GLOG_EXPORT_H
-#define GLOG_EXPORT_H
-
-#ifdef GLOG_STATIC_DEFINE
-#  define GLOG_EXPORT
-#  define GLOG_NO_EXPORT
-#else
-#  ifndef GLOG_EXPORT
-#    ifdef GOOGLE_GLOG_IS_A_DLL
-        /* We are building this library */
-#      define GLOG_EXPORT __declspec(dllexport)
-#    else
-        /* We are using this library */
-#      define GLOG_EXPORT __declspec(dllimport)
-#    endif
-#  endif
-
-#  ifndef GLOG_NO_EXPORT
-#    define GLOG_NO_EXPORT 
-#  endif
-#endif
-
-#ifndef GLOG_DEPRECATED
-#  define GLOG_DEPRECATED __declspec(deprecated)
-#endif
-
-#ifndef GLOG_DEPRECATED_EXPORT
-#  define GLOG_DEPRECATED_EXPORT GLOG_EXPORT GLOG_DEPRECATED
-#endif
-
-#ifndef GLOG_DEPRECATED_NO_EXPORT
-#  define GLOG_DEPRECATED_NO_EXPORT GLOG_NO_EXPORT GLOG_DEPRECATED
-#endif
-
-/* NOLINTNEXTLINE(readability-avoid-unconditional-preprocessor-if) */
-#if 0 /* DEFINE_NO_DEPRECATED */
-#  ifndef GLOG_NO_DEPRECATED
-#    define GLOG_NO_DEPRECATED
-#  endif
-#endif
-
-#endif /* GLOG_EXPORT_H */
diff --git a/3rdParty/ceres_2.2/include/glog/log_severity.h b/3rdParty/ceres_2.2/include/glog/log_severity.h
index aa48f53..99945a4 100644
--- a/3rdParty/ceres_2.2/include/glog/log_severity.h
+++ b/3rdParty/ceres_2.2/include/glog/log_severity.h
@@ -30,20 +30,14 @@
 #ifndef BASE_LOG_SEVERITY_H__
 #define BASE_LOG_SEVERITY_H__
 
-// The recommended semantics of the log levels are as follows:
-//
-// INFO:
-//   Use for state changes or other major events, or to aid debugging.
-// WARNING:
-//   Use for undesired but relatively expected events, which may indicate a
-//   problem
-// ERROR:
-//   Use for undesired and unexpected events that the program can recover from.
-//   All ERRORs should be actionable - it should be appropriate to file a bug
-//   whenever an ERROR occurs in production.
-// FATAL:
-//   Use for undesired and unexpected events that the program cannot recover
-//   from.
+// Annoying stuff for windows -- makes sure clients can import these functions
+#ifndef GOOGLE_GLOG_DLL_DECL
+# if defined(_WIN32) && !defined(__CYGWIN__)
+#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
+# else
+#   define GOOGLE_GLOG_DLL_DECL
+# endif
+#endif
 
 // Variables of type LogSeverity are widely taken to lie in the range
 // [0, NUM_SEVERITIES-1].  Be careful to preserve this assumption if
@@ -67,7 +61,7 @@ const int INFO = GLOG_INFO, WARNING = GLOG_WARNING,
 #define DFATAL_LEVEL FATAL
 #endif
 
-extern GLOG_EXPORT const char* const LogSeverityNames[NUM_SEVERITIES];
+extern GOOGLE_GLOG_DLL_DECL const char* const LogSeverityNames[NUM_SEVERITIES];
 
 // NDEBUG usage helpers related to (RAW_)DCHECK:
 //
diff --git a/3rdParty/ceres_2.2/include/glog/logging.h b/3rdParty/ceres_2.2/include/glog/logging.h
index 7b4b589..ba1084a 100644
--- a/3rdParty/ceres_2.2/include/glog/logging.h
+++ b/3rdParty/ceres_2.2/include/glog/logging.h
@@ -1,4 +1,4 @@
-// Copyright (c) 2022, Google Inc.
+// Copyright (c) 1999, Google Inc.
 // All rights reserved.
 //
 // Redistribution and use in source and binary forms, with or without
@@ -33,18 +33,12 @@
 // Pretty much everybody needs to #include this file so that they can
 // log various happenings.
 //
-#ifndef GLOG_LOGGING_H
-#define GLOG_LOGGING_H
+#ifndef _LOGGING_H_
+#define _LOGGING_H_
 
-#if 1 && __cplusplus >= 201103L
-#include <chrono>
-#endif
-
-#include <cerrno>
-#include <cstddef>
-#include <cstdlib>
-#include <cstring>
-#include <ctime>
+#include <errno.h>
+#include <string.h>
+#include <time.h>
 #include <iosfwd>
 #include <ostream>
 #include <sstream>
@@ -63,10 +57,13 @@
 #define GLOG_MSVC_POP_WARNING()
 #endif
 
-#include <glog/platform.h>
-
-#if 1
-#include <glog/export.h>
+// Annoying stuff for windows -- makes sure clients can import these functions
+#ifndef GOOGLE_GLOG_DLL_DECL
+# if defined(_WIN32) && !defined(__CYGWIN__)
+#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
+# else
+#   define GOOGLE_GLOG_DLL_DECL
+# endif
 #endif
 
 // We care a lot about number of bits things take up.  Unfortunately,
@@ -85,16 +82,10 @@
 #include <inttypes.h>           // a third place for uint16_t or u_int16_t
 #endif
 
-#if 0
+#if 1
 #include <gflags/gflags.h>
 #endif
 
-#if 1 && __cplusplus >= 201103L
-#include <atomic>
-#elif defined(GLOG_OS_WINDOWS)
-#include <Windows.h>
-#endif
-
 namespace google {
 
 #if 1      // the C99 format
@@ -116,62 +107,8 @@ typedef unsigned __int64 uint64;
 #error Do not know how to define a 32-bit integer quantity on your system
 #endif
 
-typedef double WallTime;
-
-struct GLOG_EXPORT LogMessageTime {
-  LogMessageTime();
-  LogMessageTime(std::tm t);
-  LogMessageTime(std::time_t timestamp, WallTime now);
-
-  const time_t& timestamp() const { return timestamp_; }
-  const int& sec() const { return time_struct_.tm_sec; }
-  const int32_t& usec() const { return usecs_; }
-  const int&(min)() const { return time_struct_.tm_min; }
-  const int& hour() const { return time_struct_.tm_hour; }
-  const int& day() const { return time_struct_.tm_mday; }
-  const int& month() const { return time_struct_.tm_mon; }
-  const int& year() const { return time_struct_.tm_year; }
-  const int& dayOfWeek() const { return time_struct_.tm_wday; }
-  const int& dayInYear() const { return time_struct_.tm_yday; }
-  const int& dst() const { return time_struct_.tm_isdst; }
-  const long int& gmtoff() const { return gmtoffset_; }
-  const std::tm& tm() const { return time_struct_; }
-
- private:
-  void init(const std::tm& t, std::time_t timestamp, WallTime now);
-  std::tm time_struct_;  // Time of creation of LogMessage
-  time_t timestamp_;     // Time of creation of LogMessage in seconds
-  int32_t usecs_;        // Time of creation of LogMessage - microseconds part
-  long int gmtoffset_;
-
-  void CalcGmtOffset();
-};
-
-#ifdef GLOG_CUSTOM_PREFIX_SUPPORT
-struct LogMessageInfo {
-  explicit LogMessageInfo(const char* const severity_,
-                          const char* const filename_,
-                          const int& line_number_,
-                          const int& thread_id_,
-                          const LogMessageTime& time_):
-      severity(severity_), filename(filename_), line_number(line_number_),
-      thread_id(thread_id_), time(time_)
-  {}
-
-  const char* const severity;
-  const char* const filename;
-  const int &line_number;
-  const int &thread_id;
-  const LogMessageTime& time;
-};
-
-typedef void(*CustomPrefixCallback)(std::ostream& s, const LogMessageInfo& l, void* data);
-
-#endif
-
 }
 
-
 // The global value of GOOGLE_STRIP_LOG. All the messages logged to
 // LOG(XXX) with severity less than GOOGLE_STRIP_LOG will not be displayed.
 // If it can be determined at compile time that the message will not be
@@ -343,13 +280,12 @@ typedef void(*CustomPrefixCallback)(std::ostream& s, const LogMessageInfo& l, vo
 //
 // Log lines have this form:
 //
-//     Lyyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg...
+//     Lmmdd hh:mm:ss.uuuuuu threadid file:line] msg...
 //
 // where the fields are defined as follows:
 //
 //   L                A single character, representing the log level
 //                    (eg 'I' for INFO)
-//   yyyy             The year
 //   mm               The month (zero padded; ie May is '05')
 //   dd               The day (zero padded)
 //   hh:mm:ss.uuuuuu  Time in hours, minutes and fractional seconds
@@ -369,36 +305,11 @@ typedef void(*CustomPrefixCallback)(std::ostream& s, const LogMessageInfo& l, vo
 // synchronized.  Hence, use caution when comparing the low bits of
 // timestamps from different machines.
 
-#pragma push_macro("DECLARE_VARIABLE")
-#pragma push_macro("DECLARE_bool")
-#pragma push_macro("DECLARE_string")
-#pragma push_macro("DECLARE_int32")
-#pragma push_macro("DECLARE_uint32")
-
-#ifdef DECLARE_VARIABLE
-#undef DECLARE_VARIABLE
-#endif
-
-#ifdef DECLARE_bool
-#undef DECLARE_bool
-#endif
-
-#ifdef DECLARE_string
-#undef DECLARE_string
-#endif
-
-#ifdef DECLARE_int32
-#undef DECLARE_int32
-#endif
-
-#ifdef DECLARE_uint32
-#undef DECLARE_uint32
-#endif
-
 #ifndef DECLARE_VARIABLE
+#define MUST_UNDEF_GFLAGS_DECLARE_MACROS
 #define DECLARE_VARIABLE(type, shorttype, name, tn)                     \
   namespace fL##shorttype {                                             \
-    extern GLOG_EXPORT type FLAGS_##name;                      \
+    extern GOOGLE_GLOG_DLL_DECL type FLAGS_##name;                      \
   }                                                                     \
   using fL##shorttype::FLAGS_##name
 
@@ -410,30 +321,15 @@ typedef void(*CustomPrefixCallback)(std::ostream& s, const LogMessageInfo& l, vo
 #define DECLARE_int32(name) \
   DECLARE_VARIABLE(google::int32, I, name, int32)
 
-#if !defined(DECLARE_uint32)
-// uint32 specialization
-#define DECLARE_uint32(name) \
-  DECLARE_VARIABLE(google::uint32, U, name, uint32)
-#endif // !defined(DECLARE_uint32) && !(0)
-
 // Special case for string, because we have to specify the namespace
 // std::string, which doesn't play nicely with our FLAG__namespace hackery.
 #define DECLARE_string(name)                                            \
   namespace fLS {                                                       \
-    extern GLOG_EXPORT std::string& FLAGS_##name;              \
+    extern GOOGLE_GLOG_DLL_DECL std::string& FLAGS_##name;              \
   }                                                                     \
   using fLS::FLAGS_##name
 #endif
 
-// Set whether appending a timestamp to the log file name
-DECLARE_bool(timestamp_in_logfile_name);
-
-// Set whether log messages go to stdout instead of logfiles
-DECLARE_bool(logtostdout);
-
-// Set color messages logged to stdout (if supported by terminal).
-DECLARE_bool(colorlogtostdout);
-
 // Set whether log messages go to stderr instead of logfiles
 DECLARE_bool(logtostderr);
 
@@ -450,9 +346,6 @@ DECLARE_int32(stderrthreshold);
 // Set whether the log prefix should be prepended to each line of output.
 DECLARE_bool(log_prefix);
 
-// Set whether the year should be included in the log prefix.
-DECLARE_bool(log_year_in_prefix);
-
 // Log messages at a level <= this flag are buffered.
 // Log messages at a higher level are flushed immediately.
 DECLARE_int32(logbuflevel);
@@ -477,16 +370,19 @@ DECLARE_string(log_link);
 
