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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
#include "sparse.h"
#include <Eigen/SparseQR>
template<typename MatrixType,typename DenseMat>
int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 150)
{
eigen_assert(maxRows >= maxCols);
typedef typename MatrixType::Scalar Scalar;
int rows = internal::random<int>(1,maxRows);
int cols = internal::random<int>(1,maxCols);
double density = (std::max)(8./(rows*cols), 0.01);
A.resize(rows,cols);
dA.resize(rows,cols);
initSparse<Scalar>(density, dA, A,ForceNonZeroDiag);
A.makeCompressed();
int nop = internal::random<int>(0, internal::random<double>(0,1) > 0.5 ? cols/2 : 0);
for(int k=0; k<nop; ++k)
{
int j0 = internal::random<int>(0,cols-1);
int j1 = internal::random<int>(0,cols-1);
Scalar s = internal::random<Scalar>();
A.col(j0) = s * A.col(j1);
dA.col(j0) = s * dA.col(j1);
}
// if(rows<cols) {
// A.conservativeResize(cols,cols);
// dA.conservativeResize(cols,cols);
// dA.bottomRows(cols-rows).setZero();
// }
return rows;
}
template<typename Scalar> void test_sparseqr_scalar()
{
typedef SparseMatrix<Scalar,ColMajor> MatrixType;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
MatrixType A;
DenseMat dA;
DenseVector refX,x,b;
SparseQR<MatrixType, COLAMDOrdering<int> > solver;
generate_sparse_rectangular_problem(A,dA);
b = dA * DenseVector::Random(A.cols());
solver.compute(A);
// Q should be MxM
VERIFY_IS_EQUAL(solver.matrixQ().rows(), A.rows());
VERIFY_IS_EQUAL(solver.matrixQ().cols(), A.rows());
// R should be MxN
VERIFY_IS_EQUAL(solver.matrixR().rows(), A.rows());
VERIFY_IS_EQUAL(solver.matrixR().cols(), A.cols());
// Q and R can be multiplied
DenseMat recoveredA = solver.matrixQ()
* DenseMat(solver.matrixR().template triangularView<Upper>())
* solver.colsPermutation().transpose();
VERIFY_IS_EQUAL(recoveredA.rows(), A.rows());
VERIFY_IS_EQUAL(recoveredA.cols(), A.cols());
// and in the full rank case the original matrix is recovered
if (solver.rank() == A.cols())
{
VERIFY_IS_APPROX(A, recoveredA);
}
if(internal::random<float>(0,1)>0.5f)
solver.factorize(A); // this checks that calling analyzePattern is not needed if the pattern do not change.
if (solver.info() != Success)
{
std::cerr << "sparse QR factorization failed\n";
exit(0);
return;
}
x = solver.solve(b);
if (solver.info() != Success)
{
std::cerr << "sparse QR factorization failed\n";
exit(0);
return;
}
VERIFY_IS_APPROX(A * x, b);
//Compare with a dense QR solver
ColPivHouseholderQR<DenseMat> dqr(dA);
refX = dqr.solve(b);
VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
if(solver.rank()==A.cols()) // full rank
VERIFY_IS_APPROX(x, refX);
// else
// VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() );
// Compute explicitly the matrix Q
MatrixType Q, QtQ, idM;
Q = solver.matrixQ();
//Check ||Q' * Q - I ||
QtQ = Q * Q.adjoint();
idM.resize(Q.rows(), Q.rows()); idM.setIdentity();
VERIFY(idM.isApprox(QtQ));
// Q to dense
DenseMat dQ;
dQ = solver.matrixQ();
VERIFY_IS_APPROX(Q, dQ);
}
void test_sparseqr()
{
for(int i=0; i<g_repeat; ++i)
{
CALL_SUBTEST_1(test_sparseqr_scalar<double>());
CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >());
}
}
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