PDF version (NAG web site
, 64-bit version, 64-bit version)
NAG Toolbox: nag_quad_md_adapt (d01fc)
Purpose
nag_quad_md_adapt (d01fc) attempts to evaluate a multidimensional integral (up to dimensions), with constant and finite limits, to a specified relative accuracy, using an adaptive subdivision strategy.
Syntax
[
minpts,
acc,
finval,
ifail] = d01fc(
a,
b,
minpts,
maxpts,
functn,
eps, 'ndim',
ndim)
[
minpts,
acc,
finval,
ifail] = nag_quad_md_adapt(
a,
b,
minpts,
maxpts,
functn,
eps, 'ndim',
ndim)
Description
nag_quad_md_adapt (d01fc) returns an estimate of a multidimensional integral over a hyper-rectangle (i.e., with constant limits), and also an estimate of the relative error. You set the relative accuracy required, return values for the integrand via a function argument
functn, and also set the minimum and maximum acceptable number of calls to
functn (in
minpts and
maxpts).
The function operates by repeated subdivision of the hyper-rectangular region into smaller hyper-rectangles. In each subregion, the integral is estimated using a seventh-degree rule, and an error estimate is obtained by comparison with a fifth-degree rule which uses a subset of the same points. The fourth differences of the integrand along each coordinate axis are evaluated, and the subregion is marked for possible future subdivision in half along that coordinate axis which has the largest absolute fourth difference.
If the estimated errors, totalled over the subregions, exceed the requested relative error (or if fewer than
minpts calls to
functn have been made), further subdivision is necessary, and is performed on the subregion with the largest estimated error, that subregion being halved along the appropriate coordinate axis.
The function will fail if the requested relative error level has not been attained by the time
maxpts calls to
functn have been made; or, if the amount
lenwrk of working storage is insufficient. A formula for the recommended value of
lenwrk is given in
Arguments. If a smaller value is used, and is exhausted in the course of execution, the function switches to a less efficient mode of operation; only if this mode also breaks down is insufficient storage reported.
nag_quad_md_adapt (d01fc) is based on the HALF function developed by
van Dooren and de Ridder (1976). It uses a different basic rule, described in
Genz and Malik (1980).
References
Genz A C and Malik A A (1980) An adaptive algorithm for numerical integration over an N-dimensional rectangular region J. Comput. Appl. Math. 6 295–302
van Dooren P and de Ridder L (1976) An adaptive algorithm for numerical integration over an N-dimensional cube J. Comput. Appl. Math. 2 207–217
Parameters
Compulsory Input Parameters
- 1:
– double array
-
The lower limits of integration,
, for .
- 2:
– double array
-
The upper limits of integration,
, for .
- 3:
– int64int32nag_int scalar
-
Must be set to the minimum number of integrand evaluations to be allowed.
- 4:
– int64int32nag_int scalar
-
The maximum number of integrand evaluations to be allowed.
Constraints:
- ;
- , where .
- 5:
– function handle or string containing name of m-file
-
functn must return the value of the integrand
at a given point.
[result] = functn(ndim, z)
Input Parameters
- 1:
– int64int32nag_int scalar
-
, the number of dimensions of the integral.
- 2:
– double array
-
The coordinates of the point at which the integrand must be evaluated.
Output Parameters
- 1:
– double scalar
-
The value of the integrand at the given point.
- 6:
– double scalar
-
The relative error acceptable to you. When the solution is zero or very small relative accuracy may not be achievable but you may still set
eps to a reasonable value and check for the error exit
.
Constraint:
.
Optional Input Parameters
- 1:
– int64int32nag_int scalar
-
Default:
the dimension of the arrays
a,
b. (An error is raised if these dimensions are not equal.)
, the number of dimensions of the integral.
Constraint:
.
Output Parameters
- 1:
– int64int32nag_int scalar
-
Contains the actual number of integrand evaluations used by nag_quad_md_adapt (d01fc).
- 2:
– double scalar
-
The estimated relative error in
finval.
- 3:
– double scalar
-
The best estimate obtained for the integral.
- 4:
– int64int32nag_int scalar
unless the function detects an error (see
Error Indicators and Warnings).
Error Indicators and Warnings
Note: nag_quad_md_adapt (d01fc) may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the function:
Cases prefixed with W are classified as warnings and
do not generate an error of type NAG:error_n. See nag_issue_warnings.
-
-
On entry, | , |
or | , |
or | maxpts is too small, |
or | , |
or | . |
- W
-
maxpts was too small to obtain the required relative accuracy
eps. On soft failure,
finval and
acc contain estimates of the integral and the relative error, but
acc will be greater than
eps.
- W
-
lenwrk was too small. On soft failure,
finval and
acc contain estimates of the integral and the relative error, but
acc will be greater than
eps.
-
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
A relative error estimate is output through the argument
acc.
Further Comments
Execution time will usually be dominated by the time taken to evaluate
functn, and hence the maximum time that could be taken will be proportional to
maxpts.
Example
This example estimates the integral
The accuracy requested is one part in .
Open in the MATLAB editor:
d01fc_example
function d01fc_example
fprintf('d01fc example results\n\n');
a = zeros(4,1);
b = ones(4,1);
minpts = int64(0);
maxpts = int64(8000);
epsilon = 0.0001;
[minpts, acc, finval, ifail] = d01fc(...
a, b, minpts, maxpts, @functn, epsilon);
fprintf('Requested accuracy = %10.2e\n', epsilon);
fprintf('Estimated value = %8.4f\n',finval);
fprintf('Estimated accuracy = %9.1e\n',acc);
function result = functn(ndim,z)
result = 4*z(1)*z(3)*z(3)*exp(2*z(1)*z(3))/(1+z(2)+z(4))^2;
d01fc example results
Requested accuracy = 1.00e-04
Estimated value = 0.5754
Estimated accuracy = 9.9e-05
PDF version (NAG web site
, 64-bit version, 64-bit version)
© The Numerical Algorithms Group Ltd, Oxford, UK. 2009–2015