| ▼NEigen | Namespace containing all symbols from the Eigen library |
| ►Ninternal | |
| CCoeffLoader | |
| CExpressionHasTensorBroadcastingOp | |
| CInitializer | Helper template to initialize Tensors from std::initializer_lists |
| Cmatrix_exp_computeUV | Compute the (17,17)-Padé approximant to the exponential |
| Cmatrix_function_compute | Class for computing matrix functions |
| Cmatrix_sqrt_compute | Helper struct for computing matrix square roots of general matrices |
| CMatrixExponentialScalingOp | Scaling operator |
| CMatrixFunctionAtomic | Helper class for computing matrix functions of atomic matrices |
| CMatrixLogarithmAtomic | Helper class for computing matrix logarithm of atomic matrices |
| CReductionReturnType | |
| CTensorAsyncExecutor | |
| CTensorExecutor | The tensor executor class |
| CTensorExecutor< Expression, DefaultDevice, true, TiledEvaluation::Off > | |
| CTensorExecutor< Expression, DefaultDevice, Vectorizable, TiledEvaluation::On > | |
| ►NTensorSycl | |
| ►Ninternal | |
| CBlockProperties | BlockProperties is a template class that provides different characteristic of a block of each Tensor processed by each workgroup |
| CGeneralScalarContraction | 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 |
| CGeneralVectorTensor | GeneralVectorTensor is a template class that provides Tensor -vector contraction operation, which is a special case of Tensor Tensor contraction |
| ►CTensorContractionKernel | TensorContractionKernel is a template class that provides Tensor -Tensor contraction operation |
| CMemHolder | 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 |
| CMemHolder< contraction_type::no_local, MemSize > | Specialization of memHolder class when no local memory kernel is used |
| CTiledMemory | 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 |
| CThreadProperties | 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 |
| CTTPanelSize | TTPanelSize, a template class used for setting the panel size required for launching General Tensor Tensor contraction kernel on various hardware devices |
| CTVPanelSize | TVPanelSize, a template class used for setting the panel size required for launching General TensorVector contraction kernel on various hardware devices |
| CAlignedVector3 | A vectorization friendly 3D vector |
| CAutoDiffScalar | A scalar type replacement with automatic differentiation capability |
| CBlockSparseMatrix | A versatile sparse matrix representation where each element is a block |
| 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 |
| CDynamicSGroup | Dynamic symmetry group |
| CEulerAngles | Represents a rotation in a 3 dimensional space as three Euler angles |
| CEulerSystem | Represents a fixed Euler rotation system |
| CGMRES | A GMRES solver for sparse square problems |
| CHybridNonLinearSolver | Finds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg") |
| 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 |
| 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 |
| 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 |
| CMatrixComplexPowerReturnValue | Proxy for the matrix power of some matrix (expression) |
| CMatrixExponentialReturnValue | Proxy for the matrix exponential of some matrix (expression) |
| CMatrixFunctionReturnValue | Proxy for the matrix function of some matrix (expression) |
| CMatrixLogarithmReturnValue | Proxy for the matrix logarithm of some matrix (expression) |
| 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 |
| CMatrixPowerReturnValue | Proxy for the matrix power of some matrix (expression) |
| CMatrixSquareRootReturnValue | Proxy for the matrix square root of some matrix (expression) |
| CMINRES | A minimal residual solver for sparse symmetric problems |
| CNNLS | Implementation of the Non-Negative Least Squares (NNLS) algorithm |
| CNumericalDiff | |
| CPolynomialSolver | A polynomial solver |
| 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 |
| CSGroup | Symmetry group, initialized from template arguments |
| CSparseInverse | Calculate sparse subset of inverse of sparse matrix |
| CSpline | A class representing multi-dimensional spline curves |
| CSplineFitting | Spline fitting methods |
| 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 | |
| CTensor | The tensor class |
| 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 |
| CTensorContractionOp | |
| 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 | A cost model used to limit the number of threads used for evaluating tensor expression |
| CTensorCustomBinaryOp | Tensor custom class |
| CTensorCustomUnaryOp | Tensor custom class |
| CTensorCwiseBinaryOp | Tensor binary expression |
| CTensorCwiseNullaryOp | Tensor nullary expression |
| CTensorCwiseUnaryOp | Tensor unary expression |
| CTensorDevice | Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...) |
| CTensorEvaluator | The tensor evaluator class |
| CTensorFFTOp | Tensor FFT class |
| CTensorFixedSize | The fixed sized version of the tensor class |
| CTensorForcedEvalOp | Tensor reshaping class |
| CTensorGeneratorOp | Tensor generator class |
| CTensorImagePatchOp | 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 |
| CTensorIndexPairOp | Tensor + Index Pair class |
| CTensorInflationOp | Tensor inflation class |
| CTensorMap | A tensor expression mapping an existing array of data |
| CTensorPaddingOp | Tensor padding class. At the moment only padding with a constant value is supported |
| CTensorPatchOp | Tensor patch class |
| CTensorReductionOp | Tensor reduction class |
| CTensorRef | A reference to a tensor expression The expression will be evaluated lazily (as much as possible) |
| CTensorRef< const PlainObjectType > | A reference to a constant tensor expression The expression will be evaluated lazily (as much as possible) |
| CTensorReshapingOp | Tensor reshaping class |
| CTensorReverseOp | Tensor reverse elements class |
| CTensorRollOp | Tensor roll (circular shift) elements class |
| CTensorScanOp | Tensor scan class |
| CTensorShufflingOp | Tensor shuffling class |
| CTensorStridingOp | Tensor striding class |
| CTensorTraceOp | Tensor Trace class |
| CTensorVolumePatchOp | Patch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows: |
| CTensorPairIndex | Converts to Tensor<Pair<Index, Scalar> > and reduces to Tensor<Index> |
| CTensorSlicing | Tensor slicing class |
1.13.2