added all necessary multi-start optimizers types
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@754763 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe
parent
8f6fd887f5
commit
c12aa3b936
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@ -0,0 +1,190 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.optimization;
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import java.util.Arrays;
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import java.util.Comparator;
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import org.apache.commons.math.ConvergenceException;
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import org.apache.commons.math.MathRuntimeException;
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import org.apache.commons.math.random.RandomVectorGenerator;
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/**
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* Special implementation of the {@link ScalarDifferentiableOptimizer} interface adding
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* multi-start features to an existing optimizer.
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* <p>
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* This class wraps a classical optimizer to use it several times in
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* turn with different starting points in order to avoid being trapped
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* into a local extremum when looking for a global one.
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* </p>
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* @version $Revision$ $Date$
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* @since 2.0
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*/
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public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferentiableOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = 9008747186334431824L;
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/** Underlying classical optimizer. */
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private final ScalarDifferentiableOptimizer optimizer;
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/** Number of evaluations already performed for all starts. */
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private int totalEvaluations;
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/** Maximal number of evaluations allowed. */
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private int maxEvaluations;
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/** Number of starts to go. */
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private int starts;
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/** Random generator for multi-start. */
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private RandomVectorGenerator generator;
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/** Found optima. */
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private ScalarPointValuePair[] optima;
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/**
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* Create a multi-start optimizer from a single-start optimizer
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* @param optimizer single-start optimizer to wrap
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* @param starts number of starts to perform (including the
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* first one), multi-start is disabled if value is less than or
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* equal to 1
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* @param generator random vector generator to use for restarts
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*/
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public MultiStartScalarDifferentiableOptimizer(final ScalarDifferentiableOptimizer optimizer,
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final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.totalEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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}
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/** Get all the optima found during the last call to {@link
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* #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}.
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* <p>The optimizer stores all the optima found during a set of
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* restarts. The {@link #optimize(ScalarObjectiveFunction, GoalType,
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* double[]) optimize} method returns the best point only. This
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* method returns all the points found at the end of each starts,
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* including the best one already returned by the {@link
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* #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}
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* method.
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* </p>
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* <p>
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* The returned array as one element for each start as specified
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* in the constructor. It is ordered with the results from the
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* runs that did converge first, sorted from best to worst
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* objective value (i.e in ascending order if minimizing and in
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* descending order if maximizing), followed by and null elements
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* corresponding to the runs that did not converge. This means all
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* elements will be null if the {@link #optimize(ScalarObjectiveFunction,
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* GoalType, double[]) optimize} method did throw a {@link
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* ConvergenceException ConvergenceException}). This also means that
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* if the first element is non null, it is the best point found across
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* all starts.</p>
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* @return array containing the optima
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* @exception IllegalStateException if {@link #optimize(ScalarObjectiveFunction,
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* GoalType, double[]) optimize} has not been called
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*/
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public ScalarPointValuePair[] getOptima() throws IllegalStateException {
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if (optima == null) {
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throw MathRuntimeException.createIllegalStateException("no optimum computed yet");
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}
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return (ScalarPointValuePair[]) optima.clone();
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}
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/** {@inheritDoc} */
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public int getEvaluations() {
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return totalEvaluations;
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}
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/** {@inheritDoc} */
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public void setMaxEvaluations(int maxEvaluations) {
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this.maxEvaluations = maxEvaluations;
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}
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/** {@inheritDoc} */
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public int getMaxEvaluations() {
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return maxEvaluations;
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}
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/** {@inheritDoc} */
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public void setConvergenceChecker(ScalarConvergenceChecker checker) {
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optimizer.setConvergenceChecker(checker);
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}
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/** {@inheritDoc} */
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public ScalarConvergenceChecker getConvergenceChecker() {
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return optimizer.getConvergenceChecker();
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}
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/** {@inheritDoc} */
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public ScalarPointValuePair optimize(final ScalarDifferentiableObjectiveFunction f,
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final GoalType goalType,
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double[] startPoint)
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throws ObjectiveException, OptimizationException {
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optima = new ScalarPointValuePair[starts];
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totalEvaluations = 0;
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// multi-start loop
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for (int i = 0; i < starts; ++i) {
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try {
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optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
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optima[i] = optimizer.optimize(f, goalType,
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(i == 0) ? startPoint : generator.nextVector());
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} catch (ObjectiveException obe) {
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optima[i] = null;
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} catch (OptimizationException ope) {
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optima[i] = null;
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}
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totalEvaluations += optimizer.getEvaluations();
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}
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// sort the optima from best to worst, followed by null elements
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Arrays.sort(optima, new Comparator<ScalarPointValuePair>() {
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public int compare(final ScalarPointValuePair o1, final ScalarPointValuePair o2) {
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if (o1 == null) {
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return (o2 == null) ? 0 : +1;
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} else if (o2 == null) {
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return -1;
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}
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final double v1 = o1.getValue();
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final double v2 = o2.getValue();
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return (goalType == GoalType.MINIMIZE) ?
