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Fix bug #836: extend SparseQR to support more columns than rows.

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This commit is contained in:
Gael Guennebaud
2014-07-01 10:24:46 +02:00
parent d73ee84d37
commit 75e574275c
2 changed files with 84 additions and 65 deletions
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35
test/sparseqr.cpp
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@@ -2,24 +2,24 @@
// 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 = 300)
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,rows);
int cols = internal::random<int>(1,maxCols);
double density = (std::max)(8./(rows*cols), 0.01);
A.resize(rows,rows);
dA.resize(rows,rows);
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);
@@ -31,6 +31,13 @@ int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows
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;
}
@@ -42,11 +49,10 @@ template<typename Scalar> void test_sparseqr_scalar()
MatrixType A;
DenseMat dA;
DenseVector refX,x,b;
SparseQR<MatrixType, AMDOrdering<int> > solver;
SparseQR<MatrixType, COLAMDOrdering<int> > solver;
generate_sparse_rectangular_problem(A,dA);
int n = A.cols();
b = DenseVector::Random(n);
b = dA * DenseVector::Random(A.cols());
solver.compute(A);
if (solver.info() != Success)
{
@@ -60,17 +66,19 @@ template<typename Scalar> void test_sparseqr_scalar()
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())
VERIFY((dA * refX - b).norm() * 2 > (A * x - b).norm() );
else
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;
@@ -88,3 +96,4 @@ void test_sparseqr()
CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >());
}
}