Large-deviation sampling laws for constrained simulation optimization on finite sets

Large-deviation sampling laws for constrained simulation optimization on finite sets. Hunter, S. R. & Pasupathy, R. In Johansson, B., Jain, S., Montoya-Torres, J., Hugan, J., & Yücesan, E., editors, Proceedings of the 2010 Winter Simulation Conference, pages 995–1002, Piscataway, NJ, 2010. Institute of Electrical and Electronics Engineers, Inc..
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We consider the problem of selecting an optimal system from among a finite set of competing systems, based on a ``stochastic'' objective function and subject to a single ``stochastic'' constraint. By strategically dividing the competing systems, we derive a large deviations sampling framework that asymptotically minimizes the probability of false selection. We provide an illustrative example where a closed-form sampling law is obtained after relaxation.

@inproceedings{2010hunpasWSC,
	Year = {2010},
	Author = {S. R. Hunter and R. Pasupathy},
	Title = {Large-deviation sampling laws for constrained simulation optimization on finite sets},
	Booktitle = {Proceedings of the 2010 Winter Simulation Conference},
	Editor = {B. Johansson and S. Jain and J. {Montoya-Torres} and J. Hugan and E. Y\"{u}cesan},
	Publisher = {Institute of Electrical and Electronics Engineers, Inc.},
	Address = {Piscataway, NJ},
	Pages = {995--1002},
	doi = {10.1109/WSC.2010.5679092},
	abstract = {We consider the problem of selecting an optimal system from among a finite set of competing systems, based on a ``stochastic'' objective function and subject to a single ``stochastic'' constraint. By strategically dividing the competing systems, we derive a large deviations sampling framework that asymptotically minimizes the probability of false selection. We provide an illustrative example where a closed-form sampling law is obtained after relaxation.},
	keywords = {simulation optimization > single-objective > stochastically constrained > ranking and selection}}

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