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• W2100081894 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." @default.
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• W2100081894 date "2012-12-09" @default.
• W2100081894 modified "2023-09-23" @default.
• W2100081894 title "Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets" @default.
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• W2100081894 doi "https://doi.org/10.5555/2429759.2429776" @default.
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