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MATH-1622: Simulated annealing entails at least one additional search. 

Also ensure that the "best list" contains at least two points.
This commit is contained in:
Gilles Sadowski 2021-08-26 01:39:46 +02:00
parent 677f5f7a3f
commit 06ec7ad0bc
1 changed files with 41 additions and 19 deletions
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60
commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/noderiv/SimplexOptimizer.java
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@ -29,7 +29,7 @@ import java.util.concurrent.CopyOnWriteArrayList;
import org.apache.commons.math4.legacy.core.MathArrays;
import org.apache.commons.math4.legacy.analysis.MultivariateFunction;
import org.apache.commons.math4.legacy.exception.MathUnsupportedOperationException;
import org.apache.commons.math4.legacy.exception.ConvergenceException;
import org.apache.commons.math4.legacy.exception.MathInternalError;
import org.apache.commons.math4.legacy.exception.util.LocalizedFormats;
import org.apache.commons.math4.legacy.optim.ConvergenceChecker;
import org.apache.commons.math4.legacy.optim.OptimizationData;
@ -90,9 +90,12 @@ import org.apache.commons.math4.legacy.optim.nonlinear.scalar.ObjectiveFunction;
* <p>
* Whenever {@link SimulatedAnnealing simulated annealing (SA)} is activated,
* and the SA phase has completed, convergence has probably not been reached
* yet: In such cases, the {@link PopulationSize} argument to method
* {@link #optimize(OptimizationData[]) optimize} will trigger an additional
* "best list" search.
* yet; whenever it's the case, an additional (non-SA) search will be performed
* (using the current best simplex point as a start point).
* <p>
* Additional "best list" searches can be requested through setting the
* {@link PopulationSize} argument of the {@link #optimize(OptimizationData[])
* optimize} method.
*
* <p>
* This implementation does not directly support constrained optimization
@ -113,8 +116,10 @@ public class SimplexOptimizer extends MultivariateOptimizer {
private Simplex initialSimplex;
/** Simulated annealing setup (optional). */
private SimulatedAnnealing simulatedAnnealing = null;
/** Number of additional optimizations (optional). */
private int bestListSize = 0;
/** User-defined number of additional optimizations (optional). */
private int populationSize = 0;
/** Actual number of additional optimizations. */
private int additionalSearch = 0;
/** Callbacks. */
private final List<Observer> callbacks = new CopyOnWriteArrayList<>();
@ -244,13 +249,18 @@ public class SimplexOptimizer extends MultivariateOptimizer {
comparator);
}
if (bestListSize != 0) {
if (additionalSearch != 0) {
// In "bestList", we must keep track of at least two points
// in order to be able to compute the new initial simplex for
// the additional search.
final int max = Math.max(additionalSearch, 2);
// Store best points.
for (int i = 0; i < currentSimplex.getSize(); i++) {
keepIfBetter(currentSimplex.get(i),
comparator,
bestList,
bestListSize);
max);
}
}
@ -258,19 +268,20 @@ public class SimplexOptimizer extends MultivariateOptimizer {
}
// No convergence.
if (bestList.isEmpty()) {
// Bail out.
throw new ConvergenceException();
} else {
if (additionalSearch > 0) {
// Additional optimizations.
// Reference to counter in the "main" search in order to retrieve
// the total number of evaluations in the "best list" search.
final IntSupplier evalCount = () -> getEvaluations();
return bestListSearch(evalFunc,
comparator,
bestList,
evalCount);
}
throw new MathInternalError(); // Should never happen.
}
/**
@ -301,7 +312,7 @@ public class SimplexOptimizer extends MultivariateOptimizer {
} else if (data instanceof SimulatedAnnealing) {
simulatedAnnealing = (SimulatedAnnealing) data;
} else if (data instanceof PopulationSize) {
bestListSize = ((PopulationSize) data).getPopulationSize();
populationSize = ((PopulationSize) data).getPopulationSize();
}
}
}
@ -335,6 +346,7 @@ public class SimplexOptimizer extends MultivariateOptimizer {
* {@link #optimize(OptimizationData[]) optimize} method.
* @throws NullPointerException if no initial simplex or no transform rule
* was passed to the {@link #optimize(OptimizationData[]) optimize} method.
* @throws IllegalArgumentException if {@link #populationSize} is negative.
*/
private void checkParameters() {
Objects.requireNonNull(updateRule, "Update rule");
@ -344,6 +356,14 @@ public class SimplexOptimizer extends MultivariateOptimizer {
getUpperBound() != null) {
throw new MathUnsupportedOperationException(LocalizedFormats.CONSTRAINT);
}
if (populationSize < 0) {
throw new IllegalArgumentException("Population size");
}
additionalSearch = simulatedAnnealing == null ?
Math.max(0, populationSize) :
Math.max(1, populationSize);
}
/**
@ -441,22 +461,24 @@ public class SimplexOptimizer extends MultivariateOptimizer {
*
* @param evalFunc Objective function.
* @param comp Fitness comparator.
* @param starts Starting points.
* @param bestList Best points encountered during the "main" search.
* List is assumed to be ordered from best to worst.
* @param evalCount Evaluation counter.
* @return the optimum.
*/
private PointValuePair bestListSearch(MultivariateFunction evalFunc,
Comparator<PointValuePair> comp,
List<PointValuePair> starts,
List<PointValuePair> bestList,
IntSupplier evalCount) {
PointValuePair best = starts.get(0); // Overall best result.
PointValuePair best = bestList.get(0); // Overall best result.
// Additional local optimizations using each of the best
// points visited during the main search.
for (PointValuePair p : starts) {
for (int i = 0; i < additionalSearch; i++) {
final PointValuePair start = bestList.get(i);
// Find shortest distance to the other points.
final double dist = shortestDistance(p, starts);
final double[] init = p.getPoint();
final double dist = shortestDistance(start, bestList);
final double[] init = start.getPoint();
// Create smaller initial simplex.
final Simplex simplex = Simplex.equalSidesAlongAxes(init.length,
SIMPLEX_SIDE_RATIO * dist);