Eigen::SparseLU< _MatrixType, _OrderingType > Class Template Reference

Sparse supernodal LU factorization for general matrices. More...

#include </home/runner/work/allwpilib/allwpilib/wpimath/src/main/native/thirdparty/eigen/include/Eigen/src/SparseLU/SparseLU.h>

Inheritance diagram for Eigen::SparseLU< _MatrixType, _OrderingType >:

Public Types
enum	{ ColsAtCompileTime = MatrixType::ColsAtCompileTime , MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime }
typedef _MatrixType	MatrixType
typedef _OrderingType	OrderingType
typedef MatrixType::Scalar	Scalar
typedef MatrixType::RealScalar	RealScalar
typedef MatrixType::StorageIndex	StorageIndex
typedef SparseMatrix< Scalar, ColMajor, StorageIndex >	NCMatrix
typedef internal::MappedSuperNodalMatrix< Scalar, StorageIndex >	SCMatrix
typedef Matrix< Scalar, Dynamic, 1 >	ScalarVector
typedef Matrix< StorageIndex, Dynamic, 1 >	IndexVector
typedef PermutationMatrix< Dynamic, Dynamic, StorageIndex >	PermutationType
typedef internal::SparseLUImpl< Scalar, StorageIndex >	Base
Public Types inherited from Eigen::internal::SparseLUImpl< _MatrixType::Scalar, _MatrixType::StorageIndex >
typedef Matrix< _MatrixType::Scalar, Dynamic, 1 >	ScalarVector
typedef Matrix< _MatrixType::StorageIndex, Dynamic, 1 >	IndexVector
typedef Matrix< _MatrixType::Scalar, Dynamic, Dynamic, ColMajor >	ScalarMatrix
typedef Map< ScalarMatrix, 0, OuterStride<> >	MappedMatrixBlock
typedef ScalarVector::RealScalar	RealScalar
typedef Ref< Matrix< _MatrixType::Scalar, Dynamic, 1 > >	BlockScalarVector
typedef Ref< Matrix< _MatrixType::StorageIndex, Dynamic, 1 > >	BlockIndexVector
typedef LU_GlobalLU_t< IndexVector, ScalarVector >	GlobalLU_t
typedef SparseMatrix< _MatrixType::Scalar, ColMajor, _MatrixType::StorageIndex >	MatrixType

Public Member Functions
	SparseLU ()
	SparseLU (const MatrixType &matrix)
	~SparseLU ()
void	analyzePattern (const MatrixType &matrix)
	Compute the column permutation to minimize the fill-in. More...
void	factorize (const MatrixType &matrix)
void	simplicialfactorize (const MatrixType &matrix)
void	compute (const MatrixType &matrix)
	Compute the symbolic and numeric factorization of the input sparse matrix. More...
const SparseLUTransposeView< false, SparseLU< _MatrixType, _OrderingType > >	transpose ()
const SparseLUTransposeView< true, SparseLU< _MatrixType, _OrderingType > >	adjoint ()
Index	rows () const
Index	cols () const
void	isSymmetric (bool sym)
	Indicate that the pattern of the input matrix is symmetric. More...
SparseLUMatrixLReturnType< SCMatrix >	matrixL () const
SparseLUMatrixUReturnType< SCMatrix, MappedSparseMatrix< Scalar, ColMajor, StorageIndex > >	matrixU () const
const PermutationType &	rowsPermutation () const
const PermutationType &	colsPermutation () const
void	setPivotThreshold (const RealScalar &thresh)
	Set the threshold used for a diagonal entry to be an acceptable pivot. More...
ComputationInfo	info () const
	Reports whether previous computation was successful. More...
std::string	lastErrorMessage () const
template<typename Rhs , typename Dest >
bool	_solve_impl (const MatrixBase< Rhs > &B, MatrixBase< Dest > &X_base) const
Scalar	absDeterminant ()
Scalar	logAbsDeterminant () const
Scalar	signDeterminant ()
Scalar	determinant ()
Index	nnzL () const
Index	nnzU () const
Public Member Functions inherited from Eigen::SparseSolverBase< SparseLU< _MatrixType, _OrderingType > >
	SparseSolverBase ()
	Default constructor. More...
	~SparseSolverBase ()
SparseLU< _MatrixType, _OrderingType > &	derived ()
const SparseLU< _MatrixType, _OrderingType > &	derived () const
const Solve< SparseLU< _MatrixType, _OrderingType >, Rhs >	solve (const MatrixBase< Rhs > &b) const
const Solve< SparseLU< _MatrixType, _OrderingType >, Rhs >	solve (const SparseMatrixBase< Rhs > &b) const
void	_solve_impl (const SparseMatrixBase< Rhs > &b, SparseMatrixBase< Dest > &dest) const

