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125 lines
3.9 KiB
125 lines
3.9 KiB
// Small bench routine for Eigen available in Eigen
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// (C) Desire NUENTSA WAKAM, INRIA
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#include <iostream>
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#include <fstream>
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#include <iomanip>
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#include <Eigen/Jacobi>
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#include <Eigen/Householder>
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#include <Eigen/IterativeLinearSolvers>
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#include <Eigen/LU>
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#include <unsupported/Eigen/SparseExtra>
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//#include <Eigen/SparseLU>
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#include <Eigen/SuperLUSupport>
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// #include <unsupported/Eigen/src/IterativeSolvers/Scaling.h>
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#include <bench/BenchTimer.h>
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#include <unsupported/Eigen/IterativeSolvers>
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using namespace std;
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using namespace Eigen;
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int main(int argc, char **args)
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{
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SparseMatrix<double, ColMajor> A;
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typedef SparseMatrix<double, ColMajor>::Index Index;
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typedef Matrix<double, Dynamic, Dynamic> DenseMatrix;
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typedef Matrix<double, Dynamic, 1> DenseRhs;
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VectorXd b, x, tmp;
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BenchTimer timer,totaltime;
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//SparseLU<SparseMatrix<double, ColMajor> > solver;
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// SuperLU<SparseMatrix<double, ColMajor> > solver;
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ConjugateGradient<SparseMatrix<double, ColMajor>, Lower,IncompleteCholesky<double,Lower> > solver;
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ifstream matrix_file;
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string line;
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int n;
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// Set parameters
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// solver.iparm(IPARM_THREAD_NBR) = 4;
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/* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
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if (argc < 2) assert(false && "please, give the matrix market file ");
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timer.start();
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totaltime.start();
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loadMarket(A, args[1]);
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cout << "End charging matrix " << endl;
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bool iscomplex=false, isvector=false;
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int sym;
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getMarketHeader(args[1], sym, iscomplex, isvector);
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if (iscomplex) { cout<< " Not for complex matrices \n"; return -1; }
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if (isvector) { cout << "The provided file is not a matrix file\n"; return -1;}
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if (sym != 0) { // symmetric matrices, only the lower part is stored
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SparseMatrix<double, ColMajor> temp;
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temp = A;
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A = temp.selfadjointView<Lower>();
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}
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timer.stop();
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n = A.cols();
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// ====== TESTS FOR SPARSE TUTORIAL ======
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// cout<< "OuterSize " << A.outerSize() << " inner " << A.innerSize() << endl;
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// SparseMatrix<double, RowMajor> mat1(A);
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// SparseMatrix<double, RowMajor> mat2;
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// cout << " norm of A " << mat1.norm() << endl; ;
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// PermutationMatrix<Dynamic, Dynamic, int> perm(n);
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// perm.resize(n,1);
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// perm.indices().setLinSpaced(n, 0, n-1);
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// mat2 = perm * mat1;
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// mat.subrows();
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// mat2.resize(n,n);
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// mat2.reserve(10);
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// mat2.setConstant();
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// std::cout<< "NORM " << mat1.squaredNorm()<< endl;
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cout<< "Time to load the matrix " << timer.value() <<endl;
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/* Fill the right hand side */
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// solver.set_restart(374);
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if (argc > 2)
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loadMarketVector(b, args[2]);
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else
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{
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b.resize(n);
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tmp.resize(n);
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// tmp.setRandom();
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for (int i = 0; i < n; i++) tmp(i) = i;
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b = A * tmp ;
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}
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// Scaling<SparseMatrix<double> > scal;
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// scal.computeRef(A);
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// b = scal.LeftScaling().cwiseProduct(b);
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/* Compute the factorization */
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cout<< "Starting the factorization "<< endl;
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timer.reset();
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timer.start();
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cout<< "Size of Input Matrix "<< b.size()<<"\n\n";
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cout<< "Rows and columns "<< A.rows() <<" " <<A.cols() <<"\n";
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solver.compute(A);
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// solver.analyzePattern(A);
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// solver.factorize(A);
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if (solver.info() != Success) {
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std::cout<< "The solver failed \n";
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return -1;
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}
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timer.stop();
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float time_comp = timer.value();
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cout <<" Compute Time " << time_comp<< endl;
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timer.reset();
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timer.start();
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x = solver.solve(b);
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// x = scal.RightScaling().cwiseProduct(x);
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timer.stop();
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float time_solve = timer.value();
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cout<< " Time to solve " << time_solve << endl;
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/* Check the accuracy */
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VectorXd tmp2 = b - A*x;
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double tempNorm = tmp2.norm()/b.norm();
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cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
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// cout << "Iterations : " << solver.iterations() << "\n";
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totaltime.stop();
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cout << "Total time " << totaltime.value() << "\n";
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// std::cout<<x.transpose()<<"\n";
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return 0;
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}
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