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International Conference on Sequences and Their Applications

SETA 2008: Sequences and Their Applications - SETA 2008 pp 1–17Cite as

Comparison of Point Sets and Sequences for Quasi-Monte Carlo and for Random Number Generation

(Invited Paper)

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5203))

Abstract

Algorithmic random number generators require recurring sequences with very long periods and good multivariate uniformity properties. Point sets and sequences for quasi-Monte Carlo numerical integration need similar multivariate uniformity properties as well. It then comes as no surprise that both types of applications share common (or similar) construction methods. However, there are some differences in both the measures of uniformity and the construction methods used in practice. We briefly survey these methods and explain some of the reasons for the differences.

This work was supported NSERC-Canada Grant Number ODGP0110050 and by a Canada Research Chair to the author.

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  1. DIRO, CIRRELT, and GERAD, Université de Montréal, Canada

    Pierre L’Ecuyer

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  1. Pierre L’Ecuyer
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Solomon W. Golomb Matthew G. Parker Alexander Pott Arne Winterhof

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L’Ecuyer, P. (2008). Comparison of Point Sets and Sequences for Quasi-Monte Carlo and for Random Number Generation. In: Golomb, S.W., Parker, M.G., Pott, A., Winterhof, A. (eds) Sequences and Their Applications - SETA 2008. SETA 2008. Lecture Notes in Computer Science, vol 5203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85912-3_1

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  • DOI: https://doi.org/10.1007/978-3-540-85912-3_1

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