separated iteration counter from function evaluation counters,
some optimizers are based on gradient/jacobian only and cannot reliably be protected by monitoring the objective function calls. We now have two or three counters for each algorithm: - iteration counter, which is checked against a max allowance to prevent infinite loops if no convergence is reached - objective function evaluations, for user information only - objective function gradient/jacobian if the function is differentiable, for user information only git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@757181 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe
parent
cfa4345431
commit
87c0ddbc6e
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@ -118,8 +118,7 @@ public class MessagesResources_fr
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{ "equals vertices {0} and {1} in simplex configuration",
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"sommets {0} et {1} \u00e9gaux dans la configuration du simplex" },
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// org.apache.commons.math.optimization.direct.DirectSearchOptimizer
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// org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer
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// org.apache.commons.math.estimation.AbstractEstimation
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{ "maximal number of evaluations exceeded ({0})",
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"nombre maximal d''\u00e9valuations d\u00e9pass\u00e9 ({0})" },
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@ -38,16 +38,22 @@ import org.apache.commons.math.random.RandomVectorGenerator;
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public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferentiableOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = 9008747186334431824L;
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private static final long serialVersionUID = 6185821146433609962L;
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/** Underlying classical optimizer. */
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private final ScalarDifferentiableOptimizer optimizer;
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/** Maximal number of iterations allowed. */
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private int maxIterations;
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/** Number of iterations already performed for all starts. */
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private int totalIterations;
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/** Number of evaluations already performed for all starts. */
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private int totalEvaluations;
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/** Maximal number of evaluations allowed. */
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private int maxEvaluations;
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/** Number of gradient evaluations already performed for all starts. */
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private int totalGradientEvaluations;
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/** Number of starts to go. */
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private int starts;
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@ -69,12 +75,14 @@ public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferenti
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public MultiStartScalarDifferentiableOptimizer(final ScalarDifferentiableOptimizer optimizer,
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final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.totalEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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this.optimizer = optimizer;
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this.maxIterations = Integer.MAX_VALUE;
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this.totalIterations = 0;
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this.totalEvaluations = 0;
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this.totalGradientEvaluations = 0;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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}
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/** Get all the optima found during the last call to {@link
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@ -110,19 +118,29 @@ public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferenti
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return (ScalarPointValuePair[]) optima.clone();
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}
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/** {@inheritDoc} */
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public void setMaxIterations(int maxIterations) {
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this.maxIterations = maxIterations;
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}
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/** {@inheritDoc} */
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public int getMaxIterations() {
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return maxIterations;
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}
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/** {@inheritDoc} */
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public int getIterations() {
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return totalIterations;
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}
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/** {@inheritDoc} */
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public int getEvaluations() {
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return totalEvaluations;
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}
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/** {@inheritDoc} */
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public void setMaxEvaluations(int maxEvaluations) {
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this.maxEvaluations = maxEvaluations;
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}
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/** {@inheritDoc} */
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public int getMaxEvaluations() {
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return maxEvaluations;
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public int getGradientEvaluations() {
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return totalGradientEvaluations;
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}
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/** {@inheritDoc} */
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@ -141,14 +159,16 @@ public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferenti
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double[] startPoint)
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throws ObjectiveException, OptimizationException {
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optima = new ScalarPointValuePair[starts];
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totalEvaluations = 0;
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optima = new ScalarPointValuePair[starts];
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totalIterations = 0;
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totalEvaluations = 0;
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totalGradientEvaluations = 0;
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// multi-start loop
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for (int i = 0; i < starts; ++i) {
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try {
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optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
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optimizer.setMaxIterations(maxIterations - totalIterations);
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optima[i] = optimizer.optimize(f, goalType,
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(i == 0) ? startPoint : generator.nextVector());
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} catch (ObjectiveException obe) {
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@ -157,7 +177,9 @@ public class MultiStartScalarDifferentiableOptimizer implements ScalarDifferenti
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optima[i] = null;
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}
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totalEvaluations += optimizer.getEvaluations();
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totalIterations += optimizer.getIterations();
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totalEvaluations += optimizer.getEvaluations();
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totalGradientEvaluations += optimizer.getGradientEvaluations();
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}
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@ -38,17 +38,20 @@ import org.apache.commons.math.random.RandomVectorGenerator;
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public class MultiStartScalarOptimizer implements ScalarOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = 6648351778723282863L;
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private static final long serialVersionUID = -7333253288301713047L;
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/** Underlying classical optimizer. */
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private final ScalarOptimizer optimizer;
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/** Maximal number of iterations allowed. */
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private int maxIterations;
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/** Number of iterations already performed for all starts. */
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private int totalIterations;
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/** Number of evaluations already performed for all starts. */
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private int totalEvaluations;
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/** Maximal number of evaluations allowed. */
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private int maxEvaluations;
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/** Number of starts to go. */
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private int starts;
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@ -69,8 +72,9 @@ public class MultiStartScalarOptimizer implements ScalarOptimizer {
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public MultiStartScalarOptimizer(final ScalarOptimizer optimizer, final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.maxIterations = Integer.MAX_VALUE;
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this.totalIterations = 0;
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this.totalEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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@ -109,21 +113,26 @@ public class MultiStartScalarOptimizer implements ScalarOptimizer {
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return (ScalarPointValuePair[]) optima.clone();
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}
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/** {@inheritDoc} */
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public void setMaxIterations(int maxIterations) {
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this.maxIterations = maxIterations;
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}
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/** {@inheritDoc} */
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public int getMaxIterations() {
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return maxIterations;
