1 package net.sf.openrocket.optimization.general.multidim;
3 import java.util.ArrayList;
4 import java.util.Collections;
5 import java.util.LinkedList;
8 import net.sf.openrocket.logging.LogHelper;
9 import net.sf.openrocket.optimization.general.FunctionCache;
10 import net.sf.openrocket.optimization.general.FunctionOptimizer;
11 import net.sf.openrocket.optimization.general.OptimizationController;
12 import net.sf.openrocket.optimization.general.OptimizationException;
13 import net.sf.openrocket.optimization.general.ParallelFunctionCache;
14 import net.sf.openrocket.optimization.general.Point;
15 import net.sf.openrocket.startup.Application;
16 import net.sf.openrocket.util.Statistics;
19 * A customized implementation of the parallel multidirectional search algorithm by Dennis and Torczon.
21 * This is a parallel pattern search optimization algorithm. The function evaluations are performed
22 * using an ExecutorService. By default a ThreadPoolExecutor is used that has as many thread defined
23 * as the system has processors.
25 * The optimization can be aborted by interrupting the current thread.
27 public class MultidirectionalSearchOptimizer implements FunctionOptimizer, Statistics {
28 private static final LogHelper log = Application.getLogger();
30 private List<Point> simplex = new ArrayList<Point>();
32 private ParallelFunctionCache functionExecutor;
34 private boolean useExpansion = false;
35 private boolean useCoordinateSearch = false;
37 private int stepCount = 0;
38 private int reflectionAcceptance = 0;
39 private int expansionAcceptance = 0;
40 private int coordinateAcceptance = 0;
41 private int reductionFallback = 0;
44 public MultidirectionalSearchOptimizer() {
48 public MultidirectionalSearchOptimizer(ParallelFunctionCache functionCache) {
49 this.functionExecutor = functionCache;
55 public void optimize(Point initial, OptimizationController control) throws OptimizationException {
56 FunctionCacheComparator comparator = new FunctionCacheComparator(functionExecutor);
58 final List<Point> pattern = SearchPattern.square(initial.dim());
59 log.info("Starting optimization at " + initial + " with pattern " + pattern);
63 boolean simplexComputed = false;
66 // Set up the current simplex
69 for (Point p : pattern) {
70 simplex.add(initial.add(p.mul(step)));
74 List<Point> reflection = new ArrayList<Point>(simplex.size());
75 List<Point> expansion = new ArrayList<Point>(simplex.size());
76 List<Point> coordinateSearch = new ArrayList<Point>(simplex.size());
79 boolean continueOptimization = true;
80 while (continueOptimization) {
82 log.debug("Starting optimization step with simplex " + simplex +
83 (simplexComputed ? "" : " (not computed)"));
86 if (!simplexComputed) {
87 // TODO: Could something be computed in parallel?
88 functionExecutor.compute(simplex);
89 functionExecutor.waitFor(simplex);
90 Collections.sort(simplex, comparator);
91 simplexComputed = true;
94 current = simplex.get(0);
95 currentValue = functionExecutor.getValue(current);
98 * Compute and queue the next points in likely order of usefulness.
99 * Expansion is unlikely as we're mainly dealing with bounded optimization.
101 createReflection(simplex, reflection);
102 if (useCoordinateSearch)
103 createCoordinateSearch(current, step, coordinateSearch);
105 createExpansion(simplex, expansion);
107 functionExecutor.compute(reflection);
108 if (useCoordinateSearch)
109 functionExecutor.compute(coordinateSearch);
111 functionExecutor.compute(expansion);
113 // Check reflection acceptance
114 log.debug("Computing reflection");
115 functionExecutor.waitFor(reflection);
117 if (accept(reflection, currentValue)) {
119 log.debug("Reflection was successful, aborting coordinate search, " +
120 (useExpansion ? "computing" : "skipping") + " expansion");
122 if (useCoordinateSearch)
123 functionExecutor.abort(coordinateSearch);
126 simplex.add(current);
127 simplex.addAll(reflection);
128 Collections.sort(simplex, comparator);
133 * Assume expansion to be unsuccessful, queue next reflection while computing expansion.
