Namespaces
	Architecture

	half_impl

	HybridNonLinearSolverSpace

	internal

	LevenbergMarquardtSpace

	numext

	TensorSycl

Classes
class	aligned_allocator
	STL compatible allocator to use with with 16 byte aligned types. More...

class	aligned_allocator_indirection

class	AlignedBox
	An axis aligned box. More...

class	AMDOrdering

class	AngleAxis
	Represents a 3D rotation as a rotation angle around an arbitrary 3D axis. More...

struct	AntiHermiticity

struct	AntiSymmetry

class	ArpackGeneralizedSelfAdjointEigenSolver

class	array

class	Array
	General-purpose arrays with easy API for coefficient-wise operations. More...

class	array< T, 0 >

class	ArrayBase
	Base class for all 1D and 2D array, and related expressions. More...

class	ArrayWrapper
	Expression of a mathematical vector or matrix as an array object. More...

struct	ArrayXpr

class	AutoDiffJacobian

class	AutoDiffScalar
	A scalar type replacement with automatic differentation capability. More...

class	AutoDiffVector

struct	BandShape

class	BDCSVD
	class Bidiagonal Divide and Conquer SVD More...

class	BiCGSTAB
	A bi conjugate gradient stabilized solver for sparse square problems. More...

class	Block
	Expression of a fixed-size or dynamic-size block. More...

class	BlockImpl

class	BlockImpl< const SparseMatrix< _Scalar, _Options, _StorageIndex >, BlockRows, BlockCols, true, Sparse >

class	BlockImpl< SparseMatrix< _Scalar, _Options, _StorageIndex >, BlockRows, BlockCols, true, Sparse >

class	BlockImpl< XprType, BlockRows, BlockCols, InnerPanel, Dense >

class	BlockImpl< XprType, BlockRows, BlockCols, InnerPanel, Sparse >

class	BlockImpl< XprType, BlockRows, BlockCols, true, Sparse >

class	BlockSparseMatrix
	A versatile sparse matrix representation where each element is a block. More...

class	BlockSparseMatrixView

class	BlockSparseTimeDenseProduct

class	BlockVectorReturn

class	BlockVectorView

class	CholmodBase
	The base class for the direct Cholesky factorization of Cholmod. More...

class	CholmodDecomposition
	A general Cholesky factorization and solver based on Cholmod. More...

class	CholmodSimplicialLDLT
	A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod. More...

class	CholmodSimplicialLLT
	A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod. More...

class	CholmodSupernodalLLT
	A supernodal Cholesky (LLT) factorization and solver based on Cholmod. More...

class	COLAMDOrdering

class	ColPivHouseholderQR
	Householder rank-revealing QR decomposition of a matrix with column-pivoting. More...

class	CommaInitializer
	Helper class used by the comma initializer operator. More...

class	CompleteOrthogonalDecomposition
	Complete orthogonal decomposition (COD) of a matrix. More...

class	ComplexEigenSolver
	Computes eigenvalues and eigenvectors of general complex matrices. More...

class	ComplexSchur
	Performs a complex Schur decomposition of a real or complex square matrix. More...

struct	Cond

class	Conjugate

class	ConjugateGradient
	A conjugate gradient solver for sparse (or dense) self-adjoint problems. More...

struct	ConversionSubExprEval

struct	ConversionSubExprEval< true, Eval, Scalar >

class	Cross

class	CwiseBinaryOp
	Generic expression where a coefficient-wise binary operator is applied to two expressions. More...

class	CwiseBinaryOpImpl

class	CwiseBinaryOpImpl< BinaryOp, Lhs, Rhs, Sparse >

class	CwiseNullaryOp
	Generic expression of a matrix where all coefficients are defined by a functor. More...

class	CwiseTernaryOp
	Generic expression where a coefficient-wise ternary operator is applied to two expressions. More...

class	CwiseTernaryOpImpl

class	CwiseUnaryOp
	Generic expression where a coefficient-wise unary operator is applied to an expression. More...

class	CwiseUnaryOpImpl

class	CwiseUnaryView
	Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector. More...

class	CwiseUnaryViewImpl

class	CwiseUnaryViewImpl< ViewOp, MatrixType, Dense >

struct	DefaultDevice

struct	Dense

class	DenseBase
	Base class for all dense matrices, vectors, and arrays. More...

class	DenseCoeffsBase

class	DenseCoeffsBase< Derived, DirectAccessors >
	Base class providing direct read-only coefficient access to matrices and arrays. More...

class	DenseCoeffsBase< Derived, DirectWriteAccessors >
	Base class providing direct read/write coefficient access to matrices and arrays. More...

class	DenseCoeffsBase< Derived, ReadOnlyAccessors >
	Base class providing read-only coefficient access to matrices and arrays. More...

class	DenseCoeffsBase< Derived, WriteAccessors >
	Base class providing read/write coefficient access to matrices and arrays. More...

struct	DenseFunctor

struct	DenseShape

struct	DenseSparseProductReturnType

class	DenseStorage

class	DenseStorage< T, 0, _Rows, _Cols, _Options >

class	DenseStorage< T, 0, _Rows, Dynamic, _Options >

class	DenseStorage< T, 0, Dynamic, _Cols, _Options >

class	DenseStorage< T, 0, Dynamic, Dynamic, _Options >

class	DenseStorage< T, Dynamic, _Rows, Dynamic, _Options >

class	DenseStorage< T, Dynamic, Dynamic, _Cols, _Options >

class	DenseStorage< T, Dynamic, Dynamic, Dynamic, _Options >

class	DenseStorage< T, Size, _Rows, Dynamic, _Options >

class	DenseStorage< T, Size, Dynamic, _Cols, _Options >

class	DenseStorage< T, Size, Dynamic, Dynamic, _Options >

class	DenseTimeSparseProduct

class	DGMRES
	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. More...

class	Diagonal
	Expression of a diagonal/subdiagonal/superdiagonal in a matrix. More...

class	DiagonalBase

class	DiagonalMatrix
	Represents a diagonal matrix with its storage. More...

class	DiagonalPreconditioner
	A preconditioner based on the digonal entries. More...

class	DiagonalProduct

struct	DiagonalShape

class	DiagonalWrapper
	Expression of a diagonal matrix. More...

struct	DimensionList

struct	DSizes

class	DynamicSGroup
	Dynamic symmetry group. More...

class	DynamicSGroupFromTemplateArgs

class	DynamicSkylineMatrix

class	DynamicSparseMatrix
	A sparse matrix class designed for matrix assembly purpose. More...

class	EigenBase

class	EigenSolver
	Computes eigenvalues and eigenvectors of general matrices. More...

class	EulerAngles
	Represents a rotation in a 3 dimensional space as three Euler angles. More...

class	EulerSystem
	Represents a fixed Euler rotation system. More...

class	EventCount

class	Flagged

class	ForceAlignedAccess
	Enforce aligned packet loads and stores regardless of what is requested. More...

class	FullPivHouseholderQR
	Householder rank-revealing QR decomposition of a matrix with full pivoting. More...

class	FullPivLU
	LU decomposition of a matrix with complete pivoting, and related features. More...

struct	general_product_to_triangular_selector

struct	general_product_to_triangular_selector< MatrixType, ProductType, UpLo, false >

struct	general_product_to_triangular_selector< MatrixType, ProductType, UpLo, true >

class	GeneralizedEigenSolver
	Computes the generalized eigenvalues and eigenvectors of a pair of general matrices. More...

class	GeneralizedSelfAdjointEigenSolver
	Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem. More...

struct	GenericNumTraits

class	GMRES
	A GMRES solver for sparse square problems. More...

struct	half

struct	Hermiticity

class	HessenbergDecomposition
	Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation. More...

class	Homogeneous
	Expression of one (or a set of) homogeneous vector(s) More...

struct	HomogeneousShape

class	HouseholderQR
	Householder QR decomposition of a matrix. More...

class	HouseholderSequence
	Sequence of Householder reflections acting on subspaces with decreasing size. More...

class	HybridNonLinearSolver
	Finds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg"). More...

class	Hyperplane
	A hyperplane. More...

class	IdentityPreconditioner
	A naive preconditioner which approximates any matrix as the identity matrix. More...

class	IncompleteCholesky
	Modified Incomplete Cholesky with dual threshold. More...

class	IncompleteLU

class	IncompleteLUT
	Incomplete LU factorization with dual-threshold strategy. More...

struct	IndexPair

class	InnerIterator
	An InnerIterator allows to loop over the element of any matrix expression. More...

class	InnerStride
	Convenience specialization of Stride to specify only an inner stride See class Map for some examples. More...

class	Inverse
	Expression of the inverse of another expression. More...

class	InverseImpl

class	InverseImpl< PermutationType, PermutationStorage >

class	IOFormat
	Stores a set of parameters controlling the way matrices are printed. More...

class	IterationController
	Controls the iterations of the iterative solvers. More...

class	IterativeSolverBase
	Base class for linear iterative solvers. More...

class	IterScaling
	iterative scaling algorithm to equilibrate rows and column norms in matrices More...

class	JacobiRotation
	Rotation given by a cosine-sine pair. More...

class	JacobiSVD
	Two-sided Jacobi SVD decomposition of a rectangular matrix. More...

class	KdBVH
	A simple bounding volume hierarchy based on AlignedBox. More...

class	KroneckerProduct
	Kronecker tensor product helper class for dense matrices. More...

class	KroneckerProductBase
	The base class of dense and sparse Kronecker product. More...

class	KroneckerProductSparse
	Kronecker tensor product helper class for sparse matrices. More...

struct	LazyProductReturnType

class	LDLT
	Robust Cholesky decomposition of a matrix with pivoting. More...

class	LeastSquareDiagonalPreconditioner
	Jacobi preconditioner for LeastSquaresConjugateGradient. More...

class	LeastSquaresConjugateGradient
	A conjugate gradient solver for sparse (or dense) least-square problems. More...

class	LevenbergMarquardt
	Performs non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm. More...

class	LLT
	Standard Cholesky decomposition (LL^T) of a matrix and associated features. More...

struct	MakePointer

class	Map
	A matrix or vector expression mapping an existing array of data. More...

class	Map< const Quaternion< _Scalar >, _Options >
	Quaternion expression mapping a constant memory buffer. More...

class	Map< const SparseMatrix< MatScalar, MatOptions, MatIndex >, Options, StrideType >

class	Map< PermutationMatrix< SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex >, _PacketAccess >

class	Map< Quaternion< _Scalar >, _Options >
	Expression of a quaternion from a memory buffer. More...

class	Map< SparseMatrix< MatScalar, MatOptions, MatIndex >, Options, StrideType >
	Specialization of class Map for SparseMatrix-like storage. More...

class	Map< Transpositions< SizeAtCompileTime, MaxSizeAtCompileTime, _StorageIndex >, PacketAccess >

class	MapBase

class	MapBase< Derived, ReadOnlyAccessors >
	Base class for dense Map and Block expression with direct access. More...

class	MapBase< Derived, WriteAccessors >
	Base class for non-const dense Map and Block expression with direct access. More...

class	MappedSkylineMatrix

class	MappedSparseMatrix
	Sparse matrix. More...

class	Matrix
	The matrix class, also used for vectors and row-vectors. More...

class	MatrixBase
	Base class for all dense matrices, vectors, and expressions. More...

class	MatrixComplexPowerReturnValue
	Proxy for the matrix power of some matrix (expression). More...

struct	MatrixExponentialReturnValue
	Proxy for the matrix exponential of some matrix (expression). More...

class	MatrixFunctionReturnValue
	Proxy for the matrix function of some matrix (expression). More...

class	MatrixLogarithmReturnValue
	Proxy for the matrix logarithm of some matrix (expression). More...

class	MatrixMarketIterator
	Iterator to browse matrices from a specified folder. More...

class	MatrixPower
	Class for computing matrix powers. More...

class	MatrixPowerAtomic
	Class for computing matrix powers. More...

class	MatrixPowerParenthesesReturnValue
	Proxy for the matrix power of some matrix. More...

class	MatrixPowerReturnValue
	Proxy for the matrix power of some matrix (expression). More...

class	MatrixSquareRootReturnValue
	Proxy for the matrix square root of some matrix (expression). More...

class	MatrixWrapper
	Expression of an array as a mathematical vector or matrix. More...

struct	MatrixXpr

struct	max_n_1

struct	max_n_1< 0 >

class	MaxSizeVector
	The MaxSizeVector class. More...

class	MetisOrdering

class	MINRES
	A minimal residual solver for sparse symmetric problems. More...

class	NaturalOrdering

class	NestByValue
	Expression which must be nested by value. More...

class	NoAlias
	Pseudo expression providing an operator = assuming no aliasing. More...

class	NonBlockingThreadPoolTempl

class	NumericalDiff

class	NumTraits
	Holds information about the various numeric (i.e. scalar) types allowed by Eigen. More...

