Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
Eigen::AlignedVector3< _Scalar >A vectorization friendly 3D vector
Eigen::AutoDiffScalar< _DerType >A scalar type replacement with automatic differentation capability
Eigen::BlockSparseMatrix< _Scalar, _BlockAtCompileTime, _Options, _StorageIndex >A versatile sparse matrix representation where each element is a block
ConvertToDeviceExpressionThis struct is used to convert the MakePointer in the host expression to the MakeGlobalPointer for the device expression. For the leafNodes containing the pointer. This is due to the fact that the address space of the pointer T* is different on the host and the device
Eigen::DGMRES< _MatrixType, _Preconditioner >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
Eigen::DynamicSGroupDynamic symmetry group
Eigen::DynamicSparseMatrix< _Scalar, _Options, _StorageIndex >A sparse matrix class designed for matrix assembly purpose
utility::tuple::ElemTypeHolder< 0, Tuple< T, Ts...> >Specialisation of the ElemTypeHolder class when the number of elements inside the tuple is 1
utility::tuple::ElemTypeHolder< 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
Eigen::EulerAngles< _Scalar, _System >Represents a rotation in a 3 dimensional space as three Euler angles
Eigen::EulerSystem< _AlphaAxis, _BetaAxis, _GammaAxis >Represents a fixed Euler rotation system
ExprConstructor
ExtractAccessor
Eigen::GMRES< _MatrixType, _Preconditioner >A GMRES solver for sparse square problems
Eigen::HybridNonLinearSolver< FunctorType, Scalar >Finds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg")
utility::tuple::IndexList< Is >Creates a list of index from the elements in the tuple
utility::tuple::IndexRange< MIN, MAX >IndexRange that returns a [MIN, MAX) index range
Eigen::IterScaling< _MatrixType >Iterative scaling algorithm to equilibrate rows and column norms in matrices
Eigen::KdBVH< _Scalar, _Dim, _Object >A simple bounding volume hierarchy based on AlignedBox
Eigen::KroneckerProduct< Lhs, Rhs >Kronecker tensor product helper class for dense matrices
Eigen::KroneckerProductBase< Derived >The base class of dense and sparse Kronecker product
Eigen::KroneckerProductSparse< Lhs, Rhs >Kronecker tensor product helper class for sparse matrices
Eigen::LevenbergMarquardt< _FunctorType >Performs non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm
Eigen::MatrixComplexPowerReturnValue< Derived >Proxy for the matrix power of some matrix (expression)
Eigen::MatrixExponentialReturnValue< Derived >Proxy for the matrix exponential of some matrix (expression)
Eigen::MatrixFunctionReturnValue< Derived >Proxy for the matrix function of some matrix (expression)
Eigen::MatrixLogarithmReturnValue< Derived >Proxy for the matrix logarithm of some matrix (expression)
Eigen::MatrixMarketIterator< Scalar >Iterator to browse matrices from a specified folder
Eigen::MatrixPower< MatrixType >Class for computing matrix powers
Eigen::MatrixPowerAtomic< MatrixType >Class for computing matrix powers
Eigen::MatrixPowerParenthesesReturnValue< MatrixType >Proxy for the matrix power of some matrix
Eigen::MatrixPowerReturnValue< Derived >Proxy for the matrix power of some matrix (expression)
Eigen::MatrixSquareRootReturnValue< Derived >Proxy for the matrix square root of some matrix (expression)
Eigen::MaxSizeVector< T >The MaxSizeVector class
Eigen::MINRES< _MatrixType, _UpLo, _Preconditioner >A minimal residual solver for sparse symmetric problems
Eigen::NumericalDiff< _Functor, mode >
Eigen::PolynomialSolver< _Scalar, _Deg >A polynomial solver
Eigen::PolynomialSolverBase< _Scalar, _Deg >Defined to be inherited by polynomial solvers: it provides convenient methods such as
Eigen::RandomSetter< SparseMatrixType, MapTraits, OuterPacketBits >The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
utility::tuple::RangeBuilder< MIN, N, Is >Collects internal details for generating index ranges [MIN, MAX) Declare primary template for index range builder
