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Sparse module:

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* enable complex support for the CHOLMOD LLT backend
   using CHOLMOD's triangular solver
 * quick fix for complex support in SparseLLT::solve
This commit is contained in:
Gael Guennebaud
2008-12-27 18:13:29 +00:00
parent 361225068d
commit ce3984844d
4 changed files with 126 additions and 58 deletions
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91
Eigen/src/Sparse/CholmodSupport.h
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@@ -25,6 +25,35 @@
#ifndef EIGEN_CHOLMODSUPPORT_H
#define EIGEN_CHOLMODSUPPORT_H
template<typename Scalar, typename CholmodType>
void ei_cholmod_configure_matrix(CholmodType& mat)
{
if (ei_is_same_type<Scalar,float>::ret)
{
mat.xtype = CHOLMOD_REAL;
mat.dtype = 1;
}
else if (ei_is_same_type<Scalar,double>::ret)
{
mat.xtype = CHOLMOD_REAL;
mat.dtype = 0;
}
else if (ei_is_same_type<Scalar,std::complex<float> >::ret)
{
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = 1;
}
else if (ei_is_same_type<Scalar,std::complex<double> >::ret)
{
mat.xtype = CHOLMOD_COMPLEX;
mat.dtype = 0;
}
else
{
ei_assert(false && "Scalar type not supported by CHOLMOD");
}
}
template<typename Scalar, int Flags>
cholmod_sparse SparseMatrix<Scalar,Flags>::asCholmodMatrix()
{
@@ -42,30 +71,7 @@ cholmod_sparse SparseMatrix<Scalar,Flags>::asCholmodMatrix()
res.dtype = 0;
res.stype = -1;
if (ei_is_same_type<Scalar,float>::ret)
{
res.xtype = CHOLMOD_REAL;
res.dtype = 1;
}
else if (ei_is_same_type<Scalar,double>::ret)
{
res.xtype = CHOLMOD_REAL;
res.dtype = 0;
}
else if (ei_is_same_type<Scalar,std::complex<float> >::ret)
{
res.xtype = CHOLMOD_COMPLEX;
res.dtype = 1;
}
else if (ei_is_same_type<Scalar,std::complex<double> >::ret)
{
res.xtype = CHOLMOD_COMPLEX;
res.dtype = 0;
}
else
{
ei_assert(false && "Scalar type not supported by CHOLMOD");
}
ei_cholmod_configure_matrix<Scalar>(res);
if (Flags & SelfAdjoint)
{
@@ -82,6 +88,24 @@ cholmod_sparse SparseMatrix<Scalar,Flags>::asCholmodMatrix()
return res;
}
template<typename Derived>
cholmod_dense ei_cholmod_map_eigen_to_dense(MatrixBase<Derived>& mat)
{
typedef typename Derived::Scalar Scalar;
cholmod_dense res;
res.nrow = mat.rows();
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = mat.derived().stride();
res.x = mat.derived().data();
res.z = 0;
ei_cholmod_configure_matrix<Scalar>(res);
return res;
}
template<typename Scalar, int Flags>
SparseMatrix<Scalar,Flags> SparseMatrix<Scalar,Flags>::Map(cholmod_sparse& cm)
{
@@ -103,7 +127,7 @@ class SparseLLT<MatrixType,Cholmod> : public SparseLLT<MatrixType>
{
protected:
typedef SparseLLT<MatrixType> Base;
using Base::Scalar;
using typename Base::Scalar;
using Base::RealScalar;
using Base::MatrixLIsDirty;
using Base::SupernodalFactorIsDirty;
@@ -155,11 +179,12 @@ void SparseLLT<MatrixType,Cholmod>::compute(const MatrixType& a)
}
cholmod_sparse A = const_cast<MatrixType&>(a).asCholmodMatrix();
m_cholmod.supernodal = CHOLMOD_AUTO;
// TODO
if (m_flags&IncompleteFactorization)
{
m_cholmod.nmethods = 1;
m_cholmod.method [0].ordering = CHOLMOD_NATURAL;
m_cholmod.method[0].ordering = CHOLMOD_NATURAL;
m_cholmod.postorder = 0;
}
else
@@ -196,13 +221,19 @@ template<typename MatrixType>
template<typename Derived>
void SparseLLT<MatrixType,Cholmod>::solveInPlace(MatrixBase<Derived> &b) const
{
if (m_status & MatrixLIsDirty)
matrixL();
const int size = m_matrix.rows();
const int size = m_cholmodFactor->n;
ei_assert(size==b.rows());
// this uses Eigen's triangular sparse solver
if (m_status & MatrixLIsDirty)
matrixL();
Base::solveInPlace(b);
// as long as our own triangular sparse solver is not fully optimal,
// let's use CHOLMOD's one:
// cholmod_dense cdb = ei_cholmod_map_eigen_to_dense(b);
// cholmod_dense* x = cholmod_solve(CHOLMOD_LDLt, m_cholmodFactor, &cdb, &m_cholmod);
// b = Matrix<typename Base::Scalar,Dynamic,1>::Map(reinterpret_cast<typename Base::Scalar*>(x->x),b.rows());
// cholmod_free_dense(&x, &m_cholmod);
}
#endif // EIGEN_CHOLMODSUPPORT_H