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Variance reduction for sequential sampling in stochastic programming

Jangho Park (), Rebecca Stockbridge () and Güzin Bayraksan ()
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Jangho Park: The Ohio State University
Güzin Bayraksan: The Ohio State University

Annals of Operations Research, 2021, vol. 300, issue 1, No 7, 204 pages

Abstract: Abstract This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming and presents a comparative computational study. It shows conditions under which the sequential sampling with AV and LHS satisfy finite stopping guarantees and are asymptotically valid, discussing LHS in detail. It computationally compares their use in both the sequential and non-sequential settings through a collection of two-stage stochastic linear programs with different characteristics. The numerical results show that while both AV and LHS can be preferable to random sampling in either setting, LHS typically dominates in the non-sequential setting while performing well sequentially and AV gains some advantages in the sequential setting. These results imply that, given the ease of implementation of these variance reduction techniques, armed with the same theoretical properties and improved empirical performance relative to random sampling, AV and LHS sequential procedures present attractive alternatives in practice for a class of stochastic programs.

Keywords: Sequential sampling; Variance reduction; Latin hypercube sampling; Antithetic variates; Stochastic optimization; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10479-020-03908-x

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