// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_UMFPACKSUPPORT_H
#define EIGEN_UMFPACKSUPPORT_H
namespace Eigen {
/* TODO extract L, extract U, compute det, etc... */
// generic double/complex<double> wrapper functions:
inline void umfpack_defaults(double control[UMFPACK_CONTROL], double)
{ umfpack_di_defaults(control); }
inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>)
{ umfpack_zi_defaults(control); }
inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double)
{ umfpack_di_report_info(control, info);}
inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>)
{ umfpack_zi_report_info(control, info);}
inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double)
{ umfpack_di_report_status(control, status);}
inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>)
{ umfpack_zi_report_status(control, status);}
inline void umfpack_report_control(double control[UMFPACK_CONTROL], double)
{ umfpack_di_report_control(control);}
inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>)
{ umfpack_zi_report_control(control);}
inline void umfpack_free_numeric(void **Numeric, double)
{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
inline void umfpack_free_numeric(void **Numeric, std::complex<double>)
{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
inline void umfpack_free_symbolic(void **Symbolic, double)
{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>)
{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
inline int umfpack_symbolic(int n_row,int n_col,
const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
{
return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
}
inline int umfpack_symbolic(int n_row,int n_col,
const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
{
return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
}
inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
void *Symbolic, void **Numeric,
const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
{
return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
}
inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
void *Symbolic, void **Numeric,
const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
{
return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
}
inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
double X[], const double B[], void *Numeric,
const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
{
return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
}
inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
std::complex<double> X[], const std::complex<double> B[], void *Numeric,
const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
{
return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
}
inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
{
return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
}
inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
{
return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
}
inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
{
return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
}
inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
{
double& lx0_real = numext::real_ref(Lx[0]);
double& ux0_real = numext::real_ref(Ux[0]);
double& dx0_real = numext::real_ref(Dx[0]);
return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
}
inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
{
return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
}
inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
{
double& mx_real = numext::real_ref(*Mx);
return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
}
/** \ingroup UmfPackSupport_Module
* \brief 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 \b compressed and \b column-major form.
* Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
*
* \implsparsesolverconcept
*
* \sa \ref TutorialSparseSolverConcept, class SparseLU
*/
template<typename _MatrixType>
class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> >
{
protected:
typedef SparseSolverBase<UmfPackLU<_MatrixType> > Base;
using Base::m_isInitialized;
public:
using Base::_solve_impl;
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Matrix<Scalar,Dynamic,1> Vector;
typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
typedef SparseMatrix<Scalar> LUMatrixType;
typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType;
typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
enum {
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
};
public:
typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
typedef Array<double, UMFPACK_INFO, 1> UmfpackInfo;
UmfPackLU()
: m_dummy(0,0), mp_matrix(m_dummy)
{
init();
}
template<typename InputMatrixType>
explicit UmfPackLU(const InputMatrixType& matrix)
: mp_matrix(matrix)
{
init();
compute(matrix);
}
~UmfPackLU()
{
if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
}
inline Index rows() const { return mp_matrix.rows(); }
inline Index cols() const { return mp_matrix.cols(); }
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
*/
ComputationInfo info() const
{
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
inline const LUMatrixType& matrixL() const
{
if (m_extractedDataAreDirty) extractData();
return m_l;
}
inline const LUMatrixType& matrixU() const
{
if (m_extractedDataAreDirty) extractData();
return m_u;
}
inline const IntColVectorType& permutationP() const
{
if (m_extractedDataAreDirty) extractData();
return m_p;
}
inline const IntRowVectorType& permutationQ() const
{
if (m_extractedDataAreDirty) extractData();
return m_q;
}
/** Computes the sparse Cholesky decomposition of \a matrix
* Note that the matrix should be column-major, and in compressed format for best performance.
* \sa SparseMatrix::makeCompressed().
*/
template<typename InputMatrixType>
void compute(const InputMatrixType& matrix)
{
if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
grab(matrix.derived());
analyzePattern_impl();
factorize_impl();
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
*
* \sa factorize(), compute()
*/
template<typename InputMatrixType>
void analyzePattern(const InputMatrixType& matrix)
{
if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
grab(matrix.derived());
analyzePattern_impl();
}
/** Provides the return status code returned by UmfPack during the numeric
* factorization.
*
* \sa factorize(), compute()
*/
inline int umfpackFactorizeReturncode() const
{
eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
return m_fact_errorCode;
}
/** Provides access to the control settings array used by UmfPack.
*
* If this array contains NaN's, the default values are used.
*
* See UMFPACK documentation for details.
*/
inline const UmfpackControl& umfpackControl() const
{
return m_control;
}
/** Provides access to the control settings array used by UmfPack.
*
* If this array contains NaN's, the default values are used.
*
* See UMFPACK documentation for details.