Protected Types
typedef SparseSolverBase< SparseLU< _MatrixType, _OrderingType > >	APIBase

Protected Member Functions
void	initperfvalues ()
Protected Member Functions inherited from Eigen::internal::SparseLUImpl< _MatrixType::Scalar, _MatrixType::StorageIndex >
Index	expand (VectorType &vec, Index &length, Index nbElts, Index keep_prev, Index &num_expansions)
	Expand the existing storage to accommodate more fill-ins. More...
Index	memInit (Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size, GlobalLU_t &glu)
	Allocate various working space for the numerical factorization phase. More...
Index	memXpand (VectorType &vec, Index &maxlen, Index nbElts, MemType memtype, Index &num_expansions)
	Expand the existing storage. More...
void	heap_relax_snode (const Index n, IndexVector &et, const Index relax_columns, IndexVector &descendants, IndexVector &relax_end)
	Identify the initial relaxed supernodes. More...
void	relax_snode (const Index n, IndexVector &et, const Index relax_columns, IndexVector &descendants, IndexVector &relax_end)
	Identify the initial relaxed supernodes. More...
Index	snode_dfs (const Index jcol, const Index kcol, const MatrixType &mat, IndexVector &xprune, IndexVector &marker, GlobalLU_t &glu)
Index	snode_bmod (const Index jcol, const Index fsupc, ScalarVector &dense, GlobalLU_t &glu)
Index	pivotL (const Index jcol, const RealScalar &diagpivotthresh, IndexVector &perm_r, IndexVector &iperm_c, Index &pivrow, GlobalLU_t &glu)
	Performs the numerical pivotin on the current column of L, and the CDIV operation. More...
void	dfs_kernel (const _MatrixType::StorageIndex jj, IndexVector &perm_r, Index &nseg, IndexVector &panel_lsub, IndexVector &segrep, Ref< IndexVector > repfnz_col, IndexVector &xprune, Ref< IndexVector > marker, IndexVector &parent, IndexVector &xplore, GlobalLU_t &glu, Index &nextl_col, Index krow, Traits &traits)
void	panel_dfs (const Index m, const Index w, const Index jcol, MatrixType &A, IndexVector &perm_r, Index &nseg, ScalarVector &dense, IndexVector &panel_lsub, IndexVector &segrep, IndexVector &repfnz, IndexVector &xprune, IndexVector &marker, IndexVector &parent, IndexVector &xplore, GlobalLU_t &glu)
	Performs a symbolic factorization on a panel of columns [jcol, jcol+w) More...
void	panel_bmod (const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector &dense, ScalarVector &tempv, IndexVector &segrep, IndexVector &repfnz, GlobalLU_t &glu)
	Performs numeric block updates (sup-panel) in topological order. More...
Index	column_dfs (const Index m, const Index jcol, IndexVector &perm_r, Index maxsuper, Index &nseg, BlockIndexVector lsub_col, IndexVector &segrep, BlockIndexVector repfnz, IndexVector &xprune, IndexVector &marker, IndexVector &parent, IndexVector &xplore, GlobalLU_t &glu)
	Performs a symbolic factorization on column jcol and decide the supernode boundary. More...
Index	column_bmod (const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector &tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t &glu)
	Performs numeric block updates (sup-col) in topological order. More...
Index	copy_to_ucol (const Index jcol, const Index nseg, IndexVector &segrep, BlockIndexVector repfnz, IndexVector &perm_r, BlockScalarVector dense, GlobalLU_t &glu)
	Performs numeric block updates (sup-col) in topological order. More...
void	pruneL (const Index jcol, const IndexVector &perm_r, const Index pivrow, const Index nseg, const IndexVector &segrep, BlockIndexVector repfnz, IndexVector &xprune, GlobalLU_t &glu)
	Prunes the L-structure. More...
void	countnz (const Index n, Index &nnzL, Index &nnzU, GlobalLU_t &glu)
	Count Nonzero elements in the factors. More...
void	fixupL (const Index n, const IndexVector &perm_r, GlobalLU_t &glu)
	Fix up the data storage lsub for L-subscripts. More...

Protected Attributes
ComputationInfo	m_info
bool	m_factorizationIsOk
bool	m_analysisIsOk
std::string	m_lastError
NCMatrix	m_mat
SCMatrix	m_Lstore
MappedSparseMatrix< Scalar, ColMajor, StorageIndex >	m_Ustore
PermutationType	m_perm_c
PermutationType	m_perm_r
IndexVector	m_etree
Base::GlobalLU_t	m_glu
bool	m_symmetricmode
internal::perfvalues	m_perfv
RealScalar	m_diagpivotthresh
Index	m_nnzL
Index	m_nnzU
Index	m_detPermR
Index	m_detPermC
Protected Attributes inherited from Eigen::SparseSolverBase< SparseLU< _MatrixType, _OrderingType > >
bool	m_isInitialized

Detailed Description

template<typename _MatrixType, typename _OrderingType>
class Eigen::SparseLU< _MatrixType, _OrderingType >

Sparse supernodal LU factorization for general matrices.

This class implements the supernodal LU factorization for general matrices. It uses the main techniques from the sequential SuperLU package (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real and complex arithmetic with single and double precision, depending on the scalar type of your input matrix. The code has been optimized to provide BLAS-3 operations during supernode-panel updates. It benefits directly from the built-in high-performant Eigen BLAS routines. Moreover, when the size of a supernode is very small, the BLAS calls are avoided to enable a better optimization from the compiler. For best performance, you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors.

An important parameter of this class is the ordering method. It is used to reorder the columns (and eventually the rows) of the matrix to reduce the number of new elements that are created during numerical factorization. The cheapest method available is COLAMD. See the OrderingMethods module for the list of built-in and external ordering methods.

Simple example with key steps

VectorXd x(n), b(n);
SparseMatrix<double> A;
SparseLU<SparseMatrix<double>, COLAMDOrdering<int> >   solver;
// fill A and b;
// Compute the ordering permutation vector from the structural pattern of A
solver.analyzePattern(A); 
// Compute the numerical factorization 
solver.factorize(A); 
//Use the factors to solve the linear system 
x = solver.solve(b); 

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.

Note

Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. If this is the case for your matrices, you can try the basic scaling method at "unsupported/Eigen/src/IterativeSolvers/Scaling.h"

Template Parameters

_MatrixType	The type of the sparse matrix. It must be a column-major SparseMatrix<>
_OrderingType	The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD

\implsparsesolverconcept

See also

TutorialSparseSolverConcept

OrderingMethods module

Member Typedef Documentation

APIBase

template<typename _MatrixType , typename _OrderingType >

typedef SparseSolverBase<SparseLU<_MatrixType,_OrderingType> > Eigen::SparseLU< _MatrixType, _OrderingType >::APIBase

protected

Base

template<typename _MatrixType , typename _OrderingType >

typedef internal::SparseLUImpl<Scalar, StorageIndex> Eigen::SparseLU< _MatrixType, _OrderingType >::Base

IndexVector

template<typename _MatrixType , typename _OrderingType >

typedef Matrix<StorageIndex,Dynamic,1> Eigen::SparseLU< _MatrixType, _OrderingType >::IndexVector

MatrixType

template<typename _MatrixType , typename _OrderingType >

typedef _MatrixType Eigen::SparseLU< _MatrixType, _OrderingType >::MatrixType

NCMatrix

template<typename _MatrixType , typename _OrderingType >

typedef SparseMatrix<Scalar,ColMajor,StorageIndex> Eigen::SparseLU< _MatrixType, _OrderingType >::NCMatrix

