org.apache.commons.math3.analysis.differentiation
public class FiniteDifferencesDifferentiator extends Object implements UnivariateFunctionDifferentiator, UnivariateVectorFunctionDifferentiator, UnivariateMatrixFunctionDifferentiator, Serializable
This class creates some wrapper objects around regular
univariate functions
(or univariate vector functions
or univariate matrix functions
). These
wrapper objects compute derivatives in addition to function
value.
The wrapper objects work by calling the underlying function on a sampling grid around the current point and performing polynomial interpolation. A finite differences scheme with n points is theoretically able to compute derivatives up to order n1, but it is generally better to have a slight margin. The step size must also be small enough in order for the polynomial approximation to be good in the current point neighborhood, but it should not be too small because numerical instability appears quickly (there are several differences of close points). Choosing the number of points and the step size is highly problem dependent.
As an example of good and bad settings, lets consider the quintic
polynomial function f(x) = (x1)*(x0.5)*x*(x+0.5)*(x+1)
.
Since it is a polynomial, finite differences with at least 6 points
should theoretically recover the exact same polynomial and hence
compute accurate derivatives for any order. However, due to numerical
errors, we get the following results for a 7 points finite differences
for abscissae in the [10, 10] range:
Constructor and Description 

FiniteDifferencesDifferentiator(int nbPoints,
double stepSize)
Build a differentiator with number of points and step size when independent variable is unbounded.

FiniteDifferencesDifferentiator(int nbPoints,
double stepSize,
double tLower,
double tUpper)
Build a differentiator with number of points and step size when independent variable is bounded.

Modifier and Type  Method and Description 

UnivariateDifferentiableFunction 
differentiate(UnivariateFunction function)
Create an implementation of a
differential from a regular function . 
UnivariateDifferentiableMatrixFunction 
differentiate(UnivariateMatrixFunction function)
Create an implementation of a
differential from a regular matrix function . 
UnivariateDifferentiableVectorFunction 
differentiate(UnivariateVectorFunction function)
Create an implementation of a
differential from a regular vector function . 
int 
getNbPoints()
Get the number of points to use.

double 
getStepSize()
Get the step size.

public FiniteDifferencesDifferentiator(int nbPoints, double stepSize) throws NotPositiveException, NumberIsTooSmallException
Beware that wrong settings for the finite differences differentiator can lead to highly unstable and inaccurate results, especially for high derivation orders. Using very small step sizes is often a bad idea.
nbPoints
 number of points to usestepSize
 step size (gap between each point)NotPositiveException
 if stepsize <= 0
(note that
NotPositiveException
extends NumberIsTooSmallException
)NumberIsTooSmallException
 nbPoint <= 1
public FiniteDifferencesDifferentiator(int nbPoints, double stepSize, double tLower, double tUpper) throws NotPositiveException, NumberIsTooSmallException, NumberIsTooLargeException
When the independent variable is bounded (tLower < t < tUpper), the sampling points used for differentiation will be adapted to ensure the constraint holds even near the boundaries. This means the sample will not be centered anymore in these cases. At an extreme case, computing derivatives exactly at the lower bound will lead the sample to be entirely on the right side of the derivation point.
Note that the boundaries are considered to be excluded for function evaluation.
Beware that wrong settings for the finite differences differentiator can lead to highly unstable and inaccurate results, especially for high derivation orders. Using very small step sizes is often a bad idea.
nbPoints
 number of points to usestepSize
 step size (gap between each point)tLower
 lower bound for independent variable (may be Double.NEGATIVE_INFINITY
if there are no lower bounds)tUpper
 upper bound for independent variable (may be Double.POSITIVE_INFINITY
if there are no upper bounds)NotPositiveException
 if stepsize <= 0
(note that
NotPositiveException
extends NumberIsTooSmallException
)NumberIsTooSmallException
 nbPoint <= 1
NumberIsTooLargeException
 stepSize * (nbPoints  1) >= tUpper  tLower
public int getNbPoints()
public double getStepSize()
public UnivariateDifferentiableFunction differentiate(UnivariateFunction function)
differential
from a regular function
.
The returned object cannot compute derivatives to arbitrary orders. The
value function will throw a NumberIsTooLargeException
if the requested
derivation order is larger or equal to the number of points.
differentiate
in interface UnivariateFunctionDifferentiator
function
 function to differentiatepublic UnivariateDifferentiableVectorFunction differentiate(UnivariateVectorFunction function)
differential
from a regular vector function
.
The returned object cannot compute derivatives to arbitrary orders. The
value function will throw a NumberIsTooLargeException
if the requested
derivation order is larger or equal to the number of points.
differentiate
in interface UnivariateVectorFunctionDifferentiator
function
 function to differentiatepublic UnivariateDifferentiableMatrixFunction differentiate(UnivariateMatrixFunction function)
differential
from a regular matrix function
.
The returned object cannot compute derivatives to arbitrary orders. The
value function will throw a NumberIsTooLargeException
if the requested
derivation order is larger or equal to the number of points.
differentiate
in interface UnivariateMatrixFunctionDifferentiator
function
 function to differentiateCopyright © 2003–2015 The Apache Software Foundation. All rights reserved.