* <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 MultiStartDifferentiableMultivariateVectorialOptimizerTest { @Test public void testTrivial() throws FunctionEvaluationException, OptimizationException { LinearProblem problem = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 }); DifferentiableMultivariateVectorialOptimizer underlyingOptimizer = new GaussNewtonOptimizer(true); JDKRandomGenerator g = new JDKRandomGenerator(); g.setSeed(16069223052l); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g)); MultiStartDifferentiableMultivariateVectorialOptimizer optimizer = new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer, 10, generator); optimizer.setMaxIterations(100); optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6)); // no optima before first optimization attempt try { optimizer.getOptima(); fail("an exception should have been thrown"); } catch (IllegalStateException ise) { // expected } VectorialPointValuePair optimum = optimizer.optimize(problem, problem.target, new double[] { 1 }, new double[] { 0 }); assertEquals(1.5, optimum.getPoint()[0], 1.0e-10); assertEquals(3.0, optimum.getValue()[0], 1.0e-10); VectorialPointValuePair[] optima = optimizer.getOptima(); assertEquals(10, optima.length); for (int i = 0; i < optima.length; ++i) { assertEquals(1.5, optima[i].getPoint()[0], 1.0e-10); assertEquals(3.0, optima[i].getValue()[0], 1.0e-10); } assertTrue(optimizer.getEvaluations() > 20); assertTrue(optimizer.getEvaluations() < 50); assertTrue(optimizer.getIterations() > 20); assertTrue(optimizer.getIterations() < 50); assertTrue(optimizer.getJacobianEvaluations() > 20); assertTrue(optimizer.getJacobianEvaluations() < 50); assertEquals(100, optimizer.getMaxIterations()); } @Test(expected = OptimizationException.class) public void testNoOptimum() throws FunctionEvaluationException, OptimizationException { DifferentiableMultivariateVectorialOptimizer underlyingOptimizer = new GaussNewtonOptimizer(true); JDKRandomGenerator g = new JDKRandomGenerator(); g.setSeed(12373523445l); RandomVectorGenerator generator = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g)); MultiStartDifferentiableMultivariateVectorialOptimizer optimizer = new MultiStartDifferentiableMultivariateVectorialOptimizer(underlyingOptimizer, 10, generator); optimizer.setMaxIterations(100); optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6)); optimizer.optimize(new DifferentiableMultivariateVectorialFunction() { public MultivariateMatrixFunction jacobian() { return null; } public double[] value(double[] point) throws FunctionEvaluationException { throw new FunctionEvaluationException(point[0]); } }, new double[] { 2 }, new double[] { 1 }, new double[] { 0 }); } private static class LinearProblem implements DifferentiableMultivariateVectorialFunction, Serializable { private static final long serialVersionUID = -8804268799379350190L; final RealMatrix factors; final double[] target; public LinearProblem(double[][] factors, double[] target) { this.factors = new BlockRealMatrix(factors); this.target = target; } public double[] value(double[] variables) { return factors.operate(variables); } public MultivariateMatrixFunction jacobian() { return new MultivariateMatrixFunction() { private static final long serialVersionUID = -8387467946663627585L; public double[][] value(double[] point) { return factors.getData(); } }; } } }

	Commons Math example source code file (MultiStartDifferentiableMultivariateVectorialOptimizerTest.java) This example Commons Math source code file (MultiStartDifferentiableMultivariateVectorialOptimizerTest.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM. Java - Commons Math tags/keywords differentiablemultivariatevectorialoptimizer, functionevaluationexception, functionevaluationexception, gaussnewtonoptimizer, io, jdkrandomgenerator, jdkrandomgenerator, linearproblem, linearproblem, multistartdifferentiablemultivariatevectorialoptimizer, multistartdifferentiablemultivariatevectorialoptimizer, multivariatematrixfunction, optimizationexception, test, vectorialpointvaluepair The Commons Math MultiStartDifferentiableMultivariateVectorialOptimizerTest.java source code /* * 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. */ package org.apache.commons.math.optimization; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; import static org.junit.Assert.fail; import java.io.Serializable; import org.apache.commons.math.FunctionEvaluationException; import org.apache.commons.math.analysis.DifferentiableMultivariateVectorialFunction; import org.apache.commons.math.analysis.MultivariateMatrixFunction; import org.apache.commons.math.linear.BlockRealMatrix; import org.apache.commons.math.linear.RealMatrix; import org.apache.commons.math.optimization.general.GaussNewtonOptimizer; import org.apache.commons.math.random.GaussianRandomGenerator; import org.apache.commons.math.random.JDKRandomGenerator; import org.apache.commons.math.random.RandomVectorGenerator; import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator; import org.junit.Test; /** * <p>Some of the unit tests are re-implementations of the MINPACK and 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>	* Minpack Copyright Notice (1999) University of Chicago. * All rights reserved * </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>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>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> * <ol>

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