| ►NEigen | : TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend |
| CAdolcForwardJacobian | |
| ►CAlignedVector3 | A vectorization friendly 3D vector |
| Cgeneric_assign_selector | |
| Cgeneric_assign_selector< XprType, 3 > | |
| Cgeneric_assign_selector< XprType, 4 > | |
| CAntiHermiticity | |
| CAntiSymmetry | |
| CArpackGeneralizedSelfAdjointEigenSolver | |
| CAutoDiffJacobian | |
| CAutoDiffScalar | A scalar type replacement with automatic differentiation capability |
| CAutoDiffVector | |
| CBiCGSTABL | |
| CBlockSparseMatrix | A versatile sparse matrix representation where each element is a block |
| CBlockSparseMatrixView | |
| CBlockSparseTimeDenseProduct | |
| CBlockVectorReturn | |
| CBlockVectorView | |
| CCleanedUpDerType | |
| CCond | |
| CConversionSubExprEval | |
| CConversionSubExprEval< true, Eval, EvalPointerType > | |
| Cdefault_fft_impl | |
| CDefaultDevice | |
| CDenseFunctor | |
| CDGMRES | 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 |
| CDimensionList | |
| CDSizes | |
| ►CDynamicSGroup | Dynamic symmetry group |
| CGenerator | |
| CGroupElement | |
| CDynamicSGroupFromTemplateArgs | |
| CDynamicSkylineMatrix | |
| CEigenConvolutionKernel | |
| CEigenConvolutionKernel< Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, Buffer_accessor, convolution_type::CONV1D > | |
| CEigenConvolutionKernel< Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, Buffer_accessor, convolution_type::CONV2D > | |
| CEigenConvolutionKernel< Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, Buffer_accessor, convolution_type::CONV3D > | |
| CEulerAngles | Represents a rotation in a 3 dimensional space as three Euler angles |
| CEulerSystem | Represents a fixed Euler rotation system |
| CFABSum | |
| CFFT | |
| Cfft_fwd_proxy | |
| Cfft_inv_proxy | |
| CGMRES | A GMRES solver for sparse square problems |
| CHermiticity | |
| ►CHybridNonLinearSolver | Finds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg") |
| CParameters | |
| CIDRS | The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square problems |
| CIDRSTABL | The IDR(s)STAB(l) is a combination of IDR(s) and BiCGSTAB(l). It is a short-recurrences Krylov method for sparse square problems. It can outperform both IDR(s) and BiCGSTAB(l). IDR(s)STAB(l) generally closely follows the optimal GMRES convergence in terms of the number of Matrix-Vector products. However, without the increasing cost per iteration of GMRES. IDR(s)STAB(l) is suitable for both indefinite systems and systems with complex eigenvalues |
| CIncompleteLU | |
| CIndexList | |
| CIndexPair | |
| CIndexPairList | |
| CIterScaling | Iterative scaling algorithm to equilibrate rows and column norms in matrices |
| CKahanSum | Kahan algorithm based accumulator |
| ►CKdBVH | A simple bounding volume hierarchy based on AlignedBox |
| CVectorComparator | |
| CKroneckerProduct | Kronecker tensor product helper class for dense matrices |
| CKroneckerProductBase | The base class of dense and sparse Kronecker product |
| CKroneckerProductSparse | Kronecker tensor product helper class for sparse matrices |
| ►CLevenbergMarquardt | Performs non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm |
| CParameters | |
| CMakeComplex | |
| CMakeComplex< false > | |
| CMakeComplex< true > | |
| CMakePointer | |
| CMappedSkylineMatrix | |
| CMatrixMarketIterator | Iterator to browse matrices from a specified folder |
| CMatrixPower | Class for computing matrix powers |
| CMatrixPowerAtomic | Class for computing matrix powers |
| CMatrixPowerParenthesesReturnValue | Proxy for the matrix power of some matrix |
| Cmax_n_1 | |
| Cmax_n_1< 0 > | |
| CMINRES | A minimal residual solver for sparse symmetric problems |
