NAG Library Routine Document
s17ajf
(airy_ai_deriv)
1
Purpose
s17ajf returns a value of the derivative of the Airy function $\mathrm{Ai}\left(x\right)$, via the function name.
2
Specification
Fortran Interface
Real (Kind=nag_wp)  ::  s17ajf  Integer, Intent (Inout)  :: 
ifail  Real (Kind=nag_wp), Intent (In)  :: 
x 

C Header Interface
#include nagmk26.h
double 
s17ajf_ (
const double *x,
Integer *ifail) 

3
Description
s17ajf evaluates an approximation to the derivative of the Airy function $\mathrm{Ai}\left(x\right)$. It is based on a number of Chebyshev expansions.
For
$x<5$,
where
$z=\frac{\pi}{4}+\zeta $,
$\zeta =\frac{2}{3}\sqrt{{x}^{3}}$ and
$a\left(t\right)$ and
$b\left(t\right)$ are expansions in variable
$t=2{\left(\frac{5}{x}\right)}^{3}1$.
For
$5\le x\le 0$,
where
$f$ and
$g$ are expansions in
$t=2{\left(\frac{x}{5}\right)}^{3}1$.
For
$0<x<4.5$,
where
$y\left(t\right)$ is an expansion in
$t=4\left(\frac{x}{9}\right)1$.
For
$4.5\le x<9$,
where
$v\left(t\right)$ is an expansion in
$t=4\left(\frac{x}{9}\right)3$.
For
$x\ge 9$,
where
$z=\frac{2}{3}\sqrt{{x}^{3}}$ and
$u\left(t\right)$ is an expansion in
$t=2\left(\frac{18}{z}\right)1$.
For $\leftx\right<\text{}$ the square of the machine precision, the result is set directly to ${\mathrm{Ai}}^{\prime}\left(0\right)$. 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 routine must fail. This occurs for $x<{\left(\frac{\sqrt{\pi}}{\epsilon}\right)}^{4/7}$, where $\epsilon $ is the machine precision.
For large positive arguments, where ${\mathrm{Ai}}^{\prime}$ decays in an essentially exponential manner, there is a danger of underflow so the routine must fail.
4
References
Abramowitz M and Stegun I A (1972) Handbook of Mathematical Functions (3rd Edition) Dover Publications
5
Arguments
 1: $\mathbf{x}$ – Real (Kind=nag_wp)Input

On entry: the argument $x$ of the function.
 2: $\mathbf{ifail}$ – IntegerInput/Output

On entry:
ifail must be set to
$0$,
$1\text{ or}1$. If you are unfamiliar with this argument you should refer to
Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{ or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{ or}\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit:
${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
${\mathbf{ifail}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
 ${\mathbf{ifail}}=1$

x is too large and positive. On soft failure, the routine returns zero. (see the
Users' Note for your implementation for details)
 ${\mathbf{ifail}}=2$

x is too large and negative. On soft failure, the routine returns zero. See also the
Users' Note for your implementation.
 ${\mathbf{ifail}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 3.9 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 3.8 in How to Use the NAG Library and its Documentation for further information.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 3.7 in How to Use the NAG Library and its Documentation for further 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,
$\epsilon $, are related in principle to the relative error in the argument,
$\delta $, by
In practice, approximate equality is the best that can be expected. When
$\delta $,
$\epsilon $ 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
Therefore it becomes impossible to calculate the function with any accuracy if
${\leftx\right}^{7/4}>\frac{\sqrt{\pi}}{\delta}$.
For large positive
$x$, the relative error amplification is considerable:
However, very large arguments are not possible due to the danger of underflow. Thus in practice error amplification is limited.
8
Parallelism and Performance
s17ajf is not threaded in any implementation.
None.
10
Example
This example reads values of the argument $x$ from a file, evaluates the function at each value of $x$ and prints the results.
10.1
Program Text
Program Text (s17ajfe.f90)
10.2
Program Data
Program Data (s17ajfe.d)
10.3
Program Results
Program Results (s17ajfe.r)