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With inspiration from other libraries
/*
* 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;
import java.util.Arrays;
import java.util.Comparator;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomVectorGenerator;
/**
* Base class for all implementations of a multi-start optimizer.
*
* This interface is mainly intended to enforce the internal coherence of
* Commons-Math. Users of the API are advised to base their code on
* {@link DifferentiableMultivariateVectorMultiStartOptimizer}.
*
* @param Type of the objective function to be optimized.
*
* @deprecated As of 3.1 (to be removed in 4.0).
* @since 3.0
*/
@Deprecated
public class BaseMultivariateVectorMultiStartOptimizer
implements BaseMultivariateVectorOptimizer {
/** Underlying classical optimizer. */
private final BaseMultivariateVectorOptimizer optimizer;
/** Maximal number of evaluations allowed. */
private int maxEvaluations;
/** Number of evaluations already performed for all starts. */
private int totalEvaluations;
/** Number of starts to go. */
private int starts;
/** Random generator for multi-start. */
private RandomVectorGenerator generator;
/** Found optima. */
private PointVectorValuePair[] optima;
/**
* Create a multi-start optimizer from a single-start optimizer.
*
* @param optimizer Single-start optimizer to wrap.
* @param starts Number of starts to perform. If {@code starts == 1},
* the {@link #optimize(int,MultivariateVectorFunction,double[],double[],double[])
* optimize} will return the same solution as {@code optimizer} would.
* @param generator Random vector generator to use for restarts.
* @throws NullArgumentException if {@code optimizer} or {@code generator}
* is {@code null}.
* @throws NotStrictlyPositiveException if {@code starts < 1}.
*/
protected BaseMultivariateVectorMultiStartOptimizer(final BaseMultivariateVectorOptimizer optimizer,
final int starts,
final RandomVectorGenerator generator) {
if (optimizer == null ||
generator == null) {
throw new NullArgumentException();
}
if (starts < 1) {
throw new NotStrictlyPositiveException(starts);
}
this.optimizer = optimizer;
this.starts = starts;
this.generator = generator;
}
/**
* Get all the optima found during the last call to {@link
* #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize}.
* The optimizer stores all the optima found during a set of
* restarts. The {@link #optimize(int,MultivariateVectorFunction,double[],double[],double[])
* optimize} method returns the best point only. This method
* returns all the points found at the end of each starts, including
* the best one already returned by the {@link
* #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize} method.
*
* The returned array as one element for each start as specified
* in the constructor. It is ordered with the results from the
* runs that did converge first, sorted from best to worst
* objective value (i.e. in ascending order if minimizing and in
* descending order if maximizing), followed by and null elements
* corresponding to the runs that did not converge. This means all
* elements will be null if the {@link
* #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize} method did
* throw a {@link ConvergenceException}). This also means that if
* the first element is not {@code null}, it is the best point found
* across all starts.
*
* @return array containing the optima
* @throws MathIllegalStateException if {@link
* #optimize(int,MultivariateVectorFunction,double[],double[],double[]) optimize} has not been
* called.
*/
public PointVectorValuePair[] getOptima() {
if (optima == null) {
throw new MathIllegalStateException(LocalizedFormats.NO_OPTIMUM_COMPUTED_YET);
}
return optima.clone();
}
/** {@inheritDoc} */
public int getMaxEvaluations() {
return maxEvaluations;
}
/** {@inheritDoc} */
public int getEvaluations() {
return totalEvaluations;
}
/** {@inheritDoc} */
public ConvergenceChecker getConvergenceChecker() {
return optimizer.getConvergenceChecker();
}
/**
* {@inheritDoc}
*/
public PointVectorValuePair optimize(int maxEval, final FUNC f,
double[] target, double[] weights,
double[] startPoint) {
maxEvaluations = maxEval;
RuntimeException lastException = null;
optima = new PointVectorValuePair[starts];
totalEvaluations = 0;
// Multi-start loop.
for (int i = 0; i < starts; ++i) {
// CHECKSTYLE: stop IllegalCatch
try {
optima[i] = optimizer.optimize(maxEval - totalEvaluations, f, target, weights,
i == 0 ? startPoint : generator.nextVector());
} catch (ConvergenceException oe) {
optima[i] = null;
} catch (RuntimeException mue) {
lastException = mue;
optima[i] = null;
}
// CHECKSTYLE: resume IllegalCatch
totalEvaluations += optimizer.getEvaluations();
}
sortPairs(target, weights);
if (optima[0] == null) {
throw lastException; // cannot be null if starts >=1
}
// Return the found point given the best objective function value.
return optima[0];
}
/**
* Sort the optima from best to worst, followed by {@code null} elements.
*
* @param target Target value for the objective functions at optimum.
* @param weights Weights for the least-squares cost computation.
*/
private void sortPairs(final double[] target,
final double[] weights) {
Arrays.sort(optima, new Comparator() {
/** {@inheritDoc} */
public int compare(final PointVectorValuePair o1,
final PointVectorValuePair o2) {
if (o1 == null) {
return (o2 == null) ? 0 : 1;
} else if (o2 == null) {
return -1;
}
return Double.compare(weightedResidual(o1), weightedResidual(o2));
}
private double weightedResidual(final PointVectorValuePair pv) {
final double[] value = pv.getValueRef();
double sum = 0;
for (int i = 0; i < value.length; ++i) {
final double ri = value[i] - target[i];
sum += weights[i] * ri * ri;
}
return sum;
}
});
}
}