// Copyright (c) 2012 INRIA Bordeaux Sud-Ouest (France), All rights reserved. // // This file is part of CGAL (www.cgal.org); 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. // // Licensees holding a valid commercial license may use this file in // accordance with the commercial license agreement provided with the software. // // This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE // WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. // // $URL$ // $Id$ // SPDX-License-Identifier: LGPL-3.0+ // // Author(s) : Gael Guennebaud #ifndef CGAL_EIGEN_SVD_H #define CGAL_EIGEN_SVD_H #include #if defined(BOOST_MSVC) # pragma warning(push) # pragma warning(disable:4244) #endif #include #include #include namespace CGAL { /*! \ingroup PkgSolver The class `Eigen_svd` provides an algorithm to solve in the least square sense a linear system with a singular value decomposition using \ref thirdpartyEigen. \cgalModels `SvdTraits` */ class Eigen_svd { public: /// \name Types /// @{ typedef double FT; typedef Eigen_vector Vector; typedef Eigen_matrix Matrix; /// @} /// Solves the system \f$ MX=B\f$ (in the least square sense if \f$ M\f$ is not /// square) using a singular value decomposition.The solution is stored in \f$ B\f$. /// \return the condition number of \f$ M\f$ static FT solve(const Matrix& M, Vector& B) { Eigen::JacobiSVD jacobiSvd(M.eigen_object(),::Eigen::ComputeThinU | ::Eigen::ComputeThinV); B.eigen_object()=jacobiSvd.solve(Vector::EigenType(B.eigen_object())); return jacobiSvd.singularValues().array().abs().maxCoeff() / jacobiSvd.singularValues().array().abs().minCoeff(); } }; } // namespace CGAL #if defined(BOOST_MSVC) # pragma warning(pop) #endif #endif // CGAL_EIGEN_SVD_H