Eigen Namespace Reference

Namespace containing all symbols from the Eigen library. More...

Namespaces
	internal
	template <class> class MakePointer_ is added to convert the host pointer to the device pointer.

Classes
class	aligned_allocator
	STL compatible allocator to use with with 16 byte aligned types. More...
class	aligned_allocator_indirection
class	AlignedBox
class	AMDOrdering
	Functor computing the approximate minimum degree ordering If the matrix is not structurally symmetric, an ordering of A^T+A is computed. More...
class	AngleAxis
struct	AntiHermiticity
struct	AntiSymmetry
class	ArpackGeneralizedSelfAdjointEigenSolver
class	Array
	General-purpose arrays with easy API for coefficient-wise operations. More...
class	array
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
	The type used to identify an array expression. More...
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	BenchTimer
	Elapsed time timer keeping the best try. 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 >
	Generic implementation of sparse Block expression. More...
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
class	ComplexSchur
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
	The type used to identify a dense storage. More...
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. 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...
struct	eigen_assert_exception
class	EigenBase
	Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T). More...
class	EigenSolver
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
class	GeneralizedSelfAdjointEigenSolver
struct	GenericNumTraits
class	GMRES
	A GMRES solver for sparse square problems. More...
struct	half
struct	Hermiticity
class	HessenbergDecomposition
class	Homogeneous
struct	HomogeneousShape
class	HouseholderQR
	Householder QR decomposition of a matrix. More...
class	HouseholderSequence
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
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
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
	The type used to identify a matrix expression. More...
struct	max_n_1
struct	max_n_1< 0 >
class	MaxSizeVector
	The MaxSizeVector class. More...
class	MetisOrdering
	Get the fill-reducing ordering from the METIS package. More...
class	MINRES
	A minimal residual solver for sparse symmetric problems. More...
class	NaturalOrdering
	Functor computing the natural ordering (identity) More...
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
	This class allows you to add a method df() to your functor, which will use numerical differentiation to compute an approximate of the derivative for the functor. More...
class	NumTraits
	Holds information about the various numeric (i.e. More...
struct	NumTraits< Array< Scalar, Rows, Cols, Options, MaxRows, MaxCols > >
struct	NumTraits< AutoDiffScalar< DerType > >
struct	NumTraits< boost::multiprecision::detail::expression< T1, T2, T3, T4, T5 > >
struct	NumTraits< double >
struct	NumTraits< Eigen::half >
struct	NumTraits< float >
struct	NumTraits< long double >
struct	NumTraits< Real >
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
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
	The type used to identify a permutation storage. More...
class	PermutationWrapper
	Class to view a vector of integers as a permutation matrix. More...
class	PlainObjectBase
	Dense storage base class for matrices and arrays. More...
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
class	QuaternionBase
class	RandomSetter
	The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access. More...
class	RealQZ
class	RealSchur
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
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
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
	Stores a skyline set of values in three structures : The diagonal elements The upper elements The lower elements. More...
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
	The type used to identify a general solver (factored) storage. More...
class	SolveWithGuess
	Pseudo expression representing a solving operation. More...
struct	Sparse
	The type used to identify a general sparse storage. More...
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 >
	class SparseMapBase More...
class	SparseMapBase< Derived, WriteAccessors >
	class SparseMapBase 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
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
	Represents a std::map. More...
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. 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
	template <class> class MakePointer_ is added to convert the host pointer to the device pointer. 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
class	Translation
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
	The type used to identify a permutation storage. More...
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
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 EIGEN_DEFAULT_DENSE_INDEX_TYPE	DenseIndex
typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE	Index
	The Index type as used for the API. More...
typedef std::complex< double >	dcomplex
typedef std::complex< float >	scomplex
typedef int	BlasIndex
typedef AngleAxis< float >	AngleAxisf
	single precision angle-axis type
typedef AngleAxis< double >	AngleAxisd
	double precision angle-axis type
typedef Quaternion< float >	Quaternionf
	single precision quaternion type
typedef Quaternion< double >	Quaterniond
	double precision quaternion type
typedef Map< Quaternion< float >, 0 >	QuaternionMapf
	Map an unaligned array of single precision scalars as a quaternion.
typedef Map< Quaternion< double >, 0 >	QuaternionMapd
	Map an unaligned array of double precision scalars as a quaternion.
typedef Map< Quaternion< float >, Aligned >	QuaternionMapAlignedf
	Map a 16-byte aligned array of single precision scalars as a quaternion.
typedef Map< Quaternion< double >, Aligned >	QuaternionMapAlignedd
	Map a 16-byte aligned array of double precision scalars as a quaternion.
typedef Rotation2D< float >	Rotation2Df
	single precision 2D rotation type
typedef Rotation2D< double >	Rotation2Dd
	double precision 2D rotation type
typedef DiagonalMatrix< float, 2 >	AlignedScaling2f
typedef DiagonalMatrix< double, 2 >	AlignedScaling2d
typedef DiagonalMatrix< float, 3 >	AlignedScaling3f
typedef DiagonalMatrix< double, 3 >	AlignedScaling3d
typedef Transform< float, 2, Isometry >	Isometry2f
typedef Transform< float, 3, Isometry >	Isometry3f
typedef Transform< double, 2, Isometry >	Isometry2d
typedef Transform< double, 3, Isometry >	Isometry3d
typedef Transform< float, 2, Affine >	Affine2f
typedef Transform< float, 3, Affine >	Affine3f
typedef Transform< double, 2, Affine >	Affine2d
typedef Transform< double, 3, Affine >	Affine3d
typedef Transform< float, 2, AffineCompact >	AffineCompact2f
typedef Transform< float, 3, AffineCompact >	AffineCompact3f
typedef Transform< double, 2, AffineCompact >	AffineCompact2d
typedef Transform< double, 3, AffineCompact >	AffineCompact3d
typedef Transform< float, 2, Projective >	Projective2f
typedef Transform< float, 3, Projective >	Projective3f
typedef Transform< double, 2, Projective >	Projective2d
typedef Transform< double, 3, Projective >	Projective3d
typedef Translation< float, 2 >	Translation2f
typedef Translation< double, 2 >	Translation2d
typedef Translation< float, 3 >	Translation3f
typedef Translation< double, 3 >	Translation3d
typedef NonBlockingThreadPoolTempl< StlThreadEnvironment >	NonBlockingThreadPool
typedef SimpleThreadPoolTempl< StlThreadEnvironment >	SimpleThreadPool
typedef Spline< float, 2 >	Spline2f
	2D float B-spline with dynamic degree. More...
typedef Spline< float, 3 >	Spline3f
	3D float B-spline with dynamic degree. More...
typedef Spline< double, 2 >	Spline2d
	2D double B-spline with dynamic degree. More...
typedef Spline< double, 3 >	Spline3d
	3D double B-spline with dynamic degree. More...

Enumerations
enum	{ CPU_TIMER = 0, REAL_TIMER = 1 }
enum	CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt }
enum	{ Large = 2, Small = 3 }
enum	{ DontAlignCols = 1 }
enum	{ StreamPrecision = -1, FullPrecision = -2 }
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 }
	Enum containing possible values for the Mode or UpLo parameter of MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. More...
enum	AlignmentType {   Unaligned =0, Aligned8 =8, Aligned16 =16, Aligned32 =32,   Aligned64 =64, Aligned128 =128, AlignedMask =255, Aligned =16,   AlignedMax = Unaligned }
	Enum for indicating whether a buffer is aligned or not. More...
enum	CornerType { TopLeft, TopRight, BottomLeft, BottomRight }
	Enum used by DenseBase::corner() in Eigen2 compatibility mode. More...
enum	DirectionType { Vertical, Horizontal, BothDirections }
	Enum containing possible values for the Direction parameter of Reverse, PartialReduxExpr and VectorwiseOp. More...
enum	TraversalType {   DefaultTraversal, LinearTraversal, InnerVectorizedTraversal, LinearVectorizedTraversal,   SliceVectorizedTraversal, InvalidTraversal, AllAtOnceTraversal }
enum	UnrollingType { NoUnrolling, InnerUnrolling, CompleteUnrolling }
enum	SpecializedType { Specialized, BuiltIn }
enum	StorageOptions { ColMajor = 0, RowMajor = 0x1, AutoAlign = 0, DontAlign = 0x2 }
	Enum containing possible values for the _Options template parameter of Matrix, Array and BandMatrix. More...
enum	SideType { OnTheLeft = 1, OnTheRight = 2 }
	Enum for specifying whether to apply or solve on the left or right. More...
enum	NoChange_t { NoChange }
enum	Sequential_t { Sequential }
enum	Default_t { Default }
enum	AmbiVectorMode { IsDense = 0, IsSparse }
enum	AccessorLevels { ReadOnlyAccessors, WriteAccessors, DirectAccessors, DirectWriteAccessors }
	Used as template parameter in DenseCoeffBase and MapBase to indicate which accessors should be provided. More...
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 with options to give to various decompositions. More...
enum	QRPreconditioners { NoQRPreconditioner, HouseholderQRPreconditioner, ColPivHouseholderQRPreconditioner, FullPivHouseholderQRPreconditioner }
	Possible values for the QRPreconditioner template parameter of JacobiSVD. More...
enum	ComputationInfo { Success = 0, NumericalIssue = 1, NoConvergence = 2, InvalidInput = 3 }
	Enum for reporting the status of a computation. More...
enum	TransformTraits { Isometry = 0x1, Affine = 0x2, AffineCompact = 0x10 | Affine, Projective = 0x20 }
	Enum used to specify how a particular transformation is stored in a matrix. More...
enum	ProductImplType {   DefaultProduct =0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode,   LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct,   GemmProduct }
enum	Action { GetAction, SetAction }
enum	SimplicialCholeskyMode { SimplicialCholeskyLLT, SimplicialCholeskyLDLT }
enum	{ StandardCompressedFormat = 2 }
enum	FFTResultType { RealPart = 0, ImagPart = 1, BothParts = 2 }
enum	FFTDirection { FFT_FORWARD = 0, FFT_REVERSE = 1 }
enum	PaddingType { PADDING_VALID = 1, PADDING_SAME = 2 }
enum	{ NegationFlag = 0x01, ConjugationFlag = 0x02 }
enum	{ GlobalRealFlag = 0x01, GlobalImagFlag = 0x02, GlobalZeroFlag = 0x03 }
enum	EulerAxis { EULER_X = 1, EULER_Y = 2, EULER_Z = 3 }
	Representation of a fixed signed rotation axis for EulerSystem. More...
enum	NumericalDiffMode { Forward, Central }
enum	AdditionalProductEvaluationMode { SkylineTimeDenseProduct, SkylineTimeSkylineProduct, DenseTimeSkylineProduct }
enum	{ IsSkyline = SkylineBit }
enum	{ SPD = 0x100, NonSymmetric = 0x0 }