struct	NumTraits< Array< Scalar, Rows, Cols, Options, MaxRows, MaxCols > >

struct	NumTraits< AutoDiffScalar< DerType > >

struct	NumTraits< double >

struct	NumTraits< Eigen::half >

struct	NumTraits< float >

struct	NumTraits< long double >

struct	NumTraits< std::complex< _Real > >

struct	NumTraits< std::string >

struct	NumTraits< void >

class	OuterStride
	Convenience specialization of Stride to specify only an outer stride See class Map for some examples. More...

struct	PacketConverter

struct	PacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 1, 2 >

struct	PacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 2, 1 >

struct	PacketConverter< TensorEvaluator, SrcPacket, TgtPacket, 4, 1 >

struct	PacketType

class	ParametrizedLine
	A parametrized line. More...

class	PardisoImpl

class	PardisoLDLT
	A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library. More...

class	PardisoLLT
	A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library. More...

class	PardisoLU
	A sparse direct LU factorization and solver based on the PARDISO library. More...

class	PartialPivLU
	LU decomposition of a matrix with partial pivoting, and related features. More...

class	PartialReduxExpr
	Generic expression of a partially reduxed matrix. More...

class	PastixBase

class	PastixLDLT
	A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library. More...

class	PastixLLT
	A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library. More...

class	PastixLU
	Interface to the PaStix solver. More...

class	PermutationBase
	Base class for permutations. More...

class	PermutationMatrix
	Permutation matrix. More...

struct	PermutationShape

struct	PermutationStorage

class	PermutationWrapper
	Class to view a vector of integers as a permutation matrix. More...

class	PlainObjectBase

class	PolynomialSolver
	A polynomial solver. More...

class	PolynomialSolver< _Scalar, 1 >

class	PolynomialSolverBase
	Defined to be inherited by polynomial solvers: it provides convenient methods such as. More...

class	Product
	Expression of the product of two arbitrary matrices or vectors. More...

class	ProductImpl

class	ProductImpl< Lhs, Rhs, Option, Dense >

struct	ProductReturnType

class	Quaternion
	The quaternion class used to represent 3D orientations and rotations. More...

class	QuaternionBase
	Base class for quaternion expressions. More...

class	RandomSetter
	The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access. More...

class	RealQZ
	Performs a real QZ decomposition of a pair of square matrices. More...

class	RealSchur
	Performs a real Schur decomposition of a square matrix. More...

class	Ref
	A matrix or vector expression mapping an existing expression. More...

class	Ref< const SparseMatrix< MatScalar, MatOptions, MatIndex >, Options, StrideType >

class	Ref< const SparseVector< MatScalar, MatOptions, MatIndex >, Options, StrideType >

class	Ref< const TPlainObjectType, Options, StrideType >

class	Ref< SparseMatrix< MatScalar, MatOptions, MatIndex >, Options, StrideType >
	A sparse matrix expression referencing an existing sparse expression. More...

class	Ref< SparseVector< MatScalar, MatOptions, MatIndex >, Options, StrideType >
	A sparse vector expression referencing an existing sparse vector expression. More...

class	RefBase

class	Replicate
	Expression of the multiple replication of a matrix or vector. More...

class	ReturnByValue

class	Reverse
	Expression of the reverse of a vector or matrix. More...

class	Rotation2D
	Represents a rotation/orientation in a 2 dimensional space. More...

class	RotationBase
	Common base class for compact rotation representations. More...

class	RunQueue

class	ScalarBinaryOpTraits
	Determines whether the given binary operation of two numeric types is allowed and what the scalar return type is. More...

struct	ScalarBinaryOpTraits< AutoDiffScalar< DerType >, typename DerType::Scalar, BinOp >

struct	ScalarBinaryOpTraits< T, T, BinaryOp >

struct	ScalarBinaryOpTraits< T, typename NumTraits< typename internal::enable_if< NumTraits< T >::IsComplex, T >::type >::Real, BinaryOp >

struct	ScalarBinaryOpTraits< T, void, BinaryOp >

struct	ScalarBinaryOpTraits< typename DerType::Scalar, AutoDiffScalar< DerType >, BinOp >

struct	ScalarBinaryOpTraits< typename NumTraits< typename internal::enable_if< NumTraits< T >::IsComplex, T >::type >::Real, T, BinaryOp >

struct	ScalarBinaryOpTraits< void, T, BinaryOp >

struct	ScalarBinaryOpTraits< void, void, BinaryOp >

struct	ScanLauncher

class	Select
	Expression of a coefficient wise version of the C++ ternary operator ?: More...

struct	selfadjoint_product_selector

struct	selfadjoint_product_selector< MatrixType, OtherType, UpLo, false >

struct	selfadjoint_product_selector< MatrixType, OtherType, UpLo, true >

struct	selfadjoint_rank1_update

struct	selfadjoint_rank1_update< Scalar, Index, ColMajor, UpLo, ConjLhs, ConjRhs >

struct	selfadjoint_rank1_update< Scalar, Index, RowMajor, UpLo, ConjLhs, ConjRhs >

class	SelfAdjointEigenSolver
	Computes eigenvalues and eigenvectors of selfadjoint matrices. More...

struct	SelfAdjointShape

class	SelfAdjointView
	Expression of a selfadjoint matrix from a triangular part of a dense matrix. More...

class	SGroup
	Symmetry group, initialized from template arguments. More...

class	SimpleThreadPoolTempl

class	SimplicialCholesky

class	SimplicialCholeskyBase
	A base class for direct sparse Cholesky factorizations. More...

class	SimplicialLDLT
	A direct sparse LDLT Cholesky factorizations without square root. More...

class	SimplicialLLT
	A direct sparse LLT Cholesky factorizations. More...

struct	Sizes

class	SkylineInplaceLU
	Inplace LU decomposition of a skyline matrix and associated features. More...

class	SkylineMatrix
	The main skyline matrix class. More...

class	SkylineMatrixBase
	Base class of any skyline matrices or skyline expressions. More...

class	SkylineProduct

struct	SkylineProductReturnType

class	SkylineStorage

class	SkylineVector

struct	SluMatrix

struct	SluMatrixMapHelper

struct	SluMatrixMapHelper< Matrix< Scalar, Rows, Cols, Options, MRows, MCols > >

struct	SluMatrixMapHelper< SparseMatrixBase< Derived > >

class	Solve
	Pseudo expression representing a solving operation. More...

class	SolveImpl

class	SolveImpl< Decomposition, RhsType, Dense >

class	SolverBase
	A base class for matrix decomposition and solvers. More...

struct	SolverShape

struct	SolverStorage

class	SolveWithGuess
	Pseudo expression representing a solving operation. More...

struct	Sparse

class	SparseCompressedBase
	Common base class for sparse [compressed]-{row\|column}-storage format. More...

class	SparseDenseOuterProduct

struct	SparseDenseProductReturnType

class	SparseDiagonalProduct

struct	SparseFunctor

class	SparseLU
	Sparse supernodal LU factorization for general matrices. More...

struct	SparseLUMatrixLReturnType

struct	SparseLUMatrixUReturnType

class	SparseMapBase

class	SparseMapBase< Derived, ReadOnlyAccessors >
	Common base class for Map and Ref instance of sparse matrix and vector. More...

class	SparseMapBase< Derived, WriteAccessors >
	Common base class for writable Map and Ref instance of sparse matrix and vector. More...

class	SparseMatrix
	A versatible sparse matrix representation. More...

class	SparseMatrixBase
	Base class of any sparse matrices or sparse expressions. More...

class	SparseQR
	Sparse left-looking rank-revealing QR factorization. More...

struct	SparseQR_QProduct

struct	SparseQRMatrixQReturnType

struct	SparseQRMatrixQTransposeReturnType

class	SparseSelfAdjointView
	Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix. More...

struct	SparseShape

class	SparseSolverBase
	A base class for sparse solvers. More...

class	SparseSparseProduct

struct	SparseSparseProductReturnType

class	SparseSymmetricPermutationProduct

class	SparseTimeDenseProduct

class	SparseVector
	a sparse vector class More...

class	SparseView
	Expression of a dense or sparse matrix with zero or too small values removed. More...

class	Spline
	A class representing multi-dimensional spline curves. More...

struct	SplineFitting
	Spline fitting methods. More...

struct	SplineTraits

struct	SplineTraits< Spline< _Scalar, _Dim, _Degree >, _DerivativeOrder >
	Compile-time attributes of the Spline class for fixed degree. More...

struct	SplineTraits< Spline< _Scalar, _Dim, _Degree >, Dynamic >
	Compile-time attributes of the Spline class for Dynamic degree. More...

class	SPQR
	Sparse QR factorization based on SuiteSparseQR library. More...

struct	SPQR_QProduct

struct	SPQRMatrixQReturnType

struct	SPQRMatrixQTransposeReturnType

class	StaticSGroup
	Static symmetry group. More...

struct	StdMapTraits

struct	StlThreadEnvironment

class	Stride
	Holds strides information for Map. More...

class	SuperLU
	A sparse direct LU factorization and solver based on the SuperLU library. More...

class	SuperLUBase
	The base class for the direct and incomplete LU factorization of SuperLU. More...

class	SVDBase
	Base class of SVD algorithms. More...

class	SwapWrapper

struct	Symmetry

class	Tensor
	The tensor class. More...

class	TensorAssignOp

class	TensorBase
	The tensor base class. More...

class	TensorBase< Derived, ReadOnlyAccessors >

class	TensorBroadcastingOp

class	TensorChippingOp

class	TensorConcatenationOp
	Tensor concatenation class. More...

struct	TensorContractionEvaluatorBase

class	TensorContractionOp

class	TensorConversionOp
	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. More...

class	TensorConvolutionOp

class	TensorCostModel

class	TensorCustomBinaryOp
	Tensor custom class. More...

class	TensorCustomUnaryOp
	Tensor custom class. More...

class	TensorCwiseBinaryOp

class	TensorCwiseNullaryOp

class	TensorCwiseTernaryOp

class	TensorCwiseUnaryOp

class	TensorDevice
	Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...) More...

class	TensorEvalToOp

class	TensorEvaluator
	A cost model used to limit the number of threads used for evaluating tensor expression. More...

struct	TensorEvaluator< const Derived, Device >

struct	TensorEvaluator< const TensorAssignOp< LeftArgType, RightArgType >, Device >

struct	TensorEvaluator< const TensorBroadcastingOp< Broadcast, ArgType >, Device >

struct	TensorEvaluator< const TensorChippingOp< DimId, ArgType >, Device >

struct	TensorEvaluator< const TensorConcatenationOp< Axis, LeftArgType, RightArgType >, Device >

struct	TensorEvaluator< const TensorContractionOp< Indices, LeftArgType, RightArgType >, Device >

struct	TensorEvaluator< const TensorConversionOp< TargetType, ArgType >, Device >

struct	TensorEvaluator< const TensorConvolutionOp< Indices, InputArgType, KernelArgType >, Device >

struct	TensorEvaluator< const TensorCustomBinaryOp< CustomBinaryFunc, LhsXprType, RhsXprType >, Device >

struct	TensorEvaluator< const TensorCustomUnaryOp< CustomUnaryFunc, XprType >, Device >

struct	TensorEvaluator< const TensorCwiseBinaryOp< BinaryOp, LeftArgType, RightArgType >, Device >

struct	TensorEvaluator< const TensorCwiseNullaryOp< NullaryOp, ArgType >, Device >

struct	TensorEvaluator< const TensorCwiseTernaryOp< TernaryOp, Arg1Type, Arg2Type, Arg3Type >, Device >

struct	TensorEvaluator< const TensorCwiseUnaryOp< UnaryOp, ArgType >, Device >

struct	TensorEvaluator< const TensorEvalToOp< ArgType, MakePointer_ >, Device >

struct	TensorEvaluator< const TensorForcedEvalOp< ArgType, MakePointer_ >, Device >

struct	TensorEvaluator< const TensorGeneratorOp< Generator, ArgType >, Device >

struct	TensorEvaluator< const TensorImagePatchOp< Rows, Cols, ArgType >, Device >

struct	TensorEvaluator< const TensorIndexTupleOp< ArgType >, Device >

struct	TensorEvaluator< const TensorInflationOp< Strides, ArgType >, Device >

struct	TensorEvaluator< const TensorLayoutSwapOp< ArgType >, Device >

struct	TensorEvaluator< const TensorPaddingOp< PaddingDimensions, ArgType >, Device >

struct	TensorEvaluator< const TensorPatchOp< PatchDim, ArgType >, Device >

struct	TensorEvaluator< const TensorReductionOp< Op, Dims, ArgType, MakePointer_ >, Device >

struct	TensorEvaluator< const TensorRef< Derived >, Device >

struct	TensorEvaluator< const TensorReshapingOp< NewDimensions, ArgType >, Device >

struct	TensorEvaluator< const TensorReverseOp< ReverseDimensions, ArgType >, Device >

struct	TensorEvaluator< const TensorScanOp< Op, ArgType >, Device >

struct	TensorEvaluator< const TensorSelectOp< IfArgType, ThenArgType, ElseArgType >, Device >

struct	TensorEvaluator< const TensorShufflingOp< Shuffle, ArgType >, Device >

struct	TensorEvaluator< const TensorSlicingOp< StartIndices, Sizes, ArgType >, Device >

struct	TensorEvaluator< const TensorStridingOp< Strides, ArgType >, Device >

struct	TensorEvaluator< const TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType >, Device >

struct	TensorEvaluator< const TensorTupleReducerOp< ReduceOp, Dims, ArgType >, Device >

struct	TensorEvaluator< const TensorVolumePatchOp< Planes, Rows, Cols, ArgType >, Device >

struct	TensorEvaluator< TensorChippingOp< DimId, ArgType >, Device >

struct	TensorEvaluator< TensorConcatenationOp< Axis, LeftArgType, RightArgType >, Device >

struct	TensorEvaluator< TensorLayoutSwapOp< ArgType >, Device >

struct	TensorEvaluator< TensorRef< Derived >, Device >

struct	TensorEvaluator< TensorReshapingOp< NewDimensions, ArgType >, Device >

struct	TensorEvaluator< TensorReverseOp< ReverseDimensions, ArgType >, Device >

struct	TensorEvaluator< TensorShufflingOp< Shuffle, ArgType >, Device >

struct	TensorEvaluator< TensorSlicingOp< StartIndices, Sizes, ArgType >, Device >

struct	TensorEvaluator< TensorStridingOp< Strides, ArgType >, Device >

struct	TensorEvaluator< TensorStridingSlicingOp< StartIndices, StopIndices, Strides, ArgType >, Device >

class	TensorFFTOp

class	TensorFixedSize
	The fixed sized version of the tensor class. More...

