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, int maxCols = 150)
     14 {
     15   eigen_assert(maxRows >= maxCols);
     16   typedef typename MatrixType::Scalar Scalar;
     17   int rows = internal::random<int>(1,maxRows);
     18   int cols = internal::random<int>(1,maxCols);
     19   double density = (std::max)(8./(rows*cols), 0.01);
     20 
     21   A.resize(rows,cols);
     22   dA.resize(rows,cols);
     23   initSparse<Scalar>(density, dA, A,ForceNonZeroDiag);
     24   A.makeCompressed();
     25   int nop = internal::random<int>(0, internal::random<double>(0,1) > 0.5 ? cols/2 : 0);
     26   for(int k=0; k<nop; ++k)
     27   {
     28     int j0 = internal::random<int>(0,cols-1);
     29     int j1 = internal::random<int>(0,cols-1);
     30     Scalar s = internal::random<Scalar>();
     31     A.col(j0)  = s * A.col(j1);
     32     dA.col(j0) = s * dA.col(j1);
     33   }
     34 
     35 //   if(rows<cols) {
     36 //     A.conservativeResize(cols,cols);
     37 //     dA.conservativeResize(cols,cols);
     38 //     dA.bottomRows(cols-rows).setZero();
     39 //   }
     40 
     41   return rows;
     42 }
     43 
     44 template<typename Scalar> void test_sparseqr_scalar()
     45 {
     46   typedef SparseMatrix<Scalar,ColMajor> MatrixType;
     47   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat;
     48   typedef Matrix<Scalar,Dynamic,1> DenseVector;
     49   MatrixType A;
     50   DenseMat dA;
     51   DenseVector refX,x,b;
     52   SparseQR<MatrixType, COLAMDOrdering<int> > solver;
     53   generate_sparse_rectangular_problem(A,dA);
     54 
     55   b = dA * DenseVector::Random(A.cols());
     56   solver.compute(A);
     57   if(internal::random<float>(0,1)>0.5f)
     58     solver.factorize(A);  // this checks that calling analyzePattern is not needed if the pattern do not change.
     59   if (solver.info() != Success)
     60   {
     61     std::cerr << "sparse QR factorization failed\n";
     62     exit(0);
     63     return;
     64   }
     65   x = solver.solve(b);
     66   if (solver.info() != Success)
     67   {
     68     std::cerr << "sparse QR factorization failed\n";
     69     exit(0);
     70     return;
     71   }
     72 
     73   VERIFY_IS_APPROX(A * x, b);
     74 
     75   //Compare with a dense QR solver
     76   ColPivHouseholderQR<DenseMat> dqr(dA);
     77   refX = dqr.solve(b);
     78 
     79   VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
     80   if(solver.rank()==A.cols()) // full rank
     81     VERIFY_IS_APPROX(x, refX);
     82 //   else
     83 //     VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() );
     84 
     85   // Compute explicitly the matrix Q
     86   MatrixType Q, QtQ, idM;
     87   Q = solver.matrixQ();
     88   //Check  ||Q' * Q - I ||
     89   QtQ = Q * Q.adjoint();
     90   idM.resize(Q.rows(), Q.rows()); idM.setIdentity();
     91   VERIFY(idM.isApprox(QtQ));
     92 
     93   // Q to dense
     94   DenseMat dQ;
     95   dQ = solver.matrixQ();
     96   VERIFY_IS_APPROX(Q, dQ);
     97 }
     98 void test_sparseqr()
     99 {
    100   for(int i=0; i<g_repeat; ++i)
    101   {
    102     CALL_SUBTEST_1(test_sparseqr_scalar<double>());
    103     CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >());
    104   }
    105 }
    106 
    107