/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
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package org.apache.commons.math3.optim.nonlinear.vector;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.linear.BlockRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.SimpleVectorValueChecker;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomVectorGenerator;
import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator;
import org.junit.Assert;
import org.junit.Test;
/**
* <p>Some of the unit tests are re-implementations of the MINPACK <a
* href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
* href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
* The redistribution policy for MINPACK is available <a
* href="http://www.netlib.org/minpack/disclaimer">here</a>, for
* convenience, it is reproduced below.</p>
*
* <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
* <tr><td>
* Minpack Copyright Notice (1999) University of Chicago.
* All rights reserved
* </td></tr>
* <tr><td>
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* <ol>
* <li>Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.</li>
* <li>Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.</li>
* <li>The end-user documentation included with the redistribution, if any,
* must include the following acknowledgment:
* <code>This product includes software developed by the University of
* Chicago, as Operator of Argonne National Laboratory.</code>
* Alternately, this acknowledgment may appear in the software itself,
* if and wherever such third-party acknowledgments normally appear.</li>
* <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
* WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
* UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
* THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
* OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
* OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
* USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
* THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
* DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
* UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
* BE CORRECTED.</strong></li>
* <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
* HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
* ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
* INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
* ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
* PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
* SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
* (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
* EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
* POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
* <ol></td></tr>
* </table>
*
* @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
* @author Burton S. Garbow (original fortran minpack tests)
* @author Kenneth E. Hillstrom (original fortran minpack tests)
* @author Jorge J. More (original fortran minpack tests)
* @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
*/
public class MultiStartMultivariateVectorOptimizerTest {
@Test(expected=NullPointerException.class)
public void testGetOptimaBeforeOptimize() {
LinearProblem problem
= new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
JacobianMultivariateVectorOptimizer underlyingOptimizer
= new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(16069223052l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
MultiStartMultivariateVectorOptimizer optimizer
= new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
optimizer.getOptima();
}
@Test
public void testTrivial() {
LinearProblem problem
= new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
JacobianMultivariateVectorOptimizer underlyingOptimizer
= new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(16069223052l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
MultiStartMultivariateVectorOptimizer optimizer
= new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
PointVectorValuePair optimum
= optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0 }));
Assert.assertEquals(1.5, optimum.getPoint()[0], 1e-10);
Assert.assertEquals(3.0, optimum.getValue()[0], 1e-10);
PointVectorValuePair[] optima = optimizer.getOptima();
Assert.assertEquals(10, optima.length);
for (int i = 0; i < optima.length; i++) {
Assert.assertEquals(1.5, optima[i].getPoint()[0], 1e-10);
Assert.assertEquals(3.0, optima[i].getValue()[0], 1e-10);
}
Assert.assertTrue(optimizer.getEvaluations() > 20);
Assert.assertTrue(optimizer.getEvaluations() < 50);
Assert.assertEquals(100, optimizer.getMaxEvaluations());
}
/**
* Test demonstrating that the user exception is fnally thrown if none
* of the runs succeed.
*/
@Test(expected=TestException.class)
public void testNoOptimum() {
JacobianMultivariateVectorOptimizer underlyingOptimizer
= new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(12373523445l);
RandomVectorGenerator generator
= new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
MultiStartMultivariateVectorOptimizer optimizer
= new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
optimizer.optimize(new MaxEval(100),
new Target(new double[] { 0 }),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0 }),
new ModelFunction(new MultivariateVectorFunction() {
public double[] value(double[] point) {
throw new TestException();
}
}));
}
private static class TestException extends RuntimeException {}
private static class LinearProblem {
private final RealMatrix factors;
private final double[] target;
public LinearProblem(double[][] factors,
double[] target) {
this.factors = new BlockRealMatrix(factors);
this.target = target;
}
public Target getTarget() {
return new Target(target);
}
public ModelFunction getModelFunction() {
return new ModelFunction(new MultivariateVectorFunction() {
public double[] value(double[] variables) {
return factors.operate(variables);
}
});
}
public ModelFunctionJacobian getModelFunctionJacobian() {
return new ModelFunctionJacobian(new MultivariateMatrixFunction() {
public double[][] value(double[] point) {
return factors.getData();
}
});
}
}
}