| // 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) ); | 
 |   } | 
 | } |