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NAG Library Function Document

nag_airy_ai_deriv (s17ajc)

▸▿ 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_airy_ai_deriv (s17ajc) returns a value of the derivative of the Airy function

Ai (x)

2

Specification

#include <nag.h>

#include <nags.h>

double

nag_airy_ai_deriv (double x, NagError *fail)

3

Description

nag_airy_ai_deriv (s17ajc) evaluates an approximation to the derivative of the Airy function

Ai (x)

. It is based on a number of Chebyshev expansions.

For

x < - 5

{Ai}^{'} (x) = \sqrt[4]{- x} [a (t) \cos z + \frac{b (t)}{ζ} \sin z],

where

z = \frac{π}{4} + ζ

ζ = \frac{2}{3} \sqrt{- x^{3}}

and

a (t)

and

b (t)

are expansions in variable

t = - 2 {(\frac{5}{x})}^{3} - 1

For

- 5 \leq x \leq 0

{Ai}^{'} (x) = x^{2} f (t) - g (t),

where

f

and

g

are expansions in

t = - 2 {(\frac{x}{5})}^{3} - 1

For

0 < x < 4.5

{Ai}^{'} (x) = e^{- 11 x / 8} y (t),

where

y (t)

is an expansion in

t = 4 (\frac{x}{9}) - 1

For

4.5 \leq x < 9

{Ai}^{'} (x) = e^{- 5 x / 2} v (t),

where

v (t)

is an expansion in

t = 4 (\frac{x}{9}) - 3

For

x \geq 9

{Ai}^{'} (x) = \sqrt[4]{x} e^{- z} u (t),

where

z = \frac{2}{3} \sqrt{x^{3}}

and

u (t)

is an expansion in

t = 2 (\frac{18}{z}) - 1

For

|x| <

the square of the machine precision, the result is set directly to

{Ai}^{'} (0)

. 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 < - {(\frac{\sqrt{π}}{ε})}^{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: $x$ – doubleInput: On entry: the argument $x$ of the function.
2: $fail$ – NagError *Input/Output: The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
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.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_REAL_ARG_GT: On entry, $x = 〈value〉$ .
Constraint: $x \leq 〈value〉$ .
x is too large and positive. The function returns zero.
NE_REAL_ARG_LT: On entry, $x = 〈value〉$ .
Constraint: $x \geq 〈value〉$ .
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 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 ≃ |x^{2} Ai (x)| δ ε ≃ |\frac{x^{2} Ai (x)}{{Ai}^{'} (x)}| δ .

In practice, approximate equality is the best that can be expected. When

δ

ε

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

\frac{{|x|}^{7 / 4}}{\sqrt{π}} .

Therefore it becomes impossible to calculate the function with any accuracy if

{|x|}^{7 / 4} > \frac{\sqrt{π}}{δ}

For large positive

x

, the relative error amplification is considerable:

\frac{ε}{δ} ≃ \sqrt{x^{3}} .

However, very large arguments are not possible due to the danger of underflow. Thus in practice error amplification is limited.

8

Parallelism and Performance

nag_airy_ai_deriv (s17ajc) is not threaded in any implementation.

9

Further Comments

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.

NAG Library Function Document

nag_airy_ai_deriv (s17ajc)

▸▿ 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

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