source: src/main/java/agents/anac/y2019/harddealer/math3/optimization/univariate/UnivariateMultiStartOptimizer.java

/*
 * 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 agents.anac.y2019.harddealer.math3.optimization.univariate;

import java.util.Arrays;
import java.util.Comparator;

import agents.anac.y2019.harddealer.math3.analysis.UnivariateFunction;
import agents.anac.y2019.harddealer.math3.exception.MathIllegalStateException;
import agents.anac.y2019.harddealer.math3.exception.NotStrictlyPositiveException;
import agents.anac.y2019.harddealer.math3.exception.NullArgumentException;
import agents.anac.y2019.harddealer.math3.exception.util.LocalizedFormats;
import agents.anac.y2019.harddealer.math3.random.RandomGenerator;
import agents.anac.y2019.harddealer.math3.optimization.GoalType;
import agents.anac.y2019.harddealer.math3.optimization.ConvergenceChecker;

/**
 * Special implementation of the {@link UnivariateOptimizer} interface
 * adding multi-start features to an existing optimizer.
 *
 * 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.
 *
 * @param <FUNC> Type of the objective function to be optimized.
 *
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 3.0
 */
@Deprecated
public class UnivariateMultiStartOptimizer<FUNC extends UnivariateFunction>
    implements BaseUnivariateOptimizer<FUNC> {
    /** Underlying classical optimizer. */
    private final BaseUnivariateOptimizer<FUNC> optimizer;
    /** Maximal number of evaluations allowed. */
    private int maxEvaluations;
    /** 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;

    /**
     * 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 NullArgumentException if {@code optimizer} or {@code generator}
     * is {@code null}.
     * @throws NotStrictlyPositiveException if {@code starts < 1}.
     */
    public UnivariateMultiStartOptimizer(final BaseUnivariateOptimizer<FUNC> optimizer,
                                             final int starts,
                                             final RandomGenerator generator) {
        if (optimizer == null ||
                generator == null) {
                throw new NullArgumentException();
        }
        if (starts < 1) {
            throw new NotStrictlyPositiveException(starts);
        }

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

    /**
     * {@inheritDoc}
     */
    public ConvergenceChecker<UnivariatePointValuePair> getConvergenceChecker() {
        return optimizer.getConvergenceChecker();
    }

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

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

    /**
     * Get all the optima found during the last call to {@link
     * #optimize(int,UnivariateFunction,GoalType,double,double) optimize}.
     * The optimizer stores all the optima found during a set of
     * restarts. The {@link #optimize(int,UnivariateFunction,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(int,UnivariateFunction,GoalType,double,double) 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 {@link
     * #optimize(int,UnivariateFunction,GoalType,double,double) 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(int,UnivariateFunction,GoalType,double,double) optimize}
     * has not been called.
     */
    public UnivariatePointValuePair[] getOptima() {
        if (optima == null) {
            throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
        }
        return optima.clone();
    }

    /** {@inheritDoc} */
    public UnivariatePointValuePair optimize(int maxEval, final FUNC f,
                                                 final GoalType goal,
                                                 final double min, final double max) {
        return optimize(maxEval, f, goal, min, max, min + 0.5 * (max - min));
    }

    /** {@inheritDoc} */
    public UnivariatePointValuePair optimize(int maxEval, final FUNC f,
                                                 final GoalType goal,
                                                 final double min, final double max,
                                                 final double startValue) {
        RuntimeException lastException = null;
        optima = new UnivariatePointValuePair[starts];
        totalEvaluations = 0;

        // Multi-start loop.
        for (int i = 0; i < starts; ++i) {
            // CHECKSTYLE: stop IllegalCatch
            try {
                final double s = (i == 0) ? startValue : min + generator.nextDouble() * (max - min);
                optima[i] = optimizer.optimize(maxEval - totalEvaluations, f, goal, min, max, s);
            } catch (RuntimeException mue) {
                lastException = mue;
                optima[i] = null;
            }
            // CHECKSTYLE: resume IllegalCatch

            totalEvaluations += optimizer.getEvaluations();
        }

        sortPairs(goal);

        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);
                }
            });
    }
}