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

#include <SuiteSparseQRSupport.h>

Public Types
typedef _MatrixType::Scalar	Scalar

typedef _MatrixType::RealScalar	RealScalar

typedef SuiteSparse_long	Index

typedef SparseMatrix< Scalar, ColMajor, Index >	MatrixType

typedef PermutationMatrix< Dynamic, Dynamic >	PermutationType

Public Member Functions
	SPQR (const _MatrixType &matrix)

void	SPQR_free ()

void	compute (const _MatrixType &matrix)

Index	rows () const
	Get the number of rows of the input matrix and the Q matrix.

Index	cols () const
	Get the number of columns of the input matrix.

template<typename Rhs >
const internal::solve_retval< SPQR, Rhs >	solve (const MatrixBase< Rhs > &B) const

template<typename Rhs , typename Dest >
void	_solve (const MatrixBase< Rhs > &b, MatrixBase< Dest > &dest) const

const MatrixType	matrixR () const

SPQRMatrixQReturnType< SPQR >	matrixQ () const
	Get an expression of the matrix Q.

PermutationType	colsPermutation () const
	Get the permutation that was applied to columns of A.

Index	rank () const
	Gets the rank of the matrix. More...

void	setSPQROrdering (int ord)
	Set the fill-reducing ordering method to be used.

void	setPivotThreshold (const RealScalar &tol)
	Set the tolerance tol to treat columns with 2-norm < =tol as zero.

cholmod_common *	cholmodCommon () const

ComputationInfo	info () const
	Reports whether previous computation was successful. More...

Protected Attributes
bool	m_isInitialized

bool	m_analysisIsOk

bool	m_factorizationIsOk

bool	m_isRUpToDate

ComputationInfo	m_info

int	m_ordering

int	m_allow_tol

RealScalar	m_tolerance

cholmod_sparse *	m_cR

MatrixType	m_R

Index *	m_E

cholmod_sparse *	m_H

Index *	m_HPinv

cholmod_dense *	m_HTau

Index	m_rank

cholmod_common	m_cc

bool	m_useDefaultThreshold

Friends
template<typename , typename >
struct	SPQR_QProduct

Detailed Description

template<typename _MatrixType>
class Eigen::SPQR< _MatrixType >

Sparse QR factorization based on SuiteSparseQR library.

This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition of sparse matrices. The result is then used to solve linear leasts_square systems. Clearly, a QR factorization is returned such that A*P = Q*R where :

P is the column permutation. Use colsPermutation() to get it.

Q is the orthogonal matrix represented as Householder reflectors. Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. You can then apply it to a vector.

R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index

Template Parameters

_MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> NOTE