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Fixed Levenberg-Marquardt optimizer that did not use the vectorial convergence checker. 

Now this optimizer can use either the general vectorial convergence checker or its own specialized convergence settings.
Minor changes had to be introduced in the test data, they have been validated
JIRA: MATH-362


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@949433 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2010-05-29 18:15:50 +00:00
parent 24e05bf7e9
commit 24218a5278
4 changed files with 48 additions and 25 deletions
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35
src/main/java/org/apache/commons/math/optimization/general/LevenbergMarquardtOptimizer.java
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@ -34,8 +34,9 @@ import org.apache.commons.math.optimization.VectorialPointValuePair;
*
* <p>The resolution engine is a simple translation of the MINPACK <a
* href="http://www.netlib.org/minpack/lmder.f">lmder</a> routine with minor
* changes. The changes include the over-determined resolution and the Q.R.
* decomposition which has been rewritten following the algorithm described in the
* changes. The changes include the over-determined resolution, the use of
* inherited convergence checker and the Q.R. decomposition which has been
* rewritten following the algorithm described in the
* P. Lascaux and R. Theodor book <i>Analyse num&eacute;rique matricielle
* appliqu&eacute;e &agrave; l'art de l'ing&eacute;nieur</i>, Masson 1986.</p>
* <p>The authors of the original fortran version are:
@ -143,6 +144,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
* Build an optimizer for least squares problems.
* <p>The default values for the algorithm settings are:
* <ul>
* <li>{@link #setConvergenceChecker vectorial convergence checker}: null</li>
* <li>{@link #setInitialStepBoundFactor initial step bound factor}: 100.0</li>
* <li>{@link #setMaxIterations maximal iterations}: 1000</li>
* <li>{@link #setCostRelativeTolerance cost relative tolerance}: 1.0e-10</li>
@ -150,6 +152,10 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
* <li>{@link #setOrthoTolerance orthogonality tolerance}: 1.0e-10</li>
* </ul>
* </p>
* <p>These default values may be overridden after construction. If the {@link
* #setConvergenceChecker vectorial convergence checker} is set to a non-null value, it
* will be used instead of the {@link #setCostRelativeTolerance cost relative tolerance}
* and {@link #setParRelativeTolerance parameters relative tolerance} settings.
*/
public LevenbergMarquardtOptimizer() {
@ -157,6 +163,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
setMaxIterations(1000);
// default values for the tuning parameters
setConvergenceChecker(null);
setInitialStepBoundFactor(100.0);
setCostRelativeTolerance(1.0e-10);
setParRelativeTolerance(1.0e-10);
@ -179,7 +186,8 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
/**
* Set the desired relative error in the sum of squares.
*
* <p>This setting is used only if the {@link #setConvergenceChecker vectorial
* convergence checker} is set to null.</p>
* @param costRelativeTolerance desired relative error in the sum of squares
*/
public void setCostRelativeTolerance(double costRelativeTolerance) {
@ -188,7 +196,8 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
/**
* Set the desired relative error in the approximate solution parameters.
*
* <p>This setting is used only if the {@link #setConvergenceChecker vectorial
* convergence checker} is set to null.</p>
* @param parRelativeTolerance desired relative error
* in the approximate solution parameters
*/
@ -198,7 +207,8 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
/**
* Set the desired max cosine on the orthogonality.
*
* <p>This setting is always used, regardless of the {@link #setConvergenceChecker
* vectorial convergence checker} being null or non-null.</p>
* @param orthoTolerance desired max cosine on the orthogonality
* between the function vector and the columns of the jacobian
*/
@ -235,11 +245,13 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
// outer loop
lmPar = 0;
boolean firstIteration = true;
VectorialPointValuePair current = new VectorialPointValuePair(point, objective);
while (true) {
incrementIterationsCounter();
// compute the Q.R. decomposition of the jacobian matrix
VectorialPointValuePair previous = current;
updateJacobian();
qrDecomposition();
@ -291,7 +303,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
}
if (maxCosine <= orthoTolerance) {
// convergence has been reached
return new VectorialPointValuePair(point, objective);
return current;
}
// rescale if necessary
@ -333,6 +345,7 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
// evaluate the function at x + p and calculate its norm
updateResidualsAndCost();
current = new VectorialPointValuePair(point, objective);
// compute the scaled actual reduction
double actRed = -1.0;
@ -401,11 +414,19 @@ public class LevenbergMarquardtOptimizer extends AbstractLeastSquaresOptimizer {
}
// tests for convergence.
if (checker != null) {
// we use the vectorial convergence checker
if (checker.converged(getIterations(), previous, current)) {
return current;
}
} else {
// we use the Levenberg-Marquardt specific convergence parameters
if (((Math.abs(actRed) <= costRelativeTolerance) &&
(preRed <= costRelativeTolerance) &&
(ratio <= 2.0)) ||
(delta <= parRelativeTolerance * xNorm)) {
return new VectorialPointValuePair(point, objective);
return current;
}
}
// tests for termination and stringent tolerances

