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MATH-413 

Convergence checker passed in the constructor.


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1164300 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2011-09-01 23:37:36 +00:00
parent 1ef23c7fb5
commit 681943d4f2
3 changed files with 136 additions and 85 deletions
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18
src/main/java/org/apache/commons/math/optimization/direct/SimplexOptimizer.java
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@ -63,10 +63,6 @@ import org.apache.commons.math.optimization.MultivariateRealOptimizer;
* re-initialized to one with the appropriate dimensions.
* </p>
* <p>
* If {@link #setConvergenceChecker(ConvergenceChecker)} is not called,
* 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.
@ -83,10 +79,18 @@ public class SimplexOptimizer
private AbstractSimplex simplex;
/**
* Default constructor.
* Constructor using a default {@link SimpleScalarValueChecker convergence
* checker}.
*/
public SimplexOptimizer() {
setConvergenceChecker(new SimpleScalarValueChecker());
this(new SimpleScalarValueChecker());
}
/**
* @param checker Convergence checker.
*/
public SimplexOptimizer(ConvergenceChecker<RealPointValuePair> checker) {
super(checker);
}
/**
@ -94,7 +98,7 @@ public class SimplexOptimizer
* @param abs Absolute threshold.
*/
public SimplexOptimizer(double rel, double abs) {
setConvergenceChecker(new SimpleScalarValueChecker(rel, abs));
this(new SimpleScalarValueChecker(rel, abs));
}
/**

101
src/test/java/org/apache/commons/math/optimization/direct/SimplexOptimizerMultiDirectionalTest.java
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@ -27,57 +27,61 @@ import org.junit.Test;
public class SimplexOptimizerMultiDirectionalTest {
@Test
public void testMinimizeMaximize() {
// the following function has 4 local extrema:
final double xM = -3.841947088256863675365;
final double yM = -1.391745200270734924416;
final double xP = 0.2286682237349059125691;
final double yP = -yM;
final double valueXmYm = 0.2373295333134216789769; // local maximum
final double valueXmYp = -valueXmYm; // local minimum
final double valueXpYm = -0.7290400707055187115322; // global minimum
final double valueXpYp = -valueXpYm; // global maximum
MultivariateRealFunction fourExtrema = new MultivariateRealFunction() {
public double value(double[] variables) {
final double x = variables[0];
final double y = variables[1];
return ((x == 0) || (y == 0)) ? 0 :
(FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y));
}
};
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
RealPointValuePair optimum;
final FourExtrema fourExtrema = new FourExtrema();
// minimization
optimum = optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
Assert.assertEquals(xM, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(valueXmYp, optimum.getValue(), 8e-13);
final RealPointValuePair optimum
= optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 8e-13);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
}
optimum = optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
Assert.assertEquals(xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(valueXpYm, optimum.getValue(), 2e-12);
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(200, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
}
// maximization
optimum = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 });
Assert.assertEquals(xM, optimum.getPoint()[0], 7e-7);
Assert.assertEquals(yM, optimum.getPoint()[1], 3e-7);
Assert.assertEquals(valueXmYm, optimum.getValue(), 2e-14);
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 });
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 2e-14);
Assert.assertTrue(optimizer.getEvaluations() > 120);
Assert.assertTrue(optimizer.getEvaluations() < 150);
}
optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1e-15, 1e-30));
optimum = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });
Assert.assertEquals(xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(valueXpYp, optimum.getValue(), 2e-12);
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleScalarValueChecker(1e-15, 1e-30));
optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 2e-12);
Assert.assertTrue(optimizer.getEvaluations() > 180);
Assert.assertTrue(optimizer.getEvaluations() < 220);
}
@ -153,6 +157,25 @@ public class SimplexOptimizerMultiDirectionalTest {
Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );
}
private static class FourExtrema implements MultivariateRealFunction {
// The following function has 4 local extrema.
final double xM = -3.841947088256863675365;
final double yM = -1.391745200270734924416;
final double xP = 0.2286682237349059125691;
final double yP = -yM;
final double valueXmYm = 0.2373295333134216789769; // Local maximum.
final double valueXmYp = -valueXmYm; // Local minimum.
final double valueXpYm = -0.7290400707055187115322; // Global minimum.
final double valueXpYp = -valueXpYm; // Global maximum.
public double value(double[] variables) {
final double x = variables[0];
final double y = variables[1];
return (x == 0 || y == 0) ? 0 :
FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y);
}
}
private static class Gaussian2D implements MultivariateRealFunction {
private final double[] maximumPosition;
private final double std;

