mirror of https://github.com/CGAL/cgal
72 lines
1.8 KiB
C++
72 lines
1.8 KiB
C++
// Copyright (c) 2012 INRIA Bordeaux Sud-Ouest (France), All rights reserved.
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//
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// This file is part of CGAL (www.cgal.org)
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//
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// $URL$
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// $Id$
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// SPDX-License-Identifier: LGPL-3.0-or-later OR LicenseRef-Commercial
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//
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// Author(s) : Gael Guennebaud
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#ifndef CGAL_EIGEN_SVD_H
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#define CGAL_EIGEN_SVD_H
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#include <boost/config.hpp>
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#if defined(BOOST_MSVC)
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# pragma warning(push)
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# pragma warning(disable:4244)
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#endif
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#include <CGAL/Eigen_matrix.h>
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#include <CGAL/Eigen_vector.h>
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#include <Eigen/SVD>
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namespace CGAL {
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/*!
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\ingroup PkgSolverInterfaceLS
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The class `Eigen_svd` provides an algorithm to solve in the least
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square sense a linear system with a singular value decomposition using
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\ref thirdpartyEigen.
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\cgalModels `SvdTraits`
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*/
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class Eigen_svd
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{
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public:
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/// \name Types
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/// @{
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typedef double FT;
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typedef Eigen_vector<FT> Vector;
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typedef Eigen_matrix<FT> Matrix;
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/// @}
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/// Solves the system \f$ MX=B\f$ (in the least square sense if \f$ M\f$ is not
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/// square) using a singular value decomposition.The solution is stored in \f$ B\f$.
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/// \return the condition number of \f$ M\f$
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static FT solve(const Matrix& M, Vector& B)
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{
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#if EIGEN_VERSION_AT_LEAST(3,4,90)
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Eigen::JacobiSVD<Matrix::EigenType, Eigen::ComputeThinU | Eigen::ComputeThinV> jacobiSvd(M.eigen_object());
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#else
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Eigen::JacobiSVD<Matrix::EigenType> jacobiSvd(M.eigen_object(), ::Eigen::ComputeThinU | ::Eigen::ComputeThinV);
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#endif
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B.eigen_object()=jacobiSvd.solve(Vector::EigenType(B.eigen_object()));
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return jacobiSvd.singularValues().array().abs().maxCoeff() /
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jacobiSvd.singularValues().array().abs().minCoeff();
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}
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};
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} // namespace CGAL
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#if defined(BOOST_MSVC)
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# pragma warning(pop)
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#endif
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#endif // CGAL_EIGEN_SVD_H
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