 DECLARE_int32(v);  // in vlog_is_on.cc
 
-DECLARE_string(vmodule); // also in vlog_is_on.cc
-
 // Sets the maximum log file size (in MB).
-DECLARE_uint32(max_log_size);
+DECLARE_int32(max_log_size);
 
 // Sets whether to avoid logging to the disk if the disk is full.
 DECLARE_bool(stop_logging_if_full_disk);
 
-// Use UTC time for logging
-DECLARE_bool(log_utc_time);
+#ifdef MUST_UNDEF_GFLAGS_DECLARE_MACROS
+#undef MUST_UNDEF_GFLAGS_DECLARE_MACROS
+#undef DECLARE_VARIABLE
+#undef DECLARE_bool
+#undef DECLARE_int32
+#undef DECLARE_string
+#endif
 
 // Log messages below the GOOGLE_STRIP_LOG level will be compiled away for
 // security reasons. See LOG(severtiy) below.
@@ -613,38 +509,18 @@ DECLARE_bool(log_utc_time);
 namespace google {
 
 // They need the definitions of integer types.
-#include <glog/log_severity.h>
-#include <glog/vlog_is_on.h>
+#include "glog/log_severity.h"
+#include "glog/vlog_is_on.h"
 
 // Initialize google's logging library. You will see the program name
 // specified by argv0 in log outputs.
-GLOG_EXPORT void InitGoogleLogging(const char* argv0);
-
-#ifdef GLOG_CUSTOM_PREFIX_SUPPORT
-GLOG_EXPORT void InitGoogleLogging(const char* argv0,
-                                   CustomPrefixCallback prefix_callback,
-                                   void* prefix_callback_data = NULL);
-#endif
-
-// Check if google's logging library has been initialized.
-GLOG_EXPORT bool IsGoogleLoggingInitialized();
+GOOGLE_GLOG_DLL_DECL void InitGoogleLogging(const char* argv0);
 
 // Shutdown google's logging library.
-GLOG_EXPORT void ShutdownGoogleLogging();
-
-#if defined(__GNUC__)
-typedef void (*logging_fail_func_t)() __attribute__((noreturn));
-#else
-typedef void (*logging_fail_func_t)();
-#endif
+GOOGLE_GLOG_DLL_DECL void ShutdownGoogleLogging();
 
 // Install a function which will be called after LOG(FATAL).
-GLOG_EXPORT void InstallFailureFunction(logging_fail_func_t fail_func);
-
-// Enable/Disable old log cleaner.
-GLOG_EXPORT void EnableLogCleaner(unsigned int overdue_days);
-GLOG_EXPORT void DisableLogCleaner();
-GLOG_EXPORT void SetApplicationFingerprint(const std::string& fingerprint);
+GOOGLE_GLOG_DLL_DECL void InstallFailureFunction(void (*fail_func)());
 
 class LogSink;  // defined below
 
@@ -678,7 +554,7 @@ class LogSink;  // defined below
 // NOTE: LOG(severity) expands to LogMessage().stream() for the specified
 // severity.
 #define LOG_TO_STRING(severity, message) \
-  LOG_TO_STRING_##severity(static_cast<std::string*>(message)).stream()
+  LOG_TO_STRING_##severity(static_cast<string*>(message)).stream()
 
 // If a non-NULL pointer is given, we push the message onto the end
 // of a vector of strings; otherwise, we report it with LOG(severity).
@@ -737,12 +613,10 @@ inline int            GetReferenceableValue(int                t) { return t; }
 inline unsigned int   GetReferenceableValue(unsigned int       t) { return t; }
 inline long           GetReferenceableValue(long               t) { return t; }
 inline unsigned long  GetReferenceableValue(unsigned long      t) { return t; }
-#if __cplusplus >= 201103L
 inline long long      GetReferenceableValue(long long          t) { return t; }
 inline unsigned long long GetReferenceableValue(unsigned long long t) {
   return t;
 }
-#endif
 
 // This is a dummy class to define the following operator.
 struct DummyClassToDefineOperator {};
@@ -768,20 +642,13 @@ inline void MakeCheckOpValueString(std::ostream* os, const T& v) {
 
 // Overrides for char types provide readable values for unprintable
 // characters.
-template <> GLOG_EXPORT
+template <> GOOGLE_GLOG_DLL_DECL
 void MakeCheckOpValueString(std::ostream* os, const char& v);
-template <> GLOG_EXPORT
+template <> GOOGLE_GLOG_DLL_DECL
 void MakeCheckOpValueString(std::ostream* os, const signed char& v);
-template <> GLOG_EXPORT
+template <> GOOGLE_GLOG_DLL_DECL
 void MakeCheckOpValueString(std::ostream* os, const unsigned char& v);
 
-// This is required because nullptr is only present in c++ 11 and later.
-#if 1 && __cplusplus >= 201103L
-// Provide printable value for nullptr_t
-template <> GLOG_EXPORT
-void MakeCheckOpValueString(std::ostream* os, const std::nullptr_t& v);
-#endif
-
 // Build the error message string. Specify no inlining for code size.
 template <typename T1, typename T2>
 std::string* MakeCheckOpString(const T1& v1, const T2& v2, const char* exprtext)
@@ -803,7 +670,7 @@ LogSeverity NormalizeSeverity(LogSeverity s);
 // base::BuildCheckOpString(exprtext, base::Print<T1>, &v1,
 // base::Print<T2>, &v2), however this approach has complications
 // related to volatile arguments and function-pointer arguments).
-class GLOG_EXPORT CheckOpMessageBuilder {
+class GOOGLE_GLOG_DLL_DECL CheckOpMessageBuilder {
  public:
   // Inserts "exprtext" and " (" to the stream.
   explicit CheckOpMessageBuilder(const char *exprtext);
@@ -936,7 +803,7 @@ typedef std::string _Check_string;
 // Helper functions for string comparisons.
 // To avoid bloat, the definitions are in logging.cc.
 #define DECLARE_CHECK_STROP_IMPL(func, expected) \
-  GLOG_EXPORT std::string* Check##func##expected##Impl( \
+  GOOGLE_GLOG_DLL_DECL std::string* Check##func##expected##Impl( \
       const char* s1, const char* s2, const char* names);
 DECLARE_CHECK_STROP_IMPL(strcmp, true)
 DECLARE_CHECK_STROP_IMPL(strcmp, false)
@@ -1024,101 +891,8 @@ PLOG_IF(FATAL, GOOGLE_PREDICT_BRANCH_NOT_TAKEN((invocation) == -1))    \
 #define LOG_OCCURRENCES LOG_EVERY_N_VARNAME(occurrences_, __LINE__)
 #define LOG_OCCURRENCES_MOD_N LOG_EVERY_N_VARNAME(occurrences_mod_n_, __LINE__)
 
-#if 1 && __cplusplus >= 201103L
-#define GLOG_CONSTEXPR constexpr
-#else
-#define GLOG_CONSTEXPR const
-#endif
-
-#define LOG_TIME_PERIOD LOG_EVERY_N_VARNAME(timePeriod_, __LINE__)
-#define LOG_PREVIOUS_TIME_RAW LOG_EVERY_N_VARNAME(previousTimeRaw_, __LINE__)
-#define LOG_TIME_DELTA LOG_EVERY_N_VARNAME(deltaTime_, __LINE__)
-#define LOG_CURRENT_TIME LOG_EVERY_N_VARNAME(currentTime_, __LINE__)
-#define LOG_PREVIOUS_TIME LOG_EVERY_N_VARNAME(previousTime_, __LINE__)
-
-#if defined(__has_feature)
-#  if __has_feature(thread_sanitizer)
-#    define GLOG_SANITIZE_THREAD 1
-#  endif
-#endif
-
-#if !defined(GLOG_SANITIZE_THREAD) && defined(__SANITIZE_THREAD__) && __SANITIZE_THREAD__
-#  define GLOG_SANITIZE_THREAD 1
-#endif
-
-#if defined(GLOG_SANITIZE_THREAD)
-#define GLOG_IFDEF_THREAD_SANITIZER(X) X
-#else
-#define GLOG_IFDEF_THREAD_SANITIZER(X)
-#endif
-
-#if defined(GLOG_SANITIZE_THREAD)
-} // namespace google
-
-// We need to identify the static variables as "benign" races
-// to avoid noisy reports from TSAN.
-extern "C" void AnnotateBenignRaceSized(
-  const char *file,
-  int line,
-  const volatile void *mem,
-  size_t size,
-  const char *description);
-
-namespace google {
-#endif
-
-#if __cplusplus >= 201103L && 1 && 1 // Have <chrono> and <atomic>
-#define SOME_KIND_OF_LOG_EVERY_T(severity, seconds) \
-  GLOG_CONSTEXPR std::chrono::nanoseconds LOG_TIME_PERIOD = std::chrono::duration_cast<std::chrono::nanoseconds>(std::chrono::duration<double>(seconds)); \
-  static std::atomic<google::int64> LOG_PREVIOUS_TIME_RAW; \
-  GLOG_IFDEF_THREAD_SANITIZER( \
-          AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_TIME_PERIOD, sizeof(google::int64), "")); \
-  GLOG_IFDEF_THREAD_SANITIZER( \
-          AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_PREVIOUS_TIME_RAW, sizeof(google::int64), "")); \
-  const auto LOG_CURRENT_TIME = std::chrono::duration_cast<std::chrono::nanoseconds>(std::chrono::steady_clock::now().time_since_epoch()); \
-  const auto LOG_PREVIOUS_TIME = LOG_PREVIOUS_TIME_RAW.load(std::memory_order_relaxed); \
-  const auto LOG_TIME_DELTA = LOG_CURRENT_TIME - std::chrono::nanoseconds(LOG_PREVIOUS_TIME); \
-  if (LOG_TIME_DELTA > LOG_TIME_PERIOD) \
-    LOG_PREVIOUS_TIME_RAW.store(std::chrono::duration_cast<std::chrono::nanoseconds>(LOG_CURRENT_TIME).count(), std::memory_order_relaxed); \
-  if (LOG_TIME_DELTA > LOG_TIME_PERIOD) google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity).stream()
-#elif defined(GLOG_OS_WINDOWS)
-#define SOME_KIND_OF_LOG_EVERY_T(severity, seconds) \
-  GLOG_CONSTEXPR LONGLONG LOG_TIME_PERIOD = (seconds) * LONGLONG(1000000000); \
-  static LARGE_INTEGER LOG_PREVIOUS_TIME; \
-  LONGLONG LOG_TIME_DELTA; \
-  { \
-    LARGE_INTEGER currTime; \
-    LARGE_INTEGER freq; \
-    QueryPerformanceCounter(&currTime); \
-    QueryPerformanceFrequency(&freq); \
-    InterlockedCompareExchange64(&LOG_PREVIOUS_TIME.QuadPart, currTime.QuadPart, 0); \
-    LOG_TIME_DELTA = (currTime.QuadPart - LOG_PREVIOUS_TIME.QuadPart) * LONGLONG(1000000000) / freq.QuadPart; \
-    if (LOG_TIME_DELTA > LOG_TIME_PERIOD) InterlockedExchange64(&LOG_PREVIOUS_TIME.QuadPart, currTime.QuadPart); \
-  } \
-  if (LOG_TIME_DELTA > LOG_TIME_PERIOD) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity).stream()
-#else
-#define SOME_KIND_OF_LOG_EVERY_T(severity, seconds) \
-  GLOG_CONSTEXPR google::int64 LOG_TIME_PERIOD(seconds * 1000000000); \
-  static google::int64 LOG_PREVIOUS_TIME; \
-  google::int64 LOG_TIME_DELTA = 0; \
-  { \
-    timespec currentTime = {}; \
-    clock_gettime(CLOCK_MONOTONIC, &currentTime); \
-    LOG_TIME_DELTA = (currentTime.tv_sec * 1000000000 + currentTime.tv_nsec) - LOG_PREVIOUS_TIME; \
-  } \
-  if (LOG_TIME_DELTA > LOG_TIME_PERIOD) __sync_add_and_fetch(&LOG_PREVIOUS_TIME, LOG_TIME_DELTA); \
-  if (LOG_TIME_DELTA > LOG_TIME_PERIOD) google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity).stream()
-#endif
-
-#if 1 && __cplusplus >= 201103L
 #define SOME_KIND_OF_LOG_EVERY_N(severity, n, what_to_do) \
-  static std::atomic<int> LOG_OCCURRENCES(0), LOG_OCCURRENCES_MOD_N(0); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES, sizeof(int), "")); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES_MOD_N, sizeof(int), "")); \
+  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
   ++LOG_OCCURRENCES; \
   if (++LOG_OCCURRENCES_MOD_N > n) LOG_OCCURRENCES_MOD_N -= n; \
   if (LOG_OCCURRENCES_MOD_N == 1) \
@@ -1127,20 +901,16 @@ namespace google {
         &what_to_do).stream()
 