| CNNLS | Implementation of the Non-Negative Least Squares (NNLS) algorithm |
| CNoOpOutputKernel | |
| CNumericalDiff | |
| CNumTraits< adtl::adouble > | |
| CNumTraits< AutoDiffScalar< DerType > > | |
| CNumTraits< mpfr::mpreal > | |
| CNumTraits< type2index< n > > | |
| CPacketConverter | |
| CPacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 1, 1 > | |
| CPacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 1, TgtCoeffRatio > | |
| CPacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 2, 1 > | |
| CPacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 4, 1 > | |
| CPacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 8, 1 > | |
| CPacketType | |
| CPair | |
| CPartOf | |
| CPartOf< ImagPart > | |
| CPartOf< RealPart > | |
| CPolynomialSolver | A polynomial solver |
| CPolynomialSolver< Scalar_, 1 > | |
| CPolynomialSolverBase | Defined to be inherited by polynomial solvers: it provides convenient methods such as |
| ►CRandomSetter | The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access |
| CScalarWrapper | |
| CScalarBinaryOpTraits< AutoDiffScalar< DerType >, typename DerType::Scalar, BinOp > | |
| CScalarBinaryOpTraits< typename DerType::Scalar, AutoDiffScalar< DerType >, BinOp > | |
| CSGroup | Symmetry group, initialized from template arguments |
| CSizes | |
| CSkylineInplaceLU | Inplace LU decomposition of a skyline matrix and associated features |
| ►CSkylineMatrix | The main skyline matrix class |
| CInnerLowerIterator | |
| CInnerUpperIterator | |
| CSkylineMatrixBase | Base class of any skyline matrices or skyline expressions |
| CSkylineProduct | |
| CSkylineProductReturnType | |
| CSkylineStorage | |
| CSkylineVector | |
| CSparseFunctor | |
| CSparseInverse | Calculate sparse subset of inverse of sparse matrix |
| CSpline | A class representing multi-dimensional spline curves |
| CSplineFitting | Spline fitting methods |
| CSplineTraits | |
| CSplineTraits< Spline< Scalar_, Dim_, Degree_ >, _DerivativeOrder > | Compile-time attributes of the Spline class for fixed degree |
| CSplineTraits< Spline< Scalar_, Dim_, Degree_ >, Dynamic > | Compile-time attributes of the Spline class for Dynamic degree |
| CStaticSGroup | Static symmetry group |
| CStdMapTraits | |
| CStdUnorderedMapTraits | |
| CStorageMemory | |
| CSymmetry | |
| ►CTensor | The tensor class |
| CisOfNormalIndex | |
| CTensorAssignOp | |
| CTensorAsyncDevice | 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. |
| CTensorBase | The tensor base class |
| CTensorBroadcastingOp | |
| CTensorChippingOp | |
| CTensorConcatenationOp | Tensor concatenation class |
| CTensorContractionEvaluatorBase | |
| CTensorContractionOp | |
| CTensorContractionParams | |
| CTensorConversionOp | 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 |
| CTensorConvolutionOp | |
| CTensorCostModel | |
| CTensorCustomBinaryOp | Tensor custom class |
| CTensorCustomUnaryOp | Tensor custom class |
| CTensorCwiseBinaryOp | |
| CTensorCwiseNullaryOp | |
| CTensorCwiseTernaryOp | |
| CTensorCwiseUnaryOp | |
| CTensorDevice | Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...) |
| CTensorEvalToOp | |
| CTensorEvaluator | A cost model used to limit the number of threads used for evaluating tensor expression |
| CTensorEvaluator< const Derived, Device > | |
| CTensorEvaluator< const TensorAssignOp< LeftArgType, RightArgType >, Device > | |
| ►CTensorEvaluator< const TensorBroadcastingOp< Broadcast, ArgType >, Device > | |
| CBlockBroadcastingIteratorState | |
| CBlockBroadcastingParams | |
| CTensorEvaluator< const TensorChippingOp< DimId, ArgType >, Device > | |