class	TensorForcedEvalOp

class	TensorGeneratorOp

class	TensorImagePatchOp

class	TensorIndexTupleOp

class	TensorInflationOp

class	TensorLayoutSwapOp

class	TensorMap
	A tensor expression mapping an existing array of data. More...

class	TensorOpCost

class	TensorPaddingOp

class	TensorPatchOp

class	TensorReductionOp

class	TensorRef
	A reference to a tensor expression The expression will be evaluated lazily (as much as possible). More...

class	TensorReshapingOp

class	TensorReverseOp

class	TensorScanOp

class	TensorSelectOp

class	TensorShufflingOp

class	TensorSlicingOp

class	TensorStorage

class	TensorStorage< T, DSizes< IndexType, NumIndices_ >, Options_ >

class	TensorStorage< T, FixedDimensions, Options_ >

class	TensorStridingOp

class	TensorStridingSlicingOp

class	TensorTupleReducerOp

class	TensorVolumePatchOp

class	ThreadPoolInterface

class	Transform
	Represents an homogeneous transformation in a N dimensional space. More...

class	Translation
	Represents a translation transformation. More...

class	Transpose
	Expression of the transpose of a matrix. More...

class	Transpose< TranspositionsBase< TranspositionsDerived > >

class	TransposeImpl

class	TransposeImpl< MatrixType, Dense >

class	TransposeImpl< MatrixType, Sparse >

class	Transpositions
	Represents a sequence of transpositions (row/column interchange) More...

class	TranspositionsBase

struct	TranspositionsShape

struct	TranspositionsStorage

class	TranspositionsWrapper

class	TriangularBase
	Base class for triangular part in a matrix. More...

struct	TriangularShape

class	TriangularView
	Expression of a triangular part in a matrix. More...

class	TriangularViewImpl

class	TriangularViewImpl< _MatrixType, _Mode, Dense >
	Base class for a triangular part in a dense matrix. More...

class	TriangularViewImpl< MatrixType, Mode, Sparse >
	Base class for a triangular part in a sparse matrix. More...

class	Tridiagonalization
	Tridiagonal decomposition of a selfadjoint matrix. More...

class	Triplet
	A small structure to hold a non zero as a triplet (i,j,value). More...

struct	Tuple

class	UmfPackLU
	A sparse LU factorization and solver based on UmfPack. More...

class	UniformScaling

class	VectorBlock
	Expression of a fixed-size or dynamic-size sub-vector. More...

class	VectorwiseOp
	Pseudo expression providing partial reduction operations. More...

class	WithFormat
	Pseudo expression providing matrix output with given format. More...

Typedefs
typedef Transform< double, 2, Affine >	Affine2d

typedef Transform< float, 2, Affine >	Affine2f

typedef Transform< double, 3, Affine >	Affine3d

typedef Transform< float, 3, Affine >	Affine3f

typedef Transform< double, 2, AffineCompact >	AffineCompact2d

typedef Transform< float, 2, AffineCompact >	AffineCompact2f

typedef Transform< double, 3, AffineCompact >	AffineCompact3d

typedef Transform< float, 3, AffineCompact >	AffineCompact3f

typedef DiagonalMatrix< double, 2 >	AlignedScaling2d

typedef DiagonalMatrix< float, 2 >	AlignedScaling2f

typedef DiagonalMatrix< double, 3 >	AlignedScaling3d

typedef DiagonalMatrix< float, 3 >	AlignedScaling3f

typedef AngleAxis< double >	AngleAxisd

typedef AngleAxis< float >	AngleAxisf

typedef int	BlasIndex

typedef std::complex< double >	dcomplex

typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE	DenseIndex

typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE	Index
	The Index type as used for the API. More...

typedef Transform< double, 2, Isometry >	Isometry2d

typedef Transform< float, 2, Isometry >	Isometry2f

typedef Transform< double, 3, Isometry >	Isometry3d

typedef Transform< float, 3, Isometry >	Isometry3f

typedef NonBlockingThreadPoolTempl< StlThreadEnvironment >	NonBlockingThreadPool

typedef Transform< double, 2, Projective >	Projective2d

typedef Transform< float, 2, Projective >	Projective2f

typedef Transform< double, 3, Projective >	Projective3d

typedef Transform< float, 3, Projective >	Projective3f

typedef Quaternion< double >	Quaterniond

typedef Quaternion< float >	Quaternionf

typedef Map< Quaternion< double >, Aligned >	QuaternionMapAlignedd

typedef Map< Quaternion< float >, Aligned >	QuaternionMapAlignedf

typedef Map< Quaternion< double >, 0 >	QuaternionMapd

typedef Map< Quaternion< float >, 0 >	QuaternionMapf

typedef Rotation2D< double >	Rotation2Dd

typedef Rotation2D< float >	Rotation2Df

typedef std::complex< float >	scomplex

typedef SimpleThreadPoolTempl< StlThreadEnvironment >	SimpleThreadPool

typedef Spline< double, 2 >	Spline2d
	2D double B-spline with dynamic degree. More...

typedef Spline< float, 2 >	Spline2f
	2D float B-spline with dynamic degree. More...

typedef Spline< double, 3 >	Spline3d
	3D double B-spline with dynamic degree. More...

typedef Spline< float, 3 >	Spline3f
	3D float B-spline with dynamic degree. More...

typedef Translation< double, 2 >	Translation2d

typedef Translation< float, 2 >	Translation2f

typedef Translation< double, 3 >	Translation3d

typedef Translation< float, 3 >	Translation3f

Enumerations
enum	{ Large = 2, Small = 3 }

enum	{ DontAlignCols = 1 }

enum	{ StreamPrecision = -1, FullPrecision = -2 }

enum	{ StandardCompressedFormat = 2 }

enum	{ NegationFlag = 0x01, ConjugationFlag = 0x02 }

enum	{ GlobalRealFlag = 0x01, GlobalImagFlag = 0x02, GlobalZeroFlag = 0x03 }

enum	{ IsSkyline = SkylineBit }

enum	{ SPD = 0x100, NonSymmetric = 0x0 }

enum	AccessorLevels { ReadOnlyAccessors, WriteAccessors, DirectAccessors, DirectWriteAccessors }

enum	Action { GetAction, SetAction }

enum	AdditionalProductEvaluationMode { SkylineTimeDenseProduct, SkylineTimeSkylineProduct, DenseTimeSkylineProduct }

enum	AlignmentType { Unaligned =0, Aligned8 =8, Aligned16 =16, Aligned32 =32, Aligned64 =64, Aligned128 =128, AlignedMask =255, Aligned =16, AlignedMax = Unaligned }

enum	AmbiVectorMode { IsDense = 0, IsSparse }

enum	CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt }

enum	ComputationInfo { Success = 0, NumericalIssue = 1, NoConvergence = 2, InvalidInput = 3 }

enum	CornerType { TopLeft, TopRight, BottomLeft, BottomRight }

enum	DecompositionOptions { Pivoting = 0x01, NoPivoting = 0x02, ComputeFullU = 0x04, ComputeThinU = 0x08, ComputeFullV = 0x10, ComputeThinV = 0x20, EigenvaluesOnly = 0x40, ComputeEigenvectors = 0x80, EigVecMask = EigenvaluesOnly \| ComputeEigenvectors, Ax_lBx = 0x100, ABx_lx = 0x200, BAx_lx = 0x400, GenEigMask = Ax_lBx \| ABx_lx \| BAx_lx }

enum	Default_t { Default }

enum	DirectionType { Vertical, Horizontal, BothDirections }

enum	EulerAxis { EULER_X = 1, EULER_Y = 2, EULER_Z = 3 }
	Representation of a fixed signed rotation axis for EulerSystem. More...

enum	FFTDirection { FFT_FORWARD = 0, FFT_REVERSE = 1 }

enum	FFTResultType { RealPart = 0, ImagPart = 1, BothParts = 2 }

enum	NoChange_t { NoChange }

enum	NumericalDiffMode { Forward, Central }

enum	PaddingType { PADDING_VALID = 1, PADDING_SAME = 2 }

enum	ProductImplType { DefaultProduct =0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct }

enum	QRPreconditioners { NoQRPreconditioner, HouseholderQRPreconditioner, ColPivHouseholderQRPreconditioner, FullPivHouseholderQRPreconditioner }

enum	Sequential_t { Sequential }

enum	SideType { OnTheLeft = 1, OnTheRight = 2 }

enum	SimplicialCholeskyMode { SimplicialCholeskyLLT, SimplicialCholeskyLDLT }

enum	SpecializedType { Specialized, BuiltIn }

enum	StorageOptions { ColMajor = 0, RowMajor = 0x1, AutoAlign = 0, DontAlign = 0x2 }

enum	TransformTraits { Isometry = 0x1, Affine = 0x2, AffineCompact = 0x10 \| Affine, Projective = 0x20 }

enum	TraversalType { DefaultTraversal, LinearTraversal, InnerVectorizedTraversal, LinearVectorizedTraversal, SliceVectorizedTraversal, InvalidTraversal, AllAtOnceTraversal }

enum	UnrollingType { NoUnrolling, InnerUnrolling, CompleteUnrolling }

enum	UpLoType { Lower =0x1, Upper =0x2, UnitDiag =0x4, ZeroDiag =0x8, UnitLower =UnitDiag\|Lower, UnitUpper =UnitDiag\|Upper, StrictlyLower =ZeroDiag\|Lower, StrictlyUpper =ZeroDiag\|Upper, SelfAdjoint =0x10, Symmetric =0x20 }

Functions
template<typename DerTypeA , typename DerTypeB >
const AutoDiffScalar< Matrix< typename internal::traits< typename internal::remove_all< DerTypeA >::type >::Scalar, Dynamic, 1 > >	atan2 (const AutoDiffScalar< DerTypeA > &a, const AutoDiffScalar< DerTypeB > &b)

template<typename ADerived , typename BDerived , typename XDerived >
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorCwiseTernaryOp< internal::scalar_betainc_op< typename XDerived::Scalar >, const ADerived, const BDerived, const XDerived >	betainc (const ADerived &a, const BDerived &b, const XDerived &x)

template<typename ArgADerived , typename ArgBDerived , typename ArgXDerived >
const Eigen::CwiseTernaryOp< Eigen::internal::scalar_betainc_op< typename ArgXDerived::Scalar >, const ArgADerived, const ArgBDerived, const ArgXDerived >	betainc (const Eigen::ArrayBase< ArgADerived > &a, const Eigen::ArrayBase< ArgBDerived > &b, const Eigen::ArrayBase< ArgXDerived > &x)

Box2d	bounding_box (const Vector2d &v)

template<typename Scalar , int Dim>
AlignedBox< Scalar, Dim >	bounding_box (const Matrix< Scalar, Dim, 1 > &v)

template<typename BVH , typename Intersector >
void	BVIntersect (const BVH &tree, Intersector &intersector)

template<typename BVH1 , typename BVH2 , typename Intersector >
void	BVIntersect (const BVH1 &tree1, const BVH2 &tree2, Intersector &intersector)

template<typename BVH , typename Minimizer >
Minimizer::Scalar	BVMinimize (const BVH &tree, Minimizer &minimizer)

template<typename BVH1 , typename BVH2 , typename Minimizer >
Minimizer::Scalar	BVMinimize (const BVH1 &tree1, const BVH2 &tree2, Minimizer &minimizer)

template<typename Polynomial >
NumTraits< typename Polynomial::Scalar >::Real	cauchy_max_bound (const Polynomial &poly)

template<typename Polynomial >
NumTraits< typename Polynomial::Scalar >::Real	cauchy_min_bound (const Polynomial &poly)

template<typename T1 , typename T2 >
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T1 &	choose (Cond< true >, const T1 &first, const T2 &)

template<typename T1 , typename T2 >
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T2 &	choose (Cond< false >, const T1 &, const T2 &second)

template<typename PointArrayType , typename KnotVectorType >
void	ChordLengths (const PointArrayType &pts, KnotVectorType &chord_lengths)
	Computes chord length parameters which are required for spline interpolation. More...

