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NAG Library Routine Document

d05bwf (volterra_weights)

▸▿ 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

d05bwf computes the quadrature weights associated with the Adams' methods of orders three to six and the Backward Differentiation Formulae (BDF) methods of orders two to five. These rules, which are referred to as reducible quadrature rules, can then be used in the solution of Volterra integral and integro-differential equations.

2

Specification

Fortran Interface

Subroutine d05bwf (

method, iorder, omega, nomg, lensw, sw, ldsw, nwt, ifail)

Integer, Intent (In)	::	iorder, nomg, ldsw, nwt
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	lensw
Real (Kind=nag_wp), Intent (Inout)	::	sw(ldsw,nwt)
Real (Kind=nag_wp), Intent (Out)	::	omega(nomg)
Character (1), Intent (In)	::	method

C Header Interface

#include nagmk26.h

void	d05bwf_ ( const char method, const Integer iorder, double omega[], const Integer nomg, Integer lensw, double sw[], const Integer ldsw, const Integer nwt, Integer *ifail, const Charlen length_method)

3

Description

d05bwf computes the weights

W_{i, j}

and

ω_{i}

for a family of quadrature rules related to the Adams' methods of orders three to six and the BDF methods of orders two to five, for approximating the integral:

\int_{0}^{t} ϕ (s) d s ≃ h \sum_{j = 0}^{p - 1} W_{i, j} ϕ (j \times h) + h \sum_{j = p}^{i} ω_{i - j} ϕ (j \times h), 0 \leq t \leq T,

(1)

with

t = i \times h

, for

i = 0, 1, \dots, n

, for some given constant

h

In (1),

h

is a uniform mesh,

p

is related to the order of the method being used and

W_{i, j}

ω_{i}

are the starting and the convolution weights respectively. The mesh size

h

is determined as

h = \frac{T}{n}

, where

n = n_{w} + p - 1

and

n_{w}

is the chosen number of convolution weights

w_{j}

, for

j = 1, 2, \dots, n_{w} - 1

. A description of how these weights can be used in the solution of a Volterra integral equation of the second kind is given in Section 9. For a general discussion of these methods, see Wolkenfelt (1982) for more details.

4

References

Lambert J D (1973) Computational Methods in Ordinary Differential Equations John Wiley

Wolkenfelt P H M (1982) The construction of reducible quadrature rules for Volterra integral and integro-differential equations IMA J. Numer. Anal. 2 131–152

5

Arguments

1: $method$ – Character(1)Input

On entry: the type of method to be used.

$method ='A'$: For Adams' type formulae.
$method ='B'$: For Backward Differentiation Formulae.

Constraint:

method ='A'

'B'

2: $iorder$ – IntegerInput

On entry: the order of the method to be used. The number of starting weights,

p

is determined by method and iorder.

method ='A'

p = iorder - 1

method ='B'

p = iorder

Constraints:

if $method ='A'$ , $3 \leq iorder \leq 6$ ;
if $method ='B'$ , $2 \leq iorder \leq 5$ .

3: $omega (nomg)$ – Real (Kind=nag_wp) arrayOutput

On exit: contains the first nomg convolution weights.

4: $nomg$ – IntegerInput

On entry: the number of convolution weights,

n_{w}

Constraint:

nomg \geq 1

5: $lensw$ – IntegerOutput

On exit: the number of rows in the weights

W_{i, j}

6: $sw (ldsw, nwt)$ – Real (Kind=nag_wp) arrayOutput

On exit:

sw (i, j + 1)

contains the weights

W_{i, j}

, for

i = 1, 2, \dots, lensw

and

j = 0, 1, \dots, nwt - 1

, where

n

is as defined in Section 3.

7: $ldsw$ – IntegerInput

On entry: the first dimension of the array sw as declared in the (sub)program from which d05bwf is called.

Constraints:

if $method ='A'$ , $ldsw \geq nomg + iorder - 2$ ;
if $method ='B'$ , $ldsw \geq nomg + iorder - 1$ .

8: $nwt$ – IntegerInput

On entry:

p

, the number of columns in the starting weights.

Constraints:

if $method ='A'$ , $nwt = iorder - 1$ ;
if $method ='B'$ , $nwt = iorder$ .

9: $ifail$ – IntegerInput/Output

On entry: ifail must be set to

0

- 1 ​ or ​ 1

. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation 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 argument, 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,

method \neq'A'

'B'

$ifail = 2$

On entry,	$iorder < 2$ or $iorder > 6$ ,
or	$nomg < 1$ .

$ifail = 3$

On entry,	$method ='A'$ and $iorder = 2$ ,
or	$method ='B'$ and $iorder = 6$ .

$ifail = 4$

On entry,	$method ='A'$ and $nwt \neq iorder - 1$ ,
or	$method ='B'$ and $nwt \neq iorder$ .

$ifail = 5$

On entry,	$method ='A'$ and $ldsw < nomg + iorder - 2$ ,
or	$method ='B'$ and $ldsw < nomg + iorder - 1$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

Not applicable.

8

Parallelism and Performance

d05bwf is not threaded in any implementation.

9

Further Comments

Reducible quadrature rules are most appropriate for solving Volterra integral equations (and integro-differential equations). In this section, we propose the following algorithm which you may find useful in solving a linear Volterra integral equation of the form

y (t) = f (t) + \int_{0}^{t} K (t, s) y (s) d s, 0 \leq t \leq T,

(2)

using d05bwf. In (2),

K (t, s)

and

f (t)

are given and the solution

y (t)

is sought on a uniform mesh of size

h

such that

T = n h

. Discretization of (2) yields

y_{i} = f (i \times h) + h \sum_{j = 0}^{p - 1} W_{i, j} K (i, h, j, h) y_{j} + h \sum_{j = p}^{i} ω_{i - j} K (i, h, j, h) y_{j},

(3)

where

y_{i} ≃ y (i \times h)

. We propose the following algorithm for computing

y_{i}

from (3) after a call to d05bwf:

(a)

Equation (3) requires starting values,

y_{j}

, for

j = 1, 2, \dots, nwt - 1

, with

y_{0} = f (0)

. These starting values can be computed by solving the linear system

y_{i} = f (i \times h) + h \sum_{j = 0}^{nwt - 1} sw (i, j + 1) K (i, h, j, h) y_{j}, i = 1, 2, \dots, nwt - 1 .

(b)

Compute the inhomogeneous terms

σ_{i} = f (i \times h) + h \sum_{j = 0}^{nwt - 1} sw (i, j + 1) K (i, h, j, h) y_{j}, i = nwt, nwt + 1, \dots, n .

(c)

Start the iteration for

i = nwt, nwt + 1, \dots, n

to compute

y_{i}

from:

(1 - h \times omega (1) K (i, h, i, h)) y_{i} = σ_{i} + h \sum_{j = nwt}^{i - 1} omega (i - j + 1) K (i, h, j, h) y_{j} .

Note that for a nonlinear integral equation, the solution of a nonlinear algebraic system is required at step (a) and a single nonlinear equation at step (c).

10

Example

The following example generates the first ten convolution and thirteen starting weights generated by the fourth-order BDF method.

10.1

Program Text

Program Text (d05bwfe.f90)

10.2

Program Data

None.

10.3

Program Results

Program Results (d05bwfe.r)

d05bwf (PDF version)

D05 (inteq) Chapter Contents

D05 (inteq) Chapter Introduction

NAG Library Manual

NAG Library Routine Document

d05bwf (volterra_weights)

▸▿ 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

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