devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add a draft (not clean ) version of the COLAMD ordering implementation

Browse Source
This commit is contained in:
Desire NUENTSA
2012-07-18 16:59:00 +02:00
parent 773804691a
commit b0cba2d988
5 changed files with 2604 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

93
Eigen/src/OrderingMethods/Ordering.h
View File

@@ -27,6 +27,7 @@
#define EIGEN_ORDERING_H
#include "Amd.h"
#include "Eigen_Colamd.h"
namespace Eigen {
namespace internal {
@@ -112,54 +113,50 @@ class NaturalOrdering
* Get the column approximate minimum degree ordering
* The matrix should be in column-major format
*/
// template<typename Scalar, typename Index>
// class COLAMDOrdering: public OrderingBase< ColamdOrdering<Scalar, Index> >
// {
// public:
// typedef OrderingBase< ColamdOrdering<Scalar, Index> > Base;
// typedef SparseMatrix<Scalar,ColMajor,Index> MatrixType;
//
// public:
// COLAMDOrdering():Base() {}
//
// COLAMDOrdering(const MatrixType& matrix):Base()
// {
// compute(matrix);
// }
// COLAMDOrdering(const MatrixType& mat, PermutationType& perm_c):Base()
// {
// compute(matrix);
// perm_c = this.get_perm();
// }
// void compute(const MatrixType& mat)
// {
// // Test if the matrix is column major...
//
// int m = mat.rows();
// int n = mat.cols();
// int nnz = mat.nonZeros();
// // Get the recommended value of Alen to be used by colamd
// int Alen = colamd_recommended(nnz, m, n);
// // Set the default parameters
// double knobs[COLAMD_KNOBS];
// colamd_set_defaults(knobs);
//
// int info;
// VectorXi p(n), A(nnz);
// for(int i=0; i < n; i++) p(i) = mat.outerIndexPtr()(i);
// for(int i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()(i);
// // Call Colamd routine to compute the ordering
// info = colamd(m, n, Alen, A,p , knobs, stats)
// eigen_assert( (info != FALSE)&& "COLAMD failed " );
//
// m_P.resize(n);
// for (int i = 0; i < n; i++) m_P(p(i)) = i;
// m_isInitialized = true;
// }
// protected:
// using Base::m_isInitialized;
// using Base m_P;
// };
template<typename Index>
class COLAMDOrdering;
#include "Eigen_Colamd.h"
template<typename Index>
class COLAMDOrdering
{
public:
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
typedef Matrix<Index, Dynamic, 1> IndexVector;
/** Compute the permutation vector form a sparse matrix */
template <typename MatrixType>
void operator() (const MatrixType& mat, PermutationType& perm)
{
int m = mat.rows();
int n = mat.cols();
int nnz = mat.nonZeros();
// Get the recommended value of Alen to be used by colamd
int Alen = eigen_colamd_recommended(nnz, m, n);
// Set the default parameters
double knobs [EIGEN_COLAMD_KNOBS];
int stats [EIGEN_COLAMD_STATS];
eigen_colamd_set_defaults(knobs);
int info;
IndexVector p(n+1), A(Alen);
for(int i=0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
for(int i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
// Call Colamd routine to compute the ordering
info = eigen_colamd(m, n, Alen, A.data(), p.data(), knobs, stats);
eigen_assert( info && "COLAMD failed " );
perm.resize(n);
for (int i = 0; i < n; i++) perm.indices()(p(i)) = i;
}
private:
};
} // end namespace Eigen
#endif