5
src/site/xdoc/changes.xml
View File

@ -52,6 +52,11 @@ The <action> type attribute can be add,update,fix,remove.
If the output is not quite correct, check for invisible trailing spaces!
-->
<release version="2.2" date="TBD" description="TBD">
<action dev="luc" type="fix" issue="MATH-362" >
Fixed Levenberg-Marquardt optimizer that did not use the vectorial convergence checker.
Now this optimizer can use either the general vectorial convergence checker or its own
specialized convergence settings.
</action>
<action dev="psteitz" type="fix" issue="MATH-371" due-to="Kevin Childs">
Fixed loss of significance error in PersonsCorrelation p-value computation causing p-values
smaller than the machine epsilon (~1E-16) to be reported as 0.

2
src/test/java/org/apache/commons/math/optimization/general/LevenbergMarquardtOptimizerTest.java
View File

@ -485,7 +485,7 @@ public class LevenbergMarquardtOptimizerTest
circle.addPoint(points[i][0], points[i][1]);
}
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-10, 1.0e-10));
optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-8, 1.0e-8));
VectorialPointValuePair optimum =
optimizer.optimize(circle, target, weights, new double[] { -12, -12 });
Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);

25
src/test/java/org/apache/commons/math/optimization/general/MinpackTest.java
View File

@ -152,14 +152,14 @@ public class MinpackTest extends TestCase {
minpackTest(new FreudensteinRothFunction(new double[] { 5.0, -20.0 },
12432.833948863, 6.9988751744895,
new double[] {
11.4130046614746,
-0.896796038685958
11.4121122022341,
-0.8968550851268697
}), false);
minpackTest(new FreudensteinRothFunction(new double[] { 50.0, -200.0 },
11426454.595762, 6.99887517242903,
new double[] {
11.4127817857886,
-0.89680510749204
11.412069435091231,
-0.8968582807605691
}), false);
}
@ -325,7 +325,7 @@ public class MinpackTest extends TestCase {
minpackTest(new JennrichSampsonFunction(10, new double[] { 0.3, 0.4 },
64.5856498144943, 11.1517793413499,
new double[] {
0.257819926636811, 0.257829976764542
0.2578330049, 0.257829976764542
}), false);
}
@ -499,8 +499,8 @@ public class MinpackTest extends TestCase {
function.getTarget(), function.getWeight(),
function.getStartPoint());
assertFalse(exceptionExpected);
assertTrue(function.checkTheoreticalMinCost(optimizer.getRMS()));
assertTrue(function.checkTheoreticalMinParams(optimum));
function.checkTheoreticalMinCost(optimizer.getRMS());
function.checkTheoreticalMinParams(optimum);
} catch (OptimizationException lsse) {
assertTrue(exceptionExpected);
} catch (FunctionEvaluationException fe) {
@ -561,24 +561,21 @@ public class MinpackTest extends TestCase {
return startParams.length;
}
public boolean checkTheoreticalMinCost(double rms) {
public void checkTheoreticalMinCost(double rms) {
double threshold = costAccuracy * (1.0 + theoreticalMinCost);
return Math.abs(Math.sqrt(m) * rms - theoreticalMinCost) <= threshold;
assertEquals(theoreticalMinCost, Math.sqrt(m) * rms, threshold);
}
public boolean checkTheoreticalMinParams(VectorialPointValuePair optimum) {
public void checkTheoreticalMinParams(VectorialPointValuePair optimum) {
double[] params = optimum.getPointRef();
if (theoreticalMinParams != null) {
for (int i = 0; i < theoreticalMinParams.length; ++i) {
double mi = theoreticalMinParams[i];
double vi = params[i];
if (Math.abs(mi - vi) > (paramsAccuracy * (1.0 + Math.abs(mi)))) {
return false;
assertEquals(mi, vi, paramsAccuracy * (1.0 + Math.abs(mi)));
}
}
}
return true;
}
public MultivariateMatrixFunction jacobian() {
return new MultivariateMatrixFunction() {