mirror of https://github.com/AxioDL/metaforce.git
97 lines
3.4 KiB
C
97 lines
3.4 KiB
C
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/* -*- c++ -*- (enables emacs c++ mode) */
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/*===========================================================================
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Copyright (C) 2002-2017 Yves Renard, Benjamin Schleimer
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This file is a part of GetFEM++
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GetFEM++ is free software; you can redistribute it and/or modify it
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under the terms of the GNU Lesser General Public License as published
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by the Free Software Foundation; either version 3 of the License, or
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(at your option) any later version along with the GCC Runtime Library
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Exception either version 3.1 or (at your option) any later version.
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This program is distributed in the hope that it will be useful, but
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WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
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or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public
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License and GCC Runtime Library Exception for more details.
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You should have received a copy of the GNU Lesser General Public License
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along with this program; if not, write to the Free Software Foundation,
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Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301, USA.
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As a special exception, you may use this file as it is a part of a free
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software library without restriction. Specifically, if other files
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instantiate templates or use macros or inline functions from this file,
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or you compile this file and link it with other files to produce an
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executable, this file does not by itself cause the resulting executable
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to be covered by the GNU Lesser General Public License. This exception
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does not however invalidate any other reasons why the executable file
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might be covered by the GNU Lesser General Public License.
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===========================================================================*/
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/**@file gmm_leastsquares_cg.h
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@author Benjamin Schleimer <bensch128 (at) yahoo (dot) com>
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@date January 23, 2007.
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@brief Conjugate gradient least squares algorithm.
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Algorithm taken from http://www.stat.washington.edu/wxs/Stat538-w05/Notes/conjugate-gradients.pdf page 6
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*/
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#ifndef GMM_LEAST_SQUARES_CG_H__
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#define GMM_LEAST_SQUARES_CG_H__
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#include "gmm_kernel.h"
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#include "gmm_iter.h"
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#include "gmm_conjugated.h"
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namespace gmm {
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template <typename Matrix, typename Vector1, typename Vector2>
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void least_squares_cg(const Matrix& C, Vector1& x, const Vector2& y,
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iteration &iter) {
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typedef typename temporary_dense_vector<Vector1>::vector_type temp_vector;
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typedef typename linalg_traits<Vector1>::value_type T;
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T rho, rho_1(0), a;
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temp_vector p(vect_size(x)), q(vect_size(y)), g(vect_size(x));
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temp_vector r(vect_size(y));
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iter.set_rhsnorm(gmm::sqrt(gmm::abs(vect_hp(y, y))));
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if (iter.get_rhsnorm() == 0.0)
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clear(x);
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else {
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mult(C, scaled(x, T(-1)), y, r);
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mult(conjugated(C), r, g);
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rho = vect_hp(g, g);
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copy(g, p);
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while (!iter.finished_vect(g)) {
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if (!iter.first()) {
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rho = vect_hp(g, g);
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add(g, scaled(p, rho / rho_1), p);
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}
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mult(C, p, q);
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a = rho / vect_hp(q, q);
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add(scaled(p, a), x);
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add(scaled(q, -a), r);
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// NOTE: how do we minimize the impact to the transpose?
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mult(conjugated(C), r, g);
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rho_1 = rho;
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++iter;
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}
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}
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}
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template <typename Matrix, typename Precond,
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typename Vector1, typename Vector2> inline
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void least_squares_cg(const Matrix& C, const Vector1& x, const Vector2& y,
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iteration &iter)
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{ least_squares_cg(C, linalg_const_cast(x), y, iter); }
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}
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#endif // GMM_SOLVER_CG_H__
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