template<typename DerType >
const AutoDiffScalar< DerType > &	conj (const AutoDiffScalar< DerType > &x)

template<typename SplineType , typename DerivativeType >
void	derivativesImpl (const SplineType &spline, typename SplineType::Scalar u, DenseIndex order, DerivativeType &der)

template<typename Dims1 , typename Dims2 >
EIGEN_DEVICE_FUNC bool	dimensions_match (Dims1 &dims1, Dims2 &dims2)

template<typename T , typename X , typename Y >
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T	divup (const X x, const Y y)

template<typename T >
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T	divup (const T x, const T y)

void	dsaupd_ (int ido, char bmat, int n, char which, int nev, double tol, double resid, int ncv, double v, int ldv, int iparam, int ipntr, double workd, double workl, int lworkl, int info)

void	dseupd_ (int rvec, char All, int select, double d, double z, int ldz, double sigma, char bmat, int n, char which, int nev, double tol, double resid, int ncv, double v, int ldv, int iparam, int ipntr, double workd, double workl, int lworkl, int ierr)

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (abs, using std::abs;return Eigen::MakeAutoDiffScalar(abs(x.value()), x.derivatives()*(x.value()< 0?-1:1));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(abs2

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (sqrt, using std::sqrt;Scalar sqrtx=sqrt(x.value());return Eigen::MakeAutoDiffScalar(sqrtx, x.derivatives()*(Scalar(0.5)/sqrtx));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cos

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (sin, using std::sin;using std::cos;return Eigen::MakeAutoDiffScalar(sin(x.value()), x.derivatives()*cos(x.value()));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(exp

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (log, using std::log;return Eigen::MakeAutoDiffScalar(log(x.value()), x.derivatives()*(Scalar(1)/x.value()));) template< typename DerType > inline const Eigen

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (tan, using std::tan;using std::cos;return Eigen::MakeAutoDiffScalar(tan(x.value()), x.derivatives()*(Scalar(1)/numext::abs2(cos(x.value()))));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(asin

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (acos, using std::sqrt;using std::acos;return Eigen::MakeAutoDiffScalar(acos(x.value()), x.derivatives()*(Scalar(-1)/sqrt(1-numext::abs2(x.value()))));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(tanh

	EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY (sinh, using std::sinh;using std::cosh;return Eigen::MakeAutoDiffScalar(sinh(x.value()), x.derivatives()*cosh(x.value()));) EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY(cosh

template<typename Derived >
const	EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE (Derived, typename Derived::Scalar, pow) pow(const Eigen

template<typename Derived >
const	EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE (typename Derived::Scalar, Derived, pow) pow(const typename Derived

bool	getMarketHeader (const std::string &filename, int &sym, bool &iscomplex, bool &isvector)

template<typename VectorsType , typename CoeffsType >
HouseholderSequence< VectorsType, CoeffsType >	householderSequence (const VectorsType &v, const CoeffsType &h)
	Convenience function for constructing a Householder sequence. More...

template<typename Derived , typename ExponentDerived >
const Eigen::CwiseBinaryOp< Eigen::internal::scalar_igamma_op< typename Derived::Scalar >, const Derived, const ExponentDerived >	igamma (const Eigen::ArrayBase< Derived > &a, const Eigen::ArrayBase< ExponentDerived > &x)

template<typename Derived , typename ExponentDerived >
const Eigen::CwiseBinaryOp< Eigen::internal::scalar_igammac_op< typename Derived::Scalar >, const Derived, const ExponentDerived >	igammac (const Eigen::ArrayBase< Derived > &a, const Eigen::ArrayBase< ExponentDerived > &x)

template<typename DerType >
DerType::Scalar	imag (const AutoDiffScalar< DerType > &)

void	initParallel ()

template<typename KnotVectorType >
void	KnotAveraging (const KnotVectorType &parameters, DenseIndex degree, KnotVectorType &knots)
	Computes knot averages.The knots are computed as $\begin{align} u_0 & = \hdots = u_p = 0 \\ u_{m-p} & = \hdots = u_{m} = 1 \\ u_{j+p} & = \frac{1}{p}\sum_{i=j}^{j+p-1}\bar{u}_i \quad\quad j=1,\hdots,n-p \end{align}$ where is the degree and the number knots of the desired interpolating spline. More...

template<typename KnotVectorType , typename ParameterVectorType , typename IndexArray >
void	KnotAveragingWithDerivatives (const ParameterVectorType &parameters, const unsigned int degree, const IndexArray &derivativeIndices, KnotVectorType &knots)
	Computes knot averages when derivative constraints are present. Note that this is a technical interpretation of the referenced article since the algorithm contained therein is incorrect as written. More...

template<typename A , typename B >
KroneckerProduct< A, B >	kroneckerProduct (const MatrixBase< A > &a, const MatrixBase< B > &b)

template<typename A , typename B >
KroneckerProductSparse< A, B >	kroneckerProduct (const EigenBase< A > &a, const EigenBase< B > &b)

std::ptrdiff_t	l1CacheSize ()

std::ptrdiff_t	l2CacheSize ()

std::ptrdiff_t	l3CacheSize ()

template<typename SparseMatrixType >
bool	loadMarket (SparseMatrixType &mat, const std::string &filename)

template<typename VectorType >
bool	loadMarketVector (VectorType &vec, const std::string &filename)

template<typename NewDerType >
AutoDiffScalar< NewDerType >	MakeAutoDiffScalar (const typename NewDerType::Scalar &value, const NewDerType &der)

template<typename MatrixType , typename ResultType >
void	matrix_sqrt_quasi_triangular (const MatrixType &arg, ResultType &result)
	Compute matrix square root of quasi-triangular matrix. More...

template<typename MatrixType , typename ResultType >
void	matrix_sqrt_triangular (const MatrixType &arg, ResultType &result)
	Compute matrix square root of triangular matrix. More...

int	nbThreads ()

template<typename U , typename V >
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool	operator!= (const Tuple< U, V > &x, const Tuple< U, V > &y)

template<typename SparseDerived , typename PermDerived >
const Product< SparseDerived, PermDerived, AliasFreeProduct >	operator* (const SparseMatrixBase< SparseDerived > &matrix, const PermutationBase< PermDerived > &perm)

template<typename SparseDerived , typename PermDerived >
const Product< PermDerived, SparseDerived, AliasFreeProduct >	operator* (const PermutationBase< PermDerived > &perm, const SparseMatrixBase< SparseDerived > &matrix)

template<typename SparseDerived , typename PermutationType >
const Product< SparseDerived, Inverse< PermutationType >, AliasFreeProduct >	operator* (const SparseMatrixBase< SparseDerived > &matrix, const InverseImpl< PermutationType, PermutationStorage > &tperm)

template<typename SparseDerived , typename PermutationType >
const Product< Inverse< PermutationType >, SparseDerived, AliasFreeProduct >	operator* (const InverseImpl< PermutationType, PermutationStorage > &tperm, const SparseMatrixBase< SparseDerived > &matrix)

template<typename MatrixDerived , typename TranspositionsDerived >
EIGEN_DEVICE_FUNC const Product< MatrixDerived, TranspositionsDerived, AliasFreeProduct >	operator* (const MatrixBase< MatrixDerived > &matrix, const TranspositionsBase< TranspositionsDerived > &transpositions)

template<typename TranspositionsDerived , typename MatrixDerived >
EIGEN_DEVICE_FUNC const Product< TranspositionsDerived, MatrixDerived, AliasFreeProduct >	operator* (const TranspositionsBase< TranspositionsDerived > &transpositions, const MatrixBase< MatrixDerived > &matrix)

template<typename OtherDerived , typename VectorsType , typename CoeffsType , int Side>
internal::matrix_type_times_scalar_type< typename VectorsType::Scalar, OtherDerived >::Type	operator* (const MatrixBase< OtherDerived > &other, const HouseholderSequence< VectorsType, CoeffsType, Side > &h)
	Computes the product of a matrix with a Householder sequence. More...

template<typename MatrixDerived , typename PermutationDerived >
EIGEN_DEVICE_FUNC const Product< MatrixDerived, PermutationDerived, AliasFreeProduct >	operator* (const MatrixBase< MatrixDerived > &matrix, const PermutationBase< PermutationDerived > &permutation)

template<typename PermutationDerived , typename MatrixDerived >
EIGEN_DEVICE_FUNC const Product< PermutationDerived, MatrixDerived, AliasFreeProduct >	operator* (const PermutationBase< PermutationDerived > &permutation, const MatrixBase< MatrixDerived > &matrix)

template<typename DenseDerived , typename SparseDerived >
EIGEN_STRONG_INLINE const CwiseBinaryOp< internal::scalar_sum_op< typename DenseDerived::Scalar, typename SparseDerived::Scalar >, const DenseDerived, const SparseDerived >	operator+ (const MatrixBase< DenseDerived > &a, const SparseMatrixBase< SparseDerived > &b)

template<typename SparseDerived , typename DenseDerived >
EIGEN_STRONG_INLINE const CwiseBinaryOp< internal::scalar_sum_op< typename SparseDerived::Scalar, typename DenseDerived::Scalar >, const SparseDerived, const DenseDerived >	operator+ (const SparseMatrixBase< SparseDerived > &a, const MatrixBase< DenseDerived > &b)

template<typename DenseDerived , typename SparseDerived >
EIGEN_STRONG_INLINE const CwiseBinaryOp< internal::scalar_difference_op< typename DenseDerived::Scalar, typename SparseDerived::Scalar >, const DenseDerived, const SparseDerived >	operator- (const MatrixBase< DenseDerived > &a, const SparseMatrixBase< SparseDerived > &b)

template<typename SparseDerived , typename DenseDerived >
EIGEN_STRONG_INLINE const CwiseBinaryOp< internal::scalar_difference_op< typename SparseDerived::Scalar, typename DenseDerived::Scalar >, const SparseDerived, const DenseDerived >	operator- (const SparseMatrixBase< SparseDerived > &a, const MatrixBase< DenseDerived > &b)

template<typename T >
std::ostream &	operator<< (std::ostream &os, const TensorBase< T, ReadOnlyAccessors > &expr)

template<typename U , typename V >
EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool	operator== (const Tuple< U, V > &x, const Tuple< U, V > &y)

template<class T , std::size_t N>
EIGEN_DEVICE_FUNC bool	operator== (const array< T, N > &lhs, const array< T, N > &rhs)

template<typename Polynomials , typename T >
T	poly_eval (const Polynomials &poly, const T &x)

template<typename Polynomials , typename T >
T	poly_eval_horner (const Polynomials &poly, const T &x)

template<typename DerivedN , typename DerivedX >
const Eigen::CwiseBinaryOp< Eigen::internal::scalar_polygamma_op< typename DerivedX::Scalar >, const DerivedN, const DerivedX >	polygamma (const Eigen::ArrayBase< DerivedN > &n, const Eigen::ArrayBase< DerivedX > &x)

template<typename DerType >
const AutoDiffScalar< DerType > &	real (const AutoDiffScalar< DerType > &x)

	return (x<=y?ADS(x):ADS(y))

	return (x >=y?ADS(x):ADS(y))

	return (x< y?ADS(x):ADS(y))

	return (x > y?ADS(x):ADS(y))

template<typename VectorsType , typename CoeffsType >
HouseholderSequence< VectorsType, CoeffsType, OnTheRight >	rightHouseholderSequence (const VectorsType &v, const CoeffsType &h)
	Convenience function for constructing a Householder sequence. More...

template<typename RootVector , typename Polynomial >
void	roots_to_monicPolynomial (const RootVector &rv, Polynomial &poly)

template<typename SparseMatrixType >
bool	saveMarket (const SparseMatrixType &mat, const std::string &filename, int sym=0)

template<typename VectorType >
bool	saveMarketVector (const VectorType &vec, const std::string &filename)

UniformScaling< float >	Scaling (float s)

