001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 018 package org.apache.commons.math.optimization; 019 020 import java.util.Arrays; 021 import java.util.Comparator; 022 023 import org.apache.commons.math.FunctionEvaluationException; 024 import org.apache.commons.math.MathRuntimeException; 025 import org.apache.commons.math.analysis.MultivariateRealFunction; 026 import org.apache.commons.math.random.RandomVectorGenerator; 027 028 /** 029 * Special implementation of the {@link MultivariateRealOptimizer} interface adding 030 * multi-start features to an existing optimizer. 031 * <p> 032 * This class wraps a classical optimizer to use it several times in 033 * turn with different starting points in order to avoid being trapped 034 * into a local extremum when looking for a global one. 035 * </p> 036 * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $ 037 * @since 2.0 038 */ 039 public class MultiStartMultivariateRealOptimizer 040 implements MultivariateRealOptimizer { 041 042 /** Underlying classical optimizer. */ 043 private final MultivariateRealOptimizer optimizer; 044 045 /** Maximal number of iterations allowed. */ 046 private int maxIterations; 047 048 /** Maximal number of evaluations allowed. */ 049 private int maxEvaluations; 050 051 /** Number of iterations already performed for all starts. */ 052 private int totalIterations; 053 054 /** Number of evaluations already performed for all starts. */ 055 private int totalEvaluations; 056 057 /** Number of starts to go. */ 058 private int starts; 059 060 /** Random generator for multi-start. */ 061 private RandomVectorGenerator generator; 062 063 /** Found optima. */ 064 private RealPointValuePair[] optima; 065 066 /** 067 * Create a multi-start optimizer from a single-start optimizer 068 * @param optimizer single-start optimizer to wrap 069 * @param starts number of starts to perform (including the 070 * first one), multi-start is disabled if value is less than or 071 * equal to 1 072 * @param generator random vector generator to use for restarts 073 */ 074 public MultiStartMultivariateRealOptimizer(final MultivariateRealOptimizer optimizer, 075 final int starts, 076 final RandomVectorGenerator generator) { 077 this.optimizer = optimizer; 078 this.totalIterations = 0; 079 this.totalEvaluations = 0; 080 this.starts = starts; 081 this.generator = generator; 082 this.optima = null; 083 setMaxIterations(Integer.MAX_VALUE); 084 setMaxEvaluations(Integer.MAX_VALUE); 085 } 086 087 /** Get all the optima found during the last call to {@link 088 * #optimize(MultivariateRealFunction, GoalType, double[]) optimize}. 089 * <p>The optimizer stores all the optima found during a set of 090 * restarts. The {@link #optimize(MultivariateRealFunction, GoalType, 091 * double[]) optimize} method returns the best point only. This 092 * method returns all the points found at the end of each starts, 093 * including the best one already returned by the {@link 094 * #optimize(MultivariateRealFunction, GoalType, double[]) optimize} 095 * method. 096 * </p> 097 * <p> 098 * The returned array as one element for each start as specified 099 * in the constructor. It is ordered with the results from the 100 * runs that did converge first, sorted from best to worst 101 * objective value (i.e in ascending order if minimizing and in 102 * descending order if maximizing), followed by and null elements 103 * corresponding to the runs that did not converge. This means all 104 * elements will be null if the {@link #optimize(MultivariateRealFunction, 105 * GoalType, double[]) optimize} method did throw a {@link 106 * org.apache.commons.math.ConvergenceException ConvergenceException}). 107 * This also means that if the first element is non null, it is the best 108 * point found across all starts.</p> 109 * @return array containing the optima 110 * @exception IllegalStateException if {@link #optimize(MultivariateRealFunction, 111 * GoalType, double[]) optimize} has not been called 112 */ 113 public RealPointValuePair[] getOptima() throws IllegalStateException { 114 if (optima == null) { 115 throw MathRuntimeException.createIllegalStateException("no optimum computed yet"); 116 } 117 return optima.clone(); 118 } 119 120 /** {@inheritDoc} */ 121 public void setMaxIterations(int maxIterations) { 122 this.maxIterations = maxIterations; 123 } 124 125 /** {@inheritDoc} */ 126 public int getMaxIterations() { 127 return maxIterations; 128 } 129 130 /** {@inheritDoc} */ 131 public void setMaxEvaluations(int maxEvaluations) { 132 this.maxEvaluations = maxEvaluations; 133 } 134 135 /** {@inheritDoc} */ 136 public int getMaxEvaluations() { 137 return maxEvaluations; 138 } 139 140 /** {@inheritDoc} */ 141 public int getIterations() { 142 return totalIterations; 143 } 144 145 /** {@inheritDoc} */ 146 public int getEvaluations() { 147 return totalEvaluations; 148 } 149 150 /** {@inheritDoc} */ 151 public void setConvergenceChecker(RealConvergenceChecker checker) { 152 optimizer.setConvergenceChecker(checker); 153 } 154 155 /** {@inheritDoc} */ 156 public RealConvergenceChecker getConvergenceChecker() { 157 return optimizer.getConvergenceChecker(); 158 } 159 160 /** {@inheritDoc} */ 161 public RealPointValuePair optimize(final MultivariateRealFunction f, 162 final GoalType goalType, 163 double[] startPoint) 164 throws FunctionEvaluationException, OptimizationException { 165 166 optima = new RealPointValuePair[starts]; 167 totalIterations = 0; 168 totalEvaluations = 0; 169 170 // multi-start loop 171 for (int i = 0; i < starts; ++i) { 172 173 try { 174 optimizer.setMaxIterations(maxIterations - totalIterations); 175 optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations); 176 optima[i] = optimizer.optimize(f, goalType, 177 (i == 0) ? startPoint : generator.nextVector()); 178 } catch (FunctionEvaluationException fee) { 179 optima[i] = null; 180 } catch (OptimizationException oe) { 181 optima[i] = null; 182 } 183 184 totalIterations += optimizer.getIterations(); 185 totalEvaluations += optimizer.getEvaluations(); 186 187 } 188 189 // sort the optima from best to worst, followed by null elements 190 Arrays.sort(optima, new Comparator<RealPointValuePair>() { 191 public int compare(final RealPointValuePair o1, final RealPointValuePair o2) { 192 if (o1 == null) { 193 return (o2 == null) ? 0 : +1; 194 } else if (o2 == null) { 195 return -1; 196 } 197 final double v1 = o1.getValue(); 198 final double v2 = o2.getValue(); 199 return (goalType == GoalType.MINIMIZE) ? 200 Double.compare(v1, v2) : Double.compare(v2, v1); 201 } 202 }); 203 204 if (optima[0] == null) { 205 throw new OptimizationException( 206 "none of the {0} start points lead to convergence", 207 starts); 208 } 209 210 // return the found point given the best objective function value 211 return optima[0]; 212 213 } 214 215 }