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}
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/** {@inheritDoc} */
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public int getIterations() {
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return totalIterations;
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}
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/** {@inheritDoc} */
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public int getEvaluations() {
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return totalEvaluations;
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}
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/** {@inheritDoc} */
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public void setMaxEvaluations(int maxEvaluations) {
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this.maxEvaluations = maxEvaluations;
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}
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/** {@inheritDoc} */
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public int getMaxEvaluations() {
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return maxEvaluations;
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}
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/** {@inheritDoc} */
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public void setConvergenceChecker(ScalarConvergenceChecker checker) {
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optimizer.setConvergenceChecker(checker);
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@ -140,14 +149,15 @@ public class MultiStartScalarOptimizer implements ScalarOptimizer {
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double[] startPoint)
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throws ObjectiveException, OptimizationException {
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optima = new ScalarPointValuePair[starts];
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optima = new ScalarPointValuePair[starts];
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totalIterations = 0;
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totalEvaluations = 0;
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// multi-start loop
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for (int i = 0; i < starts; ++i) {
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try {
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optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
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optimizer.setMaxIterations(maxIterations - totalIterations);
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optima[i] = optimizer.optimize(f, goalType,
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(i == 0) ? startPoint : generator.nextVector());
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} catch (ObjectiveException obe) {
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@ -156,6 +166,7 @@ public class MultiStartScalarOptimizer implements ScalarOptimizer {
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optima[i] = null;
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}
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totalIterations += optimizer.getIterations();
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totalEvaluations += optimizer.getEvaluations();
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}
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@ -38,20 +38,23 @@ import org.apache.commons.math.random.RandomVectorGenerator;
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public class MultiStartVectorialDifferentiableOptimizer implements VectorialDifferentiableOptimizer {
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/** Serializable version identifier. */
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private static final long serialVersionUID = -6671992853686531955L;
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private static final long serialVersionUID = -9109278856437190136L;
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/** Underlying classical optimizer. */
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private final VectorialDifferentiableOptimizer optimizer;
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/** Maximal number of iterations allowed. */
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private int maxIterations;
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/** Number of iterations already performed for all starts. */
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private int totalIterations;
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/** Number of evaluations already performed for all starts. */
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private int totalEvaluations;
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/** Number of jacobian evaluations already performed for all starts. */
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private int totalJacobianEvaluations;
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/** Maximal number of evaluations allowed. */
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private int maxEvaluations;
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/** Number of starts to go. */
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private int starts;
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@ -73,9 +76,10 @@ public class MultiStartVectorialDifferentiableOptimizer implements VectorialDiff
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final int starts,
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final RandomVectorGenerator generator) {
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this.optimizer = optimizer;
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this.maxIterations = Integer.MAX_VALUE;
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this.totalIterations = 0;
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this.totalEvaluations = 0;
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this.totalJacobianEvaluations = 0;
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this.maxEvaluations = Integer.MAX_VALUE;
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this.starts = starts;
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this.generator = generator;
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this.optima = null;
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@ -114,6 +118,21 @@ public class MultiStartVectorialDifferentiableOptimizer implements VectorialDiff
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return (VectorialPointValuePair[]) optima.clone();
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}
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/** {@inheritDoc} */
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public void setMaxIterations(int maxIterations) {
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this.maxIterations = maxIterations;
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}
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/** {@inheritDoc} */
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public int getMaxIterations() {
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return maxIterations;
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}
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/** {@inheritDoc} */
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public int getIterations() {
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return totalIterations;
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}
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/** {@inheritDoc} */
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public int getEvaluations() {
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return totalEvaluations;
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@ -124,16 +143,6 @@ public class MultiStartVectorialDifferentiableOptimizer implements VectorialDiff
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return totalJacobianEvaluations;
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}
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/** {@inheritDoc} */
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public void setMaxEvaluations(int maxEvaluations) {
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this.maxEvaluations = maxEvaluations;
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}
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/** {@inheritDoc} */
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public int getMaxEvaluations() {
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return maxEvaluations;
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}
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/** {@inheritDoc} */
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public void setConvergenceChecker(VectorialConvergenceChecker checker) {
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optimizer.setConvergenceChecker(checker);
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@ -150,15 +159,16 @@ public class MultiStartVectorialDifferentiableOptimizer implements VectorialDiff
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final double[] startPoint)
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throws ObjectiveException, OptimizationException, IllegalArgumentException {
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optima = new VectorialPointValuePair[starts];
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totalEvaluations = 0;
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optima = new VectorialPointValuePair[starts];
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totalIterations = 0;
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totalEvaluations = 0;
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totalJacobianEvaluations = 0;
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// multi-start loop
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for (int i = 0; i < starts; ++i) {
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try {
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optimizer.setMaxEvaluations(maxEvaluations - totalEvaluations);
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optimizer.setMaxIterations(maxIterations - totalIterations);
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optima[i] = optimizer.optimize(f, target, weights,
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(i == 0) ? startPoint : generator.nextVector());
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} catch (ObjectiveException obe) {
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@ -167,6 +177,7 @@ public class MultiStartVectorialDifferentiableOptimizer implements VectorialDiff
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optima[i] = null;
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}
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totalIterations += optimizer.getIterations();
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totalEvaluations += optimizer.getEvaluations();
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totalJacobianEvaluations += optimizer.getJacobianEvaluations();
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|
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@ -30,7 +30,7 @@ import org.apache.commons.math.ConvergenceException;
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public class OptimizationException extends ConvergenceException {
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/** Serializable version identifier. */
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private static final long serialVersionUID = -781139167958631145L;
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private static final long serialVersionUID = -357696069587075016L;
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/**
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* Simple constructor.
