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Matrix data structures, linear solvers, least squares methods, eigenvalue,
and singular value decompositions. For larger random dense matrices (above ~ 350 x 350)
matrix-matrix multiplication C = A.B is about 50% faster than MTJ.
The newest version!
/*
* Copyright (C) 2003-2006 Bjørn-Ove Heimsund
*
* This file is part of MTJ.
*
* This library 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 2.1 of the License, or (at your
* option) any later version.
*
* This library 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
* for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this library; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
/*
* Derived from public domain software at http://www.netlib.org/templates
*/
package no.uib.cipr.matrix.sparse;
import no.uib.cipr.matrix.Matrix;
import no.uib.cipr.matrix.NotConvergedException;
import no.uib.cipr.matrix.Vector;
/**
* Conjugate Gradients squared solver. CGS solves the unsymmetric linear system
* Ax = b
using the Conjugate Gradient Squared method.
*
* @author Templates
*/
public class CGS extends AbstractIterativeSolver {
/**
* Vectors for use in the iterative solution process
*/
private Vector p, q, u, phat, qhat, vhat, uhat, sum, r, rtilde;
/**
* Constructor for CGS. Uses the given vector as template for creating
* scratch vectors. Typically, the solution or the right hand side vector
* can be passed, and the template is not modified.
*
* @param template
* Vector to use as template for the work vectors needed in the
* solution process
*/
public CGS(Vector template) {
p = template.copy();
q = template.copy();
u = template.copy();
phat = template.copy();
qhat = template.copy();
vhat = template.copy();
uhat = template.copy();
sum = template.copy();
r = template.copy();
rtilde = template.copy();
}
public Vector solve(Matrix A, Vector b, Vector x)
throws IterativeSolverNotConvergedException {
checkSizes(A, b, x);
double rho_1 = 0, rho_2 = 0, alpha = 0, beta = 0;
A.multAdd(-1, x, r.set(b));
rtilde.set(r);
for (iter.setFirst(); !iter.converged(r, x); iter.next()) {
rho_1 = rtilde.dot(r);
if (rho_1 == 0)
throw new IterativeSolverNotConvergedException(
NotConvergedException.Reason.Breakdown, "rho", iter);
if (iter.isFirst()) {
u.set(r);
p.set(u);
} else {
beta = rho_1 / rho_2;
u.set(r).add(beta, q);
sum.set(q).add(beta, p);
p.set(u).add(beta, sum);
}
M.apply(p, phat);
A.mult(phat, vhat);
alpha = rho_1 / rtilde.dot(vhat);
q.set(-alpha, vhat).add(u);
M.apply(sum.set(u).add(q), uhat);
x.add(alpha, uhat);
A.mult(uhat, qhat);
r.add(-alpha, qhat);
rho_2 = rho_1;
}
return x;
}
}