com.joptimizer.algebra.CholeskyRCFactorizationTest.java Source code

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Here is the source code for com.joptimizer.algebra.CholeskyRCFactorizationTest.java

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/*
 * Copyright 2011-2016 joptimizer.com
 *
 * This work is licensed under the Creative Commons Attribution-NoDerivatives 4.0 
 * International License. To view a copy of this license, visit 
 *
 *        http://creativecommons.org/licenses/by-nd/4.0/ 
 *
 * or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
 */
package com.joptimizer.algebra;

import junit.framework.TestCase;

import org.apache.commons.lang3.ArrayUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.MatrixUtils;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.SingularValueDecomposition;

import cern.colt.matrix.DoubleFactory2D;

public class CholeskyRCFactorizationTest extends TestCase {

    private Log log = LogFactory.getLog(this.getClass().getName());

    public void testInvert1() throws Exception {
        log.debug("testInvert1");
        double[][] QData = new double[][] { { 1, .12, .13, .14, .15 }, { .12, 2, .23, .24, .25 },
                { .13, .23, 3, 0, 0 }, { .14, .24, 0, 4, 0 }, { .15, .25, 0, 0, 5 } };
        RealMatrix Q = MatrixUtils.createRealMatrix(QData);

        CholeskyRCFactorization myc = new CholeskyRCFactorization(DoubleFactory2D.dense.make(QData));
        myc.factorize();
        RealMatrix L = new Array2DRowRealMatrix(myc.getL().toArray());
        RealMatrix LT = new Array2DRowRealMatrix(myc.getLT().toArray());
        log.debug("L: " + ArrayUtils.toString(L.getData()));
        log.debug("LT: " + ArrayUtils.toString(LT.getData()));
        log.debug("L.LT: " + ArrayUtils.toString(L.multiply(LT).getData()));
        log.debug("LT.L: " + ArrayUtils.toString(LT.multiply(L).getData()));

        // check Q = L.LT
        double norm = L.multiply(LT).subtract(Q).getNorm();
        log.debug("norm: " + norm);
        assertTrue(norm < 1.E-15);

        RealMatrix LInv = new SingularValueDecomposition(L).getSolver().getInverse();
        log.debug("LInv: " + ArrayUtils.toString(LInv.getData()));
        RealMatrix LInvT = LInv.transpose();
        log.debug("LInvT: " + ArrayUtils.toString(LInvT.getData()));
        RealMatrix LTInv = new SingularValueDecomposition(LT).getSolver().getInverse();
        log.debug("LTInv: " + ArrayUtils.toString(LTInv.getData()));
        RealMatrix LTInvT = LTInv.transpose();
        log.debug("LTInvT: " + ArrayUtils.toString(LTInvT.getData()));
        log.debug("LInv.LInvT: " + ArrayUtils.toString(LInv.multiply(LInvT).getData()));
        log.debug("LTInv.LTInvT: " + ArrayUtils.toString(LTInv.multiply(LTInvT).getData()));

        RealMatrix Id = MatrixUtils.createRealIdentityMatrix(Q.getRowDimension());
        //check Q.(LTInv * LInv) = 1
        norm = Q.multiply(LTInv.multiply(LInv)).subtract(Id).getNorm();
        log.debug("norm: " + norm);
        assertTrue(norm < 5.E-15);

        // check Q.QInv = 1
        RealMatrix QInv = MatrixUtils.createRealMatrix(myc.getInverse().toArray());
        norm = Q.multiply(QInv).subtract(Id).getNorm();
        log.debug("norm: " + norm);
        assertTrue(norm < 1.E-15);
    }
}