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@ -42,4 +42,12 @@ public class OptimizationException extends ConvergenceException {
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super(specifier, parts);
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}
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/**
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* Create an exception with a given root cause.
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* @param cause the exception or error that caused this exception to be thrown
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*/
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public OptimizationException(Throwable cause) {
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super(cause);
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}
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}
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|
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@ -29,38 +29,45 @@ import java.io.Serializable;
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*/
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public interface ScalarDifferentiableOptimizer extends Serializable {
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/** Set the maximal number of objective function calls.
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* <p>
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* The number of objective function calls may be checked <em>after</em> a few
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* related calls have been made. This implies that in some cases this number may
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* be exceeded by a few units, depending on the dimension of the problem and kind
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* of optimizer.
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* </p>
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* @param maxEvaluations maximal number of function calls
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* .
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/** Set the maximal number of iterations of the algorithm.
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* @param maxIterations maximal number of function calls
|
||||
*/
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void setMaxEvaluations(int maxEvaluations);
|
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void setMaxIterations(int maxIterations);
|
||||
|
||||
/** Get the maximal number of objective function calls.
|
||||
* <p>
|
||||
* The number of objective function calls may be checked <em>after</em> a few
|
||||
* related calls have been made. This implies that in some cases this number may
|
||||
* be exceeded by a few units, depending on the dimension of the problem and kind
|
||||
* of optimizer.
|
||||
* </p>
|
||||
* @return maximal number of function calls
|
||||
/** Get the maximal number of iterations of the algorithm.
|
||||
* @return maximal number of iterations
|
||||
*/
|
||||
int getMaxEvaluations();
|
||||
int getMaxIterations();
|
||||
|
||||
/** Get the number of iterations realized by the algorithm.
|
||||
* <p>
|
||||
* The number of evaluations corresponds to the last call to the
|
||||
* {@link #optimize(ScalarDifferentiableObjectiveFunction, GoalType, double[]) optimize}
|
||||
* method. It is 0 if the method has not been called yet.
|
||||
* </p>
|
||||
* @return number of iterations
|
||||
*/
|
||||
int getIterations();
|
||||
|
||||
/** Get the number of evaluations of the objective function.
|
||||
* <p>
|
||||
* The number of evaluation correspond to the last call to the
|
||||
* {@link #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}
|
||||
* The number of evaluations corresponds to the last call to the
|
||||
* {@link #optimize(ScalarDifferentiableObjectiveFunction, GoalType, double[]) optimize}
|
||||
* method. It is 0 if the method has not been called yet.
|
||||
* </p>
|
||||
* @return number of evaluations of the objective function
|
||||
*/
|
||||
int getEvaluations();
|
||||
int getEvaluations();
|
||||
|
||||
/** Get the number of evaluations of the objective function gradient.
|
||||
* <p>
|
||||
* The number of evaluations corresponds to the last call to the
|
||||
* {@link #optimize(ScalarDifferentiableObjectiveFunction, GoalType, double[]) optimize}
|
||||
* method. It is 0 if the method has not been called yet.
|
||||
* </p>
|
||||
* @return number of evaluations of the objective function gradient
|
||||
*/
|
||||
int getGradientEvaluations();
|
||||
|
||||
/** Set the convergence checker.
|
||||
* @param checker object to use to check for convergence
|
||||
|
|
|
|||
|
|
@ -29,38 +29,35 @@ import java.io.Serializable;
|
|||
*/
|
||||
public interface ScalarOptimizer extends Serializable {
|
||||
|
||||
/** Set the maximal number of objective function calls.
|
||||
* <p>
|
||||
* The number of objective function calls may be checked <em>after</em> a few
|
||||
* related calls have been made. This implies that in some cases this number may
|
||||
* be exceeded by a few units, depending on the dimension of the problem and kind
|
||||
* of optimizer.
|
||||
* </p>
|
||||
* @param maxEvaluations maximal number of function calls
|
||||
* .
|
||||
/** Set the maximal number of iterations of the algorithm.
|
||||
* @param maxIterations maximal number of function calls
|
||||
*/
|
||||
void setMaxEvaluations(int maxEvaluations);
|
||||
void setMaxIterations(int maxIterations);
|
||||
|
||||
/** Get the maximal number of objective function calls.
|
||||
* <p>
|
||||
* The number of objective function calls may be checked <em>after</em> a few
|
||||
* related calls have been made. This implies that in some cases this number may
|
||||
* be exceeded by a few units, depending on the dimension of the problem and kind
|
||||
* of optimizer.
|
||||
* </p>
|
||||
* @return maximal number of function calls
|
||||
/** Get the maximal number of iterations of the algorithm.
|
||||
* @return maximal number of iterations
|
||||
*/
|
||||
int getMaxEvaluations();
|
||||
int getMaxIterations();
|
||||
|
||||
/** Get the number of iterations realized by the algorithm.
|
||||
* <p>
|
||||
* The number of evaluations corresponds to the last call to the
|
||||
* {@link #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}
|
||||
* method. It is 0 if the method has not been called yet.
