Eigen  3.3.7
Eigen::SuperLU< _MatrixType > Class Template Reference

Detailed Description

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

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

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

Template Parameters
 _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
Warning
This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.

This class follows the sparse solver concept .

Sparse solver concept, class SparseLU
Inheritance diagram for Eigen::SuperLU< _MatrixType >:

Public Member Functions

void analyzePattern (const MatrixType &matrix)

void factorize (const MatrixType &matrix)

Public Member Functions inherited from Eigen::SuperLUBase< _MatrixType, SuperLU< _MatrixType > >
void analyzePattern (const MatrixType &)

void compute (const MatrixType &matrix)

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

superlu_options_t & options ()

Public Member Functions inherited from Eigen::SparseSolverBase< SuperLU< _MatrixType > >
const Solve< SuperLU< _MatrixType >, Rhs > solve (const MatrixBase< Rhs > &b) const

const Solve< SuperLU< _MatrixType >, Rhs > solve (const SparseMatrixBase< Rhs > &b) const

SparseSolverBase ()

◆ analyzePattern()

template<typename _MatrixType >
 void Eigen::SuperLU< _MatrixType >::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.

factorize()

◆ factorize()

template<typename MatrixType >
 void Eigen::SuperLU< MatrixType >::factorize ( const MatrixType & matrix )

Performs a numeric decomposition of matrix

The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.