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..

doi abstract bibtex

doi abstract bibtex

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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S. R.","Pasupathy, R."],"bibbaseid":"hunter-pasupathy-largedeviationsamplinglawsforconstrainedsimulationoptimizationonfinitesets-2010","bibdata":{"bibtype":"inproceedings","type":"inproceedings","year":"2010","author":[{"firstnames":["S.","R."],"propositions":[],"lastnames":["Hunter"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Pasupathy"],"suffixes":[]}],"title":"Large-deviation sampling laws for constrained simulation optimization on finite sets","booktitle":"Proceedings of the 2010 Winter Simulation Conference","editor":[{"firstnames":["B."],"propositions":[],"lastnames":["Johansson"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Jain"],"suffixes":[]},{"firstnames":["J."],"propositions":[],"lastnames":["Montoya-Torres"],"suffixes":[]},{"firstnames":["J."],"propositions":[],"lastnames":["Hugan"],"suffixes":[]},{"firstnames":["E."],"propositions":[],"lastnames":["Yücesan"],"suffixes":[]}],"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","bibtex":"@inproceedings{2010hunpasWSC,\n\tYear = {2010},\n\tAuthor = {S. R. Hunter and R. Pasupathy},\n\tTitle = {Large-deviation sampling laws for constrained simulation optimization on finite sets},\n\tBooktitle = {Proceedings of the 2010 Winter Simulation Conference},\n\tEditor = {B. Johansson and S. Jain and J. {Montoya-Torres} and J. Hugan and E. Y\\\"{u}cesan},\n\tPublisher = {Institute of Electrical and Electronics Engineers, Inc.},\n\tAddress = {Piscataway, NJ},\n\tPages = {995--1002},\n\tdoi = {10.1109/WSC.2010.5679092},\n\tabstract = {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.},\n\tkeywords = {simulation optimization > single-objective > stochastically constrained > ranking and selection}}\n\n\n","author_short":["Hunter, S. R.","Pasupathy, R."],"editor_short":["Johansson, B.","Jain, S.","Montoya-Torres, J.","Hugan, J.","Yücesan, E."],"key":"2010hunpasWSC","id":"2010hunpasWSC","bibbaseid":"hunter-pasupathy-largedeviationsamplinglawsforconstrainedsimulationoptimizationonfinitesets-2010","role":"author","urls":{},"keyword":["simulation optimization > single-objective > stochastically constrained > ranking and selection"],"metadata":{"authorlinks":{"hunter, s":"https://web.ics.purdue.edu/~hunter63/","pasupathy, r":"https://bibbase.org/show?bib=http://web.ics.purdue.edu/~pasupath/rpVitapublist.bib"}}},"bibtype":"inproceedings","biburl":"http://web.ics.purdue.edu/~hunter63/PAPERS/srhunterweb.bib","downloads":20,"keywords":["simulation optimization > single-objective > stochastically constrained > ranking and selection"],"search_terms":["large","deviation","sampling","laws","constrained","simulation","optimization","finite","sets","hunter","pasupathy"],"title":"Large-deviation sampling laws for constrained simulation optimization on finite sets","year":2010,"dataSources":["ZEwmdExPMCtzAbo22","PkcXzWbdqPvM6bmCx","qnbhPCpdghcXAQgXA"]}