|
||||
* </p>
|
||||
* @return number of iterations
|
||||
*/
|
||||
int getIterations();
|
||||
|
||||
/** Get the number of evaluations of the objective function.
|
||||
* <p>
|
||||
* The number of evaluation correspond to the last call to the
|
||||
* The number of evaluations corresponds to the last call to the
|
||||
* {@link #optimize(ScalarObjectiveFunction, GoalType, double[]) optimize}
|
||||
* method. It is 0 if the method has not been called yet.
|
||||
* </p>
|
||||
* @return number of evaluations of the objective function
|
||||
*/
|
||||
int getEvaluations();
|
||||
int getEvaluations();
|
||||
|
||||
/** Set the convergence checker.
|
||||
* @param checker object to use to check for convergence
|
||||
|
|
|
|||
|
|
@ -29,28 +29,21 @@ import java.io.Serializable;
|
|||
*/
|
||||
public interface VectorialDifferentiableOptimizer extends Serializable {
|
||||
|
||||
/** Set the maximal number of objective function calls.
|
||||
* <p>
|
||||
* The number of objective function calls may be checked <em>after</em> a few
|
||||
* related calls have been made. This implies that in some cases this number may
|
||||
* be exceeded by a few units, depending on the dimension of the problem and kind
|
||||
* of optimizer.
|
||||
* </p>
|
||||
* @param maxEvaluations maximal number of function calls
|
||||
/** Set the maximal number of iterations of the algorithm.
|
||||
* @param maxIterations maximal number of function calls
|
||||
* .
|
||||
*/
|
||||
void setMaxEvaluations(int maxEvaluations);
|
||||
void setMaxIterations(int maxIterations);
|
||||
|
||||
/** Get the maximal number of objective function calls.
|
||||
* <p>
|
||||
* The number of objective function calls may be checked <em>after</em> a few
|
||||
* related calls have been made. This implies that in some cases this number may
|
||||
* be exceeded by a few units, depending on the dimension of the problem and kind
|
||||
* of optimizer.
|
||||
* </p>
|
||||
* @return maximal number of function calls
|
||||
/** Get the maximal number of iterations of the algorithm.
|
||||
* @return maximal number of iterations
|
||||
*/
|
||||
int getMaxEvaluations();
|
||||
int getMaxIterations();
|
||||
|
||||
/** Get the number of iterations realized by the algorithm.
|
||||
* @return number of iterations
|
||||
*/
|
||||
int getIterations();
|
||||
|
||||
/** Get the number of evaluations of the objective function.
|
||||
* <p>
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ import java.util.Arrays;
|
|||
import java.util.Comparator;
|
||||
|
||||
import org.apache.commons.math.MathRuntimeException;
|
||||
import org.apache.commons.math.MaxIterationsExceededException;
|
||||
import org.apache.commons.math.optimization.ScalarConvergenceChecker;
|
||||
import org.apache.commons.math.optimization.GoalType;
|
||||
import org.apache.commons.math.optimization.ObjectiveException;
|
||||
|
|
@ -28,7 +29,7 @@ import org.apache.commons.math.optimization.ScalarObjectiveFunction;
|
|||
import org.apache.commons.math.optimization.OptimizationException;
|
||||
import org.apache.commons.math.optimization.ScalarOptimizer;
|
||||
import org.apache.commons.math.optimization.ScalarPointValuePair;
|
||||
import org.apache.commons.math.optimization.SimpleValueChecker;
|
||||
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
|
||||
|
||||
/**
|
||||
* This class implements simplex-based direct search optimization
|
||||
|
|
@ -65,7 +66,7 @@ import org.apache.commons.math.optimization.SimpleValueChecker;
|
|||
* will occur.</p>
|
||||
*
|
||||
* <p>If {@link #setConvergenceChecker(ScalarConvergenceChecker)} is not called,
|
||||
* a default {@link SimpleValueChecker} is used.</p>
|
||||
* a default {@link SimpleScalarValueChecker} is used.</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>
|
||||
|
|
@ -95,11 +96,14 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
/** Convergence checker. */
|
||||
private ScalarConvergenceChecker checker;
|
||||
|
||||
/** Number of evaluations already performed for the current start. */
|
||||
private int evaluations;
|
||||
/** Maximal number of iterations allowed. */
|
||||
private int maxIterations;
|
||||
|
||||
/** Maximal number of evaluations allowed. */
|
||||
private int maxEvaluations;
|
||||
/** Number of iterations already performed. */
|
||||
private int iterations;
|
||||
|
||||
/** Number of evaluations already performed. */
|
||||
private int evaluations;
|
||||
|
||||
/** Start simplex configuration. */
|
||||
private double[][] startConfiguration;
|
||||
|
|
@ -107,8 +111,8 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
/** Simple constructor.
|
||||
*/
|
||||
protected DirectSearchOptimizer() {
|
||||
setConvergenceChecker(new SimpleValueChecker());
|
||||
setMaxEvaluations(Integer.MAX_VALUE);
|
||||
setConvergenceChecker(new SimpleScalarValueChecker());
|
||||
setMaxIterations(Integer.MAX_VALUE);
|
||||
}
|
||||
|
||||
/** Set start configuration for simplex.
