g01rtf (PDF version)
G01 (stat) Chapter Contents
G01 (stat) Chapter Introduction
NAG Library Manual
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NAG Library Routine Document
g01rtf (pdf_landau_deriv)
▸
▿
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
g01rtf
returns the value of the derivative
ϕ
′
λ
of the Landau density function, via the routine name.
2
Specification
Fortran Interface
Function g01rtf (
x
)
Real (Kind=nag_wp)
::
g01rtf
Real (Kind=nag_wp), Intent (In)
::
x
C Header Interface
#include nagmk26.h
double
g01rtf_ (
const double *
x
)
3
Description
g01rtf
evaluates an approximation to the derivative
ϕ
′
λ
of the Landau density function given by
ϕ
′
λ
=
d
ϕ
λ
d
λ
,
where
ϕ
λ
is described in
g01mtf
, using piecewise approximation by rational functions. Further details can be found in
Kölbig and Schorr (1984)
.
To obtain the value of
ϕ
λ
,
g01mtf
can be used.
4
References
Kölbig K S and Schorr B (1984) A program package for the Landau distribution
Comp. Phys. Comm.
31
97–111
5
Arguments
1:
x
– Real (Kind=nag_wp)
Input
On entry
: the argument
λ
of the function.
6
Error Indicators and Warnings
None.
7
Accuracy
At least
7
significant digits are usually correct, but occasionally only
6
. Such accuracy is normally considered to be adequate for applications in experimental physics.
Because of the asymptotic behaviour of
ϕ
′
λ
, which is of the order of
exp
-
exp
-
λ
, underflow may occur on some machines when
λ
is moderately large and negative.
8
Parallelism and Performance
g01rtf
is not threaded in any implementation.
9
Further Comments
None.
10
Example
This example evaluates
ϕ
′
λ
at
λ
=
0.5
, and prints the results.
10.1
Program Text
Program Text (g01rtfe.f90)
10.2
Program Data
Program Data (g01rtfe.d)
10.3
Program Results
Program Results (g01rtfe.r)
g01rtf (PDF version)
G01 (stat) Chapter Contents
G01 (stat) Chapter Introduction
NAG Library Manual
© The Numerical Algorithms Group Ltd, Oxford, UK. 2017