UniformScaling< double >	Scaling (double s)

template<typename RealScalar >
UniformScaling< std::complex< RealScalar > >	Scaling (const std::complex< RealScalar > &s)

template<typename Scalar >
DiagonalMatrix< Scalar, 2 >	Scaling (const Scalar &sx, const Scalar &sy)

template<typename Scalar >
DiagonalMatrix< Scalar, 3 >	Scaling (const Scalar &sx, const Scalar &sy, const Scalar &sz)

template<typename Derived >
const DiagonalWrapper< const Derived >	Scaling (const MatrixBase< Derived > &coeffs)

void	setCpuCacheSizes (std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3)

void	setNbThreads (int v)

void	ssaupd_ (int ido, char bmat, int n, char which, int nev, float tol, float resid, int ncv, float v, int ldv, int iparam, int ipntr, float workd, float workl, int lworkl, int info)

void	sseupd_ (int rvec, char All, int select, float d, float z, int ldz, float sigma, char bmat, int n, char which, int nev, float tol, float resid, int ncv, float v, int ldv, int iparam, int ipntr, float workd, float workl, int lworkl, int ierr)

template<typename T , typename Derived >
T	test_relative_error (const AlignedVector3< T > &a, const MatrixBase< Derived > &b)

template<typename Derived , typename OtherDerived >
internal::umeyama_transform_matrix_type< Derived, OtherDerived >::type	umeyama (const MatrixBase< Derived > &src, const MatrixBase< OtherDerived > &dst, bool with_scaling=true)
	Returns the transformation between two point sets. More...

void	umfpack_defaults (double control[UMFPACK_CONTROL], double)

void	umfpack_defaults (double control[UMFPACK_CONTROL], std::complex< double >)

void	umfpack_free_numeric (void **Numeric, double)

void	umfpack_free_numeric (void **Numeric, std::complex< double >)

void	umfpack_free_symbolic (void **Symbolic, double)

void	umfpack_free_symbolic (void **Symbolic, std::complex< double >)

int	umfpack_get_determinant (double Mx, double Ex, void *NumericHandle, double User_Info[UMFPACK_INFO])

int	umfpack_get_determinant (std::complex< double > Mx, double Ex, void *NumericHandle, double User_Info[UMFPACK_INFO])

int	umfpack_get_lunz (int lnz, int unz, int n_row, int n_col, int nz_udiag, void Numeric, double)

int	umfpack_get_lunz (int lnz, int unz, int n_row, int n_col, int nz_udiag, void Numeric, std::complex< double >)

int	umfpack_get_numeric (int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], int P[], int Q[], double Dx[], int do_recip, double Rs[], void Numeric)

int	umfpack_get_numeric (int Lp[], int Lj[], std::complex< double > Lx[], int Up[], int Ui[], std::complex< double > Ux[], int P[], int Q[], std::complex< double > Dx[], int do_recip, double Rs[], void Numeric)

int	umfpack_numeric (const int Ap[], const int Ai[], const double Ax[], void Symbolic, void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

int	umfpack_numeric (const int Ap[], const int Ai[], const std::complex< double > Ax[], void Symbolic, void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

void	umfpack_report_control (double control[UMFPACK_CONTROL], double)

void	umfpack_report_control (double control[UMFPACK_CONTROL], std::complex< double >)

void	umfpack_report_info (double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double)

void	umfpack_report_info (double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex< double >)

void	umfpack_report_status (double control[UMFPACK_CONTROL], int status, double)

void	umfpack_report_status (double control[UMFPACK_CONTROL], int status, std::complex< double >)

int	umfpack_solve (int sys, const int Ap[], const int Ai[], const double Ax[], double X[], const double B[], void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

int	umfpack_solve (int sys, const int Ap[], const int Ai[], const std::complex< double > Ax[], std::complex< double > X[], const std::complex< double > B[], void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

int	umfpack_symbolic (int n_row, int n_col, const int Ap[], const int Ai[], const double Ax[], void **Symbolic, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

int	umfpack_symbolic (int n_row, int n_col, const int Ap[], const int Ai[], const std::complex< double > Ax[], void **Symbolic, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])

template<typename _Scalar , int _Options, typename _StorageIndex >
cholmod_sparse	viewAsCholmod (Ref< SparseMatrix< _Scalar, _Options, _StorageIndex > > mat)

template<typename _Scalar , int _Options, typename _Index >
const cholmod_sparse	viewAsCholmod (const SparseMatrix< _Scalar, _Options, _Index > &mat)

template<typename _Scalar , int _Options, typename _Index >
const cholmod_sparse	viewAsCholmod (const SparseVector< _Scalar, _Options, _Index > &mat)

template<typename _Scalar , int _Options, typename _Index , unsigned int UpLo>
cholmod_sparse	viewAsCholmod (const SparseSelfAdjointView< const SparseMatrix< _Scalar, _Options, _Index >, UpLo > &mat)

template<typename Derived >
cholmod_dense	viewAsCholmod (MatrixBase< Derived > &mat)

template<typename Scalar , int Flags, typename StorageIndex >
MappedSparseMatrix< Scalar, Flags, StorageIndex >	viewAsEigen (cholmod_sparse &cm)

template<typename DerivedX , typename DerivedQ >
const Eigen::CwiseBinaryOp< Eigen::internal::scalar_zeta_op< typename DerivedX::Scalar >, const DerivedX, const DerivedQ >	zeta (const Eigen::ArrayBase< DerivedX > &x, const Eigen::ArrayBase< DerivedQ > &q)

Variables
const unsigned int	ActualPacketAccessBit = 0x0

EIGEN_DEPRECATED const unsigned int	AlignedBit = 0x80

const int	CoherentAccessPattern = 0x1

const unsigned int	CompressedAccessBit = 0x400

const unsigned int	DirectAccessBit = 0x40

const int	Dynamic = -1

const int	DynamicIndex = 0xffffff

EIGEN_DEPRECATED const unsigned int	EvalBeforeAssigningBit = 0x4

const unsigned int	EvalBeforeNestingBit = 0x2

Scalar	expx = exp(x.value())

const unsigned int	HereditaryBits

const int	HugeCost = 10000

const int	Infinity = -1

const int	InnerRandomAccessPattern = 0x2 \| CoherentAccessPattern

const unsigned int	LinearAccessBit = 0x10

const unsigned int	LvalueBit = 0x20

const unsigned int	NestByRefBit = 0x100

const unsigned int	NoPreferredStorageOrderBit = 0x200

const int	OuterRandomAccessPattern = 0x4 \| CoherentAccessPattern

const unsigned int	PacketAccessBit = 0x8

const int	RandomAccessPattern = 0x8 \| OuterRandomAccessPattern \| InnerRandomAccessPattern

const unsigned int	RowMajorBit = 0x1

const unsigned int	SkylineBit = 0x1200

const T &	y

Typedef Documentation

typedef int Eigen::BlasIndex

Definition at line 119 of file MKL_support.h.

typedef std::complex<double> Eigen::dcomplex

Definition at line 113 of file MKL_support.h.

typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Eigen::DenseIndex

Definition at line 25 of file Meta.h.

typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Eigen::Index

The Index type as used for the API.

To change this, #define the preprocessor symbol EIGEN_DEFAULT_DENSE_INDEX_TYPE.

See also: TopicPreprocessorDirectives, StorageIndex.

Examples:: /tmp/ws/src/hebiros/hebiros/include/hebi/Eigen/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h.

Definition at line 33 of file Meta.h.

typedef NonBlockingThreadPoolTempl<StlThreadEnvironment> Eigen::NonBlockingThreadPool

Definition at line 270 of file NonBlockingThreadPool.h.

typedef std::complex<float> Eigen::scomplex

Definition at line 114 of file MKL_support.h.

typedef SimpleThreadPoolTempl<StlThreadEnvironment> Eigen::SimpleThreadPool

Definition at line 150 of file SimpleThreadPool.h.

typedef Spline<double,2> Eigen::Spline2d

2D double B-spline with dynamic degree.

Definition at line 87 of file SplineFwd.h.

typedef Spline<float,2> Eigen::Spline2f

2D float B-spline with dynamic degree.

Definition at line 81 of file SplineFwd.h.

typedef Spline<double,3> Eigen::Spline3d

3D double B-spline with dynamic degree.

Definition at line 90 of file SplineFwd.h.

typedef Spline<float,3> Eigen::Spline3f

3D float B-spline with dynamic degree.

Definition at line 84 of file SplineFwd.h.

Enumeration Type Documentation

anonymous enum

Enumerator
Large
Small

Definition at line 16 of file GeneralProduct.h.

anonymous enum

Enumerator
DontAlignCols

Definition at line 16 of file IO.h.

anonymous enum

Enumerator
StreamPrecision
FullPrecision

Definition at line 17 of file IO.h.

anonymous enum

Enumerator
StandardCompressedFormat	used by Ref<SparseMatrix> to specify whether the input storage must be in standard compressed form

Definition at line 15 of file SparseRef.h.

anonymous enum

Enumerator
NegationFlag
ConjugationFlag

Definition at line 15 of file Symmetry.h.

anonymous enum

Enumerator
GlobalRealFlag
GlobalImagFlag
GlobalZeroFlag

Definition at line 20 of file Symmetry.h.

anonymous enum

Enumerator
IsSkyline

Definition at line 24 of file SkylineUtil.h.

anonymous enum

Enumerator
SPD
NonSymmetric

Definition at line 16 of file MatrixMarketIterator.h.

enum Eigen::Action

Enumerator
GetAction
SetAction

Definition at line 488 of file Constants.h.

enum Eigen::AdditionalProductEvaluationMode

Enumerator
SkylineTimeDenseProduct
SkylineTimeSkylineProduct
DenseTimeSkylineProduct

Definition at line 23 of file SkylineUtil.h.

enum Eigen::AmbiVectorMode

Enumerator
IsDense
IsSparse

Definition at line 356 of file Constants.h.

enum Eigen::CholmodMode

Enumerator
CholmodAuto
CholmodSimplicialLLt
CholmodSupernodalLLt
CholmodLDLt

Definition at line 162 of file CholmodSupport.h.

enum Eigen::Default_t

Enumerator
Default

Definition at line 352 of file Constants.h.

enum Eigen::EulerAxis

Representation of a fixed signed rotation axis for EulerSystem.

Values here represent:

The axis of the rotation: X, Y or Z.
The sign (i.e. direction of the rotation along the axis): positive(+) or negative(-)

Therefore, this could express all the axes {+X,+Y,+Z,-X,-Y,-Z}

For positive axis, use +EULER_{axis}, and for negative axis use -EULER_{axis}.

Enumerator
EULER_X	the X axis
EULER_Y	the Y axis
EULER_Z	the Z axis

Definition at line 55 of file EulerSystem.h.

enum Eigen::FFTDirection

Enumerator
FFT_FORWARD
FFT_REVERSE

Definition at line 82 of file TensorForwardDeclarations.h.

enum Eigen::FFTResultType

Enumerator
RealPart
ImagPart
BothParts

Definition at line 76 of file TensorForwardDeclarations.h.

enum Eigen::NoChange_t

Enumerator
NoChange

Definition at line 350 of file Constants.h.

enum Eigen::NumericalDiffMode

Enumerator
Forward
Central

Definition at line 18 of file NumericalDiff.h.

enum Eigen::PaddingType

Enumerator
PADDING_VALID
PADDING_SAME

Definition at line 265 of file TensorTraits.h.

enum Eigen::ProductImplType

Enumerator
DefaultProduct
LazyProduct
AliasFreeProduct
CoeffBasedProductMode
LazyCoeffBasedProductMode
OuterProduct
InnerProduct
GemvProduct
GemmProduct

Definition at line 483 of file Constants.h.

enum Eigen::Sequential_t

Enumerator
Sequential

Definition at line 351 of file Constants.h.

enum Eigen::SimplicialCholeskyMode

Enumerator
SimplicialCholeskyLLT
SimplicialCholeskyLDLT

Definition at line 15 of file SimplicialCholesky.h.

enum Eigen::SpecializedType

Enumerator
Specialized
BuiltIn

Definition at line 310 of file Constants.h.

enum Eigen::TraversalType

Enumerator
DefaultTraversal
LinearTraversal
InnerVectorizedTraversal
LinearVectorizedTraversal
SliceVectorizedTraversal
InvalidTraversal
AllAtOnceTraversal

Definition at line 276 of file Constants.h.

enum Eigen::UnrollingType

Enumerator
NoUnrolling
InnerUnrolling
CompleteUnrolling

Definition at line 298 of file Constants.h.

Function Documentation

template<typename DerTypeA , typename DerTypeB >

const AutoDiffScalar<Matrix<typename internal::traits<typename internal::remove_all<DerTypeA>::type>::Scalar,Dynamic,1> > Eigen::atan2	(	const AutoDiffScalar< DerTypeA > &	a,
		const AutoDiffScalar< DerTypeB > &	b
	)

inline

Definition at line 622 of file AutoDiffScalar.h.

template<typename ADerived , typename BDerived , typename XDerived >

EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorCwiseTernaryOp<internal::scalar_betainc_op<typename XDerived::Scalar>, const ADerived, const BDerived, const XDerived> Eigen::betainc	(	const ADerived &	a,
		const BDerived &	b,
		const XDerived &	x
	)

Returns: an expression of the coefficient-wise betainc(x, a, b) to the given tensors.

This function computes the regularized incomplete beta function (integral).

Definition at line 24 of file TensorGlobalFunctions.h.

template<typename ArgADerived , typename ArgBDerived , typename ArgXDerived >

const Eigen::CwiseTernaryOp<Eigen::internal::scalar_betainc_op<typename ArgXDerived::Scalar>, const ArgADerived, const ArgBDerived, const ArgXDerived> Eigen::betainc	(	const Eigen::ArrayBase< ArgADerived > &	a,
		const Eigen::ArrayBase< ArgBDerived > &	b,
		const Eigen::ArrayBase< ArgXDerived > &	x
	)

inline

Returns: an expression of the coefficient-wise betainc(x, a, b) to the given arrays.