Functions
template<typename _Scalar , int _Options, typename _StorageIndex >
cholmod_sparse	viewAsCholmod (Ref< SparseMatrix< _Scalar, _Options, _StorageIndex > > mat)
	Wraps the Eigen sparse matrix mat into a Cholmod sparse matrix object. More...
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)
	Returns a view of the Eigen sparse matrix mat as Cholmod sparse matrix. More...
template<typename Derived >
cholmod_dense	viewAsCholmod (MatrixBase< Derived > &mat)
	Returns a view of the Eigen dense matrix mat as Cholmod dense matrix. More...
template<typename Scalar , int Flags, typename StorageIndex >
MappedSparseMatrix< Scalar, Flags, StorageIndex >	viewAsEigen (cholmod_sparse &cm)
	Returns a view of the Cholmod sparse matrix cm as an Eigen sparse matrix. More...
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
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)
std::ptrdiff_t	l1CacheSize ()
std::ptrdiff_t	l2CacheSize ()
std::ptrdiff_t	l3CacheSize ()
void	setCpuCacheSizes (std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3)
	Set the cpu L1 and L2 cache sizes (in bytes). More...
void	initParallel ()
	Must be call first when calling Eigen from multiple threads.
int	nbThreads ()
void	setNbThreads (int v)
	Sets the max number of threads reserved for Eigen. More...
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)
UniformScaling< float >	Scaling (float s)
	Constructs a uniform scaling from scale factor s.
UniformScaling< double >	Scaling (double s)
	Constructs a uniform scaling from scale factor s.
template<typename RealScalar >
UniformScaling< std::complex< RealScalar > >	Scaling (const std::complex< RealScalar > &s)
	Constructs a uniform scaling from scale factor s.
template<typename Scalar >
DiagonalMatrix< Scalar, 2 >	Scaling (const Scalar &sx, const Scalar &sy)
	Constructs a 2D axis aligned scaling.
template<typename Scalar >
DiagonalMatrix< Scalar, 3 >	Scaling (const Scalar &sx, const Scalar &sy, const Scalar &sz)
	Constructs a 3D axis aligned scaling.
template<typename Derived >
const DiagonalWrapper< const Derived >	Scaling (const MatrixBase< Derived > &coeffs)
	Constructs an axis aligned scaling expression from vector expression coeffs This is an alias for coeffs.asDiagonal()
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)
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 VectorsType , typename CoeffsType >
HouseholderSequence< VectorsType, CoeffsType >	householderSequence (const VectorsType &v, const CoeffsType &h)
	\ More...
template<typename VectorsType , typename CoeffsType >
HouseholderSequence< VectorsType, CoeffsType, OnTheRight >	rightHouseholderSequence (const VectorsType &v, const CoeffsType &h)
	\ More...
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 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)
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_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])
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])
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_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_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])
template<>
Real	test_precision< Real > ()
template<typename Lhs , typename Rhs >
const Product< Lhs, Rhs >	prod (const Lhs &lhs, const Rhs &rhs)
template<typename Lhs , typename Rhs >
const Product< Lhs, Rhs, LazyProduct >	lazyprod (const Lhs &lhs, const Rhs &rhs)
template<typename DstXprType , typename SrcXprType >
EIGEN_STRONG_INLINE DstXprType &	copy_using_evaluator (const EigenBase< DstXprType > &dst, const SrcXprType &src)
template<typename DstXprType , template< typename > class StorageBase, typename SrcXprType >
EIGEN_STRONG_INLINE const DstXprType &	copy_using_evaluator (const NoAlias< DstXprType, StorageBase > &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
EIGEN_STRONG_INLINE DstXprType &	copy_using_evaluator (const PlainObjectBase< DstXprType > &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
void	add_assign_using_evaluator (const DstXprType &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
void	subtract_assign_using_evaluator (const DstXprType &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
void	multiply_assign_using_evaluator (const DstXprType &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
void	divide_assign_using_evaluator (const DstXprType &dst, const SrcXprType &src)
template<typename DstXprType , typename SrcXprType >
