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Title: Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets
Authors: Pujowidianto, N.A.
Pasupathy, R.
Hunter, S.R.
Lee, L.H. 
Chen, C.-H.
Issue Date: 2012
Citation: Pujowidianto, N.A.,Pasupathy, R.,Hunter, S.R.,Lee, L.H.,Chen, C.-H. (2012). Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets. Proceedings - Winter Simulation Conference : -. ScholarBank@NUS Repository.
Abstract: Consider the context of constrained simulation optimization (SO), that is, optimization problems where the objective function and constraints are known through a Monte Carlo simulation, with corresponding estimators possibly dependent. We identify the nature of sampling plans that characterize efficient algorithms, particularly in large countable spaces. We show that in a certain asymptotic sense, the optimal sampling characterization, that is, the sampling budget for each system that guarantees optimal convergence rates, depends on a single easily estimable quantity called the score. This result provides a useful and easily implementable sampling allocation that approximates the optimal allocation, which is otherwise intractable due to it being the solution to a difficult bilevel optimization problem. Our results point to a simple sequential algorithm for efficiently solving large-scale constrained simulation optimization problems on finite sets. © 2012 IEEE.
Source Title: Proceedings - Winter Simulation Conference
ISBN: 9781467347792
ISSN: 08917736
DOI: 10.1109/WSC.2012.6465141
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