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/* * 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.direct; import java.util.Comparator; import org.apache.commons.math3.analysis.MultivariateFunction; import org.apache.commons.math3.exception.NullArgumentException; import org.apache.commons.math3.optimization.GoalType; import org.apache.commons.math3.optimization.ConvergenceChecker; import org.apache.commons.math3.optimization.PointValuePair; import org.apache.commons.math3.optimization.SimpleValueChecker; import org.apache.commons.math3.optimization.MultivariateOptimizer; import org.apache.commons.math3.optimization.OptimizationData; /** * This class implements simplex-based direct search optimization. * * <p> * Direct search methods only use objective function values, they do * not need derivatives and don't either try to compute approximation * of the derivatives. According to a 1996 paper by Margaret H. Wright * (<a href="http://cm.bell-labs.com/cm/cs/doc/96/4-02.ps.gz">Direct * Search Methods: Once Scorned, Now Respectable</a>), they are used * when either the computation of the derivative is impossible (noisy * functions, unpredictable discontinuities) or difficult (complexity, * computation cost). In the first cases, rather than an optimum, a * <em>not too bad</em> point is desired. In the latter cases, an * optimum is desired but cannot be reasonably found. In all cases * direct search methods can be useful. * </p> * <p> * Simplex-based direct search methods are based on comparison of * the objective function values at the vertices of a simplex (which is a * set of n+1 points in dimension n) that is updated by the algorithms * steps. * <p> * <p> * The {@link #setSimplex(AbstractSimplex) setSimplex} method <em>must</em> * be called prior to calling the {@code optimize} method. * </p> * <p> * Each call to {@link #optimize(int,MultivariateFunction,GoalType,double[]) * optimize} will re-use the start configuration of the current simplex and * move it such that its first vertex is at the provided start point of the * optimization. If the {@code optimize} method is called to solve a different * problem and the number of parameters change, the simplex must be * re-initialized to one with the appropriate dimensions. * </p> * <p> * Convergence is checked by providing the <em>worst</em> points of * previous and current simplex to the convergence checker, not the best * ones. * </p> * <p> * This simplex optimizer implementation does not directly support constrained * optimization with simple bounds, so for such optimizations, either a more * dedicated method must be used like {@link CMAESOptimizer} or {@link * BOBYQAOptimizer}, or the optimized method must be wrapped in an adapter like * {@link MultivariateFunctionMappingAdapter} or {@link * MultivariateFunctionPenaltyAdapter}. * </p> * * @see AbstractSimplex * @see MultivariateFunctionMappingAdapter * @see MultivariateFunctionPenaltyAdapter * @see CMAESOptimizer * @see BOBYQAOptimizer * @version $Id: SimplexOptimizer.java 1422230 2012-12-15 12:11:13Z erans $ * @deprecated As of 3.1 (to be removed in 4.0). * @since 3.0 */ @Deprecated public class SimplexOptimizer extends BaseAbstractMultivariateOptimizer<MultivariateFunction> implements MultivariateOptimizer { /** Simplex. */ private AbstractSimplex simplex; /** * Constructor using a default {@link SimpleValueChecker convergence * checker}. * @deprecated See {@link SimpleValueChecker#SimpleValueChecker()} */ @Deprecated public SimplexOptimizer() { this(new SimpleValueChecker()); } /** * @param checker Convergence checker. */ public SimplexOptimizer(ConvergenceChecker<PointValuePair> checker) { super(checker); } /** * @param rel Relative threshold. * @param abs Absolute threshold. */ public SimplexOptimizer(double rel, double abs) { this(new SimpleValueChecker(rel, abs)); } /** * Set the simplex algorithm. * * @param simplex Simplex. * @deprecated As of 3.1. The initial simplex can now be passed as an * argument of the {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])} * method. */ @Deprecated public void setSimplex(AbstractSimplex simplex) { parseOptimizationData(simplex); } /** * Optimize an objective function. * * @param maxEval Allowed number of evaluations of the objective function. * @param f Objective function. * @param goalType Optimization type. * @param optData Optimization data. The following data will be looked for: * <ul> * <li>{@link org.apache.commons.math3.optimization.InitialGuess InitialGuess}</li> * <li>{@link AbstractSimplex}</li> * </ul> * @return the point/value pair giving the optimal value for objective * function. */ @Override protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData) { // Scan "optData" for the input specific to this optimizer. parseOptimizationData(optData); // The parent's method will retrieve the common parameters from // "optData" and call "doOptimize". return super.optimizeInternal(maxEval, f, goalType, optData); } /** * Scans the list of (required and optional) optimization data that * characterize the problem. * * @param optData Optimization data. The following data will be looked for: * <ul> * <li>{@link AbstractSimplex}</li> * </ul> */ private void parseOptimizationData(OptimizationData... optData) { // The existing values (as set by the previous call) are reused if // not provided in the argument list. for (OptimizationData data : optData) { if (data instanceof AbstractSimplex) { simplex = (AbstractSimplex) data; continue; } } } /** {@inheritDoc} */ @Override protected PointValuePair doOptimize() { if (simplex == null) { throw new NullArgumentException(); } // Indirect call to "computeObjectiveValue" in order to update the // evaluations counter. final MultivariateFunction evalFunc = new MultivariateFunction() { public double value(double[] point) { return computeObjectiveValue(point); } }; final boolean isMinim = getGoalType() == GoalType.MINIMIZE; final Comparator<PointValuePair> comparator = new Comparator<PointValuePair>() { public int compare(final PointValuePair o1, final PointValuePair o2) { final double v1 = o1.getValue(); final double v2 = o2.getValue(); return isMinim ? Double.compare(v1, v2) : Double.compare(v2, v1); } }; // Initialize search. simplex.build(getStartPoint()); simplex.evaluate(evalFunc, comparator); PointValuePair[] previous = null; int iteration = 0; final ConvergenceChecker<PointValuePair> checker = getConvergenceChecker(); while (true) { if (iteration > 0) { boolean converged = true; for (int i = 0; i < simplex.getSize(); i++) { PointValuePair prev = previous[i]; converged = converged && checker.converged(iteration, prev, simplex.getPoint(i)); } if (converged) { // We have found an optimum. return simplex.getPoint(0); } } // We still need to search. previous = simplex.getPoints(); simplex.iterate(evalFunc, comparator); ++iteration; } } }