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/*
* File: FunctionMinimizerDirectionSetPowell.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright November 5, 2007, 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.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationReferences;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction;
import gov.sandia.cognition.evaluator.Evaluator;
import gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizer;
import gov.sandia.cognition.learning.algorithm.minimization.line.LineMinimizerDerivativeFree;
import gov.sandia.cognition.learning.data.DefaultInputOutputPair;
import gov.sandia.cognition.learning.data.WeightedInputOutputPair;
import gov.sandia.cognition.math.matrix.VectorFactory;
import gov.sandia.cognition.math.matrix.Vector;
import gov.sandia.cognition.util.ObjectUtil;
import java.util.LinkedList;
import java.util.List;
/**
* Implementation of the derivative-free unconstrained nonlinear direction-set
* minimization algorithm called "Powell's Method" by Numerical Recipes.
* The method was originally known as Smith's Direction-Set Method, to which
* Powell made an ingenious improvement. Powell's Method was later improved
* upon by Brent. This algorithm creates a basis set of search directions and
* repeatedly searches along each direction until a local minimum is found
* using only function evaluations, that is, no gradient information is needed.
* This algorithm is amazingly good at finding a minimum, and is my method of
* choice for derivative-free minimization. However, be sure to check the
* performance this algorithm's cousin, the Nelder-Mead downhill simplex
* (FunctionMinimizerNelderMead) before deciding on a derivative-free
* minimization algorithm.
*
* That being said, it is sometimes more effective to use approximated
* gradients and algorithms like BFGS (FunctionMinimizerBFGS) than
* derivative-free minimization algorithms. Thus, I would try them both.
*
* @author Kevin R. Dixon
* @since 2.0
*
*/
@PublicationReferences(
references={
@PublicationReference(
author="R. Fletcher",
title="Practical Methods of Optimization, Second Edition",
type=PublicationType.Book,
year=1987,
pages={87,90},
notes="Section 4.2"
),
@PublicationReference(
author={
"William H. Press",
"Saul A. Teukolsky",
"William T. Vetterling",
"Brian P. Flannery"
},
title="Numerical Recipes in C, Second Edition",
type=PublicationType.Book,
year=1992,
pages={417,418},
notes="Section 10.5",
url="http://www.nrbook.com/a/bookcpdf.php"
)
}
)
public class FunctionMinimizerDirectionSetPowell
extends AbstractAnytimeFunctionMinimizer>
{
/**
* Default maximum number of iterations before stopping, {@value}
*/
public static final int DEFAULT_MAX_ITERATIONS = 1000;
/**
* Default tolerance, {@value}
*/
public static final double DEFAULT_TOLERANCE = 1e-5;
/**
* Test for convergence on change in x, {@value}
*/
private static final double TOLERANCE_DELTA_X = 1e-7;
/**
* Default line minimization algorithm, LineMinimizerDerivativeFree
*/
public static final LineMinimizer DEFAULT_LINE_MINIMIZER =
new LineMinimizerDerivativeFree();
/**
* Work-horse algorithm that minimizes the function along a direction
*/
private LineMinimizer lineMinimizer;
/**
* Default constructor
*/
public FunctionMinimizerDirectionSetPowell()
{
this( ObjectUtil.cloneSafe( DEFAULT_LINE_MINIMIZER ) );
}
/**
* Creates a new instance of FunctionMinimizerDirectionSetPowell
* @param lineMinimizer
* Work-horse algorithm that minimizes the function along a direction
*/
public FunctionMinimizerDirectionSetPowell(
LineMinimizer lineMinimizer )
{
this( lineMinimizer, null, DEFAULT_TOLERANCE, DEFAULT_MAX_ITERATIONS );
}
/**
* Creates a new instance of FunctionMinimizerDirectionSetPowell
*
* @param initialGuess Initial guess about the minimum of the method
* @param tolerance Tolerance of the minimization algorithm, must be >= 0.0, typically ~1e-10
* @param lineMinimizer
* Work-horse algorithm that minimizes the function along a direction
* @param maxIterations Maximum number of iterations, must be >0, typically ~100
*/
public FunctionMinimizerDirectionSetPowell(
LineMinimizer lineMinimizer,
Vector initialGuess,
double tolerance,
int maxIterations )
{
super( initialGuess, tolerance, maxIterations );
this.setLineMinimizer( lineMinimizer );
}
/**
* Matrix where the columns indicate the directions of minimization
*/
private List directionSet;
/**
* Function that maps a Evaluator onto a
* Evaluator using a set point, direction and scale factor
*/
private DirectionalVectorToScalarFunction lineFunction;
@Override
protected boolean initializeAlgorithm()
{
int numDimensions = this.initialGuess.getDimensionality();
this.directionSet = new LinkedList();
for (int j = 0; j < numDimensions; j++)
{
Vector dj = VectorFactory.getDefault().createVector( numDimensions );
dj.setElement( j, 1.0 );
this.directionSet.add( dj );
}
double fx = this.data.evaluate( this.initialGuess );
this.result = new DefaultInputOutputPair( this.initialGuess, fx );
this.lineFunction =
new DirectionalVectorToScalarFunction( this.data, null, null );
return true;
}
@Override
protected boolean step()
{
int bestIndex = -1;
double bestDecrease = Double.POSITIVE_INFINITY;
Vector xoriginal = this.result.getInput();
double foriginal = this.result.getOutput();
WeightedInputOutputPair lineResult;
double scale;
// In each iteration, loop over all direction in the set and
// find the minimum along each direction
// The "this.getKeepGoing()" part is if somebody calls "stop()" from
// another thread while we're looping
int index = 0;
for( Vector direction : this.directionSet )
{
if( !this.getKeepGoing() )
{
return false;
}
Vector xold = this.result.getInput();
double fold = this.result.getOutput();
this.lineFunction.setVectorOffset( xold );
this.lineFunction.setDirection( direction );
lineResult = this.lineMinimizer.minimizeAlongDirection(
this.lineFunction, fold, null);
scale = lineResult.getWeight();
this.result = lineResult;
// Each "deltaj" should be <= 0.0 since the line search should
// give us an fnew that is less than fold
double fnew = this.result.getOutput();
double deltaj = fnew - fold;
if( bestDecrease > deltaj )
{
bestDecrease = deltaj;
bestIndex = index;
}
// Let's scale the direction by the line-search distance
if( scale != 0.0 )
{
direction.scaleEquals( scale );
}
index++;
}
Vector xnew = this.result.getInput();
double fnew = this.result.getOutput();
// This is the termination criterion
if ((2.0 * Math.abs( foriginal - fnew )) <=
(this.tolerance * (Math.abs( foriginal ) + Math.abs( fnew ))))
{
return false;
}
// Here's the new conjugate direction
Vector direction = xnew.minus( xoriginal );
// Fletcher says that we should perform a line search along the new
// direction. I find that this helps when appended the new direction
// to the end. If we're simply replacing the best direction with
// the new direction, then this line search ends up costing us more
// function evaluations.
this.lineFunction.setVectorOffset( xnew );
this.lineFunction.setDirection( direction );
lineResult = this.lineMinimizer.minimizeAlongDirection(
this.lineFunction, fnew, null);
this.result = lineResult;
scale = lineResult.getWeight();
// See if we've converged on zero slope
double maximumDelta = 0.0;
Vector delta = this.result.getInput().minus( xoriginal );
for( int i = 0; i < delta.getDimensionality(); i++ )
{
// Normalizing coefficient: max(|xi|, 1.0), so that we're always
// reducing the values
double normalizedX = Math.max( Math.abs(this.result.getInput().getElement(i)), 1.0 );
double deltaX = Math.abs(delta.getElement(i)) / normalizedX;
if( maximumDelta < deltaX )
{
maximumDelta = deltaX;
}
}
if( maximumDelta < TOLERANCE_DELTA_X )
{
return false;
}
// Now we're going to replace the best direction with the conjugate
// direction to ensure that we don't have increasing linear dependence
if( scale != 0.0 )
{
direction.scaleEquals( scale );
this.directionSet.remove( bestIndex );
this.directionSet.add( direction );
}
return true;
}
@Override
protected void cleanupAlgorithm()
{
}
/**
* Getter for lineMinimizer
* @return
* Work-horse algorithm that minimizes the function along a direction
*/
public LineMinimizer getLineMinimizer()
{
return this.lineMinimizer;
}
/**
* Setter for lineMinimizer
* @param lineMinimizer
* Work-horse algorithm that minimizes the function along a direction
*/
public void setLineMinimizer(
LineMinimizer lineMinimizer )
{
this.lineMinimizer = lineMinimizer;
}
}