This function computes the regularized incomplete beta function (integral).

Note: This function supports only float and double scalar types in c++11 mode. To support other scalar types, or float/double in non c++11 mode, the user has to provide implementations of betainc(T,T,T) for any scalar type T to be supported.

See also: Eigen::betainc(), Eigen::lgamma()

Definition at line 90 of file SpecialFunctionsArrayAPI.h.

Box2d Eigen::bounding_box ( const Vector2d & v )

Definition at line 9 of file BVH_Example.cpp.

template<typename Scalar , int Dim>

AlignedBox<Scalar, Dim> Eigen::bounding_box ( const Matrix< Scalar, Dim, 1 > & v )

Definition at line 17 of file BVH.cpp.

template<typename BVH , typename Intersector >

void Eigen::BVIntersect	(	const BVH &	tree,
		Intersector &	intersector
	)

Given a BVH, runs the query encapsulated by intersector. The Intersector type must provide the following members:

bool intersectVolume(const BVH::Volume &volume) //returns true if volume intersects the query

bool intersectObject(const BVH::Object &object) //returns true if the search should terminate immediately

Definition at line 79 of file BVAlgorithms.h.

template<typename BVH1 , typename BVH2 , typename Intersector >

void Eigen::BVIntersect	(	const BVH1 &	tree1,
		const BVH2 &	tree2,
		Intersector &	intersector
	)

Given two BVH's, runs the query on their Cartesian product encapsulated by intersector. The Intersector type must provide the following members:

bool intersectVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2) //returns true if product of volumes intersects the query
bool intersectVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2) //returns true if the volume-object product intersects the query
bool intersectObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2) //returns true if the volume-object product intersects the query
bool intersectObjectObject(const BVH1::Object &o1, const BVH2::Object &o2) //returns true if the search should terminate immediately

Definition at line 93 of file BVAlgorithms.h.

template<typename BVH , typename Minimizer >

Minimizer::Scalar Eigen::BVMinimize	(	const BVH &	tree,
		Minimizer &	minimizer
	)

Given a BVH, runs the query encapsulated by minimizer.

Returns: the minimum value. The Minimizer type must provide the following members:
typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
Scalar minimumOnVolume(const BVH::Volume &volume)
Scalar minimumOnObject(const BVH::Object &object)

Definition at line 219 of file BVAlgorithms.h.

template<typename BVH1 , typename BVH2 , typename Minimizer >

Minimizer::Scalar Eigen::BVMinimize	(	const BVH1 &	tree1,
		const BVH2 &	tree2,
		Minimizer &	minimizer
	)

Given two BVH's, runs the query on their cartesian product encapsulated by minimizer.

Returns: the minimum value. The Minimizer type must provide the following members:
typedef Scalar //the numeric type of what is being minimized--not necessarily the Scalar type of the BVH (if it has one)
Scalar minimumOnVolumeVolume(const BVH1::Volume &v1, const BVH2::Volume &v2)
Scalar minimumOnVolumeObject(const BVH1::Volume &v1, const BVH2::Object &o2)
Scalar minimumOnObjectVolume(const BVH1::Object &o1, const BVH2::Volume &v2)
Scalar minimumOnObjectObject(const BVH1::Object &o1, const BVH2::Object &o2)

Definition at line 235 of file BVAlgorithms.h.

template<typename Polynomial >

NumTraits<typename Polynomial::Scalar>::Real Eigen::cauchy_max_bound ( const Polynomial & poly )

inline

Returns: a maximum bound for the absolute value of any root of the polynomial.

Parameters

[in] poly : the vector of coefficients of the polynomial ordered by degrees i.e. poly[i] is the coefficient of degree i of the polynomial e.g. is stored as a vector .

Precondition: the leading coefficient of the input polynomial poly must be non zero

Definition at line 75 of file PolynomialUtils.h.

template<typename Polynomial >

NumTraits<typename Polynomial::Scalar>::Real Eigen::cauchy_min_bound ( const Polynomial & poly )

inline

Returns: a minimum bound for the absolute value of any non zero root of the polynomial.

Parameters

[in] poly : the vector of coefficients of the polynomial ordered by degrees i.e. poly[i] is the coefficient of degree i of the polynomial e.g. is stored as a vector .

Definition at line 98 of file PolynomialUtils.h.

template<typename T1 , typename T2 >

EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T1& Eigen::choose	(	Cond< true >	,
		const T1 &	first,
		const T2 &
	)

Definition at line 18 of file TensorMeta.h.

template<typename T1 , typename T2 >

EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const T2& Eigen::choose	(	Cond< false >	,
		const T1 &	,
		const T2 &	second
	)

Definition at line 23 of file TensorMeta.h.

template<typename PointArrayType , typename KnotVectorType >

void Eigen::ChordLengths	(	const PointArrayType &	pts,
		KnotVectorType &	chord_lengths
	)

Computes chord length parameters which are required for spline interpolation.

Parameters

[in]	pts	The data points to which a spline should be fit.
[out]	chord_lengths	The resulting chord lenggth vector.

See also: Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data

Definition at line 189 of file SplineFitting.h.

template<typename DerType >

const AutoDiffScalar<DerType>& Eigen::conj ( const AutoDiffScalar< DerType > & x )

inline

Definition at line 542 of file AutoDiffScalar.h.

template<typename SplineType , typename DerivativeType >

void Eigen::derivativesImpl	(	const SplineType &	spline,
		typename SplineType::Scalar	u,
		DenseIndex	order,
		DerivativeType &	der
	)

Definition at line 313 of file Spline.h.

template<typename Dims1 , typename Dims2 >

EIGEN_DEVICE_FUNC bool Eigen::dimensions_match	(	Dims1 &	dims1,
		Dims2 &	dims2
	)

Definition at line 422 of file TensorDimensions.h.

template<typename T , typename X , typename Y >

EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T Eigen::divup	(	const X	x,
		const Y	y
	)

Definition at line 30 of file TensorMeta.h.

template<typename T >

EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T Eigen::divup	(	const T	x,
		const T	y
	)

Definition at line 36 of file TensorMeta.h.

void Eigen::dsaupd_	(	int *	ido,
		char *	bmat,
		int *	n,
		char *	which,
		int *	nev,
		double *	tol,
		double *	resid,
		int *	ncv,
		double *	v,
		int *	ldv,
		int *	iparam,
		int *	ipntr,
		double *	workd,
		double *	workl,
		int *	lworkl,
		int *	info
	)

void Eigen::dseupd_	(	int *	rvec,
		char *	All,
		int *	select,
		double *	d,
		double *	z,
		int *	ldz,
		double *	sigma,
		char *	bmat,
		int *	n,
		char *	which,
		int *	nev,
		double *	tol,
		double *	resid,
		int *	ncv,
		double *	v,
		int *	ldv,
		int *	iparam,
		int *	ipntr,
		double *	workd,
		double *	workl,
		int *	lworkl,
		int *	ierr
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	abs	,
		using std::abs;return Eigen::MakeAutoDiffScalar(abs(x.value()), x.derivatives()*(x.value()< 0?-1:1));
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	sqrt	,
		using std::sqrt;Scalar	sqrtx = `sqrt(x.value()); return Eigen::MakeAutoDiffScalar(sqrtx,x.derivatives() * (Scalar(0.5) / sqrtx));`
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	sin	,
		using std::sin;using std::cos;return Eigen::MakeAutoDiffScalar(sin(x.value()), x.derivatives()*cos(x.value()));
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	log	,
		using std::log;return Eigen::MakeAutoDiffScalar(log(x.value()), x.derivatives()*(Scalar(1)/x.value()));
	)		const

Definition at line 605 of file AutoDiffScalar.h.

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	tan	,
		using std::tan;using std::cos;return Eigen::MakeAutoDiffScalar(tan(x.value()), x.derivatives()*(Scalar(1)/numext::abs2(cos(x.value()))));
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	acos	,
		using std::sqrt;using std::acos;return Eigen::MakeAutoDiffScalar(acos(x.value()), x.derivatives()*(Scalar(-1)/sqrt(1-numext::abs2(x.value()))));
	)

Eigen::EIGEN_AUTODIFF_DECLARE_GLOBAL_UNARY	(	sinh	,
		using std::sinh;using std::cosh;return Eigen::MakeAutoDiffScalar(sinh(x.value()), x.derivatives()*cosh(x.value()));
	)

template<typename Derived >

const Eigen::EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE	(	Derived	,
		typename Derived::Scalar	,
		pow
	)		const

inline

Definition at line 113 of file GlobalFunctions.h.

template<typename Derived >

const Eigen::EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE	(	typename Derived::Scalar	,
		Derived	,
		pow
	)		const

inline

Definition at line 168 of file GlobalFunctions.h.

bool Eigen::getMarketHeader	(	const std::string &	filename,
		int &	sym,
		bool &	iscomplex,
		bool &	isvector
	)

inline

Definition at line 109 of file MarketIO.h.

template<typename VectorsType , typename CoeffsType >

HouseholderSequence<VectorsType,CoeffsType> Eigen::householderSequence	(	const VectorsType &	v,
		const CoeffsType &	h
	)

Convenience function for constructing a Householder sequence.

\

Returns: A HouseholderSequence constructed from the specified arguments.

Definition at line 451 of file HouseholderSequence.h.

template<typename Derived , typename ExponentDerived >

const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igamma_op<typename Derived::Scalar>, const Derived, const ExponentDerived> Eigen::igamma	(	const Eigen::ArrayBase< Derived > &	a,
		const Eigen::ArrayBase< ExponentDerived > &	x
	)

inline

Returns: an expression of the coefficient-wise igamma(a, x) to the given arrays.

This function computes the coefficient-wise incomplete gamma function.

Note: This function supports only float and double scalar types in c++11 mode. To support other scalar types, or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar type T to be supported.

See also: Eigen::igammac(), Eigen::lgamma()

Definition at line 28 of file SpecialFunctionsArrayAPI.h.

template<typename Derived , typename ExponentDerived >

const Eigen::CwiseBinaryOp<Eigen::internal::scalar_igammac_op<typename Derived::Scalar>, const Derived, const ExponentDerived> Eigen::igammac	(	const Eigen::ArrayBase< Derived > &	a,
		const Eigen::ArrayBase< ExponentDerived > &	x
	)

inline

Returns: an expression of the coefficient-wise igammac(a, x) to the given arrays.

This function computes the coefficient-wise complementary incomplete gamma function.

Note: This function supports only float and double scalar types in c++11 mode. To support other scalar types, or float/double in non c++11 mode, the user has to provide implementations of igammac(T,T) for any scalar type T to be supported.

See also: Eigen::igamma(), Eigen::lgamma()

Definition at line 48 of file SpecialFunctionsArrayAPI.h.

template<typename DerType >

DerType::Scalar Eigen::imag ( const AutoDiffScalar< DerType > & )

inline

Definition at line 546 of file AutoDiffScalar.h.

void Eigen::initParallel ( )

inline

Must be call first when calling Eigen from multiple threads

Definition at line 48 of file Parallelizer.h.

template<typename KnotVectorType >

void Eigen::KnotAveraging	(	const KnotVectorType &	parameters,
		DenseIndex	degree,
		KnotVectorType &	knots
	)

Computes knot averages.The knots are computed as

$\begin{align*} u_0 & = \hdots = u_p = 0 \\ u_{m-p} & = \hdots = u_{m} = 1 \\ u_{j+p} & = \frac{1}{p}\sum_{i=j}^{j+p-1}\bar{u}_i \quad\quad j=1,\hdots,n-p \end{align*}$

where $p$ is the degree and $m+1$ the number knots of the desired interpolating spline.

Parameters

[in]	parameters	The input parameters. During interpolation one for each data point.
[in]	degree	The spline degree which is used during the interpolation.
[out]	knots	The output knot vector.

See also: Les Piegl and Wayne Tiller, The NURBS book (2nd ed.), 1997, 9.2.1 Global Curve Interpolation to Point Data

Definition at line 45 of file SplineFitting.h.

template<typename KnotVectorType , typename ParameterVectorType , typename IndexArray >

void Eigen::KnotAveragingWithDerivatives	(	const ParameterVectorType &	parameters,
		const unsigned int	degree,
		const IndexArray &	derivativeIndices,
		KnotVectorType &	knots
	)

Computes knot averages when derivative constraints are present. Note that this is a technical interpretation of the referenced article since the algorithm contained therein is incorrect as written.

