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NAG C Library Manual

# NAG Library Function Documentnag_airy_bi (s17ahc)

## 1  Purpose

nag_airy_bi (s17ahc) returns a value of the Airy function $\mathrm{Bi}\left(x\right)$.

## 2  Specification

 #include #include
 double nag_airy_bi (double x, NagError *fail)

## 3  Description

nag_airy_bi (s17ahc) evaluates an approximation to the Airy function $\mathrm{Bi}\left(x\right)$. It is based on a number of Chebyshev expansions.
For large negative arguments, it is impossible to calculate the phase of the oscillating function with any accuracy so the function evaluation must fail. This occurs if $x<-{\left(3/2\epsilon \right)}^{2/3}$, where $\epsilon$ is the machine precision.
For large positive arguments, there is a danger of causing overflow since $\mathrm{Bi}$ 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:     xdoubleInput
On entry: the argument $x$ of the function.
2:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

## 6  Error Indicators and Warnings

NE_REAL_ARG_GT
On entry, ${\mathbf{x}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{x}}\le 〈\mathit{\text{value}}〉$.
x is too large and positive. The function returns zero.
NE_REAL_ARG_LT
On entry, x must not be less than $〈\mathit{\text{value}}〉$: ${\mathbf{x}}=〈\mathit{\text{value}}〉$.
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-like and here relative error is appropriate. The absolute error, $E$, and the relative error, $\epsilon$, are related in principle to the relative error in the argument, $\delta$, by $E\simeq \left|x{\mathrm{Bi}}^{\prime }\left(x\right)\right|\delta$, $\epsilon \simeq \left|x{\mathrm{Bi}}^{\prime }\left(x\right)/\mathrm{Bi}\left(x\right)\right|\delta$.
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 $x$, errors are strongly damped and hence will be bounded essentially by the machine precision.
For moderate to large negative $x$, the error behaviour is clearly oscillatory but the amplitude of the error grows like $\text{amplitude}\left(E/\delta \right)\sim {\left|x\right|}^{5/4}/\sqrt{\pi }$.
However the phase error will be growing roughly as $2\sqrt{{\left|x\right|}^{3}}/3$ and hence all accuracy will be lost for large negative arguments. This is due to difficulty in calculating sin and cos to any accuracy if $\left(2\sqrt{{\left|x\right|}^{3}}/3\right)>1/\delta$.
For large positive arguments, the relative error amplification is considerable, $\epsilon /\delta \sim \sqrt{{x}^{3}}$.
This means a loss of roughly two decimal places accuracy for arguments in the region of 20. However, very large arguments are not possible due to the danger of causing overflow, and errors are therefore limited in practice.

None.

## 9  Example

The following program reads values of the argument $x$ from a file, evaluates the function at each value of $x$ and prints the results.

### 9.1  Program Text

Program Text (s17ahce.c)

### 9.2  Program Data

Program Data (s17ahce.d)

### 9.3  Program Results

Program Results (s17ahce.r)