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

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Desire NUENTSA
2012-07-18 16:59:00 +02:00
parent 773804691a
commit b0cba2d988
5 changed files with 2604 additions and 56 deletions
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22
bench/spbench/test_sparseLU.cpp
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@@ -6,6 +6,7 @@
#include <iomanip>
#include <unsupported/Eigen/SparseExtra>
#include <Eigen/SparseLU>
#include <bench/BenchTimer.h>
using namespace std;
using namespace Eigen;
@@ -17,10 +18,12 @@ int main(int argc, char **args)
typedef Matrix<double, Dynamic, Dynamic> DenseMatrix;
typedef Matrix<double, Dynamic, 1> DenseRhs;
VectorXd b, x, tmp;
SparseLU<SparseMatrix<double, ColMajor>, AMDOrdering<int> > solver;
// SparseLU<SparseMatrix<double, ColMajor>, AMDOrdering<int> > solver;
SparseLU<SparseMatrix<double, ColMajor>, COLAMDOrdering<int> > solver;
ifstream matrix_file;
string line;
int n;
BenchTimer timer;
// Set parameters
/* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
@@ -53,13 +56,26 @@ int main(int argc, char **args)
/* Compute the factorization */
// solver.isSymmetric(true);
solver.compute(A);
timer.start();
// solver.compute(A);
solver.analyzePattern(A);
timer.stop();
cout << "Time to analyze " << timer.value() << std::endl;
timer.reset();
timer.start();
solver.factorize(A);
timer.stop();
cout << "Factorize Time " << timer.value() << std::endl;
timer.reset();
timer.start();
solver._solve(b, x);
timer.stop();
cout << "solve time " << timer.value() << std::endl;
/* Check the accuracy */
VectorXd tmp2 = b - A*x;
double tempNorm = tmp2.norm()/b.norm();
cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
cout << "Number of nonzeros in the factor : " << solver.nnzL() + solver.nnzU() << std::endl;
return 0;
}