UmfPackLU< _MatrixType > Class Template Reference

Detailed Description

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

A sparse LU factorization and solver based on UmfPack.

This class allows to solve for A.X = B sparse linear problems via a LU factorization using the UmfPack library. The sparse matrix A must be squared and full rank. The vectors or matrices X and B can be either dense or sparse.

Warning

The input matrix A should be in a compressed and column-major form. Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.

Template Parameters

_MatrixType

the type of the sparse matrix A, it must be a SparseMatrix<>

See also

Sparse solvers

Inherits noncopyable.

Public Member Functions
void	analyzePattern (const MatrixType &matrix)
void	compute (const MatrixType &matrix)
void	factorize (const MatrixType &matrix)
ComputationInfo	info () const
	Reports whether previous computation was successful. More...
template<typename Rhs >
const internal::solve_retval < UmfPackLU, Rhs >	solve (const MatrixBase< Rhs > &b) const
template<typename Rhs >
const internal::sparse_solve_retval < UmfPackLU, Rhs >	solve (const SparseMatrixBase< Rhs > &b) const

Member Function Documentation

void analyzePattern ( const MatrixType &  matrix )

inline

Performs a symbolic decomposition on the sparcity of matrix.

This function is particularly useful when solving for several problems having the same structure.

See also

factorize(), compute()

References Eigen::InvalidInput, and Eigen::Success.

Referenced by UmfPackLU< _MatrixType >::compute().

void compute ( const MatrixType &  matrix )

inline

Computes the sparse Cholesky decomposition of matrix Note that the matrix should be column-major, and in compressed format for best performance.

See also

SparseMatrix::makeCompressed().

References UmfPackLU< _MatrixType >::analyzePattern(), and UmfPackLU< _MatrixType >::factorize().

void factorize ( const MatrixType &  matrix )

inline

Performs a numeric decomposition of matrix

The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.

See also

analyzePattern(), compute()

References Eigen::NumericalIssue, and Eigen::Success.

Referenced by UmfPackLU< _MatrixType >::compute().

ComputationInfo info ( ) const

inline

Reports whether previous computation was successful.

Returns

Success if computation was succesful, NumericalIssue if the matrix.appears to be negative.

const internal::solve_retval<UmfPackLU, Rhs> solve ( const MatrixBase< Rhs > &  b ) const

inline

Returns

the solution x of using the current decomposition of A.

See also

compute()

const internal::sparse_solve_retval<UmfPackLU, Rhs> solve ( const SparseMatrixBase< Rhs > &  b ) const

inline

Returns

the solution x of using the current decomposition of A.

See also

compute()

References EigenBase< Derived >::derived(), and SparseMatrixBase< Derived >::rows().

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The documentation for this class was generated from the following file:

• UmfPackSupport.h