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D01gcf 1.6

Note: before using this routine, please read the Users’ Note for your implementation to check the interpretation of bold italicised terms andother implementation-dependent details.
D01GCF calculates an approximation to a definite integral in up to 20 dimensions, using the Korobov–Conroy number theoretic method.
SUBROUTINE D01GCF(NDIM, FUNCTN, REGION, NPTS, VK, NRAND, ITRANS, RES, This routine calculates an approximation to the integral using the Korobov–Conroy number The region of integration defined such that generally ci and di may be functions ofx1; x2; . . . ; xiÀ1, for i ¼ 2; 3; . . . ; n, with c1 and d1 constants. The integral is first of all transformed to anintegral over the n-cube ½0; 1Šn by the change of variables The method then uses as its basis the number theoretic formula for the n-cube, ½0; 1Šn: where fxg denotes the fractional part of x, a1; a2; . . . ; an are the so-called optimal coefficients, E is theerror, and p is a prime integer. (It is strictly only necessary that p be relatively prime to all a1; a2; . . . ; anand is in fact chosen to be even use of properties ofthe Fourier expansion of gðx1; x2; . . . ; xnÞ which is assumed to have some degree of periodicity.
Depending on the choice of a1; a2; . . . ; an the contributions from certain groups of Fourier coefficients areeliminated from the error, E. Korobov shows that a1; a2; . . . ; an can be chosen so that the error satisfies where and C are real numbers depending on the convergence rate of the Fourier series, is a constantdepending on n, and K is a constant depending on and n.
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 doesnot appear to be a critical requirement of the method.
An easily calculable error estimate is not available apart from repetition with an increasing sequence ofvalues of p which can yield erratic results. The difficulties have been studied by Cranley and Pattersonproposed a Monte Carlo error estimate arising from a stochasticintegration rule by the inclusion of a random origin shift which leaves the form of the Computing the integral for each of a sequence of random vectors 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 the user.
compute the optimal coefficients for the cases where p is a prime number or p is a product of 2 primes,respectively.
Korobov N M (1957) The approximate calculation of multiple integrals using number theoretic methodsDokl. Acad. Nauk SSSR 115 1062–1065 Korobov N M (1963) Number Theoretic Methods in Approximate Analysis Fizmatgiz, Moscow Conroy H (1967) Molecular Shroedinger equation VIII. A new method for evaluting multi-dimensionalintegrals J. Chem. Phys. 47 5307–5318 Cranley R and Patterson T N L (1976) Randomisation of number theoretic methods for mulitple integrationSIAM J. Numer. Anal. 13 904–914 On entry: the number of dimensions of the integral, n.
FUNCTN – real FUNCTION, supplied by the user.
FUNCTN must return the value of the integrand f at a given point.
On entry: the number of dimensions of the integral, n.
On entry: the co-ordinates of the point at which the integrand must be evaluated.
FUNCTN must be declared as EXTERNAL in the (sub)program from which D01GCF is called.
Parameters denoted as Input must not be changed by this procedure.
REGION – SUBROUTINE, supplied by the user.
REGION must evaluate the limits of integration in any dimension.
On entry: the number of dimensions of the integral, n.
On entry: Xð1Þ; . . . ; Xðj À 1Þ contain the current values of the first ðj À 1Þ variables,which may be used if necessary in calculating cj and dj.
On entry: the index j for which the limits of the range of integration are required.
On exit: the lower limit cj of the range of xj.
On exit: the upper limit dj of the range of xj.
REGION must be declared as EXTERNAL in the (sub)program from which D01GCF is called.
Parameters denoted as Input must not be changed by this procedure.
On entry: the Korobov rule to be used. There are two alternatives depending on the value of NPTS.
In this case one of six preset rules is chosen using 2129, 5003, 10007, 20011, 40009 or 80021points depending on the respective value of NPTS being 1, 2, 3, 4, 5 or 6.
NPTS is the number of actual points to be used with corresponding optimal coefficientssupplied in the On entry: if 6, VK must contain the n optimal coefficients (which may be calculated using6, VK need not be set.
On exit: if 6, VK is unchanged; if 6, VK contains the n optimal coefficients usedby the preset rule.
On entry: the number of random samples to be generated in the error estimation (generally a smallvalue, say 3 to 5 is sufficient). The total number of integrand evaluations will be NRAND Â On entry: indicates whether the periodising transformation is to be used: if ITRANS ¼ 0, the transformation is to be used; if ITRANS 6¼ 0, the transformation is to be suppresssed (to cover cases where the integrandmay already be periodic or where the user desires to specify a particular transformation in the On exit: an estimate of the value of the integral.
On exit: the standard error as If 1, then ERRcontains zero.
On entry: IFAIL must be set to 0, À1 or 1. Users who are unfamiliar with this parameter should On exit: IFAIL ¼ 0 unless the routine detects an For environments where it might be inappropriate to halt program execution when an error isdetected, the value À1 or 1 is recommended. If the output of error messages is undesirable, then thevalue 1 is recommended. Otherwise, for users not familiar with this parameter the recommendedvalue is 0. When the value À1 or 1 is used it is essential to test the value of IFAIL on exit.
If on entry IFAIL ¼ 0 or À1, explanatory error messages are output on the current error message unit (as Errors or warnings detected by the routine: An estimate of the absolute standard error is given by the value, on The time taken by the routine will be approximately proportional to p, where p is the numberof points used.
The exact by D01GCF will depend (within statistical limits) on thesequence of random numbers generated within the routine that the results returned by D01GCF in separate runs are identical, users before callingD01GCF; to ensure that they are cosð0:5 þ 2ðx1 þ x2 þ x3 þ x4Þ À 4Þ dx1 dx2 dx3 dx4: Note: the listing of the example program presented below uses bold italicised terms to denote precision-dependent details. Please read theUsers’ Note for your implementation to check the interpretation of these the results produced may not be identical for all implementations.
. Executable Statements .
WRITE (NOUT,*) ’D01GCF Example Program Results’NPTS = 2ITRANS = 0NRAND = 4IFAIL = 0 CALL D01GCF(NDIM,FUNCT,REGION,NPTS,VK,NRAND,ITRANS,RES,ERR,IFAIL) WRITE (NOUT,*)WRITE (NOUT,99999) ’Result =’, RES, ’ . Executable Statements .
A = 0.0e0B = 1.0e0RETURNEND . Executable Statements .
SUM = 0.0e0DO 20 J = 1, NDIM FUNCT = COS(0.5e0+2.0e0*SUM-real(NDIM))RETURNEND
  • Advice on Replacement Calls for Withdrawn/Superseded Routines
  • Implementation-specific Information
  • C05 - Roots of One or More Transcendental Equations
  • D02 - Ordinary Differential Equations
  • D02 - Ordinary Differential Equations
  • E04 - Minimizing or Maximizing a Function
  • F06 - Linear Algebra Support Routines
  • F08 - Least-squares and Eigenvalue Problems (LAPACK)
  • G01 - Simple Calculations on Statistical Data
  • G02 - Correlation and Regression Analysis
  • S - Approximations of Special Functions

    Source: http://www.hpc.science.unsw.edu.au/files/docs/nag/fl21/pdf/D01/d01gcf.pdf

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