▼NEigen | Namespace containing all symbols from the Eigen library |
CAlignedVector3 | A vectorization friendly 3D vector |
CAutoDiffScalar | A scalar type replacement with automatic differentation 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 |
CDynamicSparseMatrix | A sparse matrix class designed for matrix assembly purpose |
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") |
CIterationController | Controls the iterations of the iterative solvers |
CIterScaling | Iterative scaling algorithm to equilibrate rows and column norms in matrices |
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) |
CMaxSizeVector | The MaxSizeVector class |
CMINRES | A minimal residual solver for sparse symmetric problems |
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 |
CSkylineInplaceLU | Inplace LU decomposition of a skyline matrix and associated features |
CSkylineMatrix | The main skyline matrix class |
CSkylineMatrixBase | Base class of any skyline matrices or skyline expressions |
CSkylineStorage | |
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 | |
CTensor | The tensor class |
CTensorBase | The tensor base class |
CTensorConcatenationOp | Tensor concatenation class |
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 |
CTensorCustomBinaryOp | Tensor custom class |
CTensorCustomUnaryOp | Tensor custom class |
CTensorDevice | Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...) |
CTensorEvaluator | A cost model used to limit the number of threads used for evaluating tensor expression |
CTensorFixedSize | The fixed sized version of the tensor class |
CTensorMap | A tensor expression mapping an existing array of data |
CTensorRef | A reference to a tensor expression The expression will be evaluated lazily (as much as possible) |
▼Nutility | |
▼Ntuple | |
CElemTypeHolder | ElemTypeHolder class is used to specify the types of the elements inside the tuple |
CElemTypeHolder< 0, Tuple< T, Ts... > > | Specialisation of the ElemTypeHolder class when the number of elements inside the tuple is 1 |
CElemTypeHolder< k, Tuple< T, Ts... > > | Specialisation of the ElemTypeHolder class when the number of elements inside the tuple is bigger than 1. It recursively calls itself to detect the type of each element in the tuple |
CIndexList | Creates a list of index from the elements in the tuple |
CIndexRange | IndexRange that returns a [MIN, MAX) index range |
CRangeBuilder | Collects internal details for generating index ranges [MIN, MAX) Declare primary template for index range builder |
CRangeBuilder< MIN, MIN, Is... > | Base Step: Specialisation of the RangeBuilder when the MIN==MAX. In this case the Is... is [0 to sizeof...(tuple elements)) |
CStaticIf | The StaticIf struct is used to statically choose the type based on the condition |
CStaticIf< true, T > | Specialisation of the StaticIf when the condition is true |
CTuple | Fixed-size collection of heterogeneous values Ts... - the types of the elements that the tuple stores. Empty list is supported |
CTuple< T, Ts... > | Specialisation of the Tuple class when the tuple has at least one element |
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 |
CTensorForcedEval | Tensor reshaping class |
CTensorForcedEval | Tensor reshaping class |
CTensorGenerator | Tensor generator 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 |
CTensorIndexTuple | Tensor + Index Tuple class |
CTensorInflation | Tensor inflation class |
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 |
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 |
CTensorTupleIndex | Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index> |
CTensorVolumePatch | Patch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows: |