[136] | 1 | // Small bench routine for Eigen available in Eigen
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| 2 | // (C) Desire NUENTSA WAKAM, INRIA
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| 3 |
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| 4 | #include <iostream>
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| 5 | #include <fstream>
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| 6 | #include <iomanip>
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| 7 | #include <unsupported/Eigen/SparseExtra>
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| 8 | #include <Eigen/SparseLU>
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| 9 | #include <bench/BenchTimer.h>
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| 10 | #ifdef EIGEN_METIS_SUPPORT
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| 11 | #include <Eigen/MetisSupport>
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| 12 | #endif
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| 13 |
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| 14 | using namespace std;
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| 15 | using namespace Eigen;
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| 16 |
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| 17 | int main(int argc, char **args)
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| 18 | {
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| 19 | // typedef complex<double> scalar;
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| 20 | typedef double scalar;
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| 21 | SparseMatrix<scalar, ColMajor> A;
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| 22 | typedef SparseMatrix<scalar, ColMajor>::Index Index;
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| 23 | typedef Matrix<scalar, Dynamic, Dynamic> DenseMatrix;
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| 24 | typedef Matrix<scalar, Dynamic, 1> DenseRhs;
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| 25 | Matrix<scalar, Dynamic, 1> b, x, tmp;
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| 26 | // SparseLU<SparseMatrix<scalar, ColMajor>, AMDOrdering<int> > solver;
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| 27 | // #ifdef EIGEN_METIS_SUPPORT
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| 28 | // SparseLU<SparseMatrix<scalar, ColMajor>, MetisOrdering<int> > solver;
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| 29 | // std::cout<< "ORDERING : METIS\n";
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| 30 | // #else
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| 31 | SparseLU<SparseMatrix<scalar, ColMajor>, COLAMDOrdering<int> > solver;
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| 32 | std::cout<< "ORDERING : COLAMD\n";
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| 33 | // #endif
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| 34 |
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| 35 | ifstream matrix_file;
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| 36 | string line;
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| 37 | int n;
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| 38 | BenchTimer timer;
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| 39 |
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| 40 | // Set parameters
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| 41 | /* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
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| 42 | if (argc < 2) assert(false && "please, give the matrix market file ");
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| 43 | loadMarket(A, args[1]);
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| 44 | cout << "End charging matrix " << endl;
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| 45 | bool iscomplex=false, isvector=false;
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| 46 | int sym;
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| 47 | getMarketHeader(args[1], sym, iscomplex, isvector);
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| 48 | // if (iscomplex) { cout<< " Not for complex matrices \n"; return -1; }
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| 49 | if (isvector) { cout << "The provided file is not a matrix file\n"; return -1;}
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| 50 | if (sym != 0) { // symmetric matrices, only the lower part is stored
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| 51 | SparseMatrix<scalar, ColMajor> temp;
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| 52 | temp = A;
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| 53 | A = temp.selfadjointView<Lower>();
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| 54 | }
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| 55 | n = A.cols();
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| 56 | /* Fill the right hand side */
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| 57 |
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| 58 | if (argc > 2)
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| 59 | loadMarketVector(b, args[2]);
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| 60 | else
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| 61 | {
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| 62 | b.resize(n);
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| 63 | tmp.resize(n);
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| 64 | // tmp.setRandom();
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| 65 | for (int i = 0; i < n; i++) tmp(i) = i;
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| 66 | b = A * tmp ;
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| 67 | }
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| 68 |
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| 69 | /* Compute the factorization */
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| 70 | // solver.isSymmetric(true);
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| 71 | timer.start();
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| 72 | // solver.compute(A);
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| 73 | solver.analyzePattern(A);
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| 74 | timer.stop();
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| 75 | cout << "Time to analyze " << timer.value() << std::endl;
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| 76 | timer.reset();
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| 77 | timer.start();
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| 78 | solver.factorize(A);
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| 79 | timer.stop();
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| 80 | cout << "Factorize Time " << timer.value() << std::endl;
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| 81 | timer.reset();
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| 82 | timer.start();
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| 83 | x = solver.solve(b);
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| 84 | timer.stop();
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| 85 | cout << "solve time " << timer.value() << std::endl;
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| 86 | /* Check the accuracy */
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| 87 | Matrix<scalar, Dynamic, 1> tmp2 = b - A*x;
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| 88 | scalar tempNorm = tmp2.norm()/b.norm();
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| 89 | cout << "Relative norm of the computed solution : " << tempNorm <<"\n";
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| 90 | cout << "Number of nonzeros in the factor : " << solver.nnzL() + solver.nnzU() << std::endl;
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| 91 |
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| 92 | return 0;
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| 93 | } |
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