| CTensorEvaluator< const TensorConcatenationOp< Axis, LeftArgType, RightArgType >, Device > | |
| CTensorEvaluator< const TensorContractionOp< Indices, LeftArgType, RightArgType, OutputKernelType >, Device > | |
| ►CTensorEvaluator< const TensorContractionOp< Indices, LeftArgType, RightArgType, OutputKernelType >, Eigen::SyclDevice > | |
| Cinput_mapper_propertis | |
| CTripleDim | |
| ►CTensorEvaluator< const TensorConversionOp< TargetType, ArgType >, Device > | |
| ►CTensorConversionOpBlockFactory | |
| CXprType | |
| CTensorEvaluator< const TensorConvolutionOp< Indices, InputArgType, KernelArgType >, Device > | |
| CTensorEvaluator< const TensorConvolutionOp< Indices, InputArgType, KernelArgType >, Eigen::SyclDevice > | |
| CTensorEvaluator< const TensorCustomBinaryOp< CustomBinaryFunc, LhsXprType, RhsXprType >, Device > | |
| CTensorEvaluator< const TensorCustomUnaryOp< CustomUnaryFunc, XprType >, Device > | |
| CTensorEvaluator< const TensorCwiseBinaryOp< BinaryOp, LeftArgType, RightArgType >, Device > | |
| CTensorEvaluator< const TensorCwiseNullaryOp< NullaryOp, ArgType >, Device > | |
| CTensorEvaluator< const TensorCwiseTernaryOp< TernaryOp, Arg1Type, Arg2Type, Arg3Type >, Device > | |
| CTensorEvaluator< const TensorCwiseUnaryOp< UnaryOp, ArgType >, Device > | |
| CTensorEvaluator< const TensorEvalToOp< ArgType, MakePointer_ >, Device > | |
| CTensorEvaluator< const TensorFFTOp< FFT, ArgType, FFTResultType, FFTDir >, Device > | |
| CTensorEvaluator< const TensorForcedEvalOp< ArgType_ >, Device > | |
| ►CTensorEvaluator< const TensorGeneratorOp< Generator, ArgType >, Device > | |
| CBlockIteratorState | |
| CTensorEvaluator< const TensorImagePatchOp< Rows, Cols, ArgType >, Device > | |
| CTensorEvaluator< const TensorIndexPairOp< ArgType >, Device > | |
| CTensorEvaluator< const TensorInflationOp< Strides, ArgType >, Device > | |
| CTensorEvaluator< const TensorLayoutSwapOp< ArgType >, Device > | |
| ►CTensorEvaluator< const TensorPaddingOp< PaddingDimensions, ArgType >, Device > | |
| CBlockIteratorState | |
| CTensorEvaluator< const TensorPairReducerOp< ReduceOp, Dims, ArgType >, Device > | |
| CTensorEvaluator< const TensorPatchOp< PatchDim, ArgType >, Device > | |
| CTensorEvaluator< const TensorReductionOp< Op, Dims, ArgType, MakePointer_ >, Device > | |
| CTensorEvaluator< const TensorReductionOp< Op, Dims, ArgType, MakePointer_ >, Eigen::SyclDevice > | |
| CTensorEvaluator< const TensorRef< Derived >, Device > | |
| ►CTensorEvaluator< const TensorReshapingOp< NewDimensions, ArgType >, Device > | |
| CBlockIteratorState | |
| ►CTensorEvaluator< const TensorReverseOp< ReverseDimensions, ArgType >, Device > | |
| CBlockIteratorState | |
| CTensorEvaluator< const TensorScanOp< Op, ArgType >, Device > | |
| ►CTensorEvaluator< const TensorSelectOp< IfArgType, ThenArgType, ElseArgType >, Device > | |
| ►CTensorSelectOpBlockFactory | |
| CXprType | |
| ►CTensorEvaluator< const TensorShufflingOp< Shuffle, ArgType >, Device > | |
| CPacketLoader | |
| CPacketLoader< LoadMode, Self, true > | |
| CTensorEvaluator< const TensorSlicingOp< StartIndices, Sizes, ArgType >, Device > | |
| CTensorEvaluator< const TensorStridingOp< Strides, ArgType >, Device > | |
| CTensorEvaluator< const TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType >, Device > | |
| CTensorEvaluator< const TensorTraceOp< Dims, ArgType >, Device > | |
| CTensorEvaluator< const TensorVolumePatchOp< Planes, Rows, Cols, ArgType >, Device > | |
| CTensorEvaluator< TensorChippingOp< DimId, ArgType >, Device > | |
| CTensorEvaluator< TensorConcatenationOp< Axis, LeftArgType, RightArgType >, Device > | |
| CTensorEvaluator< TensorLayoutSwapOp< ArgType >, Device > | |