 #define SOME_KIND_OF_LOG_IF_EVERY_N(severity, condition, n, what_to_do) \
-  static std::atomic<int> LOG_OCCURRENCES(0), LOG_OCCURRENCES_MOD_N(0); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES, sizeof(int), "")); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES_MOD_N, sizeof(int), "")); \
+  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
   ++LOG_OCCURRENCES; \
-  if ((condition) && \
+  if (condition && \
       ((LOG_OCCURRENCES_MOD_N=(LOG_OCCURRENCES_MOD_N + 1) % n) == (1 % n))) \
     google::LogMessage( \
         __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
                  &what_to_do).stream()
 
 #define SOME_KIND_OF_PLOG_EVERY_N(severity, n, what_to_do) \
-  static std::atomic<int> LOG_OCCURRENCES(0), LOG_OCCURRENCES_MOD_N(0); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES, sizeof(int), "")); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES_MOD_N, sizeof(int), "")); \
+  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
   ++LOG_OCCURRENCES; \
   if (++LOG_OCCURRENCES_MOD_N > n) LOG_OCCURRENCES_MOD_N -= n; \
   if (LOG_OCCURRENCES_MOD_N == 1) \
@@ -1148,103 +918,14 @@ namespace google {
         __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
         &what_to_do).stream()
 
-#define SOME_KIND_OF_LOG_FIRST_N(severity, n, what_to_do) \
-  static std::atomic<int> LOG_OCCURRENCES(0); \
-  GLOG_IFDEF_THREAD_SANITIZER(AnnotateBenignRaceSized(__FILE__, __LINE__, &LOG_OCCURRENCES, sizeof(int), "")); \
-  if (LOG_OCCURRENCES <= n) \
-    ++LOG_OCCURRENCES; \
-  if (LOG_OCCURRENCES <= n) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#elif defined(GLOG_OS_WINDOWS)
-
-#define SOME_KIND_OF_LOG_EVERY_N(severity, n, what_to_do) \
-  static volatile unsigned LOG_OCCURRENCES = 0; \
-  static volatile unsigned LOG_OCCURRENCES_MOD_N = 0; \
-  InterlockedIncrement(&LOG_OCCURRENCES); \
-  if (InterlockedIncrement(&LOG_OCCURRENCES_MOD_N) > n) \
-    InterlockedExchangeSubtract(&LOG_OCCURRENCES_MOD_N, n); \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#define SOME_KIND_OF_LOG_IF_EVERY_N(severity, condition, n, what_to_do) \
-  static volatile unsigned LOG_OCCURRENCES = 0; \
-  static volatile unsigned LOG_OCCURRENCES_MOD_N = 0; \
-  InterlockedIncrement(&LOG_OCCURRENCES); \
-  if ((condition) && \
-    ((InterlockedIncrement(&LOG_OCCURRENCES_MOD_N), \
-     (LOG_OCCURRENCES_MOD_N > n && InterlockedExchangeSubtract(&LOG_OCCURRENCES_MOD_N, n))), \
-     LOG_OCCURRENCES_MOD_N == 1)) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-                 &what_to_do).stream()
-
-#define SOME_KIND_OF_PLOG_EVERY_N(severity, n, what_to_do) \
-  static volatile unsigned LOG_OCCURRENCES = 0; \
-  static volatile unsigned LOG_OCCURRENCES_MOD_N = 0; \
-  InterlockedIncrement(&LOG_OCCURRENCES); \
-  if (InterlockedIncrement(&LOG_OCCURRENCES_MOD_N) > n) \
-    InterlockedExchangeSubtract(&LOG_OCCURRENCES_MOD_N, n); \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::ErrnoLogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#define SOME_KIND_OF_LOG_FIRST_N(severity, n, what_to_do) \
-  static volatile unsigned LOG_OCCURRENCES = 0; \
-  if (LOG_OCCURRENCES <= n) \
-    InterlockedIncrement(&LOG_OCCURRENCES); \
-  if (LOG_OCCURRENCES <= n) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#else
-
-#define SOME_KIND_OF_LOG_EVERY_N(severity, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  __sync_add_and_fetch(&LOG_OCCURRENCES, 1); \
-  if (__sync_add_and_fetch(&LOG_OCCURRENCES_MOD_N, 1) > n) \
-    __sync_sub_and_fetch(&LOG_OCCURRENCES_MOD_N, n); \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
-#define SOME_KIND_OF_LOG_IF_EVERY_N(severity, condition, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  __sync_add_and_fetch(&LOG_OCCURRENCES, 1); \
-  if ((condition) && \
-      (__sync_add_and_fetch(&LOG_OCCURRENCES_MOD_N, 1) || true) && \
-      ((LOG_OCCURRENCES_MOD_N >= n && __sync_sub_and_fetch(&LOG_OCCURRENCES_MOD_N, n)) || true) && \
-      LOG_OCCURRENCES_MOD_N == (1 % n)) \
-    google::LogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-                 &what_to_do).stream()
-
-#define SOME_KIND_OF_PLOG_EVERY_N(severity, n, what_to_do) \
-  static int LOG_OCCURRENCES = 0, LOG_OCCURRENCES_MOD_N = 0; \
-  __sync_add_and_fetch(&LOG_OCCURRENCES, 1); \
-  if (__sync_add_and_fetch(&LOG_OCCURRENCES_MOD_N, 1) > n) \
-    __sync_sub_and_fetch(&LOG_OCCURRENCES_MOD_N, n); \
-  if (LOG_OCCURRENCES_MOD_N == 1) \
-    google::ErrnoLogMessage( \
-        __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
-        &what_to_do).stream()
-
 #define SOME_KIND_OF_LOG_FIRST_N(severity, n, what_to_do) \
   static int LOG_OCCURRENCES = 0; \
   if (LOG_OCCURRENCES <= n) \
-    __sync_add_and_fetch(&LOG_OCCURRENCES, 1); \
+    ++LOG_OCCURRENCES; \
   if (LOG_OCCURRENCES <= n) \
     google::LogMessage( \
         __FILE__, __LINE__, google::GLOG_ ## severity, LOG_OCCURRENCES, \
         &what_to_do).stream()
-#endif
 
 namespace glog_internal_namespace_ {
 template <bool>
@@ -1254,14 +935,12 @@ struct CrashReason;
 
 // Returns true if FailureSignalHandler is installed.
 // Needs to be exported since it's used by the signalhandler_unittest.
-GLOG_EXPORT bool IsFailureSignalHandlerInstalled();
+GOOGLE_GLOG_DLL_DECL bool IsFailureSignalHandlerInstalled();
 }  // namespace glog_internal_namespace_
 
 #define LOG_EVERY_N(severity, n)                                        \
   SOME_KIND_OF_LOG_EVERY_N(severity, (n), google::LogMessage::SendToLog)
 
-#define LOG_EVERY_T(severity, T) SOME_KIND_OF_LOG_EVERY_T(severity, (T))
-
 #define SYSLOG_EVERY_N(severity, n) \
   SOME_KIND_OF_LOG_EVERY_N(severity, (n), google::LogMessage::SendToSyslogAndLog)
 
@@ -1432,7 +1111,7 @@ namespace base_logging {
 // LogMessage::LogStream is a std::ostream backed by this streambuf.
 // This class ignores overflow and leaves two bytes at the end of the
 // buffer to allow for a '\n' and '\0'.
-class GLOG_EXPORT LogStreamBuf : public std::streambuf {
+class GOOGLE_GLOG_DLL_DECL LogStreamBuf : public std::streambuf {
  public:
   // REQUIREMENTS: "len" must be >= 2 to account for the '\n' and '\0'.
   LogStreamBuf(char *buf, int len) {
@@ -1440,12 +1119,12 @@ class GLOG_EXPORT LogStreamBuf : public std::streambuf {
   }
 
   // This effectively ignores overflow.
-  int_type overflow(int_type ch) {
+  virtual int_type overflow(int_type ch) {
     return ch;
   }
 
   // Legacy public ostrstream method.
-  size_t pcount() const { return static_cast<size_t>(pptr() - pbase()); }
+  size_t pcount() const { return pptr() - pbase(); }
   char* pbase() const { return std::streambuf::pbase(); }
 };
 
@@ -1460,7 +1139,7 @@ class GLOG_EXPORT LogStreamBuf : public std::streambuf {
 // You shouldn't actually use LogMessage's constructor to log things,
 // though.  You should use the LOG() macro (and variants thereof)
 // above.
-class GLOG_EXPORT LogMessage {
+class GOOGLE_GLOG_DLL_DECL LogMessage {
 public:
   enum {
     // Passing kNoLogPrefix for the line number disables the
@@ -1478,10 +1157,10 @@ public:
   // http://msdn.microsoft.com/en-us/library/3tdb471s(VS.80).aspx
   // Let's just ignore the warning.
 GLOG_MSVC_PUSH_DISABLE_WARNING(4275)
-  class GLOG_EXPORT LogStream : public std::ostream {
+  class GOOGLE_GLOG_DLL_DECL LogStream : public std::ostream {
 GLOG_MSVC_POP_WARNING()
   public:
-    LogStream(char *buf, int len, int64 ctr)
+    LogStream(char *buf, int len, int ctr)
         : std::ostream(NULL),
           streambuf_(buf, len),
           ctr_(ctr),
@@ -1489,8 +1168,8 @@ GLOG_MSVC_POP_WARNING()
       rdbuf(&streambuf_);
     }
 
-    int64 ctr() const { return ctr_; }
-    void set_ctr(int64 ctr) { ctr_ = ctr; }
+    int ctr() const { return ctr_; }
+    void set_ctr(int ctr) { ctr_ = ctr; }
     LogStream* self() const { return self_; }
 
     // Legacy std::streambuf methods.
@@ -1502,7 +1181,7 @@ GLOG_MSVC_POP_WARNING()
     LogStream(const LogStream&);
     LogStream& operator=(const LogStream&);
     base_logging::LogStreamBuf streambuf_;
-    int64 ctr_;  // Counter hack (for the LOG_EVERY_X() macro)
+    int ctr_;  // Counter hack (for the LOG_EVERY_X() macro)
     LogStream *self_;  // Consistency check hack
   };
 
@@ -1510,7 +1189,7 @@ public:
   // icc 8 requires this typedef to avoid an internal compiler error.
   typedef void (LogMessage::*SendMethod)();
 
-  LogMessage(const char* file, int line, LogSeverity severity, int64 ctr,
+  LogMessage(const char* file, int line, LogSeverity severity, int ctr,
              SendMethod send_method);
 
   // Two special constructors that generate reduced amounts of code at
@@ -1577,8 +1256,6 @@ public:
   // Must be called without the log_mutex held.  (L < log_mutex)
   static int64 num_messages(int severity);
 
-  const LogMessageTime& getLogMessageTime() const;
-
   struct LogMessageData;
 
 private:
@@ -1604,7 +1281,6 @@ private:
   // LogMessage uses less stack space.
   LogMessageData* allocated_;
   LogMessageData* data_;
-  LogMessageTime logmsgtime_;
 
   friend class LogDestination;
 
@@ -1615,7 +1291,7 @@ private:
 // This class happens to be thread-hostile because all instances share
 // a single data buffer, but since it can only be created just before
 // the process dies, we don't worry so much.
-class GLOG_EXPORT LogMessageFatal : public LogMessage {
+class GOOGLE_GLOG_DLL_DECL LogMessageFatal : public LogMessage {
  public:
   LogMessageFatal(const char* file, int line);
   LogMessageFatal(const char* file, int line, const CheckOpString& result);
@@ -1644,7 +1320,7 @@ inline void LogAtLevel(int const severity, std::string const &msg) {
 // reasonably good C++11 support, so we set LANG_CXX for it and
 // newer versions (_MSC_VER >= 1900).
 #if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L || \
-     (defined(_MSC_VER) && _MSC_VER >= 1900)) && !defined(__UCLIBCXX_MAJOR__)
+     (defined(_MSC_VER) && _MSC_VER >= 1900))
 // Helper for CHECK_NOTNULL().
 //
 // In C++11, all cases can be handled by a single function. Since the value
@@ -1676,14 +1352,15 @@ T* CheckNotNull(const char *file, int line, const char *names, T* t) {
 // Allow folks to put a counter in the LOG_EVERY_X()'ed messages. This
 // only works if ostream is a LogStream. If the ostream is not a
 // LogStream you'll get an assert saying as much at runtime.
-GLOG_EXPORT std::ostream& operator<<(std::ostream &os,
+GOOGLE_GLOG_DLL_DECL std::ostream& operator<<(std::ostream &os,
                                               const PRIVATE_Counter&);
 
 
 // Derived class for PLOG*() above.
-class GLOG_EXPORT ErrnoLogMessage : public LogMessage {
+class GOOGLE_GLOG_DLL_DECL ErrnoLogMessage : public LogMessage {
  public:
-  ErrnoLogMessage(const char* file, int line, LogSeverity severity, int64 ctr,
+
+  ErrnoLogMessage(const char* file, int line, LogSeverity severity, int ctr,
                   void (LogMessage::*send_method)());
 
   // Postpends ": strerror(errno) [errno]".
@@ -1699,7 +1376,7 @@ class GLOG_EXPORT ErrnoLogMessage : public LogMessage {
 // logging macros.  This avoids compiler warnings like "value computed
 // is not used" and "statement has no effect".
 
-class GLOG_EXPORT LogMessageVoidify {
+class GOOGLE_GLOG_DLL_DECL LogMessageVoidify {
  public:
   LogMessageVoidify() { }
   // This has to be an operator with a precedence lower than << but
@@ -1710,19 +1387,19 @@ class GLOG_EXPORT LogMessageVoidify {
 
 // Flushes all log files that contains messages that are at least of
 // the specified severity level.  Thread-safe.
-GLOG_EXPORT void FlushLogFiles(LogSeverity min_severity);
+GOOGLE_GLOG_DLL_DECL void FlushLogFiles(LogSeverity min_severity);
 
 // Flushes all log files that contains messages that are at least of
 // the specified severity level. Thread-hostile because it ignores
 // locking -- used for catastrophic failures.
-GLOG_EXPORT void FlushLogFilesUnsafe(LogSeverity min_severity);
+GOOGLE_GLOG_DLL_DECL void FlushLogFilesUnsafe(LogSeverity min_severity);
 
 //
 // Set the destination to which a particular severity level of log
 // messages is sent.  If base_filename is "", it means "don't log this
 // severity".  Thread-safe.
 //
-GLOG_EXPORT void SetLogDestination(LogSeverity severity,
+GOOGLE_GLOG_DLL_DECL void SetLogDestination(LogSeverity severity,
                                             const char* base_filename);
 
 //
@@ -1731,7 +1408,7 @@ GLOG_EXPORT void SetLogDestination(LogSeverity severity,
 // you don't call this function, the symlink basename is the
 // invocation name of the program.  Thread-safe.
 //
-GLOG_EXPORT void SetLogSymlink(LogSeverity severity,
+GOOGLE_GLOG_DLL_DECL void SetLogSymlink(LogSeverity severity,
                                         const char* symlink_basename);
 
 //
@@ -1739,7 +1416,7 @@ GLOG_EXPORT void SetLogSymlink(LogSeverity severity,
 // Users should subclass LogSink and override send to do whatever they want.
 // Implementations must be thread-safe because a shared instance will
 // be called from whichever thread ran the LOG(XXX) line.
-class GLOG_EXPORT LogSink {
+class GOOGLE_GLOG_DLL_DECL LogSink {
  public:
   virtual ~LogSink();
 
@@ -1748,13 +1425,8 @@ class GLOG_EXPORT LogSink {
   // during this call.
   virtual void send(LogSeverity severity, const char* full_filename,
                     const char* base_filename, int line,
-                    const LogMessageTime& logmsgtime, const char* message,
-                    size_t message_len);
-  // Provide an overload for compatibility purposes
-  GLOG_DEPRECATED
-  virtual void send(LogSeverity severity, const char* full_filename,
-                    const char* base_filename, int line, const std::tm* t,
-                    const char* message, size_t message_len);
+                    const struct ::tm* tm_time,
+                    const char* message, size_t message_len) = 0;
 
   // Redefine this to implement waiting for
   // the sink's logging logic to complete.
@@ -1774,13 +1446,13 @@ class GLOG_EXPORT LogSink {
   // Returns the normal text output of the log message.
   // Can be useful to implement send().
   static std::string ToString(LogSeverity severity, const char* file, int line,
-                              const LogMessageTime &logmsgtime,
+                              const struct ::tm* tm_time,
                               const char* message, size_t message_len);
 };
 
 // Add or remove a LogSink as a consumer of logging data.  Thread-safe.
-GLOG_EXPORT void AddLogSink(LogSink *destination);
-GLOG_EXPORT void RemoveLogSink(LogSink *destination);
+GOOGLE_GLOG_DLL_DECL void AddLogSink(LogSink *destination);
+GOOGLE_GLOG_DLL_DECL void RemoveLogSink(LogSink *destination);
 
 //
 // Specify an "extension" added to the filename specified via
@@ -1788,7 +1460,7 @@ GLOG_EXPORT void RemoveLogSink(LogSink *destination);
 // often used to append the port we're listening on to the logfile
 // name.  Thread-safe.
 //
-GLOG_EXPORT void SetLogFilenameExtension(
+GOOGLE_GLOG_DLL_DECL void SetLogFilenameExtension(
     const char* filename_extension);
 
 //
@@ -1796,12 +1468,12 @@ GLOG_EXPORT void SetLogFilenameExtension(
 // are logged to stderr (in addition to logging to the usual log
 // file(s)).  Thread-safe.
 //
-GLOG_EXPORT void SetStderrLogging(LogSeverity min_severity);
+GOOGLE_GLOG_DLL_DECL void SetStderrLogging(LogSeverity min_severity);
 
 //
 // Make it so that all log messages go only to stderr.  Thread-safe.
 //
-GLOG_EXPORT void LogToStderr();
+GOOGLE_GLOG_DLL_DECL void LogToStderr();
 
 //
 // Make it so that all log messages of at least a particular severity are
@@ -1809,15 +1481,15 @@ GLOG_EXPORT void LogToStderr();
 // usual log file(s)).  The list of addresses is just a string containing
 // the email addresses to send to (separated by spaces, say).  Thread-safe.
 //
-GLOG_EXPORT void SetEmailLogging(LogSeverity min_severity,
+GOOGLE_GLOG_DLL_DECL void SetEmailLogging(LogSeverity min_severity,
                                           const char* addresses);
 
 // A simple function that sends email. dest is a commma-separated
 // list of addressess.  Thread-safe.
-GLOG_EXPORT bool SendEmail(const char *dest,
+GOOGLE_GLOG_DLL_DECL bool SendEmail(const char *dest,
                                     const char *subject, const char *body);
 
-GLOG_EXPORT const std::vector<std::string>& GetLoggingDirectories();
+GOOGLE_GLOG_DLL_DECL const std::vector<std::string>& GetLoggingDirectories();
 
 // For tests only:  Clear the internal [cached] list of logging directories to
 // force a refresh the next time GetLoggingDirectories is called.
@@ -1825,15 +1497,15 @@ GLOG_EXPORT const std::vector<std::string>& GetLoggingDirectories();
 void TestOnly_ClearLoggingDirectoriesList();
 
 // Returns a set of existing temporary directories, which will be a
-// subset of the directories returned by GetLoggingDirectories().
+// subset of the directories returned by GetLogginDirectories().
 // Thread-safe.
-GLOG_EXPORT void GetExistingTempDirectories(
+GOOGLE_GLOG_DLL_DECL void GetExistingTempDirectories(
     std::vector<std::string>* list);
 
 // Print any fatal message again -- useful to call from signal handler
 // so that the last thing in the output is the fatal message.
 // Thread-hostile, but a race is unlikely.
-GLOG_EXPORT void ReprintFatalMessage();
+GOOGLE_GLOG_DLL_DECL void ReprintFatalMessage();
 
 // Truncate a log file that may be the append-only output of multiple
 // processes and hence can't simply be renamed/reopened (typically a
@@ -1842,16 +1514,17 @@ GLOG_EXPORT void ReprintFatalMessage();
 // be racing with other writers, this approach has the potential to
 // lose very small amounts of data. For security, only follow symlinks
 // if the path is /proc/self/fd/*
-GLOG_EXPORT void TruncateLogFile(const char* path, uint64 limit, uint64 keep);
+GOOGLE_GLOG_DLL_DECL void TruncateLogFile(const char *path,
+                                          int64 limit, int64 keep);
 
 // Truncate stdout and stderr if they are over the value specified by
 // --max_log_size; keep the final 1MB.  This function has the same
 // race condition as TruncateLogFile.
-GLOG_EXPORT void TruncateStdoutStderr();
+GOOGLE_GLOG_DLL_DECL void TruncateStdoutStderr();
 
 // Return the string representation of the provided LogSeverity level.
 // Thread-safe.
-GLOG_EXPORT const char* GetLogSeverityName(LogSeverity severity);
+GOOGLE_GLOG_DLL_DECL const char* GetLogSeverityName(LogSeverity severity);
 
 // ---------------------------------------------------------------------
 // Implementation details that are not useful to most clients
@@ -1866,7 +1539,7 @@ GLOG_EXPORT const char* GetLogSeverityName(LogSeverity severity);
 
 namespace base {
 
-class GLOG_EXPORT Logger {
+class GOOGLE_GLOG_DLL_DECL Logger {
  public:
   virtual ~Logger();
 
@@ -1881,7 +1554,7 @@ class GLOG_EXPORT Logger {
   virtual void Write(bool force_flush,
                      time_t timestamp,
                      const char* message,
-                     size_t message_len) = 0;
+                     int message_len) = 0;
 
   // Flush any buffered messages
   virtual void Flush() = 0;
@@ -1895,12 +1568,12 @@ class GLOG_EXPORT Logger {
 // Get the logger for the specified severity level.  The logger
 // remains the property of the logging module and should not be
 // deleted by the caller.  Thread-safe.
-extern GLOG_EXPORT Logger* GetLogger(LogSeverity level);
+extern GOOGLE_GLOG_DLL_DECL Logger* GetLogger(LogSeverity level);
 
 // Set the logger for the specified severity level.  The logger
 // becomes the property of the logging module and should not
 // be deleted by the caller.  Thread-safe.
-extern GLOG_EXPORT void SetLogger(LogSeverity level, Logger* logger);
+extern GOOGLE_GLOG_DLL_DECL void SetLogger(LogSeverity level, Logger* logger);
 