void	swap_using_evaluator (const DstXprType &dst, const SrcXprType &src)
template<typename T >
NumTraits< T >::Real	test_precision ()
template<>
float	test_precision< float > ()
template<>
double	test_precision< double > ()
template<>
long double	test_precision< long double > ()
template<>
float	test_precision< std::complex< float > > ()
template<>
double	test_precision< std::complex< double > > ()
template<>
long double	test_precision< std::complex< long double > > ()
bool	test_isApprox (const int &a, const int &b)
bool	test_isMuchSmallerThan (const int &a, const int &b)
bool	test_isApproxOrLessThan (const int &a, const int &b)
bool	test_isApprox (const float &a, const float &b)
bool	test_isMuchSmallerThan (const float &a, const float &b)
bool	test_isApproxOrLessThan (const float &a, const float &b)
bool	test_isApprox (const double &a, const double &b)
bool	test_isMuchSmallerThan (const double &a, const double &b)
bool	test_isApproxOrLessThan (const double &a, const double &b)
bool	test_isApprox (const std::complex< float > &a, const std::complex< float > &b)
bool	test_isMuchSmallerThan (const std::complex< float > &a, const std::complex< float > &b)
bool	test_isApprox (const std::complex< double > &a, const std::complex< double > &b)
bool	test_isMuchSmallerThan (const std::complex< double > &a, const std::complex< double > &b)
bool	test_isApprox (const std::complex< long double > &a, const std::complex< long double > &b)
bool	test_isMuchSmallerThan (const std::complex< long double > &a, const std::complex< long double > &b)
bool	test_isApprox (const long double &a, const long double &b)
bool	test_isMuchSmallerThan (const long double &a, const long double &b)
bool	test_isApproxOrLessThan (const long double &a, const long double &b)
bool	test_isApprox (const half &a, const half &b)
bool	test_isMuchSmallerThan (const half &a, const half &b)
bool	test_isApproxOrLessThan (const half &a, const half &b)
template<typename T1 , typename T2 >
T1::RealScalar	test_relative_error (const EigenBase< T1 > &a, const EigenBase< T2 > &b)
template<typename T1 , typename T2 >
T1::RealScalar	test_relative_error (const T1 &a, const T2 &b, const typename T1::Coefficients *=0)
template<typename T1 , typename T2 >
T1::Scalar	test_relative_error (const T1 &a, const T2 &b, const typename T1::MatrixType *=0)
template<typename S , int D>
S	test_relative_error (const Translation< S, D > &a, const Translation< S, D > &b)
template<typename S , int D, int O>
S	test_relative_error (const ParametrizedLine< S, D, O > &a, const ParametrizedLine< S, D, O > &b)
template<typename S , int D>
S	test_relative_error (const AlignedBox< S, D > &a, const AlignedBox< S, D > &b)
template<typename T1 , typename T2 >
T1::RealScalar	test_relative_error (const MatrixBase< T1 > &a, const SparseMatrixBase< T2 > &b)
template<typename T1 , typename T2 >
T1::RealScalar	test_relative_error (const SparseMatrixBase< T1 > &a, const MatrixBase< T2 > &b)
template<typename T1 , typename T2 >
T1::RealScalar	test_relative_error (const SparseMatrixBase< T1 > &a, const SparseMatrixBase< T2 > &b)
template<typename T1 , typename T2 >
NumTraits< T1 >::Real	test_relative_error (const T1 &a, const T2 &b, typename internal::enable_if< internal::is_arithmetic< typename NumTraits< T1 >::Real >::value, T1 >::type *=0)
template<typename T >
T	test_relative_error (const Rotation2D< T > &a, const Rotation2D< T > &b)
template<typename T >
T	test_relative_error (const AngleAxis< T > &a, const AngleAxis< T > &b)
template<typename Type1 , typename Type2 >
bool	test_isApprox (const Type1 &a, const Type2 &b, typename Type1::Scalar *=0)
template<typename T >
NumTraits< typename T::Scalar >::Real	get_test_precision (const T &, const typename T::Scalar *=0)
template<typename T >
NumTraits< T >::Real	get_test_precision (const T &, typename internal::enable_if< internal::is_arithmetic< typename NumTraits< T >::Real >::value, T >::type *=0)
template<typename Type1 , typename Type2 >
bool	verifyIsApprox (const Type1 &a, const Type2 &b)
template<typename Scalar , typename ScalarRef >
bool	test_isApproxWithRef (const Scalar &a, const Scalar &b, const ScalarRef &ref)
template<typename Derived1 , typename Derived2 >
bool	test_isMuchSmallerThan (const MatrixBase< Derived1 > &m1, const MatrixBase< Derived2 > &m2)
template<typename Derived >
bool	test_isMuchSmallerThan (const MatrixBase< Derived > &m, const typename NumTraits< typename internal::traits< Derived >::Scalar >::Real &s)