Parameters

[in]	parameters	The parameters at which the interpolation B-Spline will intersect the given interpolation points. The parameters are assumed to be a non-decreasing sequence.
[in]	degree	The degree of the interpolating B-Spline. This must be greater than zero.
[in]	derivativeIndices	The indices corresponding to parameters at which there are derivative constraints. The indices are assumed to be a non-decreasing sequence.
[out]	knots	The calculated knot vector. These will be returned as a non-decreasing sequence

See also: Les A. Piegl, Khairan Rajab, Volha Smarodzinana. 2008. Curve interpolation with directional constraints for engineering design. Engineering with Computers

Definition at line 78 of file SplineFitting.h.

template<typename A , typename B >

KroneckerProduct<A,B> Eigen::kroneckerProduct	(	const MatrixBase< A > &	a,
		const MatrixBase< B > &	b
	)

Computes Kronecker tensor product of two dense matrices

Warning: If you want to replace a matrix by its Kronecker product with some matrix, do NOT do this:
A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
instead, use eval() to work around this:
A = kroneckerProduct(A,B).eval();

Parameters

a	Dense matrix a
b	Dense matrix b

Returns: Kronecker tensor product of a and b

Definition at line 271 of file KroneckerTensorProduct.h.

template<typename A , typename B >

KroneckerProductSparse<A,B> Eigen::kroneckerProduct	(	const EigenBase< A > &	a,
		const EigenBase< B > &	b
	)

Computes Kronecker tensor product of two matrices, at least one of which is sparse

Warning: If you want to replace a matrix by its Kronecker product with some matrix, do NOT do this:
A = kroneckerProduct(A,B); // bug!!! caused by aliasing effect
instead, use eval() to work around this:
A = kroneckerProduct(A,B).eval();

Parameters

a	Dense/sparse matrix a
b	Dense/sparse matrix b

Returns: Kronecker tensor product of a and b, stored in a sparse matrix

Definition at line 298 of file KroneckerTensorProduct.h.

std::ptrdiff_t Eigen::l1CacheSize ( )

inline

Returns: the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.

See also: setCpuCacheSize

Definition at line 2111 of file GeneralBlockPanelKernel.h.

std::ptrdiff_t Eigen::l2CacheSize ( )

inline

Returns: the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.

See also: setCpuCacheSize

Definition at line 2120 of file GeneralBlockPanelKernel.h.

std::ptrdiff_t Eigen::l3CacheSize ( )

inline

Returns: the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size paramete\ rs.

See also: setCpuCacheSize

Definition at line 2130 of file GeneralBlockPanelKernel.h.

template<typename SparseMatrixType >

bool Eigen::loadMarket	(	SparseMatrixType &	mat,
		const std::string &	filename
	)

Definition at line 133 of file MarketIO.h.

template<typename VectorType >

bool Eigen::loadMarketVector	(	VectorType &	vec,
		const std::string &	filename
	)

Definition at line 193 of file MarketIO.h.

template<typename NewDerType >

AutoDiffScalar<NewDerType> Eigen::MakeAutoDiffScalar	(	const typename NewDerType::Scalar &	value,
		const NewDerType &	der
	)

inline

Definition at line 36 of file AutoDiffScalar.h.

template<typename MatrixType , typename ResultType >

void Eigen::matrix_sqrt_quasi_triangular	(	const MatrixType &	arg,
		ResultType &	result
	)

Compute matrix square root of quasi-triangular matrix.

Template Parameters

MatrixType	type of `arg`, the argument of matrix square root, expected to be an instantiation of the Matrix class template.
ResultType	type of `result`, where result is to be stored.

Parameters

[in]	arg	argument of matrix square root.
[out]	result	matrix square root of upper Hessenberg part of `arg`.

This function computes the square root of the upper quasi-triangular matrix stored in the upper Hessenberg part of arg. Only the upper Hessenberg part of result is updated, the rest is not touched. See MatrixBase::sqrt() for details on how this computation is implemented.

See also: MatrixSquareRoot, MatrixSquareRootQuasiTriangular

Definition at line 182 of file MatrixSquareRoot.h.

template<typename MatrixType , typename ResultType >

void Eigen::matrix_sqrt_triangular	(	const MatrixType &	arg,
		ResultType &	result
	)

Compute matrix square root of triangular matrix.

Template Parameters

MatrixType	type of `arg`, the argument of matrix square root, expected to be an instantiation of the Matrix class template.
ResultType	type of `result`, where result is to be stored.

Parameters

[in]	arg	argument of matrix square root.
[out]	result	matrix square root of upper triangular part of `arg`.

Only the upper triangular part (including the diagonal) of result is updated, the rest is not touched. See MatrixBase::sqrt() for details on how this computation is implemented.

See also: MatrixSquareRoot, MatrixSquareRootQuasiTriangular

Definition at line 206 of file MatrixSquareRoot.h.

int Eigen::nbThreads ( )

inline

Returns: the max number of threads reserved for Eigen

See also: setNbThreads

Definition at line 58 of file Parallelizer.h.

template<typename U , typename V >

EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool Eigen::operator!=	(	const Tuple< U, V > &	x,
		const Tuple< U, V > &	y
	)

Definition at line 148 of file TensorMeta.h.

template<typename SparseDerived , typename PermDerived >

const Product<SparseDerived, PermDerived, AliasFreeProduct> Eigen::operator*	(	const SparseMatrixBase< SparseDerived > &	matrix,
		const PermutationBase< PermDerived > &	perm
	)

inline

Returns: the matrix with the permutation applied to the columns

Definition at line 147 of file SparsePermutation.h.

template<typename SparseDerived , typename PermDerived >

const Product<PermDerived, SparseDerived, AliasFreeProduct> Eigen::operator*	(	const PermutationBase< PermDerived > &	perm,
		const SparseMatrixBase< SparseDerived > &	matrix
	)

inline

Returns: the matrix with the permutation applied to the rows

Definition at line 154 of file SparsePermutation.h.

template<typename SparseDerived , typename PermutationType >

const Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct> Eigen::operator*	(	const SparseMatrixBase< SparseDerived > &	matrix,
		const InverseImpl< PermutationType, PermutationStorage > &	tperm
	)

inline

Returns: the matrix with the inverse permutation applied to the columns.

Definition at line 162 of file SparsePermutation.h.

template<typename SparseDerived , typename PermutationType >

const Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct> Eigen::operator*	(	const InverseImpl< PermutationType, PermutationStorage > &	tperm,
		const SparseMatrixBase< SparseDerived > &	matrix
	)

inline

Returns: the matrix with the inverse permutation applied to the rows.

Definition at line 171 of file SparsePermutation.h.

template<typename MatrixDerived , typename TranspositionsDerived >

EIGEN_DEVICE_FUNC const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct> Eigen::operator*	(	const MatrixBase< MatrixDerived > &	matrix,
		const TranspositionsBase< TranspositionsDerived > &	transpositions
	)

Returns: the matrix with the transpositions applied to the columns.

Definition at line 338 of file Transpositions.h.

template<typename TranspositionsDerived , typename MatrixDerived >

EIGEN_DEVICE_FUNC const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct> Eigen::operator*	(	const TranspositionsBase< TranspositionsDerived > &	transpositions,
		const MatrixBase< MatrixDerived > &	matrix
	)

Returns: the matrix with the transpositions applied to the rows.

Definition at line 350 of file Transpositions.h.

template<typename OtherDerived , typename VectorsType , typename CoeffsType , int Side>

internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type Eigen::operator*	(	const MatrixBase< OtherDerived > &	other,
		const HouseholderSequence< VectorsType, CoeffsType, Side > &	h
	)

Computes the product of a matrix with a Householder sequence.

Parameters

[in]	other	Matrix being multiplied.
[in]	h	HouseholderSequence being multiplied.

Returns: Expression object representing the product.

This function computes $ MH $ where $ M $ is the matrix other and $ H $ is the Householder sequence represented by h.

Definition at line 438 of file HouseholderSequence.h.

template<typename MatrixDerived , typename PermutationDerived >

EIGEN_DEVICE_FUNC const Product<MatrixDerived, PermutationDerived, AliasFreeProduct> Eigen::operator*	(	const MatrixBase< MatrixDerived > &	matrix,
		const PermutationBase< PermutationDerived > &	permutation
	)

Returns: the matrix with the permutation applied to the columns.

Definition at line 543 of file PermutationMatrix.h.

template<typename PermutationDerived , typename MatrixDerived >

EIGEN_DEVICE_FUNC const Product<PermutationDerived, MatrixDerived, AliasFreeProduct> Eigen::operator*	(	const PermutationBase< PermutationDerived > &	permutation,
		const MatrixBase< MatrixDerived > &	matrix
	)

Returns: the matrix with the permutation applied to the rows.

Definition at line 555 of file PermutationMatrix.h.

template<typename DenseDerived , typename SparseDerived >

EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived> Eigen::operator+	(	const MatrixBase< DenseDerived > &	a,
		const SparseMatrixBase< SparseDerived > &	b
	)

Definition at line 698 of file SparseCwiseBinaryOp.h.

template<typename SparseDerived , typename DenseDerived >

EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived> Eigen::operator+	(	const SparseMatrixBase< SparseDerived > &	a,
		const MatrixBase< DenseDerived > &	b
	)

Definition at line 705 of file SparseCwiseBinaryOp.h.

template<typename DenseDerived , typename SparseDerived >

EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived> Eigen::operator-	(	const MatrixBase< DenseDerived > &	a,
		const SparseMatrixBase< SparseDerived > &	b
	)

Definition at line 712 of file SparseCwiseBinaryOp.h.

template<typename SparseDerived , typename DenseDerived >

EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived> Eigen::operator-	(	const SparseMatrixBase< SparseDerived > &	a,
		const MatrixBase< DenseDerived > &	b
	)

Definition at line 719 of file SparseCwiseBinaryOp.h.

template<typename T >

std::ostream& Eigen::operator<<	(	std::ostream &	os,
		const TensorBase< T, ReadOnlyAccessors > &	expr
	)

Definition at line 59 of file TensorIO.h.

template<typename U , typename V >

EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool Eigen::operator==	(	const Tuple< U, V > &	x,
		const Tuple< U, V > &	y
	)

Definition at line 142 of file TensorMeta.h.

template<class T , std::size_t N>

EIGEN_DEVICE_FUNC bool Eigen::operator==	(	const array< T, N > &	lhs,
		const array< T, N > &	rhs
	)

Definition at line 183 of file EmulateArray.h.

template<typename Polynomials , typename T >

T Eigen::poly_eval	(	const Polynomials &	poly,
		const T &	x
	)

inline

Returns: the evaluation of the polynomial at x using stabilized Horner algorithm.

Parameters

[in]	poly	: the vector of coefficients of the polynomial ordered by degrees i.e. poly[i] is the coefficient of degree i of the polynomial e.g. is stored as a vector .
[in]	x	: the value to evaluate the polynomial at.

Definition at line 46 of file PolynomialUtils.h.

template<typename Polynomials , typename T >

T Eigen::poly_eval_horner	(	const Polynomials &	poly,
		const T &	x
	)

inline

Returns: the evaluation of the polynomial at x using Horner algorithm.

Parameters

[in]	poly	: the vector of coefficients of the polynomial ordered by degrees i.e. poly[i] is the coefficient of degree i of the polynomial e.g. is stored as a vector .
[in]	x	: the value to evaluate the polynomial at.

Note for stability: $|x| \le 1$

Definition at line 28 of file PolynomialUtils.h.

template<typename DerivedN , typename DerivedX >

const Eigen::CwiseBinaryOp<Eigen::internal::scalar_polygamma_op<typename DerivedX::Scalar>, const DerivedN, const DerivedX> Eigen::polygamma	(	const Eigen::ArrayBase< DerivedN > &	n,
		const Eigen::ArrayBase< DerivedX > &	x
	)

inline

Returns: an expression of the coefficient-wise polygamma(n, x) to the given arrays.

It returns the n -th derivative of the digamma(psi) evaluated at x.

Note: This function supports only float and double scalar types in c++11 mode. To support other scalar types, or float/double in non c++11 mode, the user has to provide implementations of polygamma(T,T) for any scalar type T to be supported.

See also: Eigen::digamma()

Definition at line 70 of file SpecialFunctionsArrayAPI.h.

template<typename DerType >

const AutoDiffScalar<DerType>& Eigen::real ( const AutoDiffScalar< DerType > & x )

inline

Definition at line 544 of file AutoDiffScalar.h.

Eigen::return ( x<=y? ADSx):ADS(y )

Eigen::return ( x >=y? ADSx):ADS(y )

Eigen::return ( )

Eigen::return	(	x	,
		y?	ADSx):ADS(y
	)

template<typename VectorsType , typename CoeffsType >

HouseholderSequence<VectorsType,CoeffsType,OnTheRight> Eigen::rightHouseholderSequence	(	const VectorsType &	v,
		const CoeffsType &	h
	)

Convenience function for constructing a Householder sequence.

\

Returns: A HouseholderSequence constructed from the specified arguments.

This function differs from householderSequence() in that the template argument OnTheSide of the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft.

Definition at line 463 of file HouseholderSequence.h.

template<typename RootVector , typename Polynomial >

void Eigen::roots_to_monicPolynomial	(	const RootVector &	rv,
		Polynomial &	poly
	)

Given the roots of a polynomial compute the coefficients in the monomial basis of the monic polynomial with same roots and minimal degree. If RootVector is a vector of complexes, Polynomial should also be a vector of complexes.

