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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ghosh, Soumyadip Lam, Henry |
| Copyright Year | 2015 |
| Description | Author affiliation: Math Sciences Department, T.J. Watson IBM Research Center, Yorktown Heights, NY 10598, USA (Ghosh, Soumyadip) