|  | // 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" | 
|  | #include <Eigen/SparseExtra> | 
|  |  | 
|  | #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) ); | 
|  | } | 
|  | } |