gov.sandia.cognition.learning.algorithm.minimization

Class FunctionMinimizerFletcherReeves

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<EvaluatorType,InputOutputPair<InputType,OutputType>>

gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer<Vector,java.lang.Double,DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>>

gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerFletcherReeves

All Implemented Interfaces:

AnytimeAlgorithm<InputOutputPair<Vector,java.lang.Double>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>,InputOutputPair<Vector,java.lang.Double>>, BatchLearner<DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>,InputOutputPair<Vector,java.lang.Double>>, FunctionMinimizer<Vector,java.lang.Double,DifferentiableEvaluator<? super Vector,java.lang.Double,Vector>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="R. Fletcher",
                      title="Practical Methods of Optimization, Second Edition",
                      type=Book,
                      year=1987,
                      pages={80,83},
                      notes="Equation 4.1.4")
public class FunctionMinimizerFletcherReeves
extends FunctionMinimizerConjugateGradient

This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure. This is an unconstrained nonlinear optimization technique that uses first-order derivative (gradient) information to determine the direction of exact line searches. This algorithm is the original variant of conjugate gradient minimization, and is generally regarded as inferior to other variants, such as Polack-Ribiere and Liu-Storey. Furthermore, CG is generally regarded as inferior to quasi-Newton methods such as BFGS, but goes not store an NxN Hessian inverse. Thus, if you have many inputs (N), then the conjugate gradient minimization may be better than BFGS for your problem. But even if you decide to go with CG, then try the other CG variants before Fletcher-Reeves.

Since:

2.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

DEFAULT_LINE_MINIMIZER, DEFAULT_MAX_ITERATIONS, DEFAULT_TOLERANCE, lineFunction

Fields inherited from class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer

initialGuess, result, tolerance

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
FunctionMinimizerFletcherReeves() Creates a new instance of FunctionMinimizerPolakRibiere
FunctionMinimizerFletcherReeves(LineMinimizer<?> lineMinimizer) Creates a new instance of FunctionMinimizerPolakRibiere
FunctionMinimizerFletcherReeves(LineMinimizer<?> lineMinimizer, Vector initialGuess, double tolerance, int maxIterations) Creates a new instance of FunctionMinimizerConjugateGradient

Method Summary

Modifier and Type	Method and Description
protected double	computeScaleFactor(Vector gradientCurrent, Vector gradientPrevious) Computes the conjugate gradient parameter for the particular update scheme.

Methods inherited from class gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizerConjugateGradient

cleanupAlgorithm, getLineMinimizer, initializeAlgorithm, setLineMinimizer, step

Methods inherited from class gov.sandia.cognition.learning.algorithm.minimization.AbstractAnytimeFunctionMinimizer

getInitialGuess, getResult, getTolerance, setInitialGuess, setResult, setTolerance

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.learning.algorithm.minimization.FunctionMinimizer

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid, stop

Constructor Detail

FunctionMinimizerFletcherReeves

public FunctionMinimizerFletcherReeves()

Creates a new instance of FunctionMinimizerPolakRibiere

FunctionMinimizerFletcherReeves

public FunctionMinimizerFletcherReeves(LineMinimizer<?> lineMinimizer)

Creates a new instance of FunctionMinimizerPolakRibiere

Parameters:

lineMinimizer - Work-horse algorithm that minimizes the function along a direction

FunctionMinimizerFletcherReeves

public FunctionMinimizerFletcherReeves(LineMinimizer<?> lineMinimizer,
                                       Vector initialGuess,
                                       double tolerance,
                                       int maxIterations)

Creates a new instance of FunctionMinimizerConjugateGradient

Parameters:

initialGuess - Initial guess about the minimum of the method

tolerance - Tolerance of the minimization algorithm, must be >= 0.0, typically ~1e-10

lineMinimizer - Work-horse algorithm that minimizes the function along a direction

maxIterations - Maximum number of iterations, must be >0, typically ~100

Method Detail

computeScaleFactor

protected double computeScaleFactor(Vector gradientCurrent,
                                    Vector gradientPrevious)

Description copied from class: FunctionMinimizerConjugateGradient

Computes the conjugate gradient parameter for the particular update scheme.

Specified by:

computeScaleFactor in class FunctionMinimizerConjugateGradient

Parameters:

gradientCurrent - Gradient at the current evaluation point

gradientPrevious - Gradient at the previous evaluation point

Returns:

"beta" scale factor