Package com.irurueta.numerical.optimization

Class DerivativeBrentSingleOptimizer

java.lang.Object

com.irurueta.numerical.optimization.Optimizer

com.irurueta.numerical.optimization.SingleOptimizer

com.irurueta.numerical.optimization.BracketedSingleOptimizer

com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer

public class DerivativeBrentSingleOptimizer
extends BracketedSingleOptimizer

Class to compute local minimum on single dimension functions using a modification of Brent's algorithm that takes into account the function's derivative. This class will search for a local minimum within a bracket of values. A bracket is a set of points: "a" a minimum evaluation point, "b" a middle evaluation point and "c" a maximum evaluation where a <= b <= c, and where f(b) <= f(a) and f(b) <= f(c). This class is based on the implementation of Numerical Recipes 3rd ed. Section 10.4. Page 500.

Field Summary

Fields

Modifier and Type	Field	Description
static final double	DEFAULT_TOLERANCE	Default tolerance.
private SingleDimensionFunctionEvaluatorListener	derivativeListener	Listener to evaluate the functions derivative.
static final int	ITMAX	Maximum number of iterations to perform.
static final double	MIN_TOLERANCE	Minimum allowed tolerance value.
private double	tolerance	Tolerance.
static final double	ZEPS	Constant defining machine precision.

Fields inherited from class com.irurueta.numerical.optimization.BracketedSingleOptimizer

ax, bx, cx, DEFAULT_MAX_EVAL_POINT, DEFAULT_MIDDLE_EVAL_POINT, DEFAULT_MIN_EVAL_POINT, GLIMIT, GOLD, TINY

Fields inherited from class com.irurueta.numerical.optimization.SingleOptimizer

fmin, listener, resultAvailable, xmin

Fields inherited from class com.irurueta.numerical.optimization.Optimizer

iterationCompletedListener, locked

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	DerivativeBrentSingleOptimizer()	Empty constructor.
protected	DerivativeBrentSingleOptimizer(SingleDimensionFunctionEvaluatorListener listener, SingleDimensionFunctionEvaluatorListener derivativeListener, double minEvalPoint, double middleEvalPoint, double maxEvalPoint, double tolerance)	Constructor.

Method Summary

Modifier and Type	Method	Description
SingleDimensionFunctionEvaluatorListener	getDerivativeListener()	Returns derivative listener to get function derivative.
double	getTolerance()	Returns tolerance value.
private void	internalSetTolerance(double tolerance)	Internal method to set tolerance.
boolean	isDerivativeListenerAvailable()	Returns boolean indicating whether derivative listener has been provided and is available for retrieval.
boolean	isReady()	Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
void	minimize()	This function estimates a function minimum within provided or computed bracket of values.
void	setDerivativeListener(SingleDimensionFunctionEvaluatorListener derivativeListener)	Sets derivative listener that gets function derivative.
void	setTolerance(double tolerance)	Sets tolerance value.

Methods inherited from class com.irurueta.numerical.optimization.BracketedSingleOptimizer

areBracketEvaluationsAvailable, computeBracket, computeBracket, computeBracket, evaluateBracket, getEvaluationAtMax, getEvaluationAtMiddle, getEvaluationAtMin, getMaxEvaluationPoint, getMiddleEvaluationPoint, getMinEvaluationPoint, isBracketAvailable, mov3, setBracket, shift2, shift3, sign

Methods inherited from class com.irurueta.numerical.optimization.SingleOptimizer

getEvaluationAtResult, getListener, getResult, isListenerAvailable, isResultAvailable, setListener

Methods inherited from class com.irurueta.numerical.optimization.Optimizer

getOnIterationCompletedListener, isLocked, setOnIterationCompletedListener

Methods inherited from class java.lang.Object

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

Field Details

ITMAX

public static final int ITMAX

Maximum number of iterations to perform. If convergence is not found within this number of iterations, the minimum search will be considered as failed.

See Also:

• Constant Field Values

ZEPS

public static final double ZEPS

Constant defining machine precision.

See Also:

• Constant Field Values

DEFAULT_TOLERANCE

public static final double DEFAULT_TOLERANCE

Default tolerance. Estimated result will be found with an accuracy below or equal to provided tolerance value.

See Also:

• Constant Field Values

MIN_TOLERANCE

public static final double MIN_TOLERANCE

Minimum allowed tolerance value.

See Also:

• Constant Field Values

derivativeListener

private SingleDimensionFunctionEvaluatorListener derivativeListener

Listener to evaluate the functions derivative. If the function's derivative is not know (e.g. does not have a closed expression), then a DerivativeEstimator might be used inside the listener implementation.

tolerance

private double tolerance

Tolerance. Estimated result will be found with an accuracy below or equal to provided tolerance value.

Constructor Details

DerivativeBrentSingleOptimizer

protected DerivativeBrentSingleOptimizer()

Empty constructor.

DerivativeBrentSingleOptimizer

protected DerivativeBrentSingleOptimizer(SingleDimensionFunctionEvaluatorListener listener,
 SingleDimensionFunctionEvaluatorListener derivativeListener,
 double minEvalPoint,
 double middleEvalPoint,
 double maxEvalPoint,
 double tolerance)
                                  throws InvalidBracketRangeException

Constructor. Creates an instance with provided bracket of values and a listener to get single dimension function evaluations.

Parameters:

listener - Listener to evaluate a function.

derivativeListener - Listener to get function derivative.

minEvalPoint - Minimum bracket evaluation point.

middleEvalPoint - Middle bracket evaluation point.

maxEvalPoint - Maximum bracket evaluation point.

tolerance - tolerance to find result with. Estimated result will be found with an accuracy below or equal to provided tolerance value.

Throws:

InvalidBracketRangeException - Raised if the following condition is not met: minEvalPoint <= middleEvalPoint <= maxEvalPoint.

IllegalArgumentException - Raised if tolerance is negative.

Method Details

getDerivativeListener

public SingleDimensionFunctionEvaluatorListener getDerivativeListener()
                                                               throws NotAvailableException

Returns derivative listener to get function derivative.

Returns:

Derivative listener.

Throws:

NotAvailableException - Raised if derivative listener is not available for retrieval.

setDerivativeListener

public void setDerivativeListener(SingleDimensionFunctionEvaluatorListener derivativeListener)
                           throws LockedException

Sets derivative listener that gets function derivative.

Parameters:

derivativeListener - Sets derivative listener.

Throws:

LockedException - Raised if this instance is locked.

isDerivativeListenerAvailable

public boolean isDerivativeListenerAvailable()

Returns boolean indicating whether derivative listener has been provided and is available for retrieval.

Returns:

Boolean indicating whether derivative listener is available.

getTolerance

public double getTolerance()

Returns tolerance value. Estimated result will be found with an accuracy below or equal to provided tolerance value.

Returns:

Tolerance value.

setTolerance

public void setTolerance(double tolerance)
                  throws LockedException

Sets tolerance value. Estimated result will be found with an accuracy below or equal to provided tolerance value.

Parameters:

tolerance - Tolerance value.

Throws:

LockedException - Raised if this instance is locked.

IllegalArgumentException - Raised if tolerance is negative.

minimize

public void minimize()
              throws LockedException,
NotReadyException,
OptimizationException

This function estimates a function minimum within provided or computed bracket of values. Given a function f that computes a function and also its derivative function df, and given a bracketing triplet of abscissas "ax", "bx", "cx" (such that bx is between ax and cx, and f(bx) is less than both f(ax) and f(cx), this routine isolates the minimum to a fractional precision of about tolerance using a modification of Brent's method that uses derivatives. The abscissa of the minimum is returned as "xmin" and the minimum function value is returned as "fmin".

Overrides:

minimize in class Optimizer

Throws:

LockedException - Raised if this instance is locked, because estimation is being computed.

NotReadyException - Raised if this instance is not ready because either a listener or a bracket has not yet been provided or computed.

OptimizationException - Raised if the algorithm failed because of lack of convergence or because function couldn't be evaluated.

isReady

public boolean isReady()

Returns boolean indicating whether this instance is ready to start the estimation of a local minimum. This instance will be ready once a listener, derivative listener and bracket are available.

Overrides:

isReady in class SingleOptimizer

Returns:

True if ready, false otherwise.

internalSetTolerance

private void internalSetTolerance(double tolerance)

Internal method to set tolerance. Estimated result will be found with an accuracy below or equal to provided tolerance value.

Parameters:

tolerance - Tolerance value.

Throws:

IllegalArgumentException - Raised if tolerance is negative.