| CTensorEvaluator< TensorRef< Derived >, Device > | |
| CTensorEvaluator< TensorReshapingOp< NewDimensions, ArgType >, Device > | |
| CTensorEvaluator< TensorReverseOp< ReverseDimensions, ArgType >, Device > | |
| CTensorEvaluator< TensorShufflingOp< Shuffle, ArgType >, Device > | |
| CTensorEvaluator< TensorSlicingOp< StartIndices, Sizes, ArgType >, Device > | |
| CTensorEvaluator< TensorStridingOp< Strides, ArgType >, Device > | |
| CTensorEvaluator< TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType >, Device > | |
| CTensorFFTOp | |
| CTensorFixedSize | The fixed sized version of the tensor class |
| CTensorForcedEvalOp | |
| CTensorGeneratorOp | Tensor generator class |
| CTensorImagePatchOp | |
| CTensorIndexPairOp | |
| CTensorInflationOp | |
| CTensorIOFormat | |
| CTensorLayoutSwapOp | |
| CTensorMap | A tensor expression mapping an existing array of data |
| CTensorOpCost | |
| CTensorPaddingOp | |
| CTensorPairReducerOp | |
| CTensorPatchOp | |
| CTensorReductionEvaluatorBase | |
| ►CTensorReductionEvaluatorBase< const TensorReductionOp< Op, Dims, ArgType, MakePointer_ >, Device > | |
| CBlockIteratorState | |
| CTensorReductionOp | |
| CTensorRef | A reference to a tensor expression The expression will be evaluated lazily (as much as possible) |
| CTensorReshapingOp | |
| CTensorReverseOp | |
| CTensorScanOp | |
| CTensorSelectOp | |
| CTensorShufflingOp | |
| CTensorSlicingOp | |
| CTensorStorage | |
| CTensorStorage< T, DSizes< IndexType, NumIndices_ >, Options_ > | |
| CTensorStridingOp | |
| CTensorStridingSlicingOp | |
| CTensorTraceOp | |
| CTensorVolumePatchOp | |
| CTensorWithFormat | |
| CTensorWithFormat< T, ColMajor, 0 > | |
| CTensorWithFormat< T, ColMajor, rank > | |
| CTensorWithFormat< T, RowMajor, rank > | |
| Ctype2index | |
| Ctype2indexpair | |
| ►Nhip_impl | |
| CScalar_accessor | |
| ►Nstd | |
| Cnumeric_limits< Eigen::AutoDiffScalar< T & > > | |
| Cnumeric_limits< Eigen::AutoDiffScalar< T > > | |
| Cbinary_op_base | |
| Cevaluator | |
| Cfalse_type | |
| CFunctor | |
| CFunctor_ | |
| Cno_assignment_operator | |
| Cnumeric_limits | |
| CProductBase | |
| CTensorAssign | The tensor assignment class |
| CTensorBroadcasting | Tensor broadcasting class |
| CTensorContraction | Tensor contraction class |
| CTensorConvolution | Tensor convolution class |
| CTensorExecutor | The tensor executor class |
| CTensorExpr | Tensor expression classes |
| CTensorFFT | Tensor FFT class |
| CTensorForcedEval | Tensor reshaping class |
| CTensorImagePatch | 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 |
| CTensorIndexPair | Tensor + Index Pair class |
| CTensorInflation | Tensor inflation class |
| CTensorInitializer | Helper template to initialize Tensors from std::initializer_lists |
| CTensorKChippingReshaping | A chip is a thin slice, corresponding to a column or a row in a 2-d tensor |
| CTensorLayoutSwap | Swap the layout from col-major to row-major, or row-major to col-major, and invert the order of the dimensions |
| CTensorPadding | Tensor padding class. At the moment only padding with a constant value is supported |
| CTensorPairIndex | Converts to Tensor<Pair<Index, Scalar> > and reduces to Tensor<Index> |
| CTensorPatch | Tensor patch class |
| CTensorReduction | Tensor reduction class |
| CTensorReshaping | Tensor reshaping class |
| CTensorReverse | Tensor reverse elements class |
| CTensorScan | Tensor scan class |
| CTensorShuffling | Tensor shuffling class |
| CTensorSlicing | Tensor slicing class |
| CTensorStriding | Tensor striding class |
| CTensorTrace | Tensor Trace class |
| CTensorVolumePatch | Patch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows: |
| Ctraits | |
| Ctrue_type | |