OrderingType

template<typename _MatrixType , typename _OrderingType >

typedef _OrderingType Eigen::SparseLU< _MatrixType, _OrderingType >::OrderingType

PermutationType

template<typename _MatrixType , typename _OrderingType >

typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> Eigen::SparseLU< _MatrixType, _OrderingType >::PermutationType

RealScalar

template<typename _MatrixType , typename _OrderingType >

typedef MatrixType::RealScalar Eigen::SparseLU< _MatrixType, _OrderingType >::RealScalar

Scalar

template<typename _MatrixType , typename _OrderingType >

typedef MatrixType::Scalar Eigen::SparseLU< _MatrixType, _OrderingType >::Scalar

ScalarVector

template<typename _MatrixType , typename _OrderingType >

typedef Matrix<Scalar,Dynamic,1> Eigen::SparseLU< _MatrixType, _OrderingType >::ScalarVector

SCMatrix

template<typename _MatrixType , typename _OrderingType >

typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> Eigen::SparseLU< _MatrixType, _OrderingType >::SCMatrix

StorageIndex

template<typename _MatrixType , typename _OrderingType >

typedef MatrixType::StorageIndex Eigen::SparseLU< _MatrixType, _OrderingType >::StorageIndex

Member Enumeration Documentation

anonymous enum

template<typename _MatrixType , typename _OrderingType >

anonymous enum

Enumerator
ColsAtCompileTime
MaxColsAtCompileTime

Constructor & Destructor Documentation

SparseLU() [1/2]

template<typename _MatrixType , typename _OrderingType >

Eigen::SparseLU< _MatrixType, _OrderingType >::SparseLU ( )

inline

SparseLU() [2/2]

template<typename _MatrixType , typename _OrderingType >

Eigen::SparseLU< _MatrixType, _OrderingType >::SparseLU ( const MatrixType &  matrix )

inlineexplicit

~SparseLU()

template<typename _MatrixType , typename _OrderingType >

Eigen::SparseLU< _MatrixType, _OrderingType >::~SparseLU ( )

inline

Member Function Documentation

_solve_impl()

template<typename _MatrixType , typename _OrderingType >

template<typename Rhs , typename Dest >

bool Eigen::SparseLU< _MatrixType, _OrderingType >::_solve_impl ( const MatrixBase< Rhs > &  B, MatrixBase< Dest > &  X_base  ) const

inline

absDeterminant()

template<typename _MatrixType , typename _OrderingType >

Scalar Eigen::SparseLU< _MatrixType, _OrderingType >::absDeterminant ( )

inline

Returns

the absolute value of the determinant of the matrix of which *this is the QR decomposition.

Warning

a determinant can be very big or small, so for matrices of large enough dimension, there is a risk of overflow/underflow. One way to work around that is to use logAbsDeterminant() instead.

See also

logAbsDeterminant(), signDeterminant()

adjoint()

template<typename _MatrixType , typename _OrderingType >

const SparseLUTransposeView< true, SparseLU< _MatrixType, _OrderingType > > Eigen::SparseLU< _MatrixType, _OrderingType >::adjoint ( )

inline

Returns

an expression of the adjoint of the factored matrix

A typical usage is to solve for the adjoint problem A' x = b:

solver.compute(A);
x = solver.adjoint().solve(b);

For real scalar types, this function is equivalent to transpose().

See also

transpose(), solve()

analyzePattern()

template<typename MatrixType , typename OrderingType >

void Eigen::SparseLU< MatrixType, OrderingType >::analyzePattern

(

const MatrixType &

mat

)

Compute the column permutation to minimize the fill-in.

• Apply this permutation to the input matrix -
• Compute the column elimination tree on the permuted matrix
• Postorder the elimination tree and the column permutation

cols()

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::cols ( ) const

inline

colsPermutation()

template<typename _MatrixType , typename _OrderingType >

const PermutationType & Eigen::SparseLU< _MatrixType, _OrderingType >::colsPermutation ( ) const

inline

Returns

a reference to the column matrix permutation \( P_c^T \) such that \(P_r A P_c^T = L U\)

See also

rowsPermutation()

compute()

template<typename _MatrixType , typename _OrderingType >

void Eigen::SparseLU< _MatrixType, _OrderingType >::compute ( const MatrixType &  matrix )

inline

Compute the symbolic and numeric factorization of the input sparse matrix.