102
src/test/java/org/apache/commons/math/optimization/direct/SimplexOptimizerNelderMeadTest.java
View File

@ -26,62 +26,67 @@ import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.LeastSquaresConverter;
import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.util.FastMath;
import org.junit.Assert;
import org.junit.Test;
public class SimplexOptimizerNelderMeadTest {
@Test
public void testMinimizeMaximize() {
// the following function has 4 local extrema:
final double xM = -3.841947088256863675365;
final double yM = -1.391745200270734924416;
final double xP = 0.2286682237349059125691;
final double yP = -yM;
final double valueXmYm = 0.2373295333134216789769; // local maximum
final double valueXmYp = -valueXmYm; // local minimum
final double valueXpYm = -0.7290400707055187115322; // global minimum
final double valueXpYp = -valueXpYm; // global maximum
MultivariateRealFunction fourExtrema = new MultivariateRealFunction() {
public double value(double[] variables) {
final double x = variables[0];
final double y = variables[1];
return (x == 0 || y == 0) ? 0 :
(Math.atan(x) * Math.atan(x + 2) * Math.atan(y) * Math.atan(y) / (x * y));
}
};
public void testMinimize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
RealPointValuePair optimum;
final FourExtrema fourExtrema = new FourExtrema();
// minimization
optimum = optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
Assert.assertEquals(xM, optimum.getPoint()[0], 2e-7);
Assert.assertEquals(yP, optimum.getPoint()[1], 2e-5);
Assert.assertEquals(valueXmYp, optimum.getValue(), 6e-12);
final RealPointValuePair optimum
= optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { -3, 0 });
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 2e-7);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 2e-5);
Assert.assertEquals(fourExtrema.valueXmYp, optimum.getValue(), 6e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
}
optimum = optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
Assert.assertEquals(xP, optimum.getPoint()[0], 5e-6);
Assert.assertEquals(yM, optimum.getPoint()[1], 6e-6);
Assert.assertEquals(valueXpYm, optimum.getValue(), 1e-11);
@Test
public void testMinimize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(100, fourExtrema, GoalType.MINIMIZE, new double[] { 1, 0 });
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 5e-6);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 6e-6);
Assert.assertEquals(fourExtrema.valueXpYm, optimum.getValue(), 1e-11);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
}
// maximization
optimum = optimizer.optimize(100, fourExtrema, GoalType.MAXIMIZE, new double[] { -3, 0 });
Assert.assertEquals(xM, optimum.getPoint()[0], 1e-5);
Assert.assertEquals(yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(valueXmYm, optimum.getValue(), 3e-12);
@Test
public void testMaximize1() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(100, fourExtrema, GoalType.MAXIMIZE, new double[] { -3, 0 });
Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 1e-5);
Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
Assert.assertEquals(fourExtrema.valueXmYm, optimum.getValue(), 3e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
}
optimum = optimizer.optimize(100, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });
Assert.assertEquals(xP, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(yP, optimum.getPoint()[1], 5e-6);
Assert.assertEquals(valueXpYp, optimum.getValue(), 7e-12);
@Test
public void testMaximize2() {
SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
optimizer.setSimplex(new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
final FourExtrema fourExtrema = new FourExtrema();
final RealPointValuePair optimum
= optimizer.optimize(100, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });
Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 4e-6);
Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 5e-6);
Assert.assertEquals(fourExtrema.valueXpYp, optimum.getValue(), 7e-12);
Assert.assertTrue(optimizer.getEvaluations() > 60);
Assert.assertTrue(optimizer.getEvaluations() < 90);
}
@ -199,6 +204,25 @@ public class SimplexOptimizerNelderMeadTest {
optimizer.optimize(20, powell, GoalType.MINIMIZE, new double[] { 3, -1, 0, 1 });
}
private static class FourExtrema implements MultivariateRealFunction {
// The following function has 4 local extrema.
final double xM = -3.841947088256863675365;
final double yM = -1.391745200270734924416;
final double xP = 0.2286682237349059125691;
final double yP = -yM;
final double valueXmYm = 0.2373295333134216789769; // Local maximum.
final double valueXmYp = -valueXmYm; // Local minimum.
final double valueXpYm = -0.7290400707055187115322; // Global minimum.
final double valueXpYp = -valueXpYm; // Global maximum.
public double value(double[] variables) {
final double x = variables[0];
final double y = variables[1];
return (x == 0 || y == 0) ? 0 :
FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y);
}
}
private static class Rosenbrock implements MultivariateRealFunction {
private int count;