135 createReflection(simplex, reflection);
137 functionExecutor.compute(reflection);
138 functionExecutor.waitFor(expansion);
140 if (accept(expansion, currentValue)) {
141 log.debug("Expansion was successful, aborting reflection");
142 functionExecutor.abort(reflection);
145 simplex.add(current);
146 simplex.addAll(expansion);
148 Collections.sort(simplex, comparator);
149 expansionAcceptance++;
151 log.debug("Expansion failed");
152 reflectionAcceptance++;
156 reflectionAcceptance++;
161 log.debug("Reflection was unsuccessful, aborting expansion, computing coordinate search");
163 functionExecutor.abort(expansion);
166 * Assume coordinate search to be unsuccessful, queue contraction step while computing.
169 functionExecutor.compute(simplex);
171 if (useCoordinateSearch) {
172 functionExecutor.waitFor(coordinateSearch);
174 if (accept(coordinateSearch, currentValue)) {
176 log.debug("Coordinate search successful, reseting simplex");
177 List<Point> toAbort = new LinkedList<Point>(simplex);
179 simplex.add(current);
180 for (Point p : pattern) {
181 simplex.add(current.add(p.mul(step)));
183 toAbort.removeAll(simplex);
184 functionExecutor.abort(toAbort);
185 simplexComputed = false;
186 coordinateAcceptance++;
189 log.debug("Coordinate search unsuccessful, halving step.");
194 log.debug("Coordinate search not used, halving step.");
201 log.debug("Ending optimization step with simplex " + simplex);
203 continueOptimization = control.stepTaken(current, currentValue, simplex.get(0),
204 functionExecutor.getValue(simplex.get(0)), step);
206 if (Thread.interrupted()) {
207 throw new InterruptedException();
212 } catch (InterruptedException e) {
213 log.info("Optimization was interrupted with InterruptedException");
216 log.info("Finishing optimization at point " + simplex.get(0) + " value = " +
217 functionExecutor.getValue(simplex.get(0)));
218 log.info("Optimization statistics: " + getStatistics());
223 private void createReflection(List<Point> base, List<Point> reflection) {
224 Point current = base.get(0);
226 for (int i = 1; i < base.size(); i++) {
227 Point p = current.mul(2).sub(base.get(i));
232 private void createExpansion(List<Point> base, List<Point> expansion) {
233 Point current = base.get(0);
235 for (int i = 1; i < base.size(); i++) {
236 Point p = current.mul(3).sub(base.get(i).mul(2));
241 private void halveStep(List<Point> base) {
242 Point current = base.get(0);
243 for (int i = 1; i < base.size(); i++) {
244 Point p = base.get(i);
245 p = p.add(current).mul(0.5);
250 private void createCoordinateSearch(Point current, double step, List<Point> coordinateDirections) {
251 coordinateDirections.clear();
252 for (int i = 0; i < current.dim(); i++) {
253 Point p = new Point(current.dim());
255 coordinateDirections.add(current.add(p));
256 coordinateDirections.add(current.sub(p));
261 private boolean accept(List<Point> points, double currentValue) {
262 for (Point p : points) {
263 if (functionExecutor.getValue(p) < currentValue) {
273 public Point getOptimumPoint() {
274 if (simplex.size() == 0) {
275 throw new IllegalStateException("Optimization has not been called, simplex is empty");
277 return simplex.get(0);
281 public double getOptimumValue() {
282 return functionExecutor.getValue(getOptimumPoint());
286 public FunctionCache getFunctionCache() {
287 return functionExecutor;
291 public void setFunctionCache(FunctionCache functionCache) {
292 if (!(functionCache instanceof ParallelFunctionCache)) {
293 throw new IllegalArgumentException("Function cache needs to be a ParallelFunctionCache: " + functionCache);
295 this.functionExecutor = (ParallelFunctionCache) functionCache;
299 public String getStatistics() {
300 return "MultidirectionalSearchOptimizer[stepCount=" + stepCount +
301 ", reflectionAcceptance=" + reflectionAcceptance +
302 ", expansionAcceptance=" + expansionAcceptance +
303 ", coordinateAcceptance=" + coordinateAcceptance +
304 ", reductionFallback=" + reductionFallback;
308 public void resetStatistics() {
310 reflectionAcceptance = 0;
311 expansionAcceptance = 0;
312 coordinateAcceptance = 0;
313 reductionFallback = 0;