Deterministic algorithm for optimising the direction numbers of the Sobol’ sequence

Atanassov, Emanouil

Mathematics and Education in Mathematics, Tome 50 (2021), pp. 83-94

Résumé

The quasi-Monte Carlo (QMC) methods use deterministic, usually low-discrepancy sequences, to improve the order of convergence when compared with Monte Carlo methods dealing with the same problem. Although this improved convergence depends on the smoothness properties of the functions under consideration and sometimes cannot be established theoretically, good practical results were obtained in many areas, notably in Financial Mathematics. Most of the QMC methods do not depend on particular properties of the sequences used beyond their good distribution in a suitable multidimensional space. Many families of low-discrepancy sequences have been studies, with the family of the Sobol sequences being not only one of the oldest but also the most popular in practical applications. Since these sequences have many free parameters, it is desirable to have methods to obtain a good set of parameters, suitable for a wide class of problems. In this work we propose a fully deterministic method for optimisation of the direction numbers of the Sobol sequences that is flexible in taking into account smoothness properties of the potential sub-integral functions. We demonstrate how by implementing this algorithm on modern GPUs it is possible to obtain good sets of direction numbers within a reasonable timeframe. The numerical tests demonstrate the significant advantage in terms of accuracy that is observed, which makes the corresponding QMC methods even more competitive.

Détail

Keywords: quasi-Monte Carlo method, Sobol’ sequence, High Performance Computin, Primary 11K3, secondary 11K3