com.imsl.math
Class Quadrature

java.lang.Object
  com.imsl.math.Quadrature

All Implemented Interfaces:

Cloneable, Serializable

public class Quadrature

extends Object

implements Serializable, Cloneable

Quadrature is a general-purpose integrator that uses a globally adaptive scheme in order to reduce the absolute error. It subdivides the interval [A, B] and uses a
(2k + 1)-point Gauss-Kronrod rule to estimate the integral over each subinterval. The error for each subinterval is estimated by comparison with the k-point Gauss quadrature rule. The subinterval with the largest estimated error is then bisected and the same procedure is applied to both halves. The bisection process is continued until either the error criterion is satisfied, roundoff error is detected, the subintervals become too small, or the maximum number of subintervals allowed is reached. The Class Quadrature is based on the subroutine QAG by Piessens et al. (1983).

Reference

Piessens, R., E. deDoncker-Kapenga, C.W. Überhuber, and D.K. Kahaner (1983), QUADPACK, Springer-Verlag, New York.

See Also:

Integral of exp(2x), Integral of exp(-x) from 0 to infinity, Integral from -infinity to +infinity, Integral of an oscillatory function, Serialized Form

Nested Class Summary

static interface

Quadrature.Function Public interface function for the Quadrature class.

Constructor Summary

Quadrature()
Constructs a Quadrature object.

Method Summary

double	eval(Quadrature.Function objectF, double a, double b) Returns the value of the integral from a to b.
double	getErrorEstimate() Returns an estimate of the relative error in the computed result.
int	getErrorStatus() Returns the non-fatal error status.
void	setAbsoluteError(double errorAbsolute) Sets the absolute error tolerance.
void	setExtrapolation(boolean doExtrapolation) If true, the epsilon-algorithm for extrapolation is enabled.
void	setMaxSubintervals(int maxSubintervals) Sets the maximum number of subintervals allowed.
void	setRelativeError(double errorRelative) Sets the relative error tolerance.
void	setRule(int rule) Set the Gauss-Kronrod rule.

Methods inherited from class java.lang.Object

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

Constructor Detail

Quadrature

public Quadrature()

Constructs a Quadrature object.

Method Detail

eval

public double eval(Quadrature.Function objectF,
                   double a,
                   double b)

Returns the value of the integral from a to b.

Parameters:

objectF - an implementation of Function containing the function to be integrated

a - a double specifying the lower limit of integration

b - a double specifying the upper limit of integration, either or both of a and b can be Double.POSITIVE_INFINITY or Double.NEGATIVE_INFINITY

getErrorEstimate

public double getErrorEstimate()

Returns an estimate of the relative error in the computed result.

Returns:

a double specifying an estimate of the relative error in the computed result

getErrorStatus

public int getErrorStatus()

Returns the non-fatal error status.

Returns:

an int specifying the non-fatal error status:

Status	Meaning
1	Maximum number of subdivisions allowed has been achieved. One can allow more subdivisions by using setMaxSubintervals. If this yields no improvement it is advised to analyze the integrand in order to determine the integration difficulties. If the position of a local difficulty can be determined (e.g. singularity, discontinuity within the interval) one will probably gain from splitting up the interval at this point and calling the integrator on the subranges. If possible, an appropriate special-purpose integrator should be used, which is designed for handling the type of difficulty involved.
2	The occurrence of roundoff error is detected, which prevents the requested tolerance from being achieved. The error may be under-estimated.
3	Extremely bad integrand behavior occurs at some points of the integration interval.
5	The algorithm does not converge. Roundoff error is detected in the extrapolation table. It is presumed that the requested tolerance cannot be achieved, and that the returned result is the best that can be obtained.
6	The integral is probably divergent, or slowly convergent. It must be noted that divergence can occur with any other status value.

setAbsoluteError

public void setAbsoluteError(double errorAbsolute)

Sets the absolute error tolerance.

Parameters:

errorAbsolute - a double scalar value specifying the absolute error

setExtrapolation

public void setExtrapolation(boolean doExtrapolation)

If true, the epsilon-algorithm for extrapolation is enabled. The default is false (extrapolation is not used).

Parameters:

doExtrapolation - a boolean, true if the epsilon-algorithm for extrapolation is to be enabled, false otherwise

setMaxSubintervals

public void setMaxSubintervals(int maxSubintervals)

Sets the maximum number of subintervals allowed. The default value is 500.

Parameters:

maxSubintervals - an int specifying the maximum number of subintervals to be allowed. The default is 500.

setRelativeError

public void setRelativeError(double errorRelative)

Sets the relative error tolerance.

Parameters:

errorRelative - a double scalar value specifying the relative error

setRule

public void setRule(int rule)

Set the Gauss-Kronrod rule.

Rule	Data points used
1	7 - 15
2	10 - 21
3	15 - 31
4	20 - 41
5	25 - 51
6	30 - 61

The default is rule 3.

Parameters:

rule - an int specifying the rule to be used. The default is 3.