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Java example source code file (MultiStartUnivariateOptimizer.java)

This example Java source code file (MultiStartUnivariateOptimizer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

cannot, comparator, goaltype, mathillegalstateexception, maxeval, multistartunivariateoptimizer, notstrictlypositiveexception, optimizationdata, override, randomgenerator, runtimeexception, searchinterval, univariateoptimizer, univariatepointvaluepair, util

The MultiStartUnivariateOptimizer.java Java example 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,
 * 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.
 * <br/>
 * 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) {

        if (starts < 1) {
            throw new NotStrictlyPositiveException(starts);

        this.optimizer = optimizer;
        this.starts = starts;
        this.generator = generator;

    /** {@inheritDoc} */
    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.
     * <br/>
     * 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}.
    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} */
    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;
            if (optimData[i] instanceof SearchInterval) {
                optimData[i] = null;
                searchIntervalIndex = i;
        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();


        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<UnivariatePointValuePair>() {
                /** {@inheritDoc} */
                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);

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