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Double.compare(v1, v2) : Double.compare(v2, v1);
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}
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});
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if (optima[0] == null) {
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throw new OptimizationException(
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"none of the {0} start points lead to convergence",
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starts);
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}
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// return the found point given the best objective function value
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return optima[0];
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}
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}
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@ -35,7 +35,7 @@ import org.apache.commons.math.random.RandomVectorGenerator;
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* @version $Revision$ $Date$
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* @since 2.0
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*/
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public class MultiStartOptimizer implements ScalarOptimizer {
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public class MultiStartScalarOptimizer implements ScalarOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = 6648351778723282863L;
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@ -66,8 +66,8 @@ public class MultiStartOptimizer implements ScalarOptimizer {
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* equal to 1
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* @param generator random vector generator to use for restarts
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*/
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public MultiStartOptimizer(final ScalarOptimizer optimizer, final int starts,
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final RandomVectorGenerator generator) {
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public MultiStartScalarOptimizer(final ScalarOptimizer optimizer, final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.totalEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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@ -136,8 +136,8 @@ public class MultiStartOptimizer implements ScalarOptimizer {
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/** {@inheritDoc} */
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public ScalarPointValuePair optimize(final ScalarObjectiveFunction f,
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final GoalType goalType,
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double[] startPoint)
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final GoalType goalType,
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double[] startPoint)
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throws ObjectiveException, OptimizationException {
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optima = new ScalarPointValuePair[starts];
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@ -0,0 +1,207 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math.optimization;
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import java.util.Arrays;
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import java.util.Comparator;
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import org.apache.commons.math.ConvergenceException;
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import org.apache.commons.math.MathRuntimeException;
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import org.apache.commons.math.random.RandomVectorGenerator;
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/**
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* Special implementation of the {@link VectorialDifferentiableOptimizer} interface adding
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* multi-start features to an existing optimizer.
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* <p>
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* This class wraps a classical optimizer to use it several times in
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* turn with different starting points in order to avoid being trapped
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* into a local extremum when looking for a global one.
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* </p>
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* @version $Revision$ $Date$
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* @since 2.0
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*/
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public class MultiStartVectorialDifferentiableOptimizer implements VectorialDifferentiableOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = -6671992853686531955L;
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/** Underlying classical optimizer. */
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private final VectorialDifferentiableOptimizer optimizer;
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/** Number of evaluations already performed for all starts. */
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private int totalEvaluations;
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/** Number of jacobian evaluations already performed for all starts. */
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private int totalJacobianEvaluations;
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/** Maximal number of evaluations allowed. */
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private int maxEvaluations;
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/** Number of starts to go. */
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private int starts;
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/** Random generator for multi-start. */
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private RandomVectorGenerator generator;
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/** Found optima. */
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private VectorialPointValuePair[] optima;
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/**
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* Create a multi-start optimizer from a single-start optimizer
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* @param optimizer single-start optimizer to wrap
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* @param starts number of starts to perform (including the
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* first one), multi-start is disabled if value is less than or
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* equal to 1
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* @param generator random vector generator to use for restarts
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*/
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public MultiStartVectorialDifferentiableOptimizer(final VectorialDifferentiableOptimizer optimizer,
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final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.totalEvaluations = 0;
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this.totalJacobianEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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}
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/** Get all the optima found during the last call to {@link
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* #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}.
