NAG Library Function Document

nag_fresnel_c_vector (s20arc)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

+− 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1 Purpose

nag_fresnel_c_vector (s20arc) returns an array of values for the Fresnel integral

C (x)

2 Specification

#include <nag.h>

#include <nags.h>

void	nag_fresnel_c_vector (Integer n, const double x[], double f[], NagError *fail)

3 Description

nag_fresnel_c_vector (s20arc) evaluates an approximation to the Fresnel integral

C (x_{i}) = \int_{0}^{x_{i}} \cos (\frac{π}{2} t^{2}) d t

for an array of arguments

x_{i}

, for

i = 1, 2, \dots, n

Note:

C (x) = - C (- x)

, so the approximation need only consider

x \geq 0.0

The function is based on three Chebyshev expansions:

For

0 < x \leq 3

C (x) = x \underset{r = 0}{\sum^{'}} a_{r} T_{r} (t),   with ​ t = 2 {(\frac{x}{3})}^{4} - 1 .

For

x > 3

C (x) = \frac{1}{2} + \frac{f (x)}{x} \sin (\frac{π}{2} x^{2}) - \frac{g (x)}{x^{3}} \cos (\frac{π}{2} x^{2}),

where

f (x) = \underset{r = 0}{\sum^{'}} b_{r} T_{r} (t)

and

g (x) = \underset{r = 0}{\sum^{'}} c_{r} T_{r} (t)

with

t = 2 {(\frac{3}{x})}^{4} - 1

For small

x

C (x) ≃ x

. This approximation is used when

x

is sufficiently small for the result to be correct to machine precision.

For large

x

f (x) ≃ \frac{1}{π}

and

g (x) ≃ \frac{1}{π^{2}}

. Therefore for moderately large

x

, when

\frac{1}{π^{2} x^{3}}

is negligible compared with

\frac{1}{2}

, the second term in the approximation for

x > 3

may be dropped. For very large

x

, when

\frac{1}{π x}

becomes negligible,

C (x) ≃ \frac{1}{2}

. However there will be considerable difficulties in calculating

\sin (\frac{π}{2} x^{2})

accurately before this final limiting value can be used. Since

\sin (\frac{π}{2} x^{2})

is periodic, its value is essentially determined by the fractional part of

x^{2}

. If

x^{2} = N + θ

, where

N

is an integer and

0 \leq θ < 1

, then

\sin (\frac{π}{2} x^{2})

depends on

θ

and on

N

modulo

4

. By exploiting this fact, it is possible to retain some significance in the calculation of

\sin (\frac{π}{2} x^{2})

either all the way to the very large

x

limit, or at least until the integer part of

\frac{x}{2}

is equal to the maximum integer allowed on the machine.

4 References

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

5 Arguments

1: n – IntegerInput: On entry: $n$ , the number of points.
Constraint: $n \geq 0$ .
2: x[n] – const doubleInput: On entry: the argument $x_{i}$ of the function, for $i = 1, 2, \dots, n$ .
3: f[n] – doubleOutput: On exit: $C (x_{i})$ , the function values.
4: fail – NagError *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: $n \geq 0$ .
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.

7 Accuracy

Let

δ

and

ε

be the relative errors in the argument and result respectively.

δ

is somewhat larger than the machine precision (i.e if

δ

is due to data errors etc.), then

ε

and

δ

are approximately related by:

ε ≃ |\frac{x \cos (\frac{π}{2} x^{2})}{C (x)}| δ .

Figure 1 shows the behaviour of the error amplification factor

|\frac{x \cos (\frac{π}{2} x^{2})}{C (x)}|

However, if

δ

is of the same order as the machine precision, then rounding errors could make

ε

slightly larger than the above relation predicts.

For small

x

ε ≃ δ

and there is no amplification of relative error.

For moderately large values of

x

ε ≃ |2 x \cos (\frac{π}{2} x^{2})| δ

and the result will be subject to increasingly large amplification of errors. However the above relation breaks down for large values of

x

(i.e., when

\frac{1}{x^{2}}

is of the order of the machine precision); in this region the relative error in the result is essentially bounded by

\frac{2}{π x}

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.

Figure 1

8 Parallelism and Performance

Not applicable.

9 Further Comments

None.

10 Example

This example reads values of x from a file, evaluates the function at each value of

x_{i}

and prints the results.

NAG Library Function Documentnag_fresnel_c_vector (s20arc)