Home | History | Annotate | Download | only in test 
      1 // This file is part of Eigen, a lightweight C++ template library
      2 // for linear algebra.
      3 //
      4 // Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam (at) inria.fr>
      5 // Copyright (C) 2014 Gael Guennebaud <gael.guennebaud (at) inria.fr>
      6 //
      7 // This Source Code Form is subject to the terms of the Mozilla
      8 // Public License v. 2.0. If a copy of the MPL was not distributed
      9 #include "sparse.h"
     10 #include <Eigen/SparseQR>
     11 
     12 template<typename MatrixType,typename DenseMat>
     13 int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300)
     14 {
     15   typedef typename MatrixType::Scalar Scalar;
     16   int rows = internal::random<int>(1,maxRows);
     17   int cols = internal::random<int>(1,rows);
     18   double density = (std::max)(8./(rows*cols), 0.01);
     19 
     20   A.resize(rows,cols);
     21   dA.resize(rows,cols);
     22   initSparse<Scalar>(density, dA, A,ForceNonZeroDiag);
     23   A.makeCompressed();
     24   int nop = internal::random<int>(0, internal::random<double>(0,1) > 0.5 ? cols/2 : 0);
     25   for(int k=0; k<nop; ++k)
     26   {
     27     int j0 = internal::random<int>(0,cols-1);
     28     int j1 = internal::random<int>(0,cols-1);
     29     Scalar s = internal::random<Scalar>();
     30     A.col(j0)  = s * A.col(j1);
     31     dA.col(j0) = s * dA.col(j1);
     32   }
     33 
     34 //   if(rows<cols) {
     35 //     A.conservativeResize(cols,cols);
     36 //     dA.conservativeResize(cols,cols);
     37 //     dA.bottomRows(cols-rows).setZero();
     38 //   }
     39 
     40   return rows;
     41 }
     42 
     43 template<typename Scalar> void test_sparseqr_scalar()
     44 {
     45   typedef SparseMatrix<Scalar,ColMajor> MatrixType;
     46   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat;
     47   typedef Matrix<Scalar,Dynamic,1> DenseVector;
     48   MatrixType A;
     49   DenseMat dA;
     50   DenseVector refX,x,b;
     51   SparseQR<MatrixType, COLAMDOrdering<int> > solver;
     52   generate_sparse_rectangular_problem(A,dA);
     53 
     54   b = dA * DenseVector::Random(A.cols());
     55   solver.compute(A);
     56   if(internal::random<float>(0,1)>0.5)
     57     solver.factorize(A);  // this checks that calling analyzePattern is not needed if the pattern do not change.
     58   if (solver.info() != Success)
     59   {
     60     std::cerr << "sparse QR factorization failed\n";
     61     exit(0);
     62     return;
     63   }
     64   x = solver.solve(b);
     65   if (solver.info() != Success)
     66   {
     67     std::cerr << "sparse QR factorization failed\n";
     68     exit(0);
     69     return;
     70   }
     71 
     72   VERIFY_IS_APPROX(A * x, b);
     73 
     74   //Compare with a dense QR solver
     75   ColPivHouseholderQR<DenseMat> dqr(dA);
     76   refX = dqr.solve(b);
     77 
     78   VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
     79   if(solver.rank()==A.cols()) // full rank
     80     VERIFY_IS_APPROX(x, refX);
     81 //   else
     82 //     VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() );
     83 
     84   // Compute explicitly the matrix Q
     85   MatrixType Q, QtQ, idM;
     86   Q = solver.matrixQ();
     87   //Check  ||Q' * Q - I ||
     88   QtQ = Q * Q.adjoint();
     89   idM.resize(Q.rows(), Q.rows()); idM.setIdentity();
     90   VERIFY(idM.isApprox(QtQ));
     91 }
     92 void test_sparseqr()
     93 {
     94   for(int i=0; i<g_repeat; ++i)
     95   {
     96     CALL_SUBTEST_1(test_sparseqr_scalar<double>());
     97     CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >());
     98   }
     99 }
    100 
    101