The input matrix should be in column-major storage.

determinant()

template<typename _MatrixType , typename _OrderingType >

Scalar Eigen::SparseLU< _MatrixType, _OrderingType >::determinant ( )

inline

Returns

The determinant of the matrix.

See also

absDeterminant(), logAbsDeterminant()

factorize()

template<typename MatrixType , typename OrderingType >

void Eigen::SparseLU< MatrixType, OrderingType >::factorize

(

const MatrixType &

matrix

)

• Numerical factorization

• Interleaved with the symbolic factorization On exit, info is

  = 0: successful factorization

‍0: if info = i, and i is

<= A->ncol: U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, and division by zero will occur if it is used to solve a system of equations.

> A->ncol: number of bytes allocated when memory allocation failure occurred, plus A->ncol. If lwork = -1, it is the estimated amount of space needed, plus A->ncol.

info()

template<typename _MatrixType , typename _OrderingType >

ComputationInfo Eigen::SparseLU< _MatrixType, _OrderingType >::info ( ) const

inline

Reports whether previous computation was successful.

Returns

Success if computation was successful, NumericalIssue if the LU factorization reports a problem, zero diagonal for instance InvalidInput if the input matrix is invalid

See also

iparm()

initperfvalues()

template<typename _MatrixType , typename _OrderingType >

void Eigen::SparseLU< _MatrixType, _OrderingType >::initperfvalues ( )

inlineprotected

isSymmetric()

template<typename _MatrixType , typename _OrderingType >

void Eigen::SparseLU< _MatrixType, _OrderingType >::isSymmetric ( bool  sym )

inline

Indicate that the pattern of the input matrix is symmetric.

lastErrorMessage()

template<typename _MatrixType , typename _OrderingType >

std::string Eigen::SparseLU< _MatrixType, _OrderingType >::lastErrorMessage ( ) const

inline

Returns

A string describing the type of error

logAbsDeterminant()

template<typename _MatrixType , typename _OrderingType >

Scalar Eigen::SparseLU< _MatrixType, _OrderingType >::logAbsDeterminant ( ) const

inline

Returns

the natural log of the absolute value of the determinant of the matrix of which **this is the QR decomposition

Note

This method is useful to work around the risk of overflow/underflow that's inherent to the determinant computation.

See also

absDeterminant(), signDeterminant()

matrixL()

template<typename _MatrixType , typename _OrderingType >

SparseLUMatrixLReturnType< SCMatrix > Eigen::SparseLU< _MatrixType, _OrderingType >::matrixL ( ) const

inline

Returns

an expression of the matrix L, internally stored as supernodes The only operation available with this expression is the triangular solve

y = b; matrixL().solveInPlace(y);

matrixU()

template<typename _MatrixType , typename _OrderingType >

SparseLUMatrixUReturnType< SCMatrix, MappedSparseMatrix< Scalar, ColMajor, StorageIndex > > Eigen::SparseLU< _MatrixType, _OrderingType >::matrixU ( ) const

inline

Returns

an expression of the matrix U, The only operation available with this expression is the triangular solve

y = b; matrixU().solveInPlace(y);

nnzL()

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::nnzL ( ) const

inline

nnzU()

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::nnzU ( ) const

inline

rows()

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::rows ( ) const

inline

rowsPermutation()

template<typename _MatrixType , typename _OrderingType >

const PermutationType & Eigen::SparseLU< _MatrixType, _OrderingType >::rowsPermutation ( ) const

inline

Returns

a reference to the row matrix permutation \( P_r \) such that \(P_r A P_c^T = L U\)

See also

colsPermutation()

setPivotThreshold()

template<typename _MatrixType , typename _OrderingType >

void Eigen::SparseLU< _MatrixType, _OrderingType >::setPivotThreshold ( const RealScalar &  thresh )

inline

Set the threshold used for a diagonal entry to be an acceptable pivot.