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* <p>The optimizer stores all the optima found during a set of
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* restarts. The {@link #optimize(ScalarObjectiveFunction, GoalType,
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* double[]) optimize} method returns the best point only. This
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* method returns all the points found at the end of each starts,
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* including the best one already returned by the {@link
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* #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}
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* method.
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* </p>
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* <p>
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* The returned array as one element for each start as specified
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* in the constructor. It is ordered with the results from the
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* runs that did converge first, sorted from best to worst
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* objective value (i.e in ascending order if minimizing and in
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* descending order if maximizing), followed by and null elements
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* corresponding to the runs that did not converge. This means all
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* elements will be null if the {@link #optimize(ScalarObjectiveFunction,
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* GoalType, double[]) optimize} method did throw a {@link
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* ConvergenceException ConvergenceException}). This also means that
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* if the first element is non null, it is the best point found across
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* all starts.</p>
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* @return array containing the optima
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* @exception IllegalStateException if {@link #optimize(ScalarObjectiveFunction,
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* GoalType, double[]) optimize} has not been called
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*/
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public VectorialPointValuePair[] getOptima() throws IllegalStateException {
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if (optima == null) {
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throw MathRuntimeException.createIllegalStateException("no optimum computed yet");
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}
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return (VectorialPointValuePair[]) optima.clone();
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}
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/** {@inheritDoc} */
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public int getEvaluations() {
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return totalEvaluations;
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}
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/** {@inheritDoc} */
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public int getJacobianEvaluations() {
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return totalJacobianEvaluations;
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}
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/** {@inheritDoc} */
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public void setMaxEvaluations(int maxEvaluations) {
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this.maxEvaluations = maxEvaluations;
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}
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/** {@inheritDoc} */
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public int getMaxEvaluations() {
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return maxEvaluations;
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}
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/** {@inheritDoc} */
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public void setConvergenceChecker(VectorialConvergenceChecker checker) {
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optimizer.setConvergenceChecker(checker);
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}
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/** {@inheritDoc} */
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public VectorialConvergenceChecker getConvergenceChecker() {
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return optimizer.getConvergenceChecker();
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}
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/** {@inheritDoc} */
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public VectorialPointValuePair optimize(final VectorialDifferentiableObjectiveFunction f,
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final double[] target, final double[] weights,
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final double[] startPoint)
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throws ObjectiveException, OptimizationException, IllegalArgumentException {
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optima = new VectorialPointValuePair[starts];
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totalEvaluations = 0;
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totalJacobianEvaluations = 0;
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// multi-start loop
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for (int i = 0; i < starts; ++i) {
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try {
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optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
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optima[i] = optimizer.optimize(f, target, weights,
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(i == 0) ? startPoint : generator.nextVector());
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} catch (ObjectiveException obe) {
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optima[i] = null;
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} catch (OptimizationException ope) {
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optima[i] = null;
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}
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totalEvaluations += optimizer.getEvaluations();
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totalJacobianEvaluations += optimizer.getJacobianEvaluations();
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}
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// sort the optima from best to worst, followed by null elements
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Arrays.sort(optima, new Comparator<VectorialPointValuePair>() {
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public int compare(final VectorialPointValuePair o1, final VectorialPointValuePair o2) {
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if (o1 == null) {
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return (o2 == null) ? 0 : +1;
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} else if (o2 == null) {
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return -1;
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}
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return Double.compare(weightedResidual(o1), weightedResidual(o2));
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}
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private double weightedResidual(final VectorialPointValuePair pv) {
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final double[] value = pv.getValueRef();
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double sum = 0;
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for (int i = 0; i < value.length; ++i) {
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final double ri = value[i] - target[i];
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sum += weights[i] * ri * ri;
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}
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return sum;
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}
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});
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if (optima[0] == null) {
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throw new OptimizationException(
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"none of the {0} start points lead to convergence",
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starts);
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}
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// return the found point given the best objective function value
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return optima[0];
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}
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}
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