 Eigen  3.4.90 (git rev a4098ac676528a83cfb73d4d26ce1b42ec05f47c) 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< Derived >
template<typename Rhs >
const Solve< Derived, Rhs > solve (const MatrixBase< Rhs > &b) const

template<typename Rhs >
const Solve< Derived, 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.