Commons Math example source code file (MultiStartUnivariateRealOptimizer.java)

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

Java - Commons Math tags/keywords

convergenceexception, convergenceexception, functionevaluationexception, functionevaluationexception, goaltype, goaltype, illegalstateexception, multistartunivariaterealoptimizer, optimizationexception, randomgenerator, univariaterealfunction, univariaterealoptimizer, univariaterealoptimizer

The Commons Math MultiStartUnivariateRealOptimizer.java 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,
 * 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.math.optimization;

import org.apache.commons.math.ConvergenceException;
import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.MathRuntimeException;
import org.apache.commons.math.analysis.UnivariateRealFunction;
import org.apache.commons.math.random.RandomGenerator;

/**
 * Special implementation of the {@link UnivariateRealOptimizer} interface adding
 * multi-start features to an existing optimizer.
 * <p>
 * This class wraps a classical optimizer to use it several times in
 * turn with different starting points in order to avoid being trapped
 * into a local extremum when looking for a global one.
 * </p>
 * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $
 * @since 2.0
 */
public class MultiStartUnivariateRealOptimizer implements UnivariateRealOptimizer {

    /** Serializable version identifier. */
    private static final long serialVersionUID = 5983375963110961019L;

    /** Underlying classical optimizer. */
    private final UnivariateRealOptimizer optimizer;

    /** Maximal number of iterations allowed. */
    private int maxIterations;

    /** Maximal number of evaluations allowed. */
    private int maxEvaluations;

    /** Number of iterations already performed for all starts. */
    private int totalIterations;

    /** 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 double[] optima;

    /** Found function values at optima. */
    private double[] optimaValues;

    /**
     * Create a multi-start optimizer from a single-start optimizer
     * @param optimizer single-start optimizer to wrap
     * @param starts number of starts to perform (including the
     * first one), multi-start is disabled if value is less than or
     * equal to 1
     * @param generator random generator to use for restarts
     */
    public MultiStartUnivariateRealOptimizer(final UnivariateRealOptimizer optimizer,
                                             final int starts,
                                             final RandomGenerator generator) {
        this.optimizer        = optimizer;
        this.totalIterations  = 0;
        this.starts           = starts;
        this.generator        = generator;
        this.optima           = null;
        setMaximalIterationCount(Integer.MAX_VALUE);
        setMaxEvaluations(Integer.MAX_VALUE);
    }

    /** {@inheritDoc} */
    public double getFunctionValue() {
        return optimizer.getFunctionValue();
    }

    /** {@inheritDoc} */
    public double getResult() {
        return optimizer.getResult();
    }

    /** {@inheritDoc} */
    public double getAbsoluteAccuracy() {
        return optimizer.getAbsoluteAccuracy();
    }

    /** {@inheritDoc} */
    public int getIterationCount() {
        return totalIterations;
    }

    /** {@inheritDoc} */
    public int getMaximalIterationCount() {
        return maxIterations;
    }

    /** {@inheritDoc} */
    public int getMaxEvaluations() {
        return maxEvaluations;
    }

    /** {@inheritDoc} */
    public int getEvaluations() {
        return totalEvaluations;
    }

    /** {@inheritDoc} */
    public double getRelativeAccuracy() {
        return optimizer.getRelativeAccuracy();
    }

    /** {@inheritDoc} */
    public void resetAbsoluteAccuracy() {
        optimizer.resetAbsoluteAccuracy();
    }

    /** {@inheritDoc} */
    public void resetMaximalIterationCount() {
        optimizer.resetMaximalIterationCount();
    }

    /** {@inheritDoc} */
    public void resetRelativeAccuracy() {
        optimizer.resetRelativeAccuracy();
    }

    /** {@inheritDoc} */
    public void setAbsoluteAccuracy(double accuracy) {
        optimizer.setAbsoluteAccuracy(accuracy);
    }

    /** {@inheritDoc} */
    public void setMaximalIterationCount(int count) {
        this.maxIterations = count;
    }

    /** {@inheritDoc} */
    public void setMaxEvaluations(int maxEvaluations) {
        this.maxEvaluations = maxEvaluations;
    }

    /** {@inheritDoc} */
    public void setRelativeAccuracy(double accuracy) {
        optimizer.setRelativeAccuracy(accuracy);
    }

