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* 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,
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* See the License for the specific language governing permissions and
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package org.apache.commons.math.optimization;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import org.apache.commons.math.ConvergenceException;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.analysis.MultivariateRealFunction;
import org.apache.commons.math.optimization.direct.NelderMead;
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;
public class MultiStartMultivariateRealOptimizerTest {
@Test
public void testRosenbrock()
throws FunctionEvaluationException, ConvergenceException {
Rosenbrock rosenbrock = new Rosenbrock();
NelderMead underlying = new NelderMead();
underlying.setStartConfiguration(new double[][] {
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
});
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(16069223052l);
RandomVectorGenerator generator =
new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
MultiStartMultivariateRealOptimizer optimizer =
new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
optimizer.setMaxIterations(100);
RealPointValuePair optimum =
optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });
assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
assertTrue(optimizer.getEvaluations() > 20);
assertTrue(optimizer.getEvaluations() < 250);
assertTrue(optimum.getValue() < 8.0e-4);
}
private static class Rosenbrock implements MultivariateRealFunction {
private int count;
public Rosenbrock() {
count = 0;
}
public double value(double[] x) throws FunctionEvaluationException {
++count;
double a = x[1] - x[0] * x[0];
double b = 1.0 - x[0];
return 100 * a * a + b * b;
}
public int getCount() {
return count;
}
}
}