/*
* File: MinimizationStoppingCriterion.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Jul 4, 2008, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.learning.algorithm.minimization;
import gov.sandia.cognition.math.matrix.Vector;
/**
* Implementation of almost zero-gradient convergence test for function
* minimizers.
* @author Kevin R. Dixon
* @since 2.1
*/
public class MinimizationStoppingCriterion
{
/**
* Test for convergence on change in x, {@value}
*/
private static final double TOLERANCE_DELTA_X = 1e-7;
/**
* Tests for convergence on approximately zero slope and nonmovement along
* the x-axis
* @param xnew
* Present x-axis value
* @param fxnew
* Present y-value at xnew, may be null if unknown
* @param gradient
* Gradient at the new point
* @param delta
* Change in x-axis between iterations
* @param tolerance
* Tolerance of the stopping criterion, typically ~1e-5
* @return
* True if converged, false otherwise
*/
public static boolean convergence(
Vector xnew,
Double fxnew,
Vector gradient,
Vector delta,
double tolerance )
{
// See if we've converged on zero slope
double maximumDelta = 0.0;
double maximumGradient = 0.0;
double gradDenom = (fxnew==null) ? 1.0 : Math.max( fxnew, 1.0 );
for( int i = 0; i < xnew.getDimensionality(); i++ )
{
// Normalizing coefficient: max(|xi|, 1.0), so that we're always
// reducing the values
double normalizedX = Math.max( Math.abs(xnew.getElement(i)), 1.0 );
double deltaX = Math.abs(delta.getElement(i)) / normalizedX;
if( maximumDelta < deltaX )
{
maximumDelta = deltaX;
}
double gradX = Math.abs(gradient.getElement(i)) * normalizedX / gradDenom;
if( maximumGradient < gradX )
{
maximumGradient = gradX;
}
}
// This is the normal stopping criteria
if( (maximumDelta < TOLERANCE_DELTA_X) ||
(maximumGradient < tolerance) )
{
return true;
}
else
{
return false;
}
}
}