template<typename Derived >
bool	test_isUnitary (const MatrixBase< Derived > &m)
template<typename T , typename U >
bool	test_is_equal (const T &actual, const U &expected, bool expect_equal=true)
template<typename MatrixType >
void	createRandomPIMatrixOfRank (Index desired_rank, Index rows, Index cols, MatrixType &m)
	Creates a random Partial Isometry matrix of given rank. More...
template<typename PermutationVectorType >
void	randomPermutationVector (PermutationVectorType &v, Index size)
template<typename T >
bool	isNotNaN (const T &x)
template<typename T >
bool	isPlusInf (const T &x)
template<typename T >
bool	isMinusInf (const T &x)
Box2d	bounding_box (const Vector2d &v)
template<typename Dims1 , typename Dims2 >
EIGEN_DEVICE_FUNC bool	dimensions_match (Dims1 &dims1, Dims2 &dims2)
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 T >
std::ostream &	operator<< (std::ostream &os, const TensorBase< T, ReadOnlyAccessors > &expr)
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 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)
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 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 NewDerType >
AutoDiffScalar< NewDerType >	MakeAutoDiffScalar (const typename NewDerType::Scalar &value, const NewDerType &der)
template<typename DerType >
const AutoDiffScalar< DerType > &	conj (const AutoDiffScalar< DerType > &x)
template<typename DerType >
const AutoDiffScalar< DerType > &	real (const AutoDiffScalar< DerType > &x)
template<typename DerType >
DerType::Scalar	imag (const AutoDiffScalar< DerType > &)
	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))
	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
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)
	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 BVH , typename Intersector >
void	BVIntersect (const BVH &tree, Intersector &intersector)
	Given a BVH, runs the query encapsulated by intersector. More...
template<typename BVH1 , typename BVH2 , typename Intersector >
void	BVIntersect (const BVH1 &tree1, const BVH2 &tree2, Intersector &intersector)
	Given two BVH's, runs the query on their Cartesian product encapsulated by intersector. More...
template<typename BVH , typename Minimizer >
Minimizer::Scalar	BVMinimize (const BVH &tree, Minimizer &minimizer)
	Given a BVH, runs the query encapsulated by minimizer. More...
template<typename BVH1 , typename BVH2 , typename Minimizer >
Minimizer::Scalar	BVMinimize (const BVH1 &tree1, const BVH2 &tree2, Minimizer &minimizer)
	Given two BVH's, runs the query on their cartesian product encapsulated by minimizer. More...
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)
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)
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)
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...
template<typename Polynomials , typename T >
T	poly_eval_horner (const Polynomials &poly, const T &x)
template<typename Polynomials , typename T >
T	poly_eval (const Polynomials &poly, const T &x)
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 RootVector , typename Polynomial >
void	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. More...
bool	getMarketHeader (const std::string &filename, int &sym, bool &iscomplex, bool &isvector)
template<typename SparseMatrixType >
bool	loadMarket (SparseMatrixType &mat, const std::string &filename)
template<typename VectorType >
bool	loadMarketVector (VectorType &vec, const std::string &filename)
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)
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 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 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)
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)
template<typename SplineType , typename DerivativeType >
void	derivativesImpl (const SplineType &spline, typename SplineType::Scalar u, DenseIndex order, DerivativeType &der)
template<typename KnotVectorType >
void	KnotAveraging (const KnotVectorType &parameters, DenseIndex degree, KnotVectorType &knots)
	Computes knot averages. 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. More...
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 T , typename Derived >
T	test_relative_error (const AlignedVector3< T > &a, const MatrixBase< Derived > &b)
template<typename Scalar , int Dim>
AlignedBox< Scalar, Dim >	bounding_box (const Matrix< Scalar, Dim, 1 > &v)