Parameters

[in]	rv	: a vector containing the roots of a polynomial.
[out]	poly	: the vector of coefficients of the polynomial ordered by degrees i.e. poly[i] is the coefficient of degree i of the polynomial e.g. is stored as a vector .

Definition at line 127 of file PolynomialUtils.h.

template<typename SparseMatrixType >

bool Eigen::saveMarket	(	const SparseMatrixType &	mat,
		const std::string &	filename,
		int	sym = `0`
	)

Definition at line 225 of file MarketIO.h.

template<typename VectorType >

bool Eigen::saveMarketVector	(	const VectorType &	vec,
		const std::string &	filename
	)

Definition at line 251 of file MarketIO.h.

void Eigen::setCpuCacheSizes	(	std::ptrdiff_t	l1,
		std::ptrdiff_t	l2,
		std::ptrdiff_t	l3
	)

inline

Set the cpu L1 and L2 cache sizes (in bytes). These values are use to adjust the size of the blocks for the algorithms working per blocks.

See also: computeProductBlockingSizes

Definition at line 2142 of file GeneralBlockPanelKernel.h.

void Eigen::setNbThreads ( int v )

inline

Sets the max number of threads reserved for Eigen

See also: nbThreads

Definition at line 67 of file Parallelizer.h.

void Eigen::ssaupd_	(	int *	ido,
		char *	bmat,
		int *	n,
		char *	which,
		int *	nev,
		float *	tol,
		float *	resid,
		int *	ncv,
		float *	v,
		int *	ldv,
		int *	iparam,
		int *	ipntr,
		float *	workd,
		float *	workl,
		int *	lworkl,
		int *	info
	)

void Eigen::sseupd_	(	int *	rvec,
		char *	All,
		int *	select,
		float *	d,
		float *	z,
		int *	ldz,
		float *	sigma,
		char *	bmat,
		int *	n,
		char *	which,
		int *	nev,
		float *	tol,
		float *	resid,
		int *	ncv,
		float *	v,
		int *	ldv,
		int *	iparam,
		int *	ipntr,
		float *	workd,
		float *	workl,
		int *	lworkl,
		int *	ierr
	)

template<typename T , typename Derived >

T Eigen::test_relative_error	(	const AlignedVector3< T > &	a,
		const MatrixBase< Derived > &	b
	)

Definition at line 16 of file alignedvector3.cpp.

void Eigen::umfpack_defaults	(	double	control[UMFPACK_CONTROL],
		double
	)

inline

Definition at line 20 of file UmfPackSupport.h.

void Eigen::umfpack_defaults	(	double	control[UMFPACK_CONTROL],
		std::complex< double >
	)

inline

Definition at line 23 of file UmfPackSupport.h.

void Eigen::umfpack_free_numeric	(	void **	Numeric,
		double
	)

inline

Definition at line 44 of file UmfPackSupport.h.

void Eigen::umfpack_free_numeric	(	void **	Numeric,
		std::complex< double >
	)

inline

Definition at line 47 of file UmfPackSupport.h.

void Eigen::umfpack_free_symbolic	(	void **	Symbolic,
		double
	)

inline

Definition at line 50 of file UmfPackSupport.h.

void Eigen::umfpack_free_symbolic	(	void **	Symbolic,
		std::complex< double >
	)

inline

Definition at line 53 of file UmfPackSupport.h.

int Eigen::umfpack_get_determinant	(	double *	Mx,
		double *	Ex,
		void *	NumericHandle,
		double	User_Info[UMFPACK_INFO]
	)

inline

Definition at line 124 of file UmfPackSupport.h.

int Eigen::umfpack_get_determinant	(	std::complex< double > *	Mx,
		double *	Ex,
		void *	NumericHandle,
		double	User_Info[UMFPACK_INFO]
	)

inline

Definition at line 129 of file UmfPackSupport.h.

int Eigen::umfpack_get_lunz	(	int *	lnz,
		int *	unz,
		int *	n_row,
		int *	n_col,
		int *	nz_udiag,
		void *	Numeric,
		double
	)

inline

Definition at line 98 of file UmfPackSupport.h.

int Eigen::umfpack_get_lunz	(	int *	lnz,
		int *	unz,
		int *	n_row,
		int *	n_col,
		int *	nz_udiag,
		void *	Numeric,
		std::complex< double >
	)

inline

Definition at line 103 of file UmfPackSupport.h.

int Eigen::umfpack_get_numeric	(	int	Lp[],
		int	Lj[],
		double	Lx[],
		int	Up[],
		int	Ui[],
		double	Ux[],
		int	P[],
		int	Q[],
		double	Dx[],
		int *	do_recip,
		double	Rs[],
		void *	Numeric
	)

inline

Definition at line 108 of file UmfPackSupport.h.

int Eigen::umfpack_get_numeric	(	int	Lp[],
		int	Lj[],
		std::complex< double >	Lx[],
		int	Up[],
		int	Ui[],
		std::complex< double >	Ux[],
		int	P[],
		int	Q[],
		std::complex< double >	Dx[],
		int *	do_recip,
		double	Rs[],
		void *	Numeric
	)

inline

Definition at line 114 of file UmfPackSupport.h.

int Eigen::umfpack_numeric	(	const int	Ap[],
		const int	Ai[],
		const double	Ax[],
		void *	Symbolic,
		void **	Numeric,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 70 of file UmfPackSupport.h.

int Eigen::umfpack_numeric	(	const int	Ap[],
		const int	Ai[],
		const std::complex< double >	Ax[],
		void *	Symbolic,
		void **	Numeric,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 77 of file UmfPackSupport.h.

void Eigen::umfpack_report_control	(	double	control[UMFPACK_CONTROL],
		double
	)

inline

Definition at line 38 of file UmfPackSupport.h.

void Eigen::umfpack_report_control	(	double	control[UMFPACK_CONTROL],
		std::complex< double >
	)

inline

Definition at line 41 of file UmfPackSupport.h.

void Eigen::umfpack_report_info	(	double	control[UMFPACK_CONTROL],
		double	info[UMFPACK_INFO],
		double
	)

inline

Definition at line 26 of file UmfPackSupport.h.

void Eigen::umfpack_report_info	(	double	control[UMFPACK_CONTROL],
		double	info[UMFPACK_INFO],
		std::complex< double >
	)

inline

Definition at line 29 of file UmfPackSupport.h.

void Eigen::umfpack_report_status	(	double	control[UMFPACK_CONTROL],
		int	status,
		double
	)

inline

Definition at line 32 of file UmfPackSupport.h.

void Eigen::umfpack_report_status	(	double	control[UMFPACK_CONTROL],
		int	status,
		std::complex< double >
	)

inline

Definition at line 35 of file UmfPackSupport.h.

int Eigen::umfpack_solve	(	int	sys,
		const int	Ap[],
		const int	Ai[],
		const double	Ax[],
		double	X[],
		const double	B[],
		void *	Numeric,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 84 of file UmfPackSupport.h.

int Eigen::umfpack_solve	(	int	sys,
		const int	Ap[],
		const int	Ai[],
		const std::complex< double >	Ax[],
		std::complex< double >	X[],
		const std::complex< double >	B[],
		void *	Numeric,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 91 of file UmfPackSupport.h.

int Eigen::umfpack_symbolic	(	int	n_row,
		int	n_col,
		const int	Ap[],
		const int	Ai[],
		const double	Ax[],
		void **	Symbolic,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 56 of file UmfPackSupport.h.

int Eigen::umfpack_symbolic	(	int	n_row,
		int	n_col,
		const int	Ap[],
		const int	Ai[],
		const std::complex< double >	Ax[],
		void **	Symbolic,
		const double	Control[UMFPACK_CONTROL],
		double	Info[UMFPACK_INFO]
	)

inline

Definition at line 63 of file UmfPackSupport.h.

template<typename _Scalar , int _Options, typename _StorageIndex >

cholmod_sparse Eigen::viewAsCholmod ( Ref< SparseMatrix< _Scalar, _Options, _StorageIndex > > mat )

Wraps the Eigen sparse matrix mat into a Cholmod sparse matrix object. Note that the data are shared.

Definition at line 58 of file CholmodSupport.h.

template<typename _Scalar , int _Options, typename _Index >

const cholmod_sparse Eigen::viewAsCholmod ( const SparseMatrix< _Scalar, _Options, _Index > & mat )

Definition at line 105 of file CholmodSupport.h.

template<typename _Scalar , int _Options, typename _Index >

const cholmod_sparse Eigen::viewAsCholmod ( const SparseVector< _Scalar, _Options, _Index > & mat )

Definition at line 112 of file CholmodSupport.h.

template<typename _Scalar , int _Options, typename _Index , unsigned int UpLo>

cholmod_sparse Eigen::viewAsCholmod ( const SparseSelfAdjointView< const SparseMatrix< _Scalar, _Options, _Index >, UpLo > & mat )

Returns a view of the Eigen sparse matrix mat as Cholmod sparse matrix. The data are not copied but shared.

Definition at line 121 of file CholmodSupport.h.

template<typename Derived >

cholmod_dense Eigen::viewAsCholmod ( MatrixBase< Derived > & mat )

Returns a view of the Eigen dense matrix mat as Cholmod dense matrix. The data are not copied but shared.

Definition at line 134 of file CholmodSupport.h.

template<typename Scalar , int Flags, typename StorageIndex >

MappedSparseMatrix<Scalar,Flags,StorageIndex> Eigen::viewAsEigen ( cholmod_sparse & cm )

Returns a view of the Cholmod sparse matrix cm as an Eigen sparse matrix. The data are not copied but shared.

Definition at line 155 of file CholmodSupport.h.

template<typename DerivedX , typename DerivedQ >

const Eigen::CwiseBinaryOp<Eigen::internal::scalar_zeta_op<typename DerivedX::Scalar>, const DerivedX, const DerivedQ> Eigen::zeta	(	const Eigen::ArrayBase< DerivedX > &	x,
		const Eigen::ArrayBase< DerivedQ > &	q
	)

inline

Returns: an expression of the coefficient-wise zeta(x, q) to the given arrays.

It returns the Riemann zeta function of two arguments x and q:

Parameters

x	is the exposent, it must be > 1
q	is the shift, it must be > 0

Note: This function supports only float and double scalar types. To support other scalar types, the user has to provide implementations of zeta(T,T) for any scalar type T to be supported.

See also: ArrayBase::zeta()

Definition at line 114 of file SpecialFunctionsArrayAPI.h.

Variable Documentation

const unsigned int Eigen::ActualPacketAccessBit = 0x0

Definition at line 102 of file Constants.h.

const int Eigen::CoherentAccessPattern = 0x1

Definition at line 47 of file SparseUtil.h.

const int Eigen::Dynamic = -1

This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is stored in some runtime variable.

Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.

Definition at line 21 of file Constants.h.

const int Eigen::DynamicIndex = 0xffffff

This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value has to be specified at runtime.

Definition at line 26 of file Constants.h.

Scalar Eigen::expx = exp(x.value())

Definition at line 602 of file AutoDiffScalar.h.

const unsigned int Eigen::HereditaryBits

Initial value:

= RowMajorBit

| EvalBeforeNestingBit

Eigen::RowMajorBit

const unsigned int RowMajorBit

Definition: Constants.h:61

Eigen::EvalBeforeNestingBit

const unsigned int EvalBeforeNestingBit

Definition: Constants.h:65

Definition at line 190 of file Constants.h.

const int Eigen::HugeCost = 10000

This value means that the cost to evaluate an expression coefficient is either very expensive or cannot be known at compile time.

This value has to be positive to (1) simplify cost computation, and (2) allow to distinguish between a very expensive and very very expensive expressions. It thus must also be large enough to make sure unrolling won't happen and that sub expressions will be evaluated, but not too large to avoid overflow.

Definition at line 39 of file Constants.h.

const int Eigen::Infinity = -1

This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>(). The value Infinity there means the L-infinity norm.

Definition at line 31 of file Constants.h.

const int Eigen::InnerRandomAccessPattern = 0x2 | CoherentAccessPattern

Definition at line 48 of file SparseUtil.h.

const unsigned int Eigen::NestByRefBit = 0x100

Definition at line 164 of file Constants.h.

const int Eigen::OuterRandomAccessPattern = 0x4 | CoherentAccessPattern

Definition at line 49 of file SparseUtil.h.

const int Eigen::RandomAccessPattern = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern

Definition at line 50 of file SparseUtil.h.

const unsigned int Eigen::SkylineBit = 0x1200

Definition at line 21 of file SkylineUtil.h.

const AutoDiffScalar< DerType > & Eigen::y

Initial value:

{

typedef AutoDiffScalar<typename Eigen::internal::remove_all<DerType>::type::PlainObject> ADS

Eigen::AutoDiffScalar

A scalar type replacement with automatic differentation capability.

Definition: AutoDiffScalar.h:33

Definition at line 548 of file AutoDiffScalar.h.

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