 }
 
@@ -1915,14 +1588,14 @@ extern GLOG_EXPORT void SetLogger(LogSeverity level, Logger* logger);
 // cases, you do not need to check the error code and you can directly
 // use the value of "buf". It will never have an undefined value.
 // DEPRECATED: Use StrError(int) instead.
-GLOG_EXPORT int posix_strerror_r(int err, char *buf, size_t len);
+GOOGLE_GLOG_DLL_DECL int posix_strerror_r(int err, char *buf, size_t len);
 
 // A thread-safe replacement for strerror(). Returns a string describing the
 // given POSIX error code.
-GLOG_EXPORT std::string StrError(int err);
+GOOGLE_GLOG_DLL_DECL std::string StrError(int err);
 
 // A class for which we define operator<<, which does nothing.
-class GLOG_EXPORT NullStream : public LogMessage::LogStream {
+class GOOGLE_GLOG_DLL_DECL NullStream : public LogMessage::LogStream {
  public:
   // Initialize the LogStream so the messages can be written somewhere
   // (they'll never be actually displayed). This will be needed if a
@@ -1951,19 +1624,12 @@ inline NullStream& operator<<(NullStream &str, const T &) { return str; }
 
 // Similar to NullStream, but aborts the program (without stack
 // trace), like LogMessageFatal.
-class GLOG_EXPORT NullStreamFatal : public NullStream {
+class GOOGLE_GLOG_DLL_DECL NullStreamFatal : public NullStream {
  public:
   NullStreamFatal() { }
   NullStreamFatal(const char* file, int line, const CheckOpString& result) :
       NullStream(file, line, result) { }
-#if defined(_MSC_VER)
-#pragma warning(push)
-#pragma warning(disable : 4722)
-#endif // _MSC_VER
-  __declspec(noreturn) ~NullStreamFatal() throw () { _exit(EXIT_FAILURE); }
-#if defined(_MSC_VER)
-#pragma warning(pop)
-#endif // _MSC_VER
+  __declspec(noreturn) ~NullStreamFatal() throw () { _exit(1); }
 };
 
 // Install a signal handler that will dump signal information and a stack
@@ -1982,21 +1648,15 @@ class GLOG_EXPORT NullStreamFatal : public NullStream {
 // to use the failure signal handler for all threads.  The stack trace
 // will be shown only for the thread that receives the signal.  In other
 // words, stack traces of other threads won't be shown.
-GLOG_EXPORT void InstallFailureSignalHandler();
+GOOGLE_GLOG_DLL_DECL void InstallFailureSignalHandler();
 
 // Installs a function that is used for writing the failure dump.  "data"
 // is the pointer to the beginning of a message to be written, and "size"
 // is the size of the message.  You should not expect the data is
 // terminated with '\0'.
-GLOG_EXPORT void InstallFailureWriter(
-    void (*writer)(const char* data, size_t size));
+GOOGLE_GLOG_DLL_DECL void InstallFailureWriter(
+    void (*writer)(const char* data, int size));
 
 }
 
-#pragma pop_macro("DECLARE_VARIABLE")
-#pragma pop_macro("DECLARE_bool")
-#pragma pop_macro("DECLARE_string")
-#pragma pop_macro("DECLARE_int32")
-#pragma pop_macro("DECLARE_uint32")
-
-#endif // GLOG_LOGGING_H
+#endif // _LOGGING_H_
diff --git a/3rdParty/ceres_2.2/include/glog/platform.h b/3rdParty/ceres_2.2/include/glog/platform.h
deleted file mode 100644
index e614411..0000000
--- a/3rdParty/ceres_2.2/include/glog/platform.h
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) 2008, Google Inc.
-// All rights reserved.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are
-// met:
-//
-//     * Redistributions of source code must retain the above copyright
-// notice, this list of conditions and the following disclaimer.
-//     * Redistributions in binary form must reproduce the above
-// copyright notice, this list of conditions and the following disclaimer
-// in the documentation and/or other materials provided with the
-// distribution.
-//     * Neither the name of Google Inc. nor the names of its
-// contributors may be used to endorse or promote products derived from
-// this software without specific prior written permission.
-//
-// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
-// Author: Shinichiro Hamaji
-//
-// Detect supported platforms.
-
-#ifndef GLOG_PLATFORM_H
-#define GLOG_PLATFORM_H
-
-#if defined(WIN32) || defined(_WIN32) || defined(__WIN32__)
-#define GLOG_OS_WINDOWS
-#elif defined(__CYGWIN__) || defined(__CYGWIN32__)
-#define GLOG_OS_CYGWIN
-#elif defined(linux) || defined(__linux) || defined(__linux__)
-#ifndef GLOG_OS_LINUX
-#define GLOG_OS_LINUX
-#endif
-#elif defined(macintosh) || defined(__APPLE__) || defined(__APPLE_CC__)
-#define GLOG_OS_MACOSX
-#elif defined(__FreeBSD__)
-#define GLOG_OS_FREEBSD
-#elif defined(__NetBSD__)
-#define GLOG_OS_NETBSD
-#elif defined(__OpenBSD__)
-#define GLOG_OS_OPENBSD
-#else
-// TODO(hamaji): Add other platforms.
-#error Platform not supported by glog. Please consider to contribute platform information by submitting a pull request on Github.
-#endif
-
-#endif // GLOG_PLATFORM_H
diff --git a/3rdParty/ceres_2.2/include/glog/raw_logging.h b/3rdParty/ceres_2.2/include/glog/raw_logging.h
index 1012e96..cf3f27d 100644
--- a/3rdParty/ceres_2.2/include/glog/raw_logging.h
+++ b/3rdParty/ceres_2.2/include/glog/raw_logging.h
@@ -33,20 +33,23 @@
 // acquire any locks, and can therefore be used by low-level memory
 // allocation and synchronization code.
 
-#ifndef GLOG_RAW_LOGGING_H
-#define GLOG_RAW_LOGGING_H
+#ifndef BASE_RAW_LOGGING_H_
+#define BASE_RAW_LOGGING_H_
 
-#include <ctime>
+#include <time.h>
 
 namespace google {
 
-#include <glog/log_severity.h>
-#include <glog/logging.h>
-#include <glog/vlog_is_on.h>
+#include "glog/log_severity.h"
+#include "glog/vlog_is_on.h"
 
-#if defined(__GNUC__)
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wvariadic-macros"
+// Annoying stuff for windows -- makes sure clients can import these functions
+#ifndef GOOGLE_GLOG_DLL_DECL
+# if defined(_WIN32) && !defined(__CYGWIN__)
+#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
+# else
+#   define GOOGLE_GLOG_DLL_DECL
+# endif
 #endif
 
 // This is similar to LOG(severity) << format... and VLOG(level) << format..,
@@ -61,8 +64,8 @@ namespace google {
 //   RAW_LOG(ERROR, "Failed foo with %i: %s", status, error);
 //   RAW_VLOG(3, "status is %i", status);
 // These will print an almost standard log lines like this to stderr only:
-//   E20200821 211317 file.cc:123] RAW: Failed foo with 22: bad_file
-//   I20200821 211317 file.cc:142] RAW: status is 20
+//   E0821 211317 file.cc:123] RAW: Failed foo with 22: bad_file
+//   I0821 211317 file.cc:142] RAW: status is 20
 #define RAW_LOG(severity, ...) \
   do { \
     switch (google::GLOG_ ## severity) {  \
@@ -85,7 +88,7 @@ namespace google {
 
 // The following STRIP_LOG testing is performed in the header file so that it's
 // possible to completely compile out the logging code and the log messages.
-#if !defined(STRIP_LOG) || STRIP_LOG == 0
+#if STRIP_LOG == 0
 #define RAW_VLOG(verboselevel, ...) \
   do { \
     if (VLOG_IS_ON(verboselevel)) { \
@@ -96,35 +99,35 @@ namespace google {
 #define RAW_VLOG(verboselevel, ...) RawLogStub__(0, __VA_ARGS__)
 #endif // STRIP_LOG == 0
 
-#if !defined(STRIP_LOG) || STRIP_LOG == 0
+#if STRIP_LOG == 0
 #define RAW_LOG_INFO(...) google::RawLog__(google::GLOG_INFO, \
                                    __FILE__, __LINE__, __VA_ARGS__)
 #else
 #define RAW_LOG_INFO(...) google::RawLogStub__(0, __VA_ARGS__)
 #endif // STRIP_LOG == 0
 
-#if !defined(STRIP_LOG) || STRIP_LOG <= 1
+#if STRIP_LOG <= 1
 #define RAW_LOG_WARNING(...) google::RawLog__(google::GLOG_WARNING,   \
                                       __FILE__, __LINE__, __VA_ARGS__)
 #else
 #define RAW_LOG_WARNING(...) google::RawLogStub__(0, __VA_ARGS__)
 #endif // STRIP_LOG <= 1
 
-#if !defined(STRIP_LOG) || STRIP_LOG <= 2
+#if STRIP_LOG <= 2
 #define RAW_LOG_ERROR(...) google::RawLog__(google::GLOG_ERROR,       \
                                     __FILE__, __LINE__, __VA_ARGS__)
 #else
 #define RAW_LOG_ERROR(...) google::RawLogStub__(0, __VA_ARGS__)
 #endif // STRIP_LOG <= 2
 
-#if !defined(STRIP_LOG) || STRIP_LOG <= 3
+#if STRIP_LOG <= 3
 #define RAW_LOG_FATAL(...) google::RawLog__(google::GLOG_FATAL,       \
                                     __FILE__, __LINE__, __VA_ARGS__)
 #else
 #define RAW_LOG_FATAL(...) \
   do { \
     google::RawLogStub__(0, __VA_ARGS__);        \
-    exit(EXIT_FAILURE); \
+    exit(1); \
   } while (0)
 #endif // STRIP_LOG <= 3
 
@@ -157,10 +160,6 @@ namespace google {
 
 #endif  // NDEBUG
 
-#if defined(__GNUC__)
-#pragma GCC diagnostic pop
-#endif
-
 // Stub log function used to work around for unused variable warnings when
 // building with STRIP_LOG > 0.
 static inline void RawLogStub__(int /* ignored */, ...) {
@@ -170,10 +169,12 @@ static inline void RawLogStub__(int /* ignored */, ...) {
 // Logs format... at "severity" level, reporting it
 // as called from file:line.
 // This does not allocate memory or acquire locks.
-GLOG_EXPORT void RawLog__(LogSeverity severity, const char* file, int line,
-                          const char* format, ...)
-    ;
+GOOGLE_GLOG_DLL_DECL void RawLog__(LogSeverity severity,
+                                   const char* file,
+                                   int line,
+                                   const char* format, ...)
+   ;
 
 }
 
-#endif  // GLOG_RAW_LOGGING_H
+#endif  // BASE_RAW_LOGGING_H_
diff --git a/3rdParty/ceres_2.2/include/glog/stl_logging.h b/3rdParty/ceres_2.2/include/glog/stl_logging.h
index ed200dd..40a15aa 100644
--- a/3rdParty/ceres_2.2/include/glog/stl_logging.h
+++ b/3rdParty/ceres_2.2/include/glog/stl_logging.h
@@ -59,7 +59,7 @@
 #include <utility>
 #include <vector>
 
-#if defined(GLOG_STL_LOGGING_FOR_UNORDERED) && __cplusplus >= 201103L
+#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
 # include <unordered_map>
 # include <unordered_set>
 #endif
@@ -130,7 +130,7 @@ inline std::ostream& operator<<(std::ostream& out, \
 
 OUTPUT_FOUR_ARG_CONTAINER(std::map)
 OUTPUT_FOUR_ARG_CONTAINER(std::multimap)
-#if defined(GLOG_STL_LOGGING_FOR_UNORDERED) && __cplusplus >= 201103L
+#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
 OUTPUT_FOUR_ARG_CONTAINER(std::unordered_set)
 OUTPUT_FOUR_ARG_CONTAINER(std::unordered_multiset)
 #endif
@@ -153,7 +153,7 @@ inline std::ostream& operator<<(std::ostream& out, \
   return out; \
 }
 
