Latin supercube sampling for very high-dimensional simulations

Abstract

This article introduces Latin supercube sampling (LSS) for very high-dimensional simulations such as arise in particle transport, finance, and queueing. LSS is developed as a combination of two widely used methods: Latin hypercube sampling (LHS) and quasi-Monte Carlo (QMC). In LSS, the input variables are grouped into subsets, and a lower-dimensional QMC method is used within each subset. The QMC points are presented in random order within subsets. QMC methods have been observed to lose effectiveness in high-dimensional problems. This article shows that LSS can extend the benefits of QMC to much higher dimensions, when one can make a good grouping of input variables. Some suggestions for grouping variables are given for the motivating examples. Even a poor grouping can still be expected to do as well as LHS. The article also extends LHS and LSS to infinite-dimensional problems. The paper includes a survey of QMC methods, randomized versions of them (RQMC), and previous methods for extending QMC to higher dimensions. Furthermore it shows that LSS applied with RQMC is more reliable than LSS with QMC.