1 // Small bench routine for Eigen available in Eigen
2 // (C) Desire NUENTSA WAKAM, INRIA
3
4 #include <iostream>
5 #include <fstream>
6 #include <iomanip>
7 #include <unsupported/Eigen/SparseExtra>
8 #include <Eigen/SparseLU>
9 #include <bench/BenchTimer.h>
10 #ifdef EIGEN_METIS_SUPPORT
11 #include <Eigen/MetisSupport>
12 #endif
13
14 using namespace std;
15 using namespace Eigen;
16
main(int argc,char ** args)17 int main(int argc, char **args)
18 {
19 // typedef complex<double> scalar;
20 typedef double scalar;
21 SparseMatrix<scalar, ColMajor> A;
22 typedef SparseMatrix<scalar, ColMajor>::Index Index;
23 typedef Matrix<scalar, Dynamic, Dynamic> DenseMatrix;
24 typedef Matrix<scalar, Dynamic, 1> DenseRhs;
25 Matrix<scalar, Dynamic, 1> b, x, tmp;
26 // SparseLU<SparseMatrix<scalar, ColMajor>, AMDOrdering<int> > solver;
27 // #ifdef EIGEN_METIS_SUPPORT
28 // SparseLU<SparseMatrix<scalar, ColMajor>, MetisOrdering<int> > solver;
29 // std::cout<< "ORDERING : METIS\n";
30 // #else
31 SparseLU<SparseMatrix<scalar, ColMajor>, COLAMDOrdering<int> > solver;
32 std::cout<< "ORDERING : COLAMD\n";
33 // #endif
34
35 ifstream matrix_file;
36 string line;
37 int n;
38 BenchTimer timer;
39
40 // Set parameters
41 /* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
42 if (argc < 2) assert(false && "please, give the matrix market file ");
43 loadMarket(A, args[1]);
44 cout << "End charging matrix " << endl;
45 bool iscomplex=false, isvector=false;
46 int sym;
47 getMarketHeader(args[1], sym, iscomplex, isvector);
48 // if (iscomplex) { cout<< " Not for complex matrices \n"; return -1; }
49 if (isvector) { cout << "The provided file is not a matrix file\n"; return -1;}
50 if (sym != 0) { // symmetric matrices, only the lower part is stored
51 SparseMatrix<scalar, ColMajor> temp;
52 temp = A;
53 A = temp.selfadjointView<Lower>();
54 }
55 n = A.cols();
56 /* Fill the right hand side */
57
58 if (argc > 2)
59 loadMarketVector(b, args[2]);
60 else
61 {
62 b.resize(n);
63 tmp.resize(n);
64 // tmp.setRandom();
65 for (int i = 0; i < n; i++) tmp(i) = i;
66 b = A * tmp ;
67 }
68
69 /* Compute the factorization */
70 // solver.isSymmetric(true);
71 timer.start();
72 // solver.compute(A);
73 solver.analyzePattern(A);
74 timer.stop();
75 cout << "Time to analyze " << timer.value() << std::endl;
76 timer.reset();
77 timer.start();
78 solver.factorize(A);
79 timer.stop();
80 cout << "Factorize Time " << timer.value() << std::endl;
81 timer.reset();
82 timer.start();
83 x = solver.solve(b);
84 timer.stop();
85 cout << "solve time " << timer.value() << std::endl;
86 /* Check the accuracy */
87 Matrix<scalar, Dynamic, 1> tmp2 = b - A*x;
88 scalar tempNorm = tmp2.norm()/b.norm();
89 cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
90 cout << "Number of nonzeros in the factor : " << solver.nnzL() + solver.nnzU() << std::endl;
91
92 return 0;
93 }