NAG Library Routine Document

S13AAF

Warning. The specification of the parameter X changed at Mark 21:

X < 0.0

is no longer regarded as an input error.

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

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

S13AAF returns the value of the exponential integral

E_{1} (x)

, via the function name.

2 Specification

FUNCTION S13AAF (

X, IFAIL)

REAL (KIND=nag_wp) S13AAF

INTEGER	IFAIL
REAL (KIND=nag_wp)	X

3 Description

S13AAF calculates an approximate value for

E_{1} (x) = - Ei (- x) = \int_{x}^{\infty} \frac{e^{- u}}{u} d u .

using Chebyshev expansions, where

x

is real. For

x < 0

, the real part of the principal value of the integral is taken. The value

E_{1} (0)

is infinite, and so, when

x = 0

, S13AAF exits with an error and returns the largest representable machine number.

For

0 < x \leq 4

E_{1} (x) = y (t) - \ln x = \underset{r}{\sum^{'}} a_{r} T_{r} (t) - \ln x,

where

t = \frac{1}{2} x - 1

For

x > 4

E_{1} (x) = \frac{e^{- x}}{x} y (t) = \frac{e^{- x}}{x} \underset{r}{\sum^{'}} a_{r} T_{r} (t),

where

t = - 1.0 + \frac{14.5}{(x + 3.25)} = \frac{11.25 - x}{3.25 + x}

In both cases,

- 1 \leq t \leq + 1

For

x < 0

, the approximation is based on expansions proposed by Cody and Thatcher Jr. (1969). Precautions are taken to maintain good relative accuracy in the vicinity of

x_{0} \approx - 0.372507 \dots

, which corresponds to a simple zero of Ei(

- x

S13AAF guards against producing underflows and overflows by using the parameter

x_{hi}

; see the Users' Note for your implementation for the value of

x_{hi}

. To guard against overflow, if

x < - x_{hi}

the routine terminates and returns the negative of the largest representable machine number. To guard against underflow, if

x > x_{hi}

the result is set directly to zero.

4 References

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

Cody W J and Thatcher Jr. H C (1969) Rational Chebyshev approximations for the exponential integral Ei

(x)

Math. Comp. 23 289–303

5 Parameters

1: $X$ – REAL (KIND=nag_wp)Input: On entry: the argument $x$ of the function.

Constraint: $- x_{hi} \leq X < 0.0$ or $X > 0.0$ .
2: $IFAIL$ – INTEGERInput/Output: On entry: IFAIL must be set to $0$ , $- 1 or 1$ . If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value $- 1 or 1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is $0$ . When the value $- 1 or 1$ is used it is essential to test the value of IFAIL on exit.

On exit: $IFAIL = 0$ unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

IFAIL = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by X04AAF).

Errors or warnings detected by the routine:

$IFAIL = 1$: On entry, $X = 0.0$ and the function is infinite. The result returned is the largest representable machine number.

$IFAIL = 2$: The evaluation has been abandoned due to the likelihood of overflow. The argument $X < - x_{hi}$ , and the result is returned as the negative of the largest representable machine number.

$IFAIL = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.

$IFAIL = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.

$IFAIL = - 999$: Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7 Accuracy

Unless stated otherwise, it is assumed that

x > 0

δ

and

ε

are the relative errors in argument and result respectively, then in principle,

|ε| ≃ |\frac{e^{- x}}{E_{1} (x)} \times δ|

so the relative error in the argument is amplified in the result by at least a factor

e^{- x} / E_{1} (x)

. The equality should hold if

δ

is greater than the machine precision (

δ

due to data errors etc.) but if

δ

is simply a result of round-off in the machine representation, it is possible that an extra figure may be lost in internal calculation and round-off.

The behaviour of this amplification factor is shown in the following graph:

Figure 1

It should be noted that, for absolutely small

x

, the amplification factor tends to zero and eventually the error in the result will be limited by machine precision.

For absolutely large

x

ε \sim x δ = Δ,

the absolute error in the argument.

For

x < 0

, empirical tests have shown that the maximum relative error is a loss of approximately

1

decimal place.

8 Parallelism and Performance

Not applicable.

9 Further Comments

None.

10 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.

NAG Library Routine DocumentS13AAF