/*
* 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.math3.optimization.univariate;
import org.apache.commons.math3.analysis.QuinticFunction;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.function.Sin;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
@Deprecated
public class UnivariateMultiStartOptimizerTest {
@Test
public void testSinMin() {
UnivariateFunction f = new Sin();
UnivariateOptimizer underlying = new BrentOptimizer(1e-10, 1e-14);
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(44428400075l);
UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 10, g);
optimizer.optimize(300, f, GoalType.MINIMIZE, -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() {
// The quintic function has zeros at 0, +-0.5 and +-1.
// The function has extrema (first derivative is zero) at 0.27195613 and 0.82221643,
UnivariateFunction f = new QuinticFunction();
UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(4312000053L);
UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 5, g);
UnivariatePointValuePair optimum
= optimizer.optimize(300, f, GoalType.MINIMIZE, -0.3, -0.2);
Assert.assertEquals(-0.2719561293, 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() {
public double value(double x) {
if (x < 0) {
throw new LocalException();
}
return 0;
}
};
UnivariateOptimizer underlying = new BrentOptimizer(1e-9, 1e-14);
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(4312000053L);
UnivariateMultiStartOptimizer<UnivariateFunction> optimizer =
new UnivariateMultiStartOptimizer<UnivariateFunction>(underlying, 5, g);
try {
optimizer.optimize(300, f, GoalType.MINIMIZE, -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;
}
}