-#if defined(GLOG_STL_LOGGING_FOR_UNORDERED) && __cplusplus >= 201103L
+#ifdef GLOG_STL_LOGGING_FOR_UNORDERED
 OUTPUT_FIVE_ARG_CONTAINER(std::unordered_map)
 OUTPUT_FIVE_ARG_CONTAINER(std::unordered_multimap)
 #endif
diff --git a/3rdParty/ceres_2.2/include/glog/vlog_is_on.h b/3rdParty/ceres_2.2/include/glog/vlog_is_on.h
index 52036b5..02b0b86 100644
--- a/3rdParty/ceres_2.2/include/glog/vlog_is_on.h
+++ b/3rdParty/ceres_2.2/include/glog/vlog_is_on.h
@@ -41,7 +41,7 @@
 //     // that can't be accomplished e.g. via just VLOG(2) << ...;
 //   }
 //
-// The truth value that VLOG_IS_ON(level) returns is determined by
+// The truth value that VLOG_IS_ON(level) returns is determined by 
 // the three verbosity level flags:
 //   --v=<n>  Gives the default maximal active V-logging level;
 //            0 is the default.
@@ -61,7 +61,16 @@
 #ifndef BASE_VLOG_IS_ON_H_
 #define BASE_VLOG_IS_ON_H_
 
-#include <glog/log_severity.h>
+#include "glog/log_severity.h"
+
+// Annoying stuff for windows -- makes sure clients can import these functions
+#ifndef GOOGLE_GLOG_DLL_DECL
+# if defined(_WIN32) && !defined(__CYGWIN__)
+#   define GOOGLE_GLOG_DLL_DECL  __declspec(dllimport)
+# else
+#   define GOOGLE_GLOG_DLL_DECL
+# endif
+#endif
 
 #if defined(__GNUC__)
 // We emit an anonymous static int* variable at every VLOG_IS_ON(n) site.
@@ -72,11 +81,12 @@
 // parsing of --vmodule flag and/or SetVLOGLevel calls.
 #define VLOG_IS_ON(verboselevel)                                \
   __extension__  \
-  ({ static google::SiteFlag vlocal__ = {NULL, NULL, 0, NULL};       \
+  ({ static google::int32* vlocal__ = &google::kLogSiteUninitialized;           \
      google::int32 verbose_level__ = (verboselevel);                    \
-     (vlocal__.level == NULL ? google::InitVLOG3__(&vlocal__, &FLAGS_v, \
-                        __FILE__, verbose_level__) : *vlocal__.level >= verbose_level__); \
-  })
+     (*vlocal__ >= verbose_level__) &&                          \
+     ((vlocal__ != &google::kLogSiteUninitialized) ||                   \
+      (google::InitVLOG3__(&vlocal__, &FLAGS_v,                         \
+                   __FILE__, verbose_level__))); })
 #else
 // GNU extensions not available, so we do not support --vmodule.
 // Dynamic value of FLAGS_v always controls the logging level.
@@ -91,16 +101,16 @@
 //	 one needs to supply the exact --vmodule pattern that applied to them.
 //       (If no --vmodule pattern applied to them
 //       the value of FLAGS_v will continue to control them.)
-extern GLOG_EXPORT int SetVLOGLevel(const char* module_pattern, int log_level);
+extern GOOGLE_GLOG_DLL_DECL int SetVLOGLevel(const char* module_pattern,
+                                             int log_level);
 
 // Various declarations needed for VLOG_IS_ON above: =========================
 
-struct SiteFlag {
-  google::int32* level;
-  const char* base_name;
-  size_t base_len;
-  SiteFlag* next;
-};
+// Special value used to indicate that a VLOG_IS_ON site has not been
+// initialized.  We make this a large value, so the common-case check
+// of "*vlocal__ >= verbose_level__" in VLOG_IS_ON definition
+// passes in such cases and InitVLOG3__ is then triggered.
+extern google::int32 kLogSiteUninitialized;
 
 // Helper routine which determines the logging info for a particalur VLOG site.
 //   site_flag     is the address of the site-local pointer to the controlling
@@ -110,9 +120,10 @@ struct SiteFlag {
 //   verbose_level is the argument to VLOG_IS_ON
 // We will return the return value for VLOG_IS_ON
 // and if possible set *site_flag appropriately.
-extern GLOG_EXPORT bool InitVLOG3__(
-    google::SiteFlag* site_flag,
-    google::int32* site_default, const char* fname,
+extern GOOGLE_GLOG_DLL_DECL bool InitVLOG3__(
+    google::int32** site_flag,
+    google::int32* site_default,
+    const char* fname,
     google::int32 verbose_level);
 
 #endif  // BASE_VLOG_IS_ON_H_
diff --git a/3rdParty/ceres_2.2/lib/ceres-debug.lib b/3rdParty/ceres_2.2/lib/ceres-debug.lib
index 56ad1ee3c219b1f51a1951568e17fa7ed0ca1ef0..b9f2ce2482acf6ed2de50ae5209fc0e1ff1f2097 100644
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diff --git a/3rdParty/ceres_2.2/lib/ceres_cuda_kernels.lib b/3rdParty/ceres_2.2/lib/ceres_cuda_kernels.lib
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zVFb(Qn1^T4M3ULhmW@6-X}7ra(Mg|;HaaX@ShpZ88gSaUq|L)LA}KZoO(T*j4{b!!
zYglTdktPRSB+_i6>Y|Y@4>=+hO;J0|SUDcEVUZ*n<|Qtv;YsbHEm16@VT*+t4Sf}2
zS{-4?q|K`eASEiRz)b5alVs`xOG2dvX1hWoLBbDh*sF<5mw_AwEMZgcA;{sUNi7Gi
zZ!Bf5i7rQP&iKzV0ZF?8vrIs8{&o{!ughUKx;D9`8#(iiOuCV?;AT5=X?2fmM>aj~
zwj-Z<C(L%t(_pPe8(a@QyZt;hMk!ILfh8UzQrRcLlNmN=xZ5d5Ame=12&dBZG;f`-
z4w$i9tHyoqf=DkJ=1mi|AF1m>r<aV>f=HSSebd9Vb;LT@ayK|>w9{ajVl+)#NGzJN
ze<H<b+G3eBnzkEMYGidThNO|6Z2;8U1q0eJ>TNN)T+7^5ZZaT^{@Ld0W}r*rx)tjP
zUF$ANhXpNoVp}I5AnG&`MLlQWTZ4Sl@`w6~`!dcSs(9ovHmz;$Rvc5GRY1vFL#YR3
zs~t0Ywx%tRG}xxoR!Ae5lq+^^7{o}4F{MyhBLm2M)L@&XR#9@c_K7Wh-Cdn6J)L5F
zhoV{=M-j$+2u<5o(bL`4(b?AFHlxvsPoZh;68l=Z+j}}%dd+CGo_Z}!PhVSGpKDu(
zZJM?Yv8$!Kt*^xmsoULCPsSLV<Y64$7={uK(DZe*c6GQtW^psnXg#3R_bEw}V`F0D
aFuknq;5yZ!G3;R(U4GY2(TSUv^Zx;5c8dJ~

literal 0
HcmV?d00001

diff --git a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfig.cmake b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfig.cmake
index f377218..ba73951 100644
--- a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfig.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfig.cmake
@@ -179,7 +179,7 @@ include(CMakeFindDependencyMacro)
 # Optional dependencies
 
 
-
+find_dependency(CUDAToolkit 12.4.99)
 find_dependency (Threads)
 
 # As imported CMake targets are not re-exported when a dependent target is
@@ -191,7 +191,7 @@ find_dependency (Threads)
 
 # Eigen.
 # Flag set during configuration and build of Ceres.
-set(CERES_EIGEN_VERSION 3.4.1)
+set(CERES_EIGEN_VERSION 3.4.0)
 # Search quietly to control the timing of the error message if not found. The
 # search should be for an exact match, but for usability reasons do a soft
 # match and reject with an explanation below.
@@ -220,7 +220,7 @@ endif (Eigen3_FOUND)
 #
 # Flags set during configuration and build of Ceres.
 set(CERES_USES_MINIGLOG OFF)
-set(CERES_GLOG_VERSION 0.6.0)
+set(CERES_GLOG_VERSION 0.4.0)
 set(CERES_GLOG_WAS_BUILT_WITH_CMAKE 1)
 
 set(CERES_USES_GFLAGS ON)
diff --git a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfigVersion.cmake b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfigVersion.cmake
index 3589bbd..dd40f26 100644
--- a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfigVersion.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresConfigVersion.cmake
@@ -52,6 +52,11 @@ else()
 endif()
 
 
+# if the installed project requested no architecture check, don't perform the check
+if("FALSE")
+  return()
+endif()
+
 # if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
 if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "")
   return()
diff --git a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-debug.cmake b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-debug.cmake
index c53d109..e5b749f 100644
--- a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-debug.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-debug.cmake
@@ -5,6 +5,16 @@
 # Commands may need to know the format version.
 set(CMAKE_IMPORT_FILE_VERSION 1)
 
+# Import target "Ceres::ceres_cuda_kernels" for configuration "Debug"
+set_property(TARGET Ceres::ceres_cuda_kernels APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
+set_target_properties(Ceres::ceres_cuda_kernels PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_DEBUG "CUDA"
+  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels-debug.lib"
+  )
+
+list(APPEND _cmake_import_check_targets Ceres::ceres_cuda_kernels )
+list(APPEND _cmake_import_check_files_for_Ceres::ceres_cuda_kernels "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels-debug.lib" )
+
 # Import target "Ceres::ceres" for configuration "Debug"
 set_property(TARGET Ceres::ceres APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
 set_target_properties(Ceres::ceres PROPERTIES
diff --git a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-release.cmake b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-release.cmake
index fcadc4f..5e877a0 100644
--- a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-release.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets-release.cmake
@@ -5,6 +5,16 @@
 # Commands may need to know the format version.
 set(CMAKE_IMPORT_FILE_VERSION 1)
 
+# Import target "Ceres::ceres_cuda_kernels" for configuration "Release"
+set_property(TARGET Ceres::ceres_cuda_kernels APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(Ceres::ceres_cuda_kernels PROPERTIES
+  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CUDA"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels.lib"
+  )
+
+list(APPEND _cmake_import_check_targets Ceres::ceres_cuda_kernels )
+list(APPEND _cmake_import_check_files_for_Ceres::ceres_cuda_kernels "${_IMPORT_PREFIX}/lib/ceres_cuda_kernels.lib" )
+
 # Import target "Ceres::ceres" for configuration "Release"
 set_property(TARGET Ceres::ceres APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
 set_target_properties(Ceres::ceres PROPERTIES
diff --git a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets.cmake b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets.cmake
index abd1284..efbad2c 100644
--- a/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/Ceres/CeresTargets.cmake
@@ -3,11 +3,11 @@
 if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
    message(FATAL_ERROR "CMake >= 2.8.0 required")
 endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.12")
-   message(FATAL_ERROR "CMake >= 2.8.12 required")
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
 endif()
 cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.12...3.28)
+cmake_policy(VERSION 2.8.3...3.23)
 #----------------------------------------------------------------
 # Generated CMake target import file.
 #----------------------------------------------------------------
@@ -19,7 +19,7 @@ set(CMAKE_IMPORT_FILE_VERSION 1)
 set(_cmake_targets_defined "")
 set(_cmake_targets_not_defined "")
 set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS Ceres::ceres)
+foreach(_cmake_expected_target IN ITEMS Ceres::ceres_cuda_kernels Ceres::ceres)
   list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
   if(TARGET "${_cmake_expected_target}")
     list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
@@ -55,6 +55,9 @@ if(_IMPORT_PREFIX STREQUAL "/")
   set(_IMPORT_PREFIX "")
 endif()
 
+# Create imported target Ceres::ceres_cuda_kernels
+add_library(Ceres::ceres_cuda_kernels STATIC IMPORTED)
+
 # Create imported target Ceres::ceres
 add_library(Ceres::ceres SHARED IMPORTED)
 
