/* * 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; import java.util.Arrays; import java.util.Comparator; import org.apache.commons.math3.analysis.MultivariateFunction; import org.apache.commons.math3.exception.MathIllegalStateException; import org.apache.commons.math3.exception.NotStrictlyPositiveException; import org.apache.commons.math3.exception.NullArgumentException; import org.apache.commons.math3.exception.util.LocalizedFormats; import org.apache.commons.math3.random.RandomVectorGenerator; /** * Base class for all implementations of a multi-start optimizer. * * This interface is mainly intended to enforce the internal coherence of * Commons-Math. Users of the API are advised to base their code on * {@link MultivariateMultiStartOptimizer} or on * {@link DifferentiableMultivariateMultiStartOptimizer}. * * @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 BaseMultivariateMultiStartOptimizer<FUNC extends MultivariateFunction> implements BaseMultivariateOptimizer<FUNC> { /** Underlying classical optimizer. */ private final BaseMultivariateOptimizer<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 RandomVectorGenerator generator; /** Found optima. */ private PointValuePair[] 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 {@link #optimize(int,MultivariateFunction,GoalType,double[]) * optimize} will return the same solution as {@code optimizer} would. * @param generator Random vector generator to use for restarts. * @throws NullArgumentException if {@code optimizer} or {@code generator} * is {@code null}. * @throws NotStrictlyPositiveException if {@code starts < 1}. */ protected BaseMultivariateMultiStartOptimizer(final BaseMultivariateOptimizer<FUNC> optimizer, final int starts, final RandomVectorGenerator 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; } /** * Get all the optima found during the last call to {@link * #optimize(int,MultivariateFunction,GoalType,double[]) optimize}. * The optimizer stores all the optima found during a set of * restarts. The {@link #optimize(int,MultivariateFunction,GoalType,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,MultivariateFunction,GoalType,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 and null elements * corresponding to the runs that did not converge. This means all * elements will be null if the {@link #optimize(int,MultivariateFunction,GoalType,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,MultivariateFunction,GoalType,double[]) optimize} * has not been called. */ public PointValuePair[] getOptima() { if (optima == null) { throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET); } return optima.clone(); } /** {@inheritDoc} */ public int getMaxEvaluations() { return maxEvaluations; } /** {@inheritDoc} */ public int getEvaluations() { return totalEvaluations; } /** {@inheritDoc} */ public ConvergenceChecker<PointValuePair> getConvergenceChecker() { return optimizer.getConvergenceChecker(); } /** * {@inheritDoc} */ public PointValuePair optimize(int maxEval, final FUNC f, final GoalType goal, double[] startPoint) { maxEvaluations = maxEval; RuntimeException lastException = null; optima = new PointValuePair[starts]; totalEvaluations = 0; // Multi-start loop. for (int i = 0; i < starts; ++i) { // CHECKSTYLE: stop IllegalCatch try { optima[i] = optimizer.optimize(maxEval - totalEvaluations, f, goal, i == 0 ? startPoint : generator.nextVector()); } 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 found point given 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<PointValuePair>() { /** {@inheritDoc} */ public int compare(final PointValuePair o1, final PointValuePair 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); } }); } }