SobolRsg Class Reference

Sobol low-discrepancy sequence generator. More...

#include <ql/math/randomnumbers/sobolrsg.hpp>

Public Types
enum	DirectionIntegers {   Unit, Jaeckel, SobolLevitan, SobolLevitanLemieux,   JoeKuoD5, JoeKuoD6, JoeKuoD7, Kuo,   Kuo2, Kuo3 }
typedef Sample< std::vector< Real > >	sample_type

Public Member Functions
	SobolRsg (Size dimensionality, unsigned long seed=0, DirectionIntegers directionIntegers=Jaeckel)
void	skipTo (boost::uint_least32_t n)
const std::vector< boost::uint_least32_t > &	nextInt32Sequence () const
const SobolRsg::sample_type &	nextSequence () const
const sample_type &	lastSequence () const
Size	dimension () const

Detailed Description

Sobol low-discrepancy sequence generator.

A Gray code counter and bitwise operations are used for very fast sequence generation.

The implementation relies on primitive polynomials modulo two from the book "Monte Carlo Methods in Finance" by Peter Jäckel.

21 200 primitive polynomials modulo two are provided in QuantLib. Jäckel has calculated 8 129 334 polynomials: if you need that many dimensions you can replace the primitivepolynomials.cpp file included in QuantLib with the one provided in the CD of the "Monte Carlo Methods in Finance" book.

The choice of initialization numbers (also know as free direction integers) is crucial for the homogeneity properties of the sequence. Sobol defines two homogeneity properties: Property A and Property A'.

The unit initialization numbers suggested in "Numerical Recipes in C", 2nd edition, by Press, Teukolsky, Vetterling, and Flannery (section 7.7) fail the test for Property A even for low dimensions.

Bratley and Fox published coefficients of the free direction integers up to dimension 40, crediting unpublished work of Sobol' and Levitan. See Bratley, P., Fox, B.L. (1988) "Algorithm 659: Implementing Sobol's quasirandom sequence generator," ACM Transactions on Mathematical Software 14:88-100. These values satisfy Property A for d<=20 and d = 23, 31, 33, 34, 37; Property A' holds for d<=6.

Jäckel provides in his book (section 8.3) initialization numbers up to dimension 32. Coefficients for d<=8 are the same as in Bradley-Fox, so Property A' holds for d<=6 but Property A holds for d<=32.

The implementation of Lemieux, Cieslak, and Luttmer includes coefficients of the free direction integers up to dimension

1. Coefficients for d<=40 are the same as in Bradley-Fox. For dimension 40<d<=360 the coefficients have been calculated as optimal values based on the "resolution" criterion. See "RandQMC user's guide - A package for randomized quasi-Monte Carlo methods in C," by C. Lemieux, M. Cieslak, and K. Luttmer, version January 13 2004, and references cited there (http://www.math.ucalgary.ca/~lemieux/randqmc.html). The values up to d<=360 has been provided to the QuantLib team by Christiane Lemieux, private communication, September 2004.

For more info on Sobol' sequences see also "Monte Carlo Methods in Financial Engineering," by P. Glasserman, 2004, Springer, section 5.2.3

The Joe–Kuo numbers and the Kuo numbers are due to Stephen Joe and Frances Kuo.

S. Joe and F. Y. Kuo, Constructing Sobol sequences with better two-dimensional projections, preprint Nov 22 2007

See http://web.maths.unsw.edu.au/~fkuo/sobol/ for more information.

The Joe-Kuo numbers are available under a BSD-style license available at the above link.

Note that the Kuo numbers were generated to work with a different ordering of primitive polynomials for the first 40 or so dimensions which is why we have the Alternative Primitive Polynomials.

Tests:

• the correctness of the returned values is tested by reproducing known good values.
• the correctness of the returned values is tested by checking their discrepancy against known good values.

Constructor & Destructor Documentation

SobolRsg()

SobolRsg ( Size  dimensionality, unsigned long  seed = 0, DirectionIntegers  directionIntegers = Jaeckel  )

explicit

Precondition

dimensionality must be <= PPMT_MAX_DIM

Member Function Documentation

skipTo()

void skipTo

(

boost::uint_least32_t

n

)

skip to the n-th sample in the low-discrepancy sequence