@@ -64,6 +67,10 @@ set_target_properties(Ceres::ceres PROPERTIES
   INTERFACE_LINK_LIBRARIES "glog::glog;gflags;Eigen3::Eigen"
 )
 
+if(CMAKE_VERSION VERSION_LESS 2.8.12)
+  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
+endif()
+
 # Load information for each installed configuration.
 file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/CeresTargets-*.cmake")
 foreach(_cmake_config_file IN LISTS _cmake_config_files)
@@ -77,12 +84,9 @@ set(_IMPORT_PREFIX)
 
 # Loop over all imported files and verify that they actually exist
 foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  if(CMAKE_VERSION VERSION_LESS "3.28"
-      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
-      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
-    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-      if(NOT EXISTS "${_cmake_file}")
-        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
    \"${_cmake_file}\"
 but this file does not exist.  Possible reasons include:
 * The file was deleted, renamed, or moved to another location.
@@ -91,9 +95,8 @@ but this file does not exist.  Possible reasons include:
    \"${CMAKE_CURRENT_LIST_FILE}\"
 but not all the files it references.
 ")
-      endif()
-    endforeach()
-  endif()
+    endif()
+  endforeach()
   unset(_cmake_file)
   unset("_cmake_import_check_files_for_${_cmake_target}")
 endforeach()
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
index 1043644..b65d787 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-debug.cmake
@@ -15,5 +15,15 @@ set_target_properties(gflags_shared PROPERTIES
 list(APPEND _cmake_import_check_targets gflags_shared )
 list(APPEND _cmake_import_check_files_for_gflags_shared "${_IMPORT_PREFIX}/lib/gflags_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_debug.dll" )
 
+# Import target "gflags_nothreads_shared" for configuration "Debug"
+set_property(TARGET gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
+set_target_properties(gflags_nothreads_shared PROPERTIES
+  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib"
+  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll"
+  )
+
+list(APPEND _cmake_import_check_targets gflags_nothreads_shared )
+list(APPEND _cmake_import_check_files_for_gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll" )
+
 # Commands beyond this point should not need to know the version.
 set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
index 84a5294..dc014ca 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets-release.cmake
@@ -15,5 +15,15 @@ set_target_properties(gflags_shared PROPERTIES
 list(APPEND _cmake_import_check_targets gflags_shared )
 list(APPEND _cmake_import_check_files_for_gflags_shared "${_IMPORT_PREFIX}/lib/gflags.lib" "${_IMPORT_PREFIX}/bin/gflags.dll" )
 
+# Import target "gflags_nothreads_shared" for configuration "Release"
+set_property(TARGET gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(gflags_nothreads_shared PROPERTIES
+  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll"
+  )
+
+list(APPEND _cmake_import_check_targets gflags_nothreads_shared )
+list(APPEND _cmake_import_check_files_for_gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll" )
+
 # Commands beyond this point should not need to know the version.
 set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets.cmake
index 1aff00c..fb14f4e 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-nonamespace-targets.cmake
@@ -3,11 +3,11 @@
 if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
    message(FATAL_ERROR "CMake >= 2.8.0 required")
 endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.12")
-   message(FATAL_ERROR "CMake >= 2.8.12 required")
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
 endif()
 cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.12...3.28)
+cmake_policy(VERSION 2.8.3...3.23)
 #----------------------------------------------------------------
 # Generated CMake target import file.
 #----------------------------------------------------------------
@@ -19,7 +19,7 @@ set(CMAKE_IMPORT_FILE_VERSION 1)
 set(_cmake_targets_defined "")
 set(_cmake_targets_not_defined "")
 set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS gflags_shared)
+foreach(_cmake_expected_target IN ITEMS gflags_shared gflags_nothreads_shared)
   list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
   if(TARGET "${_cmake_expected_target}")
     list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
@@ -64,6 +64,19 @@ set_target_properties(gflags_shared PROPERTIES
   INTERFACE_LINK_LIBRARIES "shlwapi.lib"
 )
 
+# Create imported target gflags_nothreads_shared
+add_library(gflags_nothreads_shared SHARED IMPORTED)
+
+set_target_properties(gflags_nothreads_shared PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
+)
+
+if(CMAKE_VERSION VERSION_LESS 2.8.12)
+  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
+endif()
+
 # Load information for each installed configuration.
 file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/gflags-nonamespace-targets-*.cmake")
 foreach(_cmake_config_file IN LISTS _cmake_config_files)
@@ -77,12 +90,9 @@ set(_IMPORT_PREFIX)
 
 # Loop over all imported files and verify that they actually exist
 foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  if(CMAKE_VERSION VERSION_LESS "3.28"
-      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
-      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
-    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-      if(NOT EXISTS "${_cmake_file}")
-        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
    \"${_cmake_file}\"
 but this file does not exist.  Possible reasons include:
 * The file was deleted, renamed, or moved to another location.
@@ -91,9 +101,8 @@ but this file does not exist.  Possible reasons include:
    \"${CMAKE_CURRENT_LIST_FILE}\"
 but not all the files it references.
 ")
-      endif()
-    endforeach()
-  endif()
+    endif()
+  endforeach()
   unset(_cmake_file)
   unset("_cmake_import_check_files_for_${_cmake_target}")
 endforeach()
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-debug.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-debug.cmake
index c722bd5..94a1996 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-debug.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-debug.cmake
@@ -15,5 +15,15 @@ set_target_properties(gflags::gflags_shared PROPERTIES
 list(APPEND _cmake_import_check_targets gflags::gflags_shared )
 list(APPEND _cmake_import_check_files_for_gflags::gflags_shared "${_IMPORT_PREFIX}/lib/gflags_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_debug.dll" )
 
+# Import target "gflags::gflags_nothreads_shared" for configuration "Debug"
+set_property(TARGET gflags::gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
+set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
+  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib"
+  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll"
+  )
+
+list(APPEND _cmake_import_check_targets gflags::gflags_nothreads_shared )
+list(APPEND _cmake_import_check_files_for_gflags::gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads_debug.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads_debug.dll" )
+
 # Commands beyond this point should not need to know the version.
 set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-release.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-release.cmake
index 54efa6b..abda2c0 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-release.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets-release.cmake
@@ -15,5 +15,15 @@ set_target_properties(gflags::gflags_shared PROPERTIES
 list(APPEND _cmake_import_check_targets gflags::gflags_shared )
 list(APPEND _cmake_import_check_files_for_gflags::gflags_shared "${_IMPORT_PREFIX}/lib/gflags.lib" "${_IMPORT_PREFIX}/bin/gflags.dll" )
 
+# Import target "gflags::gflags_nothreads_shared" for configuration "Release"
+set_property(TARGET gflags::gflags_nothreads_shared APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
+  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib"
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll"
+  )
+
+list(APPEND _cmake_import_check_targets gflags::gflags_nothreads_shared )
+list(APPEND _cmake_import_check_files_for_gflags::gflags_nothreads_shared "${_IMPORT_PREFIX}/lib/gflags_nothreads.lib" "${_IMPORT_PREFIX}/bin/gflags_nothreads.dll" )
+
 # Commands beyond this point should not need to know the version.
 set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets.cmake b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets.cmake
index 7746aef..c2adf25 100644
--- a/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/gflags/gflags-targets.cmake
@@ -3,11 +3,11 @@
 if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
    message(FATAL_ERROR "CMake >= 2.8.0 required")
 endif()
-if(CMAKE_VERSION VERSION_LESS "2.8.12")
-   message(FATAL_ERROR "CMake >= 2.8.12 required")
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
 endif()
 cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.12...3.28)
+cmake_policy(VERSION 2.8.3...3.23)
 #----------------------------------------------------------------
 # Generated CMake target import file.
 #----------------------------------------------------------------
@@ -19,7 +19,7 @@ set(CMAKE_IMPORT_FILE_VERSION 1)
 set(_cmake_targets_defined "")
 set(_cmake_targets_not_defined "")
 set(_cmake_expected_targets "")
-foreach(_cmake_expected_target IN ITEMS gflags::gflags_shared)
+foreach(_cmake_expected_target IN ITEMS gflags::gflags_shared gflags::gflags_nothreads_shared)
   list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
   if(TARGET "${_cmake_expected_target}")
     list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
@@ -64,6 +64,19 @@ set_target_properties(gflags::gflags_shared PROPERTIES
   INTERFACE_LINK_LIBRARIES "shlwapi.lib"
 )
 
+# Create imported target gflags::gflags_nothreads_shared
+add_library(gflags::gflags_nothreads_shared SHARED IMPORTED)
+
+set_target_properties(gflags::gflags_nothreads_shared PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "GFLAGS_IS_A_DLL=1"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "shlwapi.lib"
+)
+
+if(CMAKE_VERSION VERSION_LESS 2.8.12)
+  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
+endif()
+
 # Load information for each installed configuration.
 file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/gflags-targets-*.cmake")
 foreach(_cmake_config_file IN LISTS _cmake_config_files)
@@ -77,12 +90,9 @@ set(_IMPORT_PREFIX)
 
 # Loop over all imported files and verify that they actually exist
 foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  if(CMAKE_VERSION VERSION_LESS "3.28"
-      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
-      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
-    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-      if(NOT EXISTS "${_cmake_file}")
-        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
    \"${_cmake_file}\"
 but this file does not exist.  Possible reasons include:
 * The file was deleted, renamed, or moved to another location.
@@ -91,9 +101,8 @@ but this file does not exist.  Possible reasons include:
    \"${CMAKE_CURRENT_LIST_FILE}\"
 but not all the files it references.
 ")
-      endif()
-    endforeach()
-  endif()
+    endif()
+  endforeach()
   unset(_cmake_file)
   unset("_cmake_import_check_files_for_${_cmake_target}")
 endforeach()
diff --git a/3rdParty/ceres_2.2/lib/cmake/glog/glog-config-version.cmake b/3rdParty/ceres_2.2/lib/cmake/glog/glog-config-version.cmake
index 91ca415..87f98bb 100644
--- a/3rdParty/ceres_2.2/lib/cmake/glog/glog-config-version.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/glog/glog-config-version.cmake
@@ -9,19 +9,19 @@
 # The variable CVF_VERSION must be set before calling configure_file().
 
 
-set(PACKAGE_VERSION "0.6.0")
+set(PACKAGE_VERSION "0.4.0")
 
 if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
   set(PACKAGE_VERSION_COMPATIBLE FALSE)
 else()
 
-  if("0.6.0" MATCHES "^([0-9]+)\\.")
+  if("0.4.0" MATCHES "^([0-9]+)\\.")
     set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
     if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
       string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
     endif()
   else()
-    set(CVF_VERSION_MAJOR "0.6.0")
+    set(CVF_VERSION_MAJOR "0.4.0")
   endif()
 
   if(PACKAGE_FIND_VERSION_RANGE)
@@ -52,6 +52,11 @@ else()
 endif()
 
 
+# if the installed project requested no architecture check, don't perform the check
+if("FALSE")
+  return()
+endif()
+
 # if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
 if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "")
   return()
diff --git a/3rdParty/ceres_2.2/lib/cmake/glog/glog-config.cmake b/3rdParty/ceres_2.2/lib/cmake/glog/glog-config.cmake
index 7f9d570..dbb0e2d 100644
--- a/3rdParty/ceres_2.2/lib/cmake/glog/glog-config.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/glog/glog-config.cmake
@@ -1,6 +1,6 @@
-if (CMAKE_VERSION VERSION_LESS 3.0)
-  message (FATAL_ERROR "CMake >= 3.0 required")
-endif (CMAKE_VERSION VERSION_LESS 3.0)
+if (CMAKE_VERSION VERSION_LESS 3.9)
+  message (FATAL_ERROR "CMake >= 3.9 required")
+endif (CMAKE_VERSION VERSION_LESS 3.9)
 
 
 ####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
@@ -19,9 +19,7 @@ endmacro()
 ####################################################################################
 
 include (CMakeFindDependencyMacro)
-include (${CMAKE_CURRENT_LIST_DIR}/glog-modules.cmake)
 
