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Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 1234]
 ▼NEigenNamespace containing all symbols from the Eigen library
 ►Ninternal
 ►NTensorSycl
 CAlignedVector3A vectorization friendly 3D vector
 CAutoDiffScalarA scalar type replacement with automatic differentiation capability
 CBlockSparseMatrixA versatile sparse matrix representation where each element is a block
 CDGMRESA 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
 CDynamicSGroupDynamic symmetry group
 CDynamicSparseMatrixA sparse matrix class designed for matrix assembly purpose
 CEulerAnglesRepresents a rotation in a 3 dimensional space as three Euler angles
 CEulerSystemRepresents a fixed Euler rotation system
 CGMRESA GMRES solver for sparse square problems
 CHybridNonLinearSolverFinds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg")
 CIterationControllerControls the iterations of the iterative solvers
 CIterScalingIterative scaling algorithm to equilibrate rows and column norms in matrices
 CKdBVHA simple bounding volume hierarchy based on AlignedBox
 CKroneckerProductKronecker tensor product helper class for dense matrices
 CKroneckerProductBaseThe base class of dense and sparse Kronecker product
 CKroneckerProductSparseKronecker tensor product helper class for sparse matrices
 CLevenbergMarquardtPerforms non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm
 CMatrixComplexPowerReturnValueProxy for the matrix power of some matrix (expression)
 CMatrixExponentialReturnValueProxy for the matrix exponential of some matrix (expression)
 CMatrixFunctionReturnValueProxy for the matrix function of some matrix (expression)
 CMatrixLogarithmReturnValueProxy for the matrix logarithm of some matrix (expression)
 CMatrixMarketIteratorIterator to browse matrices from a specified folder
 CMatrixPowerClass for computing matrix powers
 CMatrixPowerAtomicClass for computing matrix powers
 CMatrixPowerParenthesesReturnValueProxy for the matrix power of some matrix
 CMatrixPowerReturnValueProxy for the matrix power of some matrix (expression)
 CMatrixSquareRootReturnValueProxy for the matrix square root of some matrix (expression)
 CMaxSizeVectorThe MaxSizeVector class
 CMINRESA minimal residual solver for sparse symmetric problems
 CNumericalDiff
 CPolynomialSolverA polynomial solver
 CPolynomialSolverBaseDefined to be inherited by polynomial solvers: it provides convenient methods such as
 CRandomSetterThe RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
 CSGroupSymmetry group, initialized from template arguments
 CSkylineInplaceLUInplace LU decomposition of a skyline matrix and associated features
 CSkylineMatrixThe main skyline matrix class
 CSkylineMatrixBaseBase class of any skyline matrices or skyline expressions
 CSkylineStorage
 CSplineA class representing multi-dimensional spline curves
 CSplineFittingSpline 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
 CStaticSGroupStatic symmetry group
 CStdMapTraits
 CTensorThe tensor class
 CTensorAssignOp
 CTensorBaseThe tensor base class
 CTensorBroadcastingOp
 CTensorChippingOp
 CTensorConcatenationOpTensor concatenation class
 CTensorContractionOpTensor contraction class
 CTensorConversionOpTensor 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
 CTensorCostModelA cost model used to limit the number of threads used for evaluating tensor expression
 CTensorCustomBinaryOpTensor custom class
 CTensorCustomUnaryOpTensor custom class
 CTensorCwiseBinaryOpTensor binary expression
 CTensorCwiseNullaryOpTensor nullary expression
 CTensorCwiseUnaryOpTensor unary expression
 CTensorDevicePseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...)
 CTensorEvaluatorThe tensor evaluator class
 CTensorFixedSizeThe fixed sized version of the tensor class
 CTensorForcedEvalOpTensor reshaping class
 CTensorGeneratorOpTensor generator class
 CTensorImagePatchOpPatch 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
 CTensorInflationOpTensor inflation class
 CTensorMapA tensor expression mapping an existing array of data
 CTensorPaddingOpTensor padding class. At the moment only padding with a constant value is supported
 CTensorPatchOpTensor patch class
 CTensorReductionOpTensor reduction class
 CTensorRefA reference to a tensor expression The expression will be evaluated lazily (as much as possible)
 CTensorReshapingOpTensor reshaping class
 CTensorReverseOpTensor reverse elements class
 CTensorScanOpTensor scan class
 CTensorShufflingOpTensor shuffling class
 CTensorStridingOpTensor striding class
 CTensorVolumePatchOpPatch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows:
 ▼Nutility
 ►Ntuple
 CTensorIndexTupleTensor + Index Tuple class
 CTensorSlicingTensor slicing class
 CTensorTupleIndexConverts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>