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MATH-413 

Passing convergence checker in constructor.
Added default constructor.


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1159233 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski 2011-08-18 14:10:28 +00:00
parent 6c9487d625
commit 290621a860
2 changed files with 94 additions and 37 deletions
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39
src/main/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizer.java
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@ -28,6 +28,7 @@ import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.SingularMatrixException;
import org.apache.commons.math.optimization.VectorialPointValuePair;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
/**
* Gauss-Newton least-squares solver.
@ -49,14 +50,46 @@ public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {
/**
* Simple constructor with default settings.
* The convergence check is set to a {@link
* org.apache.commons.math.optimization.SimpleVectorialValueChecker}.
* The normal equations will be solved using LU decomposition and the
* convergence check is set to a {@link SimpleVectorialValueChecker}
* with default tolerances.
*/
public GaussNewtonOptimizer() {
this(true);
}
/**
* Simple constructor with default settings.
* The normal equations will be solved using LU decomposition.
*
* @param useLU if {@code true}, the normal equations will be solved
* @param checker Convergence checker.
*/
public GaussNewtonOptimizer(ConvergenceChecker<VectorialPointValuePair> checker) {
this(true, checker);
}
/**
* Simple constructor with default settings.
* The convergence check is set to a {@link SimpleVectorialValueChecker}
* with default tolerances.
*
* @param useLU If {@code true}, the normal equations will be solved
* using LU decomposition, otherwise they will be solved using QR
* decomposition.
*/
public GaussNewtonOptimizer(final boolean useLU) {
this(useLU, new SimpleVectorialValueChecker());
}
/**
* @param useLU If {@code true}, the normal equations will be solved
* using LU decomposition, otherwise they will be solved using QR
* decomposition.
* @param checker Convergence checker.
*/
public GaussNewtonOptimizer(final boolean useLU,
ConvergenceChecker<VectorialPointValuePair> checker) {
super(checker);
this.useLU = useLU;
}

92
src/test/java/org/apache/commons/math/optimization/general/GaussNewtonOptimizerTest.java
View File

@ -105,8 +105,10 @@ public class GaussNewtonOptimizerTest {
public void testTrivial() throws MathUserException {
LinearProblem problem =
new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -121,8 +123,9 @@ public class GaussNewtonOptimizerTest {
new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
new double[] { 4.0, 6.0, 1.0 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -144,8 +147,10 @@ public class GaussNewtonOptimizerTest {
{ 0, 0, 0, 0, 2, 0 },
{ 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.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
@ -163,8 +168,10 @@ public class GaussNewtonOptimizerTest {
{ -1, 1, 0 },
{ 0, -1, 1 }
}, new double[] { 1, 1, 1});
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
@ -186,8 +193,9 @@ public class GaussNewtonOptimizerTest {
{ 0, 0, 0, 0, 1, 1 }
}, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
new double[] { 0, 0, 0, 0, 0, 0 });
@ -209,8 +217,10 @@ public class GaussNewtonOptimizerTest {
{ 2, 1, 3 },
{ -3, 0, -9 }
}, new double[] { 1, 1, 1 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
}
@ -222,8 +232,10 @@ public class GaussNewtonOptimizerTest {
{ 8.0, 6.0, 10.0, 9.0 },
{ 7.0, 5.0, 9.0, 10.0 }
}, new double[] { 32, 23, 33, 31 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum1 =
optimizer.optimize(100, problem1, problem1.target, new double[] { 1, 1, 1, 1 },
new double[] { 0, 1, 2, 3 });
@ -259,8 +271,9 @@ public class GaussNewtonOptimizerTest {
{ 2.0, 0.0, 1.0, 0.0 }
}, new double[] { 7.0, 3.0, 5.0 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
new double[] { 7, 6, 5, 4 });
}
@ -274,8 +287,10 @@ public class GaussNewtonOptimizerTest {
{ 0.0, 0.0, -1.0, 1.0, 0.0, 1.0 },
{ 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.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
new double[] { 2, 2, 2, 2, 2, 2 });
}
@ -288,8 +303,9 @@ public class GaussNewtonOptimizerTest {
{ 1.0, 3.0 }
}, new double[] { 3.0, 1.0, 5.0 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 },
new double[] { 1, 1 });
@ -306,8 +322,9 @@ public class GaussNewtonOptimizerTest {
{ 1.0, 3.0 }
}, new double[] { 3.0, 1.0, 4.0 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
Assert.assertTrue(optimizer.getRMS() > 0.1);
@ -317,8 +334,9 @@ public class GaussNewtonOptimizerTest {
public void testInconsistentSizes1() throws MathUserException {
LinearProblem problem =
new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
@ -335,8 +353,9 @@ public class GaussNewtonOptimizerTest {
public void testInconsistentSizes2() throws MathUserException {
LinearProblem problem =
new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
@ -357,8 +376,9 @@ public class GaussNewtonOptimizerTest {
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialPointChecker(1.0e-30, 1.0e-30));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-30, 1.0e-30));
optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
new double[] { 1, 1, 1, 1, 1 },
@ -373,8 +393,10 @@ public class GaussNewtonOptimizerTest {
circle.addPoint(110.0, -20.0);
circle.addPoint( 35.0, 15.0);
circle.addPoint( 45.0, 97.0);
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-13, 1.0e-13));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-13, 1.0e-13));
VectorialPointValuePair optimum =
optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
new double[] { 1, 1, 1, 1, 1 },
@ -397,8 +419,9 @@ public class GaussNewtonOptimizerTest {
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
}
@ -414,8 +437,9 @@ public class GaussNewtonOptimizerTest {
for (int i = 0; i < points.length; ++i) {
circle.addPoint(points[i][0], points[i][1]);
}
GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
GaussNewtonOptimizer optimizer
= new GaussNewtonOptimizer(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
VectorialPointValuePair optimum =
optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });