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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.optim.univariate;
import java.util.Arrays;
import java.util.Comparator;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
import org.apache.commons.math3.optim.OptimizationData;
/**
* Special implementation of the {@link UnivariateOptimizer} interface
* adding multi-start features to an existing optimizer.
*
* This class wraps an optimizer in order to use it several times in
* turn with different starting points (trying to avoid being trapped
* in a local extremum when looking for a global one).
*
* @since 3.0
*/
public class MultiStartUnivariateOptimizer
extends UnivariateOptimizer {
/** Underlying classical optimizer. */
private final UnivariateOptimizer optimizer;
/** Number of evaluations already performed for all starts. */
private int totalEvaluations;
/** Number of starts to go. */
private int starts;
/** Random generator for multi-start. */
private RandomGenerator generator;
/** Found optima. */
private UnivariatePointValuePair[] optima;
/** Optimization data. */
private OptimizationData[] optimData;
/**
* Location in {@link #optimData} where the updated maximum
* number of evaluations will be stored.
*/
private int maxEvalIndex = -1;
/**
* Location in {@link #optimData} where the updated start value
* will be stored.
*/
private int searchIntervalIndex = -1;
/**
* Create a multi-start optimizer from a single-start optimizer.
*
* @param optimizer Single-start optimizer to wrap.
* @param starts Number of starts to perform. If {@code starts == 1},
* the {@code optimize} methods will return the same solution as
* {@code optimizer} would.
* @param generator Random generator to use for restarts.
* @throws NotStrictlyPositiveException if {@code starts < 1}.
*/
public MultiStartUnivariateOptimizer(final UnivariateOptimizer optimizer,
final int starts,
final RandomGenerator generator) {
super(optimizer.getConvergenceChecker());
if (starts < 1) {
throw new NotStrictlyPositiveException(starts);
}
this.optimizer = optimizer;
this.starts = starts;
this.generator = generator;
}
/** {@inheritDoc} */
@Override
public int getEvaluations() {
return totalEvaluations;
}
/**
* Gets all the optima found during the last call to {@code optimize}.
* The optimizer stores all the optima found during a set of
* restarts. The {@code optimize} method returns the best point only.
* This method returns all the points found at the end of each starts,
* including the best one already returned by the {@code optimize} method.
*
* The returned array as one element for each start as specified
* in the constructor. It is ordered with the results from the
* runs that did converge first, sorted from best to worst
* objective value (i.e in ascending order if minimizing and in
* descending order if maximizing), followed by {@code null} elements
* corresponding to the runs that did not converge. This means all
* elements will be {@code null} if the {@code optimize} method did throw
* an exception.
* This also means that if the first element is not {@code null}, it is
* the best point found across all starts.
*
* @return an array containing the optima.
* @throws MathIllegalStateException if {@link #optimize(OptimizationData[])
* optimize} has not been called.
*/
public UnivariatePointValuePair[] getOptima() {
if (optima == null) {
throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
}
return optima.clone();
}
/**
* {@inheritDoc}
*
* @throws MathIllegalStateException if {@code optData} does not contain an
* instance of {@link MaxEval} or {@link SearchInterval}.
*/
@Override
public UnivariatePointValuePair optimize(OptimizationData... optData) {
// Store arguments in order to pass them to the internal optimizer.
optimData = optData;
// Set up base class and perform computations.
return super.optimize(optData);
}
/** {@inheritDoc} */
@Override
protected UnivariatePointValuePair doOptimize() {
// Remove all instances of "MaxEval" and "SearchInterval" from the
// array that will be passed to the internal optimizer.
// The former is to enforce smaller numbers of allowed evaluations
// (according to how many have been used up already), and the latter
// to impose a different start value for each start.
for (int i = 0; i < optimData.length; i++) {
if (optimData[i] instanceof MaxEval) {
optimData[i] = null;
maxEvalIndex = i;
continue;
}
if (optimData[i] instanceof SearchInterval) {
optimData[i] = null;
searchIntervalIndex = i;
continue;
}
}
if (maxEvalIndex == -1) {
throw new MathIllegalStateException();
}
if (searchIntervalIndex == -1) {
throw new MathIllegalStateException();
}
RuntimeException lastException = null;
optima = new UnivariatePointValuePair[starts];
totalEvaluations = 0;
final int maxEval = getMaxEvaluations();
final double min = getMin();
final double max = getMax();
final double startValue = getStartValue();
// Multi-start loop.
for (int i = 0; i < starts; i++) {
// CHECKSTYLE: stop IllegalCatch
try {
// Decrease number of allowed evaluations.
optimData[maxEvalIndex] = new MaxEval(maxEval - totalEvaluations);
// New start value.
final double s = (i == 0) ?
startValue :
min + generator.nextDouble() * (max - min);
optimData[searchIntervalIndex] = new SearchInterval(min, max, s);
// Optimize.
optima[i] = optimizer.optimize(optimData);
} catch (RuntimeException mue) {
lastException = mue;
optima[i] = null;
}
// CHECKSTYLE: resume IllegalCatch
totalEvaluations += optimizer.getEvaluations();
}
sortPairs(getGoalType());
if (optima[0] == null) {
throw lastException; // Cannot be null if starts >= 1.
}
// Return the point with the best objective function value.
return optima[0];
}
/**
* Sort the optima from best to worst, followed by {@code null} elements.
*
* @param goal Goal type.
*/
private void sortPairs(final GoalType goal) {
Arrays.sort(optima, new Comparator() {
public int compare(final UnivariatePointValuePair o1,
final UnivariatePointValuePair o2) {
if (o1 == null) {
return (o2 == null) ? 0 : 1;
} else if (o2 == null) {
return -1;
}
final double v1 = o1.getValue();
final double v2 = o2.getValue();
return (goal == GoalType.MINIMIZE) ?
Double.compare(v1, v2) : Double.compare(v2, v1);
}
});
}
}