utility::tuple::RangeBuilder< MIN, MIN, Is...>Base Step: Specialisation of the RangeBuilder when the MIN==MAX. In this case the Is... is [0 to sizeof...(tuple elements))
Eigen::SGroup< Gen >Symmetry group, initialized from template arguments
Eigen::SkylineInplaceLU< MatrixType >Inplace LU decomposition of a skyline matrix and associated features
Eigen::SkylineMatrix< _Scalar, _Options >The main skyline matrix class
Eigen::SkylineMatrixBase< Derived >Base class of any skyline matrices or skyline expressions
Eigen::SkylineStorage< Scalar >
Eigen::Spline< _Scalar, _Dim, _Degree >A class representing multi-dimensional spline curves
Eigen::SplineFitting< SplineType >Spline fitting methods
Eigen::SplineTraits< Spline< _Scalar, _Dim, _Degree >, _DerivativeOrder >Compile-time attributes of the Spline class for fixed degree
Eigen::SplineTraits< Spline< _Scalar, _Dim, _Degree >, Dynamic >Compile-time attributes of the Spline class for Dynamic degree
StaticIfThe StaticIf struct is used to statically choose the type based on the condition
utility::tuple::StaticIf< true, T >Specialisation of the StaticIf when the condition is true
Eigen::StaticSGroup< Gen >Static symmetry group
Eigen::StdMapTraits< Scalar >
Eigen::Tensor< Scalar_, NumIndices_, Options_, IndexType_ >The tensor class
TensorAssignThe tensor assignment class
Eigen::TensorBase< Derived, AccessLevel >The tensor base class
TensorBroadcastingTensor broadcasting class
Eigen::TensorConcatenationOp< Axis, LhsXprType, RhsXprType >Tensor concatenation class
TensorContractionTensor contraction class
Eigen::TensorConversionOp< TargetType, XprType >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
TensorConvolutionTensor convolution class
Eigen::TensorCustomBinaryOp< CustomBinaryFunc, LhsXprType, RhsXprType >Tensor custom class
Eigen::TensorCustomUnaryOp< CustomUnaryFunc, XprType >Tensor custom class
Eigen::TensorDevice< ExpressionType, DeviceType >Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...)
Eigen::TensorEvaluator< Derived, Device >A cost model used to limit the number of threads used for evaluating tensor expression
TensorExecutorThe tensor executor class
TensorExprTensor expression classes
Eigen::TensorFixedSize< Scalar_, Dimensions_, Options_, IndexType >The fixed sized version of the tensor class
TensorForcedEvalTensor reshaping class
TensorForcedEvalTensor reshaping class
TensorGeneratorTensor generator class
TensorImagePatchPatch 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
TensorIndexTupleTensor + Index Tuple class
TensorInflationTensor inflation class
TensorKChippingReshapingA chip is a thin slice, corresponding to a column or a row in a 2-d tensor
TensorLayoutSwapSwap the layout from col-major to row-major, or row-major to col-major, and invert the order of the dimensions
Eigen::TensorMap< PlainObjectType, Options_, MakePointer_ >A tensor expression mapping an existing array of data
TensorPaddingTensor padding class. At the moment only padding with a constant value is supported
TensorPatchTensor patch class
TensorReductionTensor reduction class
Eigen::TensorRef< PlainObjectType >A reference to a tensor expression The expression will be evaluated lazily (as much as possible)
TensorReshapingTensor reshaping class
TensorReverseTensor reverse elements class
TensorScanTensor scan class
TensorShufflingTensor shuffling class
TensorSlicingTensor slicing class
TensorStridingTensor striding class
TensorTupleIndexConverts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>
TensorVolumePatchPatch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows:
utility::tuple::Tuple< Ts >Fixed-size collection of heterogeneous values Ts... - the types of the elements that the tuple stores. Empty list is supported
utility::tuple::Tuple< T, Ts...>Specialisation of the Tuple class when the tuple has at least one element
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