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