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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.

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/*
 * 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
 */

package no.uib.cipr.matrix.sparse;

import no.uib.cipr.matrix.Vector;

/**
 * Reports on the progress of an iterative solver.
 */
public interface IterationReporter {

    /**
     * Registers current information
     * 
     * @param r
     *            Current residual norm
     * @param x
     *            Current state vector
     * @param i
     *            Current iteration number
     */
    void monitor(double r, Vector x, int i);

    /**
     * Registers current information
     * 
     * @param r
     *            Current residual norm
     * @param i
     *            Current iteration number
     */
    void monitor(double r, int i);
}