Package com.irurueta.numerical

Class DirectionalDerivativeEvaluator

java.lang.Object

com.irurueta.numerical.DirectionalEvaluator

com.irurueta.numerical.DirectionalDerivativeEvaluator

public class DirectionalDerivativeEvaluator
extends DirectionalEvaluator

This class evaluates a multidimensional function and obtains its gradient along a line; such line is defined by an input point and a given direction. Using provided input point and direction, the multidimensional function's input parameters are determined, so they all lay on a line.

Field Summary

Fields

Modifier and Type	Field	Description
(package private) final double[]	dft	Array containing gradient at point p.
private GradientFunctionEvaluatorListener	gradientListener	Listener to evaluate a multidimensional function's gradient.

Fields inherited from class com.irurueta.numerical.DirectionalEvaluator

direction, listener, p, point

Constructor Summary

Constructors

Constructor	Description
DirectionalDerivativeEvaluator(MultiDimensionFunctionEvaluatorListener listener, GradientFunctionEvaluatorListener gradientListener, double[] point, double[] direction)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	differentiateAt(double x)	Computes derivative on current direction of a function at distance x from current point and using current listener and gradient listener.
GradientFunctionEvaluatorListener	getGradientListener()	Returns gradient listener that evaluates a multidimensional function gradient.
void	setGradientListener(GradientFunctionEvaluatorListener gradientListener)	Sets gradient listener that evaluates a multidimensional function gradient.

Methods inherited from class com.irurueta.numerical.DirectionalEvaluator

evaluateAt, getDirection, getListener, getPoint, setListener, setPointAndDirection

Methods inherited from class java.lang.Object

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

Field Details

gradientListener

private GradientFunctionEvaluatorListener gradientListener

Listener to evaluate a multidimensional function's gradient.

dft

final double[] dft

Array containing gradient at point p. This is used internally.

Constructor Details

DirectionalDerivativeEvaluator

public DirectionalDerivativeEvaluator(MultiDimensionFunctionEvaluatorListener listener,
 GradientFunctionEvaluatorListener gradientListener,
 double[] point,
 double[] direction)

Constructor.

Parameters:

listener - Listener to evaluate a multidimensional function.

gradientListener - Listener to evaluate a multidimensional function's gradient.

point - Point used as a reference to determine the function's input parameters along a line.

direction - Vector indicating the direction of the line where the function is evaluated.

Throws:

IllegalArgumentException - Raised if point and direction don't have the same length.

Method Details

getGradientListener

public GradientFunctionEvaluatorListener getGradientListener()

Returns gradient listener that evaluates a multidimensional function gradient. If the gradient expression is not known (e.g. is not a closed expression), then a GradientEstimator can be used internally in the listener implementation.

Returns:

Gradient listener.

setGradientListener

public void setGradientListener(GradientFunctionEvaluatorListener gradientListener)

Sets gradient listener that evaluates a multidimensional function gradient. If the gradient expression is not known (e.g. is not a closed expression), then a GradientEstimator can be used internally in the listener implementation being provided.

Parameters:

gradientListener - Gradient listener

differentiateAt

public double differentiateAt(double x)
                       throws EvaluationException

Computes derivative on current direction of a function at distance x from current point and using current listener and gradient listener.

Parameters:

x - Distance from current point using current direction where function is being evaluated.

Returns:

Result of evaluating function.

Throws:

EvaluationException - Thrown if function evaluation fails.