+find_dependency (gflags 2.2.2 NO_MODULE)
 
-
-
-include (${CMAKE_CURRENT_LIST_DIR}/glog-targets.cmake)
+include ("${CMAKE_CURRENT_LIST_DIR}/glog-targets.cmake")
diff --git a/3rdParty/ceres_2.2/lib/cmake/glog/glog-modules.cmake b/3rdParty/ceres_2.2/lib/cmake/glog/glog-modules.cmake
deleted file mode 100644
index 7aed125..0000000
--- a/3rdParty/ceres_2.2/lib/cmake/glog/glog-modules.cmake
+++ /dev/null
@@ -1,18 +0,0 @@
-cmake_policy (PUSH)
-cmake_policy (SET CMP0057 NEW)
-
-if (CMAKE_VERSION VERSION_LESS 3.3)
-  message (FATAL_ERROR "glog-modules.cmake requires the consumer "
-    "to use CMake 3.3 (or newer)")
-endif (CMAKE_VERSION VERSION_LESS 3.3)
-
-set (glog_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/../../../share/glog/cmake")
-list (APPEND CMAKE_MODULE_PATH ${glog_MODULE_PATH})
-
-if (NOT glog_MODULE_PATH IN_LIST CMAKE_MODULE_PATH)
-  message (FATAL_ERROR "Cannot add '${glog_MODULE_PATH}' to "
-    "CMAKE_MODULE_PATH. This will cause glog-config.cmake to fail at "
-    "locating required find modules. Make sure CMAKE_MODULE_PATH is not a cache variable.")
-endif (NOT glog_MODULE_PATH IN_LIST CMAKE_MODULE_PATH)
-
-cmake_policy (POP)
diff --git a/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets-debug.cmake b/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets-debug.cmake
new file mode 100644
index 0000000..447e3bc
--- /dev/null
+++ b/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets-debug.cmake
@@ -0,0 +1,19 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Debug".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "glog::glog" for configuration "Debug"
+set_property(TARGET glog::glog APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
+set_target_properties(glog::glog PROPERTIES
+  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/lib/glogd.lib"
+  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/glogd.dll"
+  )
+
+list(APPEND _cmake_import_check_targets glog::glog )
+list(APPEND _cmake_import_check_files_for_glog::glog "${_IMPORT_PREFIX}/lib/glogd.lib" "${_IMPORT_PREFIX}/bin/glogd.dll" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets.cmake b/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets.cmake
index 5050259..0201ac5 100644
--- a/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets.cmake
+++ b/3rdParty/ceres_2.2/lib/cmake/glog/glog-targets.cmake
@@ -7,7 +7,7 @@ if(CMAKE_VERSION VERSION_LESS "2.8.3")
    message(FATAL_ERROR "CMake >= 2.8.3 required")
 endif()
 cmake_policy(PUSH)
-cmake_policy(VERSION 2.8.3...3.28)
+cmake_policy(VERSION 2.8.3...3.23)
 #----------------------------------------------------------------
 # Generated CMake target import file.
 #----------------------------------------------------------------
@@ -59,10 +59,15 @@ endif()
 add_library(glog::glog SHARED IMPORTED)
 
 set_target_properties(glog::glog PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "GLOG_NO_ABBREVIATED_SEVERITIES;GLOG_CUSTOM_PREFIX_SUPPORT"
+  INTERFACE_COMPILE_DEFINITIONS "GLOG_NO_ABBREVIATED_SEVERITIES;GOOGLE_GLOG_DLL_DECL=__declspec(dllimport);GOOGLE_GLOG_DLL_DECL_FOR_UNITTESTS=__declspec(dllimport)"
   INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include"
+  INTERFACE_LINK_LIBRARIES "dbghelp;gflags"
 )
 
+if(CMAKE_VERSION VERSION_LESS 2.8.12)
+  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
+endif()
+
 # Load information for each installed configuration.
 file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/glog-targets-*.cmake")
 foreach(_cmake_config_file IN LISTS _cmake_config_files)
@@ -76,12 +81,9 @@ set(_IMPORT_PREFIX)
 
 # Loop over all imported files and verify that they actually exist
 foreach(_cmake_target IN LISTS _cmake_import_check_targets)
-  if(CMAKE_VERSION VERSION_LESS "3.28"
-      OR NOT DEFINED _cmake_import_check_xcframework_for_${_cmake_target}
-      OR NOT IS_DIRECTORY "${_cmake_import_check_xcframework_for_${_cmake_target}}")
-    foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
-      if(NOT EXISTS "${_cmake_file}")
-        message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
    \"${_cmake_file}\"
 but this file does not exist.  Possible reasons include:
 * The file was deleted, renamed, or moved to another location.
@@ -90,9 +92,8 @@ but this file does not exist.  Possible reasons include:
    \"${CMAKE_CURRENT_LIST_FILE}\"
 but not all the files it references.
 ")
-      endif()
-    endforeach()
-  endif()
+    endif()
+  endforeach()
   unset(_cmake_file)
   unset("_cmake_import_check_files_for_${_cmake_target}")
 endforeach()
diff --git a/3rdParty/ceres_2.2/lib/gflags.lib b/3rdParty/ceres_2.2/lib/gflags.lib
index cd364e346e77f020095dbc0472f33f2760ed995f..fb7153d7d8ff3a11a8304458231b7a576bc21306 100644
GIT binary patch
delta 106
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delta 106
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loZ^uRQ=vZD&x-@1s)FU-R^|tjt32V_l@XFsn~S_Uv;gC#ExP~!

diff --git a/3rdParty/ceres_2.2/lib/gflags_debug.lib b/3rdParty/ceres_2.2/lib/gflags_debug.lib
index 87d66beaaa43a1e173839cea8fed06901ee55fa3..4837d75905ceaa5d4c4406ab0cf757eca575dcbf 100644
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diff --git a/3rdParty/ceres_2.2/lib/gflags_nothreads.lib b/3rdParty/ceres_2.2/lib/gflags_nothreads.lib
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diff --git a/3rdParty/ceres_2.2/lib/gflags_nothreads_debug.lib b/3rdParty/ceres_2.2/lib/gflags_nothreads_debug.lib
new file mode 100644
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diff --git a/3rdParty/ceres_2.2/lib/glogd.lib b/3rdParty/ceres_2.2/lib/glogd.lib
index 1f9018cb566990ffcea56e36dc9d8d7025a19e75..44fb071d41efcebe1ed6a1954d742a4c16449445 100644
GIT binary patch
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diff --git a/3rdParty/ceres_2.2/lib/pkgconfig/libglog.pc b/3rdParty/ceres_2.2/lib/pkgconfig/libglog.pc
deleted file mode 100644
index 9c4e6b4..0000000
--- a/3rdParty/ceres_2.2/lib/pkgconfig/libglog.pc
+++ /dev/null
@@ -1,11 +0,0 @@
-prefix=E:/research/ceres2.2/glog-0.6.0/install
-exec_prefix=E:/research/ceres2.2/glog-0.6.0/install/bin
-libdir=E:/research/ceres2.2/glog-0.6.0/install/lib
-includedir=E:/research/ceres2.2/glog-0.6.0/install/include
-
-Name: libglog
-Description: Google Log (glog) C++ logging framework
-Version: 0.6.0
-Libs: -L${libdir} -lglog
-Libs.private: -ldbghelp
-Cflags: -I${includedir}
diff --git a/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Config.cmake b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Config.cmake
new file mode 100644
index 0000000..398a051
--- /dev/null
+++ b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Config.cmake
@@ -0,0 +1,37 @@
+# This file exports the Eigen3::Eigen CMake target which should be passed to the
+# target_link_libraries command.
+
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was Eigen3Config.cmake.in                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+####################################################################################
+
+if (NOT TARGET eigen)
+  include ("${CMAKE_CURRENT_LIST_DIR}/Eigen3Targets.cmake")
+endif ()
+
+# Legacy variables, do *not* use. May be removed in the future.
+
+set (EIGEN3_FOUND 1)
+set (EIGEN3_USE_FILE    "${CMAKE_CURRENT_LIST_DIR}/UseEigen3.cmake")
+
+set (EIGEN3_DEFINITIONS  "")
+set (EIGEN3_INCLUDE_DIR  "${PACKAGE_PREFIX_DIR}/include/eigen3")
+set (EIGEN3_INCLUDE_DIRS "${PACKAGE_PREFIX_DIR}/include/eigen3")
+set (EIGEN3_ROOT_DIR     "${PACKAGE_PREFIX_DIR}")
+
+set (EIGEN3_VERSION_STRING "3.4.0")
+set (EIGEN3_VERSION_MAJOR  "3")
+set (EIGEN3_VERSION_MINOR  "4")
+set (EIGEN3_VERSION_PATCH  "0")
diff --git a/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3ConfigVersion.cmake b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3ConfigVersion.cmake
new file mode 100644
index 0000000..944ed77
--- /dev/null
+++ b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3ConfigVersion.cmake
@@ -0,0 +1,70 @@
+# This is a basic version file for the Config-mode of find_package().
+# It is used by write_basic_package_version_file() as input file for configure_file()
+# to create a version-file which can be installed along a config.cmake file.
+#
+# The created file sets PACKAGE_VERSION_EXACT if the current version string and
+# the requested version string are exactly the same and it sets
+# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
+# but only if the requested major version is the same as the current one.
+# The variable CVF_VERSION must be set before calling configure_file().
+
+
+set(PACKAGE_VERSION "3.4.0")
+
+if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+
+  if("3.4.0" MATCHES "^([0-9]+)\\.")
+    set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
+      string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
+    endif()
+  else()
+    set(CVF_VERSION_MAJOR "3.4.0")
+  endif()
+
+  if(PACKAGE_FIND_VERSION_RANGE)
+    # both endpoints of the range must have the expected major version
+    math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1")
+    if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
+        OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR)
+          OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT)))
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
+        AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX)
+        OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX)))
+      set(PACKAGE_VERSION_COMPATIBLE TRUE)
+    else()
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    endif()
+  else()
+    if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR)
+      set(PACKAGE_VERSION_COMPATIBLE TRUE)
+    else()
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    endif()
+
+    if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION)
+      set(PACKAGE_VERSION_EXACT TRUE)
+    endif()
+  endif()
+endif()
+
+
+# if the installed project requested no architecture check, don't perform the check
+if("FALSE")
+  return()
+endif()
+
+# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
+if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "" STREQUAL "")
+  return()
+endif()
+
+# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
+if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "")
+  math(EXPR installedBits " * 8")
+  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
+  set(PACKAGE_VERSION_UNSUITABLE TRUE)
+endif()
diff --git a/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Targets.cmake b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Targets.cmake
new file mode 100644
index 0000000..e22f2f4
--- /dev/null
+++ b/3rdParty/ceres_2.2/share/eigen3/cmake/Eigen3Targets.cmake
@@ -0,0 +1,106 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 2.8.0 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.8.3...3.23)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS Eigen3::Eigen)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target Eigen3::Eigen
+add_library(Eigen3::Eigen INTERFACE IMPORTED)
+
+set_target_properties(Eigen3::Eigen PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include/eigen3"
+)
+
+if(CMAKE_VERSION VERSION_LESS 3.0.0)
+  message(FATAL_ERROR "This file relies on consumers using CMake 3.0.0 or greater.")
+endif()
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/Eigen3Targets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)
diff --git a/3rdParty/ceres_2.2/share/eigen3/cmake/UseEigen3.cmake b/3rdParty/ceres_2.2/share/eigen3/cmake/UseEigen3.cmake
new file mode 100644
index 0000000..a38bac8
--- /dev/null
+++ b/3rdParty/ceres_2.2/share/eigen3/cmake/UseEigen3.cmake
@@ -0,0 +1,6 @@
+#                                               -*- cmake -*-
+#
+#  UseEigen3.cmake
+
+add_definitions     ( ${EIGEN3_DEFINITIONS} )
+include_directories ( ${EIGEN3_INCLUDE_DIRS} )