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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,
* 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.math4.optim.univariate;
import org.apache.commons.math4.analysis.QuinticFunction;
import org.apache.commons.math4.analysis.UnivariateFunction;
import org.apache.commons.math4.analysis.function.Sin;
import org.apache.commons.math4.exception.MathIllegalStateException;
import org.apache.commons.math4.optim.MaxEval;
import org.apache.commons.math4.optim.nonlinear.scalar.GoalType;
import org.apache.commons.math4.optim.univariate.BrentOptimizer;
import org.apache.commons.math4.optim.univariate.MultiStartUnivariateOptimizer;
import org.apache.commons.math4.optim.univariate.SearchInterval;
import org.apache.commons.math4.optim.univariate.UnivariateObjectiveFunction;
import org.apache.commons.math4.optim.univariate.UnivariateOptimizer;
import org.apache.commons.math4.optim.univariate.UnivariatePointValuePair;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.simple.RandomSource;
import org.apache.commons.math4.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
public class MultiStartUnivariateOptimizerTest {
@Test(expected=MathIllegalStateException.class)
public void testMissingMaxEval() {
UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
UniformRandomProvider g = RandomSource.create(RandomSource.TWO_CMRES, 44428400075l);
MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 10, g);
optimizer.optimize(new UnivariateObjectiveFunction(new Sin()),
GoalType.MINIMIZE,
new SearchInterval(-1, 1));
}
@Test(expected=MathIllegalStateException.class)
public void testMissingSearchInterval() {
UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
UniformRandomProvider g = RandomSource.create(RandomSource.TWO_CMRES, 44428400075l);
MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 10, g);
optimizer.optimize(new MaxEval(300),
new UnivariateObjectiveFunction(new Sin()),
GoalType.MINIMIZE);
}
@Test
public void testSinMin() {
UnivariateFunction f = new Sin();
UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
UniformRandomProvider g = RandomSource.create(RandomSource.TWO_CMRES, 44428400075l);
MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 10, g);
optimizer.optimize(new MaxEval(300),
new UnivariateObjectiveFunction(f),
GoalType.MINIMIZE,
new SearchInterval(-100.0, 100.0));
UnivariatePointValuePair[] optima = optimizer.getOptima();
for (int i = 1; i < optima.length; ++i) {
double d = (optima[i].getPoint() - optima[i-1].getPoint()) / (2 * FastMath.PI);
Assert.assertTrue(FastMath.abs(d - FastMath.rint(d)) < 1.0e-8);
Assert.assertEquals(-1.0, f.value(optima[i].getPoint()), 1.0e-10);
Assert.assertEquals(f.value(optima[i].getPoint()), optima[i].getValue(), 1.0e-10);
}
Assert.assertTrue(optimizer.getEvaluations() > 200);
Assert.assertTrue(optimizer.getEvaluations() < 300);
}
@Test
public void testQuinticMin() {
UnivariateFunction f = new QuinticFunction();
UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
UniformRandomProvider g = RandomSource.create(RandomSource.TWO_CMRES, 4312000053L);
MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 5, g);
UnivariatePointValuePair optimum
= optimizer.optimize(new MaxEval(300),
new UnivariateObjectiveFunction(f),
GoalType.MINIMIZE,
new SearchInterval(-0.3, -0.2));
Assert.assertEquals(-0.2719561295, optimum.getPoint(), 1e-9);
Assert.assertEquals(-0.0443342695, optimum.getValue(), 1e-9);
UnivariatePointValuePair[] optima = optimizer.getOptima();
for (int i = 0; i < optima.length; ++i) {
Assert.assertEquals(f.value(optima[i].getPoint()), optima[i].getValue(), 1e-9);
}
Assert.assertTrue(optimizer.getEvaluations() >= 50);
Assert.assertTrue(optimizer.getEvaluations() <= 100);
}
@Test
public void testBadFunction() {
UnivariateFunction f = new UnivariateFunction() {
@Override
public double value(double x) {
if (x < 0) {
throw new LocalException();
}
return 0;
}
};
UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
UniformRandomProvider g = RandomSource.create(RandomSource.TWO_CMRES, 4312000053L);
MultiStartUnivariateOptimizer optimizer = new MultiStartUnivariateOptimizer(underlying, 5, g);
try {
optimizer.optimize(new MaxEval(300),
new UnivariateObjectiveFunction(f),
GoalType.MINIMIZE,
new SearchInterval(-0.3, -0.2));
Assert.fail();
} catch (LocalException e) {
// Expected.
}
// Ensure that the exception was thrown because no optimum was found.
Assert.assertTrue(optimizer.getOptima()[0] == null);
}
private static class LocalException extends RuntimeException {
private static final long serialVersionUID = 1194682757034350629L;
}
}