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author | Desire NUENTSA <desire.nuentsa_wakam@inria.fr> | 2013-01-11 17:16:14 +0100 |
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committer | Desire NUENTSA <desire.nuentsa_wakam@inria.fr> | 2013-01-11 17:16:14 +0100 |
commit | 91b3b3aaab19fc11db18d95a28c1a0be9ae9d9cd (patch) | |
tree | d5858697f227ce67812944a181627a593df76552 /test/sparseqr.cpp | |
parent | 1ccd90a927e7386574ff845ff0d326733352e9d1 (diff) |
Add a sparse QR factorization and update the elimination tree in SparseLU
Diffstat (limited to 'test/sparseqr.cpp')
-rw-r--r-- | test/sparseqr.cpp | 62 |
1 files changed, 62 insertions, 0 deletions
diff --git a/test/sparseqr.cpp b/test/sparseqr.cpp new file mode 100644 index 000000000..d34f7c48d --- /dev/null +++ b/test/sparseqr.cpp @@ -0,0 +1,62 @@ +// 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 +#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) +{ + 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,rows); + dA.resize(rows,rows); + initSparse<Scalar>(density, dA, A,ForceNonZeroDiag); + A.makeCompressed(); + return rows; +} + +template<typename Scalar> void test_sparseqr_scalar() +{ + typedef SparseMatrix<Scalar,ColMajor> MatrixType; + MatrixType A; + Matrix<Scalar,Dynamic,Dynamic> dA; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + DenseVector refX,x,b; + SparseQR<MatrixType, AMDOrdering<int> > solver; + generate_sparse_rectangular_problem(A,dA); + + int n = A.cols(); + b = DenseVector::Random(n); + 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 QR solver + refX = dA.colPivHouseholderQr().solve(b); + VERIFY(x.isApprox(refX,test_precision<Scalar>())); +} +void test_sparseqr() +{ + CALL_SUBTEST_1(test_sparseqr_scalar<double>()); + CALL_SUBTEST_2(test_sparseqr_scalar<std::complex<double> >()); +}
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