*/
inline UmfpackControl& umfpackControl()
{
return m_control;
}
/** Performs a numeric decomposition of \a matrix
*
* The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
*
* \sa analyzePattern(), compute()
*/
template<typename InputMatrixType>
void factorize(const InputMatrixType& matrix)
{
eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
if(m_numeric)
umfpack_free_numeric(&m_numeric,Scalar());
grab(matrix.derived());
factorize_impl();
}
/** Prints the current UmfPack control settings.
*
* \sa umfpackControl()
*/
void umfpackReportControl()
{
umfpack_report_control(m_control.data(), Scalar());
}
/** Prints statistics collected by UmfPack.
*
* \sa analyzePattern(), compute()
*/
void umfpackReportInfo()
{
eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
umfpack_report_info(m_control.data(), m_umfpackInfo.data(), Scalar());
}
/** Prints the status of the previous factorization operation performed by UmfPack (symbolic or numerical factorization).
*
* \sa analyzePattern(), compute()
*/
void umfpackReportStatus() {
eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
umfpack_report_status(m_control.data(), m_fact_errorCode, Scalar());
}
/** \internal */
template<typename BDerived,typename XDerived>
bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
Scalar determinant() const;
void extractData() const;
protected:
void init()
{
m_info = InvalidInput;
m_isInitialized = false;
m_numeric = 0;
m_symbolic = 0;
m_extractedDataAreDirty = true;
umfpack_defaults(m_control.data(), Scalar());
}
void analyzePattern_impl()
{
m_fact_errorCode = umfpack_symbolic(internal::convert_index<int>(mp_matrix.rows()),
internal::convert_index<int>(mp_matrix.cols()),
mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
&m_symbolic, m_control.data(), m_umfpackInfo.data());
m_isInitialized = true;
m_info = m_fact_errorCode ? InvalidInput : Success;
m_analysisIsOk = true;
m_factorizationIsOk = false;
m_extractedDataAreDirty = true;
}
void factorize_impl()
{
m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
m_symbolic, &m_numeric, m_control.data(), m_umfpackInfo.data());
m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
m_factorizationIsOk = true;
m_extractedDataAreDirty = true;
}
template<typename MatrixDerived>
void grab(const EigenBase<MatrixDerived> &A)
{
mp_matrix.~UmfpackMatrixRef();
::new (&mp_matrix) UmfpackMatrixRef(A.derived());
}
void grab(const UmfpackMatrixRef &A)
{
if(&(A.derived()) != &mp_matrix)
{
mp_matrix.~UmfpackMatrixRef();
::new (&mp_matrix) UmfpackMatrixRef(A);
}
}
// cached data to reduce reallocation, etc.
mutable LUMatrixType m_l;
int m_fact_errorCode;
UmfpackControl m_control;
mutable UmfpackInfo m_umfpackInfo;
mutable LUMatrixType m_u;
mutable IntColVectorType m_p;
mutable IntRowVectorType m_q;
UmfpackMatrixType m_dummy;
UmfpackMatrixRef mp_matrix;
void* m_numeric;
void* m_symbolic;
mutable ComputationInfo m_info;
int m_factorizationIsOk;
int m_analysisIsOk;
mutable bool m_extractedDataAreDirty;
private:
UmfPackLU(const UmfPackLU& ) { }
};
template<typename MatrixType>
void UmfPackLU<MatrixType>::extractData() const
{
if (m_extractedDataAreDirty)
{
// get size of the data
int lnz, unz, rows, cols, nz_udiag;
umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
// allocate data
m_l.resize(rows,(std::min)(rows,cols));
m_l.resizeNonZeros(lnz);
m_u.resize((std::min)(rows,cols),cols);
m_u.resizeNonZeros(unz);
m_p.resize(rows);
m_q.resize(cols);
// extract
umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
m_extractedDataAreDirty = false;
}
}
template<typename MatrixType>
typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
{
Scalar det;
umfpack_get_determinant(&det, 0, m_numeric, 0);
return det;
}
template<typename MatrixType>
template<typename BDerived,typename XDerived>
bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
{
Index rhsCols = b.cols();
eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
int errorCode;
Scalar* x_ptr = 0;
Matrix<Scalar,Dynamic,1> x_tmp;
if(x.innerStride()!=1)
{
x_tmp.resize(x.rows());
x_ptr = x_tmp.data();
}
for (int j=0; j<rhsCols; ++j)
{
if(x.innerStride()==1)
x_ptr = &x.col(j).coeffRef(0);
errorCode = umfpack_solve(UMFPACK_A,
mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
x_ptr, &b.const_cast_derived().col(j).coeffRef(0), m_numeric, m_control.data(), m_umfpackInfo.data());
if(x.innerStride()!=1)
x.col(j) = x_tmp;
if (errorCode!=0)
return false;
}
return true;
}
} // end namespace Eigen
#endif // EIGEN_UMFPACKSUPPORT_H