    /** Get all the optima found during the last call to {@link
     * #optimize(UnivariateRealFunction, GoalType, double, double) optimize}.
     * <p>The optimizer stores all the optima found during a set of
     * restarts. The {@link #optimize(UnivariateRealFunction, GoalType,
     * double, double) 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 {@link
     * #optimize(UnivariateRealFunction, GoalType, double, double) optimize}
     * method.
     * </p>
     * <p>
     * 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 Double.NaN elements
     * corresponding to the runs that did not converge. This means all
     * elements will be NaN if the {@link #optimize(UnivariateRealFunction,
     * GoalType, double, double) optimize} method did throw a {@link
     * ConvergenceException ConvergenceException}). This also means that
     * if the first element is not NaN, it is the best point found across
     * all starts.</p>
     * @return array containing the optima
     * @exception IllegalStateException if {@link #optimize(UnivariateRealFunction,
     * GoalType, double, double) optimize} has not been called
     * @see #getOptimaValues()
     */
    public double[] getOptima() throws IllegalStateException {
        if (optima == null) {
            throw MathRuntimeException.createIllegalStateException("no optimum computed yet");
        }
        return optima.clone();
    }

    /** Get all the function values at optima found during the last call to {@link
     * #optimize(UnivariateRealFunction, GoalType, double, double) optimize}.
     * <p>
     * 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 Double.NaN elements
     * corresponding to the runs that did not converge. This means all
     * elements will be NaN if the {@link #optimize(UnivariateRealFunction,
     * GoalType, double, double) optimize} method did throw a {@link
     * ConvergenceException ConvergenceException}). This also means that
     * if the first element is not NaN, it is the best point found across
     * all starts.</p>
     * @return array containing the optima
     * @exception IllegalStateException if {@link #optimize(UnivariateRealFunction,
     * GoalType, double, double) optimize} has not been called
     * @see #getOptima()
     */
    public double[] getOptimaValues() throws IllegalStateException {
        if (optimaValues == null) {
            throw MathRuntimeException.createIllegalStateException("no optimum computed yet");
        }
        return optimaValues.clone();
    }

    /** {@inheritDoc} */
    public double optimize(final UnivariateRealFunction f, final GoalType goalType,
                           final double min, final double max)
        throws ConvergenceException,
            FunctionEvaluationException {

        optima           = new double[starts];
        optimaValues     = new double[starts];
        totalIterations  = 0;
        totalEvaluations = 0;

        // multi-start loop
        for (int i = 0; i < starts; ++i) {

            try {
                optimizer.setMaximalIterationCount(maxIterations - totalIterations);
                optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
                final double bound1 = (i == 0) ? min : min + generator.nextDouble() * (max - min);
                final double bound2 = (i == 0) ? max : min + generator.nextDouble() * (max - min);
                optima[i]       = optimizer.optimize(f, goalType,
                                                     Math.min(bound1, bound2),
                                                     Math.max(bound1, bound2));
                optimaValues[i] = optimizer.getFunctionValue();
            } catch (FunctionEvaluationException fee) {
                optima[i]       = Double.NaN;
                optimaValues[i] = Double.NaN;
            } catch (ConvergenceException ce) {
                optima[i]       = Double.NaN;
                optimaValues[i] = Double.NaN;
            }

            totalIterations  += optimizer.getIterationCount();
            totalEvaluations += optimizer.getEvaluations();

        }

        // sort the optima from best to worst, followed by NaN elements
        int lastNaN = optima.length;
        for (int i = 0; i < lastNaN; ++i) {
            if (Double.isNaN(optima[i])) {
                optima[i] = optima[--lastNaN];
                optima[lastNaN + 1] = Double.NaN;
                optimaValues[i] = optimaValues[--lastNaN];
                optimaValues[lastNaN + 1] = Double.NaN;
            }
        }

        double currX = optima[0];
        double currY = optimaValues[0];
        for (int j = 1; j < lastNaN; ++j) {
            final double prevY = currY;
            currX = optima[j];
            currY = optimaValues[j];
            if ((goalType == GoalType.MAXIMIZE) ^ (currY < prevY)) {
                // the current element should be inserted closer to the beginning
                int i = j - 1;
                double mIX = optima[i];
                double mIY = optimaValues[i];
                while ((i >= 0) && ((goalType == GoalType.MAXIMIZE) ^ (currY < mIY))) {
                    optima[i + 1]       = mIX;
                    optimaValues[i + 1] = mIY;
                    if (i-- != 0) {
                        mIX = optima[i];
                        mIY = optimaValues[i];
                    } else {
                        mIX = Double.NaN;
                        mIY = Double.NaN;
                    }
                }
                optima[i + 1]       = currX;
                optimaValues[i + 1] = currY;
                currX = optima[j];
                currY = optimaValues[j];
            }
        }

        if (Double.isNaN(optima[0])) {
            throw new OptimizationException(
                    "none of the {0} start points lead to convergence",
                    starts);
        }

        // return the found point given the best objective function value
        return optima[0];

    }

    /** {@inheritDoc} */
    public double optimize(final UnivariateRealFunction f, final GoalType goalType,
                           final double min, final double max, final double startValue)
            throws ConvergenceException, FunctionEvaluationException {
        return optimize(f, goalType, min, max);
    }

}