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NAG Toolbox: nag_inteq_abel_weak_weights (d05by)
Purpose
nag_inteq_abel_weak_weights (d05by) computes the fractional quadrature weights associated with the Backward Differentiation Formulae (BDF) of orders , and . These weights can then be used in the solution of weakly singular equations of Abel type.
Syntax
Description
nag_inteq_abel_weak_weights (d05by) computes the weights
and
for a family of quadrature rules related to a BDF method for approximating the integral:
with
, for some given
. In
(1),
is the order of the BDF method used and
,
are the fractional starting and the fractional convolution weights respectively. The algorithm for the generation of
is based on Newton's iteration. Fast Fourier transform (FFT) techniques are used for computing these weights and subsequently
(see
Baker and Derakhshan (1987) and
Henrici (1979) for practical details and
Lubich (1986) for theoretical details). Some special functions can be represented as the fractional integrals of simpler functions and fractional quadratures can be employed for their computation (see
Lubich (1986)). A description of how these weights can be used in the solution of weakly singular equations of Abel type is given in
Further Comments.
References
Baker C T H and Derakhshan M S (1987) Computational approximations to some power series Approximation Theory (eds L Collatz, G Meinardus and G Nürnberger) 81 11–20
Henrici P (1979) Fast Fourier methods in computational complex analysis SIAM Rev. 21 481–529
Lubich Ch (1986) Discretized fractional calculus SIAM J. Math. Anal. 17 704–719
Parameters
Compulsory Input Parameters
- 1:
– int64int32nag_int scalar
-
, the order of the BDF method to be used.
Constraint:
.
- 2:
– int64int32nag_int scalar
-
Determines the number of weights to be computed. By setting
iq to a value,
fractional convolution weights are computed.
Constraint:
.
- 3:
– int64int32nag_int scalar
-
The dimension of the array
wt.
Constraint:
.
Optional Input Parameters
None.
Output Parameters
- 1:
– double array
-
The first
elements of
wt contains the fractional convolution weights
, for
. The remainder of the array is used as workspace.
- 2:
– double array
-
contains the fractional starting weights , for and , where .
- 3:
– int64int32nag_int scalar
unless the function detects an error (see
Error Indicators and Warnings).
Error Indicators and Warnings
Errors or warnings detected by the function:
-
-
On entry, | or , |
or | , |
or | , |
or | , |
or | . |
-
An unexpected error has been triggered by this routine. Please
contact
NAG.
-
Your licence key may have expired or may not have been installed correctly.
-
Dynamic memory allocation failed.
Accuracy
Not applicable.
Further Comments
Fractional quadrature weights can be used for solving weakly singular integral equations of Abel type. In this section, we propose the following algorithm which you may find useful in solving a linear weakly singular integral equation of the form
using
nag_inteq_abel_weak_weights (d05by). In
(2),
and
are given and the solution
is sought on a uniform mesh of size
such that
. Discretization of
(2) yields
where
, for
. We propose the following algorithm for computing
from
(3) after a call to
nag_inteq_abel_weak_weights (d05by):
(a) |
Set and . |
(b) |
Equation (3) requires starting values, , for , with . These starting values can be computed by solving the system
|
(c) |
Compute the inhomogeneous terms
|
(d) |
Start the iteration for to compute from:
|
Note that for nonlinear weakly singular equations, the solution of a nonlinear algebraic system is required at step
(b) and a single nonlinear equation at step
(d).
Example
The following example generates the first fractional convolution and fractional starting weights generated by the fourth-order BDF method.
Open in the MATLAB editor:
d05by_example
function d05by_example
fprintf('d05by example results\n\n');
iorder = int64(4);
iq = int64(3);
lenfw = int64(32);
[wt, sw, ifail] = d05by( ...
iorder, iq, lenfw);
fprintf('\nFractional convolution weights\n\n');
itiq = double(2^(iq+1));
n = [0:itiq-1]';
w(1:itiq) = wt(1:itiq,1);
fprintf('%3d %10.4f\n',[n w']');
fprintf('\nFractional starting weights W\n\n');
ldsw = double(itiq+2*iorder-1);
n = [0:ldsw-1]';
fprintf('%5d%9.4f%9.4f%9.4f%9.4f%9.4f%9.4f%9.4f\n',[n sw]');
d05by example results
Fractional convolution weights
0 0.6928
1 0.6651
2 0.4589
3 0.3175
4 0.2622
5 0.2451
6 0.2323
7 0.2164
8 0.2006
9 0.1878
10 0.1780
11 0.1700
12 0.1629
13 0.1566
14 0.1508
15 0.1457
Fractional starting weights W
0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
1 0.0565 2.8928 -6.7497 11.6491 -11.1355 5.5374 -1.1223
2 0.0371 1.7401 -2.8628 6.5207 -6.4058 3.2249 -0.6583
3 0.0300 1.3207 -2.4642 6.3612 -5.4478 2.7025 -0.5481
4 0.0258 1.1217 -2.2620 5.3683 -3.7553 2.2132 -0.4549
5 0.0230 0.9862 -2.0034 4.5005 -3.2772 2.7262 -0.4320
6 0.0208 0.9001 -1.8989 4.2847 -3.5881 2.8201 0.2253
7 0.0190 0.8506 -1.9250 4.4164 -4.0181 2.7932 0.1564
8 0.0173 0.8177 -1.9697 4.5348 -4.2425 2.7458 -0.0697
9 0.0160 0.7886 -1.9781 4.5318 -4.2769 2.6997 -0.2127
10 0.0149 0.7603 -1.9548 4.4545 -4.2332 2.6541 -0.2620
11 0.0140 0.7338 -1.9198 4.3619 -4.1782 2.6059 -0.2716
12 0.0132 0.7097 -1.8842 4.2754 -4.1246 2.5544 -0.2767
13 0.0125 0.6880 -1.8497 4.1933 -4.0662 2.5011 -0.2845
14 0.0119 0.6681 -1.8153 4.1109 -4.0004 2.4479 -0.2915
15 0.0114 0.6497 -1.7805 4.0279 -3.9304 2.3962 -0.2951
16 0.0110 0.6327 -1.7461 3.9463 -3.8598 2.3466 -0.2958
17 0.0105 0.6168 -1.7126 3.8677 -3.7907 2.2990 -0.2950
18 0.0102 0.6020 -1.6804 3.7926 -3.7238 2.2536 -0.2935
19 0.0098 0.5882 -1.6495 3.7209 -3.6589 2.2101 -0.2917
20 0.0095 0.5752 -1.6199 3.6523 -3.5961 2.1686 -0.2895
21 0.0093 0.5631 -1.5916 3.5867 -3.5356 2.1291 -0.2871
22 0.0090 0.5517 -1.5644 3.5240 -3.4774 2.0914 -0.2844
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