|
||||
|
|
@ -208,13 +212,23 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public void setMaxEvaluations(int maxEvaluations) {
|
||||
this.maxEvaluations = maxEvaluations;
|
||||
public void setMaxIterations(int maxIterations) {
|
||||
this.maxIterations = maxIterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getMaxEvaluations() {
|
||||
return maxEvaluations;
|
||||
public int getMaxIterations() {
|
||||
return maxIterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getIterations() {
|
||||
return iterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getEvaluations() {
|
||||
return evaluations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
|
|
@ -229,7 +243,7 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
|
||||
/** {@inheritDoc} */
|
||||
public ScalarPointValuePair optimize(final ScalarObjectiveFunction f, final GoalType goalType,
|
||||
final double[] startPoint)
|
||||
final double[] startPoint)
|
||||
throws ObjectiveException, OptimizationException, IllegalArgumentException {
|
||||
|
||||
if (startConfiguration == null) {
|
||||
|
|
@ -251,15 +265,15 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
};
|
||||
|
||||
// initialize search
|
||||
iterations = 0;
|
||||
evaluations = 0;
|
||||
buildSimplex(startPoint);
|
||||
evaluateSimplex(comparator);
|
||||
|
||||
ScalarPointValuePair[] previous = new ScalarPointValuePair[simplex.length];
|
||||
int iterations = 0;
|
||||
while (evaluations <= maxEvaluations) {
|
||||
while (true) {
|
||||
|
||||
if (++iterations > 1) {
|
||||
if (iterations > 0) {
|
||||
boolean converged = true;
|
||||
for (int i = 0; i < simplex.length; ++i) {
|
||||
converged &= checker.converged(iterations, previous[i], simplex[i]);
|
||||
|
|
@ -276,22 +290,24 @@ public abstract class DirectSearchOptimizer implements ScalarOptimizer {
|
|||
|
||||
}
|
||||
|
||||
throw new OptimizationException(
|
||||
"maximal number of evaluations exceeded ({0})",
|
||||
evaluations);
|
||||
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getEvaluations() {
|
||||
return evaluations;
|
||||
/** Increment the iterations counter by 1.
|
||||
* @exception OptimizationException if the maximal number
|
||||
* of iterations is exceeded
|
||||
*/
|
||||
protected void incrementIterationsCounter()
|
||||
throws OptimizationException {
|
||||
if (++iterations > maxIterations) {
|
||||
throw new OptimizationException(new MaxIterationsExceededException(maxIterations));
|
||||
}
|
||||
}
|
||||
|
||||
/** Compute the next simplex of the algorithm.
|
||||
* @param comparator comparator to use to sort simplex vertices from best to worst
|
||||
* @exception ObjectiveException if the function cannot be evaluated at
|
||||
* some point
|
||||
* @exception OptimizationException if the algorithm failed to converge
|
||||
* @exception OptimizationException if the algorithm fails to converge
|
||||
* @exception IllegalArgumentException if the start point dimension is wrong
|
||||
*/
|
||||
protected abstract void iterateSimplex(final Comparator<ScalarPointValuePair> comparator)
|
||||
|
|
|
|||
|
|
@ -62,8 +62,9 @@ public class MultiDirectional extends DirectSearchOptimizer {
|
|||
protected void iterateSimplex(final Comparator<ScalarPointValuePair> comparator)
|
||||
throws ObjectiveException, OptimizationException, IllegalArgumentException {
|
||||
|
||||
final int max = getMaxEvaluations();
|
||||
while (getEvaluations() < max) {
|
||||
while (true) {
|
||||
|
||||
incrementIterationsCounter();
|
||||
|
||||
// save the original vertex
|
||||
final ScalarPointValuePair[] original = simplex;
|
||||
|
|
@ -94,10 +95,6 @@ public class MultiDirectional extends DirectSearchOptimizer {
|
|||
|
||||
}
|
||||
|
||||
throw new OptimizationException(
|
||||
"maximal number of evaluations exceeded ({0})",
|
||||
getEvaluations());
|
||||
|
||||
}
|
||||
|
||||
/** Compute and evaluate a new simplex.
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ package org.apache.commons.math.optimization.direct;
|
|||
import java.util.Comparator;
|
||||
|
||||
import org.apache.commons.math.optimization.ObjectiveException;
|
||||
import org.apache.commons.math.optimization.OptimizationException;
|
||||
import org.apache.commons.math.optimization.ScalarPointValuePair;
|
||||
|
||||
/**
|
||||
|
|
@ -73,7 +74,9 @@ public class NelderMead extends DirectSearchOptimizer {
|
|||
|
||||
/** {@inheritDoc} */
|
||||
protected void iterateSimplex(final Comparator<ScalarPointValuePair> comparator)
|
||||
throws ObjectiveException {
|
||||
throws ObjectiveException, OptimizationException {
|
||||
|
||||
incrementIterationsCounter();
|
||||
|
||||
// the simplex has n+1 point if dimension is n
|
||||
final int n = simplex.length - 1;
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@
|
|||
|
||||
package org.apache.commons.math.optimization.general;
|
||||
|
||||
import org.apache.commons.math.MaxIterationsExceededException;
|
||||
import org.apache.commons.math.linear.InvalidMatrixException;
|
||||
import org.apache.commons.math.linear.MatrixUtils;
|
||||
import org.apache.commons.math.linear.RealMatrix;
|
||||
|
|
@ -40,20 +41,23 @@ import org.apache.commons.math.optimization.VectorialPointValuePair;
|
|||
public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferentiableOptimizer {
|
||||
|
||||
/** Serializable version identifier */
|
||||
private static final long serialVersionUID = -3080152374642370722L;
|
||||
private static final long serialVersionUID = 5413193243329026789L;
|
||||
|
||||
/** Default maximal number of objective function evaluations allowed. */
|
||||
public static final int DEFAULT_MAX_EVALUATIONS = 100;
|
||||
/** Default maximal number of iterations allowed. */
|
||||
public static final int DEFAULT_MAX_ITERATIONS = 100;
|
||||
|
||||
/** Number of evaluations already performed for the current start. */
|
||||
/** Maximal number of iterations allowed. */
|
||||
private int maxIterations;
|
||||
|
||||
/** Number of iterations already performed. */
|
||||
private int iterations;
|
||||
|
||||
/** Number of evaluations already performed. */
|
||||
private int objectiveEvaluations;
|
||||
|
||||
/** Number of jacobian evaluations. */
|
||||
private int jacobianEvaluations;
|
||||
|
||||
/** Maximal number of evaluations allowed. */
|
||||
private int maxEvaluations;
|
||||
|
||||
/** Convergence checker. */
|
||||
protected VectorialConvergenceChecker checker;
|
||||
|
||||
|
|
@ -99,17 +103,22 @@ public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferen
|
|||
*/
|
||||
protected AbstractLeastSquaresOptimizer() {
|
||||
setConvergenceChecker(new SimpleVectorialValueChecker());
|
||||
setMaxEvaluations(DEFAULT_MAX_EVALUATIONS);
|
||||
setMaxIterations(DEFAULT_MAX_ITERATIONS);
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public void setMaxEvaluations(int maxEvaluations) {
|
||||
this.maxEvaluations = maxEvaluations;
|
||||
public void setMaxIterations(int maxIterations) {
|
||||
this.maxIterations = maxIterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getMaxEvaluations() {
|
||||
return maxEvaluations;
|
||||
public int getMaxIterations() {
|
||||
return maxIterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
public int getIterations() {
|
||||
return iterations;
|
||||
}
|
||||
|
||||
/** {@inheritDoc} */
|
||||
|
|
@ -132,13 +141,26 @@ public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferen
|
|||
return checker;
|
||||
}
|
||||
|
||||
/** Increment the iterations counter by 1.
|
||||
* @exception OptimizationException if the maximal number
|
||||
* of iterations is exceeded
|
||||
*/
|
||||
protected void incrementIterationsCounter()
|
||||
throws OptimizationException {
|
||||
if (++iterations > maxIterations) {
|
||||
if (++iterations > maxIterations) {
|
||||
throw new OptimizationException(new MaxIterationsExceededException(maxIterations));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update the jacobian matrix.
|
||||
* @exception ObjectiveException if the function jacobian
|
||||
* cannot be evaluated or its dimension doesn't match problem dimension
|
||||
*/
|
||||
protected void updateJacobian() throws ObjectiveException {
|
||||
incrementJacobianEvaluationsCounter();
|
||||
++jacobianEvaluations;
|
||||
jacobian = f.jacobian(variables, objective);
|
||||
if (jacobian.length != rows) {
|
||||
throw new ObjectiveException("dimension mismatch {0} != {1}",
|
||||
|
|
@ -153,28 +175,13 @@ public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferen
|
|||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Increment the jacobian evaluations counter.
|
||||
*/
|
||||
protected final void incrementJacobianEvaluationsCounter() {
|
||||
++jacobianEvaluations;
|
||||
}
|
||||
|
||||
/**
|
||||
* Update the residuals array and cost function value.
|
||||
* @exception ObjectiveException if the function cannot be evaluated
|
||||
* or its dimension doesn't match problem dimension
|
||||
* @exception OptimizationException if the number of cost evaluations
|
||||
* exceeds the maximum allowed
|
||||
*/
|
||||
protected void updateResidualsAndCost()
|
||||
throws ObjectiveException, OptimizationException {
|
||||
|
||||
if (++objectiveEvaluations > maxEvaluations) {
|
||||
throw new OptimizationException(
|
||||
"maximal number of evaluations exceeded ({0})",
|
||||
objectiveEvaluations);
|
||||
}
|
||||
throws ObjectiveException {
|
||||
|
||||
objective = f.objective(variables);
|
||||
if (objective.length != rows) {
|
||||
|
|
@ -298,6 +305,7 @@ public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferen
|
|||
}
|
||||
|
||||
// reset counters
|
||||
iterations = 0;
|
||||
objectiveEvaluations = 0;
|
||||
jacobianEvaluations = 0;
|
||||
|
||||
|
|
@ -327,6 +335,6 @@ public abstract class AbstractLeastSquaresOptimizer implements VectorialDifferen
|
|||
* @exception IllegalArgumentException if the start point dimension is wrong
|
||||
*/
|
||||
abstract protected VectorialPointValuePair doOptimize()
|
||||
throws ObjectiveException, OptimizationException, IllegalArgumentException;
|
||||
throws ObjectiveException, OptimizationException, IllegalArgumentException;
|
||||
|
||||
}
|
||||
|
|
@ -53,7 +53,7 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
/** Simple constructor with default settings.
|
||||
* <p>The convergence check is set to a {@link SimpleVectorialValueChecker}
|
||||
* and the maximal number of evaluation is set to
|
||||
* {@link AbstractLeastSquaresOptimizer#DEFAULT_MAX_EVALUATIONS}.
|
||||
* {@link AbstractLeastSquaresOptimizer#DEFAULT_MAX_ITERATIONS}.
