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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;

/** When computing eigenvalues, this indicates which eigenvalues to locate. */
enum JobEigRange {

    /** All eigenvalues will be computed */
    All,

    /** The eigenvalues with the given indices are computed */
    Indices,

    /** Eigenvalues in a given interval will be found */
    Interval;

    /**
     * @return the netlib character version of this designation, for use with
     *         F2J.
     */
    public String netlib() {
        switch (this) {
            case All :
                return "A";
            case Indices :
                return "I";
            default :
                return "V";
        }
    }
}