NAG Library Function Document
nag_airy_bi (s17ahc)
1 Purpose
nag_airy_bi (s17ahc) returns a value of the Airy function, .
2 Specification
#include <nag.h> |
#include <nags.h> |
double |
nag_airy_bi (double x,
NagError *fail) |
|
3 Description
nag_airy_bi (s17ahc) evaluates an approximation to the Airy function . It is based on a number of Chebyshev expansions.
For
,
where
and
and
are expansions in the variable
.
For
,
where
and
are expansions in
.
For
,
where
is an expansion in
.
For
,
where
is an expansion in
.
For
,
where
and
is an expansion in
.
For , the result is set directly to . This both saves time and avoids possible intermediate underflows.
For large negative arguments, it becomes impossible to calculate the phase of the oscillating function with any accuracy so the function must fail. This occurs if , where is the machine precision.
For large positive arguments, there is a danger of causing overflow since Bi grows in an essentially exponential manner, 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:
x – doubleInput
On entry: the argument of the function.
- 2:
fail – NagError *Input/Output
-
The NAG error argument (see
Section 3.6 in the Essential Introduction).
6 Error Indicators and Warnings
- 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.
- NE_REAL_ARG_GT
-
On entry,
.
Constraint:
.
x is too large and positive. The function returns zero.
- NE_REAL_ARG_LT
-
On entry,
.
Constraint:
.
x is too large and negative. The function returns zero.
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-like and here relative error is appropriate. The absolute error,
, and the relative error,
, are related in principle to the relative error in the argument,
, by
In practice, approximate equality is the best that can be expected. When
,
or
is of the order of the
machine precision, the errors in the result will be somewhat larger.
For small , errors are strongly damped and hence will be bounded essentially by the machine precision.
For moderate to large negative , the error behaviour is clearly oscillatory but the amplitude of the error grows like amplitude .
However the phase error will be growing roughly as and hence all accuracy will be lost for large negative arguments. This is due to the impossibility of calculating sin and cos to any accuracy if .
For large positive arguments, the relative error amplification is considerable:
This means a loss of roughly two decimal places accuracy for arguments in the region of
. However very large arguments are not possible due to the danger of causing overflow and errors are therefore limited in practice.
8 Parallelism and Performance
Not applicable.
None.
10 Example
This example reads values of the argument from a file, evaluates the function at each value of and prints the results.
10.1 Program Text
Program Text (s17ahce.c)
10.2 Program Data
Program Data (s17ahce.d)
10.3 Program Results
Program Results (s17ahce.r)