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Check bounds in multi-start vector optimizers. 

JIRA: MATH-914

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1454746 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2013-03-09 17:37:30 +00:00
parent 5f9169496b
commit f00bd95151
3 changed files with 81 additions and 13 deletions
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3
src/changes/changes.xml
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@ -55,6 +55,9 @@ This is a minor release: It combines bug fixes and new features.
Changes to existing features were made in a backwards-compatible
way such as to allow drop-in replacement of the v3.1[.1] JAR file.
">
<action dev="luc" type="add" issue="MATH-914" >
Check bounds in multi-start vector optimizers.
</action>
<action dev="luc" type="add" issue="MATH-941" due-to="Piotr Wydrych" >
Added discrete distributions.
</action>

36
src/main/java/org/apache/commons/math3/optim/BaseMultiStartMultivariateOptimizer.java
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@ -18,6 +18,7 @@ package org.apache.commons.math3.optim;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.random.RandomVectorGenerator;
/**
@ -59,7 +60,15 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
/**
* Create a multi-start optimizer from a single-start optimizer.
*
* <p>
* Note that if there are bounds constraints (see {@link #getLowerBound()}
* and {@link #getUpperBound()}), then a simple rejection algorithm is used
* at each restart. This implies that the random vector generator should have
* a good probability to generate vectors in the bounded domain, otherwise the
* rejection algorithm will hit the {@link #getMaxEvaluations()} count without
* generating a proper restart point. Users must be take great care of the <a
* href="http://en.wikipedia.org/wiki/Curse_of_dimensionality">curse of dimensionality</a>.
* </p>
* @param optimizer Single-start optimizer to wrap.
* @param starts Number of starts to perform. If {@code starts == 1},
* the {@link #optimize(OptimizationData[]) optimize} will return the
@ -157,8 +166,8 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
clear();
final int maxEval = getMaxEvaluations();
final double[] min = getLowerBound(); // XXX Should be used to enforce bounds (see below).
final double[] max = getUpperBound(); // XXX Should be used to enforce bounds (see below).
final double[] min = getLowerBound();
final double[] max = getUpperBound();
final double[] startPoint = getStartPoint();
// Multi-start loop.
@ -168,9 +177,24 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
// Decrease number of allowed evaluations.
optimData[maxEvalIndex] = new MaxEval(maxEval - totalEvaluations);
// New start value.
final double[] s = (i == 0) ?
startPoint :
generator.nextVector(); // XXX This does not enforce bounds!
double[] s = null;
if (i == 0) {
s = startPoint;
} else {
int attempts = 0;
while (s == null) {
if (attempts++ >= getMaxEvaluations()) {
throw new TooManyEvaluationsException(getMaxEvaluations());
}
s = generator.nextVector();
for (int k = 0; s != null && k < s.length; ++k) {
if ((min != null && s[k] < min[k]) || (max != null && s[k] > max[k])) {
// reject the vector
s = null;
}
}
}
}
optimData[initialGuessIndex] = new InitialGuess(s);
// Optimize.
final PAIR result = optimizer.optimize(optimData);

55
src/test/java/org/apache/commons/math3/optim/nonlinear/vector/MultiStartMultivariateVectorOptimizerTest.java
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@ -16,14 +16,16 @@
*/
package org.apache.commons.math3.optim.nonlinear.vector;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.linear.BlockRealMatrix;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.SimpleBounds;
import org.apache.commons.math3.optim.SimpleVectorValueChecker;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
import org.apache.commons.math3.random.GaussianRandomGenerator;
@ -96,10 +98,10 @@ import org.junit.Test;
* @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
*/
public class MultiStartMultivariateVectorOptimizerTest {
@Test(expected=NullPointerException.class)
public void testGetOptimaBeforeOptimize() {
LinearProblem problem
= new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
JacobianMultivariateVectorOptimizer underlyingOptimizer
= new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
JDKRandomGenerator g = new JDKRandomGenerator();
@ -145,8 +147,45 @@ public class MultiStartMultivariateVectorOptimizerTest {
Assert.assertEquals(100, optimizer.getMaxEvaluations());
}
@Test
public void testIssue914() {
LinearProblem problem = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
JacobianMultivariateVectorOptimizer underlyingOptimizer =
new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6)) {
public PointVectorValuePair optimize(OptimizationData... optData) {
// filter out simple bounds, as they are not supported
// by the underlying optimizer, and we don't really care for this test
OptimizationData[] filtered = optData.clone();
for (int i = 0; i < filtered.length; ++i) {
if (filtered[i] instanceof SimpleBounds) {
filtered[i] = null;
}
}
return super.optimize(filtered);
}
};
JDKRandomGenerator g = new JDKRandomGenerator();
g.setSeed(16069223052l);
RandomVectorGenerator generator =
new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
MultiStartMultivariateVectorOptimizer optimizer =
new MultiStartMultivariateVectorOptimizer(underlyingOptimizer, 10, generator);
optimizer.optimize(new MaxEval(100),
problem.getModelFunction(),
problem.getModelFunctionJacobian(),
problem.getTarget(),
new Weight(new double[] { 1 }),
new InitialGuess(new double[] { 0 }),
new SimpleBounds(new double[] { -1.0e-10 }, new double[] { 1.0e-10 }));
PointVectorValuePair[] optima = optimizer.getOptima();
// only the first start should have succeeded
Assert.assertEquals(1, optima.length);
}
/**
* Test demonstrating that the user exception is fnally thrown if none
* Test demonstrating that the user exception is finally thrown if none
* of the runs succeed.
*/
@Test(expected=TestException.class)
@ -170,7 +209,9 @@ public class MultiStartMultivariateVectorOptimizerTest {
}));
}
private static class TestException extends RuntimeException {}
private static class TestException extends RuntimeException {
private static final long serialVersionUID = 1L;}
private static class LinearProblem {
private final RealMatrix factors;