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 (solver.info() != Success)
     58   {
     59     std::cerr << "sparse QR factorization failed\n";
     60     exit(0);
     61     return;
     62   }
     63   x = solver.solve(b);
     64   if (solver.info() != Success)
     65   {
     66     std::cerr << "sparse QR factorization failed\n";
     67     exit(0);
     68     return;
     69   }
     70 
     71   VERIFY_IS_APPROX(A * x, b);
     72 
     73   //Compare with a dense QR solver
     74   ColPivHouseholderQR<DenseMat> dqr(dA);
     75   refX = dqr.solve(b);
     76 
     77   VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
     78   if(solver.rank()==A.cols()) // full rank
     79     VERIFY_IS_APPROX(x, refX);
     80 //   else
     81 //     VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() );
     82 
     83   // Compute explicitly the matrix Q
     84   MatrixType Q, QtQ, idM;
     85   Q = solver.matrixQ();
     86   //Check  ||Q' * Q - I ||
     87   QtQ = Q * Q.adjoint();
     88   idM.resize(Q.rows(), Q.rows()); idM.setIdentity();
     89   VERIFY(idM.isApprox(QtQ));
     90 }
     91 void test_sparseqr()
     92 {
     93   for(int i=0; i<g_repeat; ++i)
     94   {
     95     CALL_SUBTEST_1(test_sparseqr_scalar<double>());
     96     CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >());
     97   }
     98 }
     99 
    100