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Diffstat (limited to 'unsupported/test/sparse_ldlt.cpp')
-rw-r--r-- | unsupported/test/sparse_ldlt.cpp | 68 |
1 files changed, 68 insertions, 0 deletions
diff --git a/unsupported/test/sparse_ldlt.cpp b/unsupported/test/sparse_ldlt.cpp new file mode 100644 index 000000000..8671807c9 --- /dev/null +++ b/unsupported/test/sparse_ldlt.cpp @@ -0,0 +1,68 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> +// +// Eigen is free software; you can redistribute it and/or +// modify it under the terms of the GNU Lesser General Public +// License as published by the Free Software Foundation; either +// version 3 of the License, or (at your option) any later version. +// +// Alternatively, you can redistribute it and/or +// modify it under the terms of the GNU General Public License as +// published by the Free Software Foundation; either version 2 of +// the License, or (at your option) any later version. +// +// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY +// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS +// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the +// GNU General Public License for more details. +// +// You should have received a copy of the GNU Lesser General Public +// License and a copy of the GNU General Public License along with +// Eigen. If not, see <http://www.gnu.org/licenses/>. + +#include "sparse.h" + +#ifdef EIGEN_TAUCS_SUPPORT +#include <Eigen/TaucsSupport> +#endif + +template<typename Scalar> void sparse_ldlt(int rows, int cols) +{ + double density = std::max(8./(rows*cols), 0.01); + typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; + typedef Matrix<Scalar,Dynamic,1> DenseVector; + + SparseMatrix<Scalar> m2(rows, cols); + DenseMatrix refMat2(rows, cols); + + DenseVector b = DenseVector::Random(cols); + DenseVector refX(cols), x(cols); + + initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0); + for(int i=0; i<rows; ++i) + m2.coeffRef(i,i) = refMat2(i,i) = ei_abs(ei_real(refMat2(i,i))); + + refX = refMat2.template selfadjointView<Upper>().ldlt().solve(b); + typedef SparseMatrix<Scalar,Upper|SelfAdjoint> SparseSelfAdjointMatrix; + x = b; + SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2); + if (ldlt.succeeded()) + ldlt.solveInPlace(x); + else + std::cerr << "warning LDLT failed\n"; + + VERIFY_IS_APPROX(refMat2.template selfadjointView<Upper>() * x, b); + VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default"); +} + +void test_sparse_ldlt() +{ + for(int i = 0; i < g_repeat; i++) { + CALL_SUBTEST_1(sparse_ldlt<double>(8, 8) ); + int s = ei_random<int>(1,300); + CALL_SUBTEST_2(sparse_ldlt<std::complex<double> >(s,s) ); + CALL_SUBTEST_1(sparse_ldlt<double>(s,s) ); + } +} |