D01GCF calculates an approximation to the integral
using the Korobov–Conroy number theoretic method (see
Korobov (1957),
Korobov (1963) and
Conroy (1967)). The region of integration defined in
(1) is such that generally
${c}_{i}$ and
${d}_{i}$ may be functions of
${x}_{1},{x}_{2},\dots ,{x}_{i-1}$, for
$i=2,3,\dots ,n$, with
${c}_{1}$ and
${d}_{1}$ constants. The integral is first of all transformed to an integral over the
$n$-cube
${\left[0,1\right]}^{n}$ by the change of variables
The method then uses as its basis the number theoretic formula for the
$n$-cube,
${\left[0,1\right]}^{n}$:
where
$\left\{x\right\}$ denotes the fractional part of
$x$,
${a}_{1},{a}_{2},\dots ,{a}_{n}$ are the so-called optimal coefficients,
$E$ is the error, and
$p$ is a prime integer. (It is strictly only necessary that
$p$ be relatively prime to all
${a}_{1},{a}_{2},\dots ,{a}_{n}$ and is in fact chosen to be even for some cases in
Conroy (1967).) The method makes use of properties of the Fourier expansion of
$g\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)$ which is assumed to have some degree of periodicity. Depending on the choice of
${a}_{1},{a}_{2},\dots ,{a}_{n}$ the contributions from certain groups of Fourier coefficients are eliminated from the error,
$E$. Korobov shows that
${a}_{1},{a}_{2},\dots ,{a}_{n}$ can be chosen so that the error satisfies
where
$\alpha $ and
$C$ are real numbers depending on the convergence rate of the Fourier series,
$\beta $ is a constant depending on
$n$, and
$K$ is a constant depending on
$\alpha $ and
$n$. There are a number of procedures for calculating these optimal coefficients. Korobov imposes the constraint that
and gives a procedure for calculating the parameter,
$a$, to satisfy the optimal conditions.
In this routine the periodisation is achieved by the simple transformation
More sophisticated periodisation procedures are available but in practice the degree of periodisation does not appear to be a critical requirement of the method.
An easily calculable error estimate is not available apart from repetition with an increasing sequence of values of
$p$ which can yield erratic results. The difficulties have been studied by
Cranley and Patterson (1976) who have proposed a Monte–Carlo error estimate arising from converting
(2) into a stochastic integration rule by the inclusion of a random origin shift which leaves the form of the error
(3) unchanged; i.e., in the formula
(2),
$\left\{k\frac{{a}_{i}}{p}\right\}$ is replaced by
$\left\{{\alpha}_{i}+k\frac{{a}_{i}}{p}\right\}$, for
$i=1,2,\dots ,n$, where each
${\alpha}_{i}$, is uniformly distributed over
$\left[0,1\right]$. Computing the integral for each of a sequence of random vectors
$\alpha $ allows a ‘standard error’ to be estimated.
This routine provides built-in sets of optimal coefficients, corresponding to six different values of
$p$. Alternatively, the optimal coefficients may be supplied by you. Routines
D01GYF and
D01GZF compute the optimal coefficients for the cases where
$p$ is a prime number or
$p$ is a product of two primes, respectively.
Conroy H (1967) Molecular Shroedinger equation VIII. A new method for evaluting multi-dimensional integrals J. Chem. Phys. 47 5307–5318
Cranley R and Patterson T N L (1976) Randomisation of number theoretic methods for mulitple integration SIAM J. Numer. Anal. 13 904–914
Korobov N M (1957) The approximate calculation of multiple integrals using number theoretic methods Dokl. Acad. Nauk SSSR 115 1062–1065
If on entry
${\mathbf{IFAIL}}={\mathbf{0}}$ or
${-{\mathbf{1}}}$, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
An estimate of the absolute standard error is given by the value, on exit, of
ERR.
The exact values of
RES and
ERR on return will depend (within statistical limits) on the sequence of random numbers generated within D01GCF by calls to
G05SAF. Separate runs will produce identical answers.
This example calculates the integral
None.