nag_airy_ai_deriv_vector (s17awc) (PDF version)
s Chapter Contents
s Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_airy_ai_deriv_vector (s17awc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_airy_ai_deriv_vector (s17awc) returns an array of values of the derivative of the Airy function Aix.

2  Specification

#include <nag.h>
#include <nags.h>
void  nag_airy_ai_deriv_vector (Integer n, const double x[], double f[], Integer ivalid[], NagError *fail)

3  Description

nag_airy_ai_deriv_vector (s17awc) evaluates an approximation to the derivative of the Airy function Aixi for an array of arguments xi, for i=1,2,,n. It is based on a number of Chebyshev expansions.
For x<-5,
Aix=-x4 atcosz+btζsinz ,
where z= π4+ζ, ζ= 23-x3 and at and bt are expansions in variable t=-2 5x 3-1.
For -5x0,
Aix=x2ft-gt,
where f and g are expansions in t=-2 x5 3-1.
For 0<x<4.5,
Aix=e-11x/8yt,
where yt is an expansion in t=4 x9-1.
For 4.5x<9,
Aix=e-5x/2vt,
where vt is an expansion in t=4 x9-3.
For x9,
Aix = x 4 e-z ut ,
where z= 23x3 and ut is an expansion in t=2 18z-1.
For x< the square of the machine precision, the result is set directly to Ai0. This both saves time and avoids possible intermediate underflows.
For large negative arguments, it becomes impossible to calculate a result for the oscillating function with any accuracy and so the function must fail. This occurs for x<- πε 4/7 , where ε is the machine precision.
For large positive arguments, where Ai decays in an essentially exponential manner, there is a danger of underflow so the function must fail.

4  References

Abramowitz M and Stegun I A (1972) Handbook of Mathematical Functions (3rd Edition) Dover Publications

5  Arguments

1:     nIntegerInput
On entry: n, the number of points.
Constraint: n0.
2:     x[n]const doubleInput
On entry: the argument xi of the function, for i=1,2,,n.
3:     f[n]doubleOutput
On exit: Aixi, the function values.
4:     ivalid[n]IntegerOutput
On exit: ivalid[i-1] contains the error code for xi, for i=1,2,,n.
ivalid[i-1]=0
No error.
ivalid[i-1]=1
xi is too large and positive. f[i-1] contains zero. The threshold value is the same as for fail.code= NE_REAL_ARG_GT in nag_airy_ai_deriv (s17ajc), as defined in the Users' Note for your implementation.
ivalid[i-1]=2
xi is too large and negative. f[i-1] contains zero. The threshold value is the same as for fail.code= NE_REAL_ARG_LT in nag_airy_ai_deriv (s17ajc), as defined in the Users' Note for your implementation.
5:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, n=value.
Constraint: n0.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NW_IVALID
On entry, at least one value of x was invalid.
Check ivalid for more information.

7  Accuracy

For negative arguments the function is oscillatory and hence absolute error is the appropriate measure. In the positive region the function is essentially exponential in character and here relative error is needed. The absolute error, E, and the relative error, ε, are related in principle to the relative error in the argument, δ, by
E x2 Aix δε x2 Aix Aix δ.
In practice, approximate equality is the best that can be expected. When δ, ε or E is of the order of the machine precision, the errors in the result will be somewhat larger.
For small x, positive or negative, errors are strongly attenuated by the function and hence will be roughly bounded by the machine precision.
For moderate to large negative x, the error, like the function, is oscillatory; however the amplitude of the error grows like
x7/4π.
Therefore it becomes impossible to calculate the function with any accuracy if x7/4> πδ .
For large positive x, the relative error amplification is considerable:
εδx3.
However, very large arguments are not possible due to the danger of underflow. Thus in practice error amplification is limited.

8  Further Comments

None.

9  Example

This example reads values of x from a file, evaluates the function at each value of xi and prints the results.

9.1  Program Text

Program Text (s17awce.c)

9.2  Program Data

Program Data (s17awce.d)

9.3  Program Results

Program Results (s17awce.r)


nag_airy_ai_deriv_vector (s17awc) (PDF version)
s Chapter Contents
s Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012