Variables
const int	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. More...
const int	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.
const int	Infinity = -1
	This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>(). More...
const int	HugeCost = 10000
	This value means that the cost to evaluate an expression coefficient is either very expensive or cannot be known at compile time. More...
const unsigned int	RowMajorBit = 0x1
	for a matrix, this means that the storage order is row-major. More...
const unsigned int	EvalBeforeNestingBit = 0x2
	means the expression should be evaluated by the calling expression
EIGEN_DEPRECATED const unsigned int	EvalBeforeAssigningBit = 0x4
const unsigned int	PacketAccessBit = 0x8
	Short version: means the expression might be vectorized. More...
const unsigned int	ActualPacketAccessBit = 0x0
const unsigned int	LinearAccessBit = 0x10
	Short version: means the expression can be seen as 1D vector. More...
const unsigned int	LvalueBit = 0x20
	Means the expression has a coeffRef() method, i.e. More...
const unsigned int	DirectAccessBit = 0x40
	Means that the underlying array of coefficients can be directly accessed as a plain strided array. More...
EIGEN_DEPRECATED const unsigned int	AlignedBit = 0x80
const unsigned int	NestByRefBit = 0x100
const unsigned int	NoPreferredStorageOrderBit = 0x200
	for an expression, this means that the storage order can be either row-major or column-major. More...
const unsigned int	CompressedAccessBit = 0x400
	Means that the underlying coefficients can be accessed through pointers to the sparse (un)compressed storage format, that is, the expression provides: More...
const unsigned int	HereditaryBits
const int	CoherentAccessPattern = 0x1
const int	InnerRandomAccessPattern = 0x2 | CoherentAccessPattern
const int	OuterRandomAccessPattern = 0x4 | CoherentAccessPattern
const int	RandomAccessPattern = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern
const T &	y
Scalar	expx = exp(x.value())
const unsigned int	SkylineBit = 0x1200

Detailed Description

Namespace containing all symbols from the Eigen library.

Typedef Documentation

Index

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

Preprocessor directives, StorageIndex.

Examples:

/tmp/tmprenbv5zb/third-party-libs/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h.

Spline2d

typedef Spline<double,2> Eigen::Spline2d

2D double B-spline with dynamic degree.

Spline2f

typedef Spline<float,2> Eigen::Spline2f

2D float B-spline with dynamic degree.

Spline3d

typedef Spline<double,3> Eigen::Spline3d

3D double B-spline with dynamic degree.

Spline3f

typedef Spline<float,3> Eigen::Spline3f

3D float B-spline with dynamic degree.

Enumeration Type Documentation

anonymous enum

anonymous enum

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

EulerAxis

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

Function Documentation

betainc() [1/2]

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).

betainc() [2/2]

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()

BVIntersect() [1/2]

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

BVIntersect() [2/2]

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

BVMinimize() [1/2]

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)

BVMinimize() [2/2]

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)

cauchy_max_bound()

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. \( 1 + 3x^2 \) is stored as a vector \( [ 1, 0, 3 ] \).

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

cauchy_min_bound()

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. \( 1 + 3x^2 \) is stored as a vector \( [ 1, 0, 3 ] \).

createRandomPIMatrixOfRank()

template<typename MatrixType >

void Eigen::createRandomPIMatrixOfRank	(	Index	desired_rank,
		Index	rows,
		Index	cols,
		MatrixType &	m
	)

Creates a random Partial Isometry matrix of given rank.

A partial isometry is a matrix all of whose singular values are either 0 or 1. This is very useful to test rank-revealing algorithms.

igamma()

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()

igammac()

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()

kroneckerProduct() [1/2]

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

kroneckerProduct() [2/2]

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

l1CacheSize()

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

l2CacheSize()

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

l3CacheSize()

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

matrix_sqrt_quasi_triangular()

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

matrix_sqrt_triangular()

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

nbThreads()

int Eigen::nbThreads ( )

inline

Returns

the max number of threads reserved for Eigen

See also

setNbThreads

operator*() [1/9]

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

operator*() [2/9]

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

operator*() [3/9]

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.

operator*() [4/9]

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.

operator*() [5/9]

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.

operator*() [6/9]

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.

operator*() [7/9]

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.

operator*() [8/9]

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.

operator*() [9/9]

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.

poly_eval()

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. \( 1 + 3x^2 \) is stored as a vector \( [ 1, 0, 3 ] \).
[in]	x	: the value to evaluate the polynomial at.

poly_eval_horner()

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. \( 1 + 3x^2 \) is stored as a vector \( [ 1, 0, 3 ] \).
[in]	x	: the value to evaluate the polynomial at.

Note for stability: \( |x| \le 1 \)

polygamma()

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()

roots_to_monicPolynomial()

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. \( 3 + x^2 \) is stored as a vector \( [ 3, 0, 1 ] \).

setCpuCacheSizes()

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

setNbThreads()

void Eigen::setNbThreads ( int  v )

inline

Sets the max number of threads reserved for Eigen.

See also

nbThreads

viewAsCholmod() [1/3]

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.

viewAsCholmod() [2/3]

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.

viewAsCholmod() [3/3]

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.

viewAsEigen()

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.

zeta()

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()

Variable Documentation

Dynamic

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.

HereditaryBits

const unsigned int Eigen::HereditaryBits

Initial value:

= RowMajorBit
                                  | EvalBeforeNestingBit

HugeCost

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.

Infinity

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.

y

const AutoDiffScalar< DerType > & Eigen::y

Initial value:

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