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A comprehensive collection of matrix data structures, linear solvers, least squares methods,
eigenvalue, and singular value decompositions.
Forked from: https://github.com/fommil/matrix-toolkits-java
and added support for eigenvalue computation of general matrices
/*
* 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.DenseMatrix;
import no.uib.cipr.matrix.DenseVector;
import no.uib.cipr.matrix.GivensRotation;
import no.uib.cipr.matrix.Matrix;
import no.uib.cipr.matrix.UpperTriangDenseMatrix;
import no.uib.cipr.matrix.Vector;
import no.uib.cipr.matrix.Vector.Norm;
/**
* GMRES solver. GMRES solves the unsymmetric linear system Ax = b
* using the Generalized Minimum Residual method. The GMRES iteration is
* restarted after a given number of iterations. By default it is restarted
* after 30 iterations.
*
* @author Templates
*/
public class GMRES extends AbstractIterativeSolver {
/**
* After this many iterations, the GMRES will be restarted.
*/
private int restart;
/**
* Vectors for use in the iterative solution process
*/
private Vector w, u, r;
/**
* Vectors spanning the subspace
*/
private Vector[] v;
/**
* Restart vector
*/
private DenseVector s;
/**
* Hessenberg matrix
*/
private DenseMatrix H;
/**
* Givens rotations for the QR factorization
*/
private GivensRotation[] rotation;
/**
* Constructor for GMRES. 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. The iteration is
* restarted every 30 iterations
*
* @param template
* Vector to use as template for the work vectors needed in the
* solution process
*/
public GMRES(Vector template) {
this(template, 30);
}
/**
* Constructor for GMRES. 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
* @param restart
* GMRES iteration is restarted after this number of iterations
*/
public GMRES(Vector template, int restart) {
w = template.copy();
u = template.copy();
r = template.copy();
setRestart(restart);
}
/**
* Sets the restart parameter
*
* @param restart
* GMRES iteration is restarted after this number of iterations
*/
public void setRestart(int restart) {
this.restart = restart;
if (restart <= 0)
throw new IllegalArgumentException(
"restart must be a positive integer");
s = new DenseVector(restart + 1);
H = new DenseMatrix(restart + 1, restart);
rotation = new GivensRotation[restart + 1];
v = new Vector[restart + 1];
for (int i = 0; i < v.length; ++i)
v[i] = r.copy().zero();
}
public Vector solve(Matrix A, Vector b, Vector x)
throws IterativeSolverNotConvergedException {
checkSizes(A, b, x);
A.multAdd(-1, x, u.set(b));
M.apply(u, r);
double normr = r.norm(Norm.Two);
M.apply(b, u);
// Outer iteration
for (iter.setFirst(); !iter.converged(r, x); iter.next()) {
v[0].set(1 / normr, r);
s.zero().set(0, normr);
int i = 0;
// Inner iteration
for (; i < restart && !iter.converged(Math.abs(s.get(i))); i++, iter
.next()) {
A.mult(v[i], u);
M.apply(u, w);
for (int k = 0; k <= i; k++) {
H.set(k, i, w.dot(v[k]));
w.add(-H.get(k, i), v[k]);
}
H.set(i + 1, i, w.norm(Norm.Two));
v[i + 1].set(1. / H.get(i + 1, i), w);
// QR factorization of H using Givens rotations
for (int k = 0; k < i; ++k)
rotation[k].apply(H, i, k, k + 1);
rotation[i] = new GivensRotation(H.get(i, i), H.get(i + 1, i));
rotation[i].apply(H, i, i, i + 1);
rotation[i].apply(s, i, i + 1);
}
// Update solution in current subspace
new UpperTriangDenseMatrix(H, i, false).solve(s, s);
for (int j = 0; j < i; j++)
x.add(s.get(j), v[j]);
A.multAdd(-1, x, u.set(b));
M.apply(u, r);
normr = r.norm(Norm.Two);
}
return x;
}
}