hide long namesshow long names
hide short namesshow short names
Integer type:  int32  int64  nag_int  show int32  show int32  show int64  show int64  show nag_int  show nag_int

PDF version (NAG web site, 64-bit version, 64-bit version)
Chapter Contents
Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_specfun_bessel_i0_real (s18ae)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_specfun_bessel_i0_real (s18ae) returns the value of the modified Bessel function I0x, via the function name.

Syntax

[result, ifail] = s18ae(x)
[result, ifail] = nag_specfun_bessel_i0_real(x)

Description

nag_specfun_bessel_i0_real (s18ae) evaluates an approximation to the modified Bessel function of the first kind I0x.
Note:  I0-x=I0x, so the approximation need only consider x0.
The function is based on three Chebyshev expansions:
For 0<x4,
I0x=exr=0arTrt,   where ​ t=2 x4 -1.  
For 4<x12,
I0x=exr=0brTrt,   where ​ t=x-84.  
For x>12,
I0x=exx r=0crTrt,   where ​ t=2 12x -1.  
For small x, I0x1. This approximation is used when x is sufficiently small for the result to be correct to machine precision.
For large x, the function must fail because of the danger of overflow in calculating ex.

References

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

Parameters

Compulsory Input Parameters

1:     x – double scalar
The argument x of the function.

Optional Input Parameters

None.

Output Parameters

1:     result – double scalar
The result of the function.
2:     ifail int64int32nag_int scalar
ifail=0 unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:
   ifail=1
x is too large. On soft failure the function returns the approximate value of I0x at the nearest valid argument.
   ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
   ifail=-399
Your licence key may have expired or may not have been installed correctly.
   ifail=-999
Dynamic memory allocation failed.

Accuracy

Let δ and ε be the relative errors in the argument and result respectively.
If 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 x I1x I0 x δ.  
Figure 1 shows the behaviour of the error amplification factor
xI1x I0x .  
Figure 1
Figure 1
However if δ is of the same order as machine precision, then rounding errors could make ε slightly larger than the above relation predicts.
For small x the amplification factor is approximately x22 , which implies strong attenuation of the error, but in general ε can never be less than the machine precision.
For large x, εxδ and we have strong amplification of errors. However the function must fail for quite moderate values of x, because I0x would overflow; hence in practice the loss of accuracy for large x is not excessive. Note that for large x the errors will be dominated by those of the standard function exp.

Further Comments

None.

Example

This example reads values of the argument x from a file, evaluates the function at each value of x and prints the results.
function s18ae_example


fprintf('s18ae example results\n\n');

x = [0    0.5    1    3    6    8    10    15    20   -1];
n = size(x,2);
result = x;

for j=1:n
  [result(j), ifail] = s18ae(x(j));
end

disp('      x          I_0(x)');
fprintf('%12.3e%12.3e\n',[x; result]);

s18ae_plot;



function s18ae_plot
  x = [0:0.2:4];
  for j = 1:numel(x)
    [I0(j), ifail] = s18ae(x(j));
  end

  fig1 = figure;
  plot(x,I0,'-r');
  xlabel('x');
  ylabel('I_0(x)');
  title('Bessel Function I_0(x)');
  axis([0 4 0 12]);

s18ae example results

      x          I_0(x)
   0.000e+00   1.000e+00
   5.000e-01   1.063e+00
   1.000e+00   1.266e+00
   3.000e+00   4.881e+00
   6.000e+00   6.723e+01
   8.000e+00   4.276e+02
   1.000e+01   2.816e+03
   1.500e+01   3.396e+05
   2.000e+01   4.356e+07
  -1.000e+00   1.266e+00
s18ae_fig1.png

PDF version (NAG web site, 64-bit version, 64-bit version)
Chapter Contents
Chapter Introduction
NAG Toolbox

© The Numerical Algorithms Group Ltd, Oxford, UK. 2009–2015