Homec4science
Diffusion Diffusion limited mixed aggregation (master) Browse

dlma/eigen-3.4.0/test/spqr_support.cpp
68bb631292e1master

Last Change
Raw File

spqr_support.cpp
View Options

// 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>
//
// 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
#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS
#include "sparse.h"
#include <Eigen/SPQRSupport>
template<typename MatrixType,typename DenseMat>
int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 300)
{
eigen_assert(maxRows >= maxCols);
typedef typename MatrixType::Scalar Scalar;
int rows = internal::random<int>(1,maxRows);
int cols = internal::random<int>(1,rows);
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();
return rows;
}
template<typename Scalar> void test_spqr_scalar()
{
typedef SparseMatrix<Scalar,ColMajor> MatrixType;
MatrixType A;
Matrix<Scalar,Dynamic,Dynamic> dA;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
DenseVector refX,x,b;
SPQR<MatrixType> solver;
generate_sparse_rectangular_problem(A,dA);
Index m = A.rows();
b = DenseVector::Random(m);
solver.compute(A);
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;
}
//Compare with a dense solver
refX = dA.colPivHouseholderQr().solve(b);
VERIFY(x.isApprox(refX,test_precision<Scalar>()));
}
EIGEN_DECLARE_TEST(spqr_support)
{
CALL_SUBTEST_1(test_spqr_scalar<double>());
CALL_SUBTEST_2(test_spqr_scalar<std::complex<double> >());
}
c4science · Help