signDeterminant()

template<typename _MatrixType , typename _OrderingType >

Scalar Eigen::SparseLU< _MatrixType, _OrderingType >::signDeterminant ( )

inline

Returns

A number representing the sign of the determinant

See also

absDeterminant(), logAbsDeterminant()

simplicialfactorize()

template<typename _MatrixType , typename _OrderingType >

void Eigen::SparseLU< _MatrixType, _OrderingType >::simplicialfactorize

(

const MatrixType &

matrix

)

transpose()

template<typename _MatrixType , typename _OrderingType >

const SparseLUTransposeView< false, SparseLU< _MatrixType, _OrderingType > > Eigen::SparseLU< _MatrixType, _OrderingType >::transpose ( )

inline

Returns

an expression of the transposed of the factored matrix.

A typical usage is to solve for the transposed problem A^T x = b:

solver.compute(A);
x = solver.transpose().solve(b);

See also

adjoint(), solve()

Member Data Documentation

m_analysisIsOk

template<typename _MatrixType , typename _OrderingType >

bool Eigen::SparseLU< _MatrixType, _OrderingType >::m_analysisIsOk

protected

m_detPermC

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::m_detPermC

protected

m_detPermR

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::m_detPermR

protected

m_diagpivotthresh

template<typename _MatrixType , typename _OrderingType >

RealScalar Eigen::SparseLU< _MatrixType, _OrderingType >::m_diagpivotthresh

protected

m_etree

template<typename _MatrixType , typename _OrderingType >

IndexVector Eigen::SparseLU< _MatrixType, _OrderingType >::m_etree

protected

m_factorizationIsOk

template<typename _MatrixType , typename _OrderingType >

bool Eigen::SparseLU< _MatrixType, _OrderingType >::m_factorizationIsOk

protected

m_glu

template<typename _MatrixType , typename _OrderingType >

Base::GlobalLU_t Eigen::SparseLU< _MatrixType, _OrderingType >::m_glu

protected

m_info

template<typename _MatrixType , typename _OrderingType >

ComputationInfo Eigen::SparseLU< _MatrixType, _OrderingType >::m_info

mutableprotected

m_lastError

template<typename _MatrixType , typename _OrderingType >

std::string Eigen::SparseLU< _MatrixType, _OrderingType >::m_lastError

protected

m_Lstore

template<typename _MatrixType , typename _OrderingType >

SCMatrix Eigen::SparseLU< _MatrixType, _OrderingType >::m_Lstore

protected

m_mat

template<typename _MatrixType , typename _OrderingType >

NCMatrix Eigen::SparseLU< _MatrixType, _OrderingType >::m_mat

protected

m_nnzL

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::m_nnzL

protected

m_nnzU

template<typename _MatrixType , typename _OrderingType >

Index Eigen::SparseLU< _MatrixType, _OrderingType >::m_nnzU

protected

m_perfv

template<typename _MatrixType , typename _OrderingType >

internal::perfvalues Eigen::SparseLU< _MatrixType, _OrderingType >::m_perfv

protected

m_perm_c

template<typename _MatrixType , typename _OrderingType >

PermutationType Eigen::SparseLU< _MatrixType, _OrderingType >::m_perm_c

protected

m_perm_r

template<typename _MatrixType , typename _OrderingType >

PermutationType Eigen::SparseLU< _MatrixType, _OrderingType >::m_perm_r

protected

m_symmetricmode

template<typename _MatrixType , typename _OrderingType >

bool Eigen::SparseLU< _MatrixType, _OrderingType >::m_symmetricmode

protected

m_Ustore

template<typename _MatrixType , typename _OrderingType >

MappedSparseMatrix<Scalar,ColMajor,StorageIndex> Eigen::SparseLU< _MatrixType, _OrderingType >::m_Ustore

protected

---

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

• /home/runner/work/allwpilib/allwpilib/wpimath/src/main/native/thirdparty/eigen/include/Eigen/src/SparseLU/SparseLU.h