|
||||
* @param useLU if true, the normal equations will be solved using LU
|
||||
* decomposition, otherwise they will be solved using QR decomposition
|
||||
*/
|
||||
|
|
@ -67,8 +67,9 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
|
||||
// iterate until convergence is reached
|
||||
VectorialPointValuePair current = null;
|
||||
boolean converged = false;
|
||||
for (int iteration = 1; ! converged; ++iteration) {
|
||||
for (boolean converged = false; !converged;) {
|
||||
|
||||
incrementIterationsCounter();
|
||||
|
||||
// evaluate the objective function and its jacobian
|
||||
VectorialPointValuePair previous = current;
|
||||
|
|
@ -122,7 +123,7 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
|
||||
// check convergence
|
||||
if (previous != null) {
|
||||
converged = checker.converged(++iteration, previous, current);
|
||||
converged = checker.converged(getIterations(), previous, current);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -146,7 +146,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
* <p>The default values for the algorithm settings are:
|
||||
* <ul>
|
||||
* <li>{@link #setInitialStepBoundFactor initial step bound factor}: 100.0</li>
|
||||
* <li>{@link #setMaxCostEval maximal cost evaluations}: 1000</li>
|
||||
* <li>{@link #setMaxIterations maximal iterations}: 1000</li>
|
||||
* <li>{@link #setCostRelativeTolerance cost relative tolerance}: 1.0e-10</li>
|
||||
* <li>{@link #setParRelativeTolerance parameters relative tolerance}: 1.0e-10</li>
|
||||
* <li>{@link #setOrthoTolerance orthogonality tolerance}: 1.0e-10</li>
|
||||
|
|
@ -156,7 +156,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
public LevenbergMarquardtOptimizer() {
|
||||
|
||||
// set up the superclass with a default max cost evaluations setting
|
||||
setMaxEvaluations(1000);
|
||||
setMaxIterations(1000);
|
||||
|
||||
// default values for the tuning parameters
|
||||
setInitialStepBoundFactor(100.0);
|
||||
|
|
@ -237,6 +237,8 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
|
|||
boolean firstIteration = true;
|
||||
while (true) {
|
||||
|
||||
incrementIterationsCounter();
|
||||
|
||||
// compute the Q.R. decomposition of the jacobian matrix
|
||||
updateJacobian();
|
||||
qrDecomposition();
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ import org.apache.commons.math.optimization.GoalType;
|
|||
import org.apache.commons.math.optimization.ObjectiveException;
|
||||
import org.apache.commons.math.optimization.ScalarObjectiveFunction;
|
||||
import org.apache.commons.math.optimization.ScalarPointValuePair;
|
||||
import org.apache.commons.math.optimization.SimpleValueChecker;
|
||||
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
|
||||
|
||||
public class MultiDirectionalTest
|
||||
extends TestCase {
|
||||
|
|
@ -94,8 +94,8 @@ public class MultiDirectionalTest
|
|||
};
|
||||
|
||||
MultiDirectional optimizer = new MultiDirectional();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(1.0e-10, 1.0e-30));
|
||||
optimizer.setMaxEvaluations(200);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-10, 1.0e-30));
|
||||
optimizer.setMaxIterations(200);
|
||||
optimizer.setStartConfiguration(new double[] { 0.2, 0.2 });
|
||||
ScalarPointValuePair optimum;
|
||||
|
||||
|
|
@ -147,8 +147,8 @@ public class MultiDirectionalTest
|
|||
|
||||
count = 0;
|
||||
MultiDirectional optimizer = new MultiDirectional();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(-1, 1.0e-3));
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setStartConfiguration(new double[][] {
|
||||
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
|
||||
});
|
||||
|
|
@ -180,8 +180,8 @@ public class MultiDirectionalTest
|
|||
|
||||
count = 0;
|
||||
MultiDirectional optimizer = new MultiDirectional();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(-1.0, 1.0e-3));
|
||||
optimizer.setMaxEvaluations(1000);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-3));
|
||||
optimizer.setMaxIterations(1000);
|
||||
ScalarPointValuePair optimum =
|
||||
optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 });
|
||||
assertEquals(count, optimizer.getEvaluations());
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ import org.apache.commons.math.optimization.GoalType;
|
|||
import org.apache.commons.math.optimization.ObjectiveException;
|
||||
import org.apache.commons.math.optimization.ScalarObjectiveFunction;
|
||||
import org.apache.commons.math.optimization.ScalarPointValuePair;
|
||||
import org.apache.commons.math.optimization.SimpleValueChecker;
|
||||
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
|
||||
|
||||
public class NelderMeadTest
|
||||
extends TestCase {
|
||||
|
|
@ -94,8 +94,8 @@ public class NelderMeadTest
|
|||
};
|
||||
|
||||
NelderMead optimizer = new NelderMead();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(1.0e-10, 1.0e-30));
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-10, 1.0e-30));
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setStartConfiguration(new double[] { 0.2, 0.2 });
|
||||
ScalarPointValuePair optimum;
|
||||
|
||||
|
|
@ -147,8 +147,8 @@ public class NelderMeadTest
|
|||
|
||||
count = 0;
|
||||
NelderMead optimizer = new NelderMead();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(-1, 1.0e-3));
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setStartConfiguration(new double[][] {
|
||||
{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
|
||||
});
|
||||
|
|
@ -180,8 +180,8 @@ public class NelderMeadTest
|
|||
|
||||
count = 0;
|
||||
NelderMead optimizer = new NelderMead();
|
||||
optimizer.setConvergenceChecker(new SimpleValueChecker(-1.0, 1.0e-3));
|
||||
optimizer.setMaxEvaluations(200);
|
||||
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-3));
|
||||
optimizer.setMaxIterations(200);
|
||||
ScalarPointValuePair optimum =
|
||||
optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 });
|
||||
assertEquals(count, optimizer.getEvaluations());
|
||||
|
|
|
|||
|
|
@ -106,7 +106,7 @@ extends TestCase {
|
|||
LinearProblem problem =
|
||||
new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1 }, new double[] { 0 });
|
||||
|
|
@ -122,7 +122,7 @@ extends TestCase {
|
|||
new double[] { 4.0, 6.0, 1.0 });
|
||||
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
|
||||
|
|
@ -145,7 +145,7 @@ extends TestCase {
|
|||
{ 0, 0, 0, 0, 0, 2 }
|
||||
}, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
|
||||
|
|
@ -164,7 +164,7 @@ extends TestCase {
|
|||
{ 0, -1, 1 }
|
||||
}, new double[] { 1, 1, 1});
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
|
||||
|
|
@ -187,7 +187,7 @@ extends TestCase {
|
|||
}, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
|
||||
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
|
||||
|
|
@ -210,7 +210,7 @@ extends TestCase {
|
|||
{ -3, 0, -9 }
|
||||
}, new double[] { 1, 1, 1 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
try {
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
|
||||
|
|
@ -230,7 +230,7 @@ extends TestCase {
|
|||
{ 7.0, 5.0, 9.0, 10.0 }
|
||||
}, new double[] { 32, 23, 33, 31 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum1 =
|
||||
optimizer.optimize(problem1, problem1.target, new double[] { 1, 1, 1, 1 },
|
||||
|
|
@ -267,7 +267,7 @@ extends TestCase {
|
|||
}, new double[] { 7.0, 3.0, 5.0 });
|
||||
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
try {
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 },
|
||||
|
|
@ -290,7 +290,7 @@ extends TestCase {
|
|||
{ 0.0, 0.0, 0.0, -1.0, 1.0, 0.0 }
|
||||
}, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
try {
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
|
||||
|
|
@ -311,7 +311,7 @@ extends TestCase {
|
|||
}, new double[] { 3.0, 1.0, 5.0 });
|
||||
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 },
|
||||
|
|
@ -330,7 +330,7 @@ extends TestCase {
|
|||
}, new double[] { 3.0, 1.0, 4.0 });
|
||||
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
|
||||
assertTrue(optimizer.getRMS() > 0.1);
|
||||
|
|
@ -341,7 +341,7 @@ extends TestCase {
|
|||
LinearProblem problem =
|
||||
new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
|
||||
VectorialPointValuePair optimum =
|
||||
|
|
@ -382,7 +382,7 @@ extends TestCase {
|
|||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-15, 1.0e-15));
|
||||
try {
|
||||
optimizer.optimize(circle, new double[] { 0, 0, 0, 0, 0 },
|
||||
|
|
@ -404,7 +404,7 @@ extends TestCase {
|
|||
circle.addPoint( 35.0, 15.0);
|
||||
circle.addPoint( 45.0, 97.0);
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-13, 1.0e-13));
|
||||
VectorialPointValuePair optimum =
|
||||
optimizer.optimize(circle, new double[] { 0, 0, 0, 0, 0 },
|
||||
|
|
@ -458,7 +458,7 @@ extends TestCase {
|
|||
circle.addPoint(points[i][0], points[i][1]);
|
||||
}
|
||||
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
|
||||
optimizer.setMaxEvaluations(100);
|
||||
optimizer.setMaxIterations(100);
|
||||
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
|
||||
try {
|
||||
optimizer.optimize(circle, target, weights, new double[] { -12, -12 });
|
||||
|
|
|
|||
|
|
@ -379,7 +379,7 @@ public class LevenbergMarquardtOptimizerTest
|
|||
try {
|
||||
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
|
||||
optimizer.setInitialStepBoundFactor(initialStepBoundFactor);
|
||||
optimizer.setMaxEvaluations(maxCostEval);
|
||||
optimizer.setMaxIterations(maxCostEval);
|
||||
optimizer.setCostRelativeTolerance(costRelativeTolerance);
|
||||
optimizer.setParRelativeTolerance(parRelativeTolerance);
|
||||
optimizer.setOrthoTolerance(orthoTolerance);
|
||||
|
|
|
|||
|
|
@ -219,7 +219,7 @@ public class MinpackTest extends TestCase {
|
|||
0.188053165007911,
|
||||
0.122430604321144,
|
||||
0.134575665392506
|
||||
}), true);
|
||||
}), false);
|
||||
}
|
||||
|
||||
public void testMinpackMeyer()
|
||||
|
|
@ -505,7 +505,7 @@ public class MinpackTest extends TestCase {
|
|||
|
||||
private void minpackTest(MinpackFunction function, boolean exceptionExpected) {
|
||||
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
|
||||
optimizer.setMaxEvaluations(100 * (function.getN() + 1));
|
||||
optimizer.setMaxIterations(100 * (function.getN() + 1));
|
||||
optimizer.setCostRelativeTolerance(Math.sqrt(2.22044604926e-16));
|
||||
optimizer.setParRelativeTolerance(Math.sqrt(2.22044604926e-16));
|
||||
optimizer.setOrthoTolerance(2.22044604926e-16);
|
||||
|
|
|
|||
Loading…
Reference in New Issue