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NAG Toolbox: nag_specfun_fresnel_s (s20ac)
Purpose
nag_specfun_fresnel_s (s20ac) returns a value for the Fresnel integral , via the function name.
Syntax
Description
nag_specfun_fresnel_s (s20ac) evaluates an approximation to the Fresnel integral
Note: , so the approximation need only consider
.
The function is based on three Chebyshev expansions:
For
,
For
,
where
,
and ,
with .
For small
,
. This approximation is used when
is sufficiently small for the result to be correct to
machine precision. For very small
, this approximation would underflow; the result is then set exactly to zero.
For large , and . Therefore for moderately large , when is negligible compared with , the second term in the approximation for may be dropped. For very large , when becomes negligible, . However there will be considerable difficulties in calculating accurately before this final limiting value can be used. Since is periodic, its value is essentially determined by the fractional part of . If where is an integer and , then depends on and on modulo . By exploiting this fact, it is possible to retain significance in the calculation of either all the way to the very large limit, or at least until the integer part of is equal to the maximum integer allowed on the machine.
References
Abramowitz M and Stegun I A (1972) Handbook of Mathematical Functions (3rd Edition) Dover Publications
Parameters
Compulsory Input Parameters
- 1:
– double scalar
-
The argument of the function.
Optional Input Parameters
None.
Output Parameters
- 1:
– double scalar
The result of the function.
- 2:
– int64int32nag_int scalar
unless the function detects an error (see
Error Indicators and Warnings).
Error Indicators and Warnings
There are no failure exits from
nag_specfun_fresnel_s (s20ac). The argument
ifail has been included for consistency with other functions in this chapter.
Accuracy
Let and be the relative errors in the argument and result respectively.
If
is somewhat larger than the
machine precision (i.e., if
is due to data errors etc.), then
and
are approximately related by:
Figure 1 shows the behaviour of the error amplification factor
.
However if
is of the same order as the
machine precision, then rounding errors could make
slightly larger than the above relation predicts.
For small , and hence there is only moderate amplification of relative error. Of course for very small where the correct result would underflow and exact zero is returned, relative error-control is lost.
For moderately large values of
,
and the result will be subject to increasingly large amplification of errors. However the above relation breaks down for large values of
(i.e., when
is of the order of the
machine precision); in this region the relative error in the result is essentially bounded by
.
Hence the effects of error amplification are limited and at worst the relative error loss should not exceed half the possible number of significant figures.
Further Comments
None.
Example
This example reads values of the argument from a file, evaluates the function at each value of and prints the results.
Open in the MATLAB editor:
s20ac_example
function s20ac_example
fprintf('s20ac example results\n\n');
x = [0 0.5 1 2 4 5 6 8 10 -1 1000];
n = size(x,2);
result = x;
for j=1:n
[result(j), ifail] = s20ac(x(j));
end
disp(' x S(x)');
fprintf('%12.3e%12.3e\n',[x; result]);
s20ac_plot;
function s20ac_plot
x = [-10:0.02:10];
for j = 1:numel(x)
[S(j), ifail] = s20ac(x(j));
end
fig1 = figure;
plot(x,S,'-r');
xlabel('x');
ylabel('S(x)');
title('Fresnel Integral S(x)');
s20ac example results
x S(x)
0.000e+00 0.000e+00
5.000e-01 6.473e-02
1.000e+00 4.383e-01
2.000e+00 3.434e-01
4.000e+00 4.205e-01
5.000e+00 4.992e-01
6.000e+00 4.470e-01
8.000e+00 4.602e-01
1.000e+01 4.682e-01
-1.000e+00 -4.383e-01
1.000e+03 4.997e-01
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