Variance Reduction via Lattice Rules

Pierre L'Ecuyer and Christiane Lemieux
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Pierre L'Ecuyer: Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, H3C 3J7, Canada, www.iro.umontreal.ca/~lecuyer
Christiane Lemieux: Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, H3C 3J7, Canada, www.math.vcalgary.ca/~lemieux

Management Science, 2000, vol. 46, issue 9, 1214-1235

Abstract: This is a review article on lattice methods for multiple integration over the unit hypercube, with a variance-reduction viewpoint. It also contains some new results and ideas. The aim is to examine the basic principles supporting these methods and how they can be used effectively for the simulation models that are typically encountered in the area of management science. These models can usually be reformulated as integration problems over the unit hypercube with a large (sometimes infinite) number of dimensions. We examine selection criteria for the lattice rules and suggest criteria which take into account the quality of the projections of the lattices over selected low-dimensional subspaces. The criteria are strongly related to those used for selecting linear congruential and multiple recursive random number generators. Numerical examples illustrate the effectiveness of the approach.

Keywords: simulation; variance reduction; quasi-Monte Carlo; low discrepancy; lattice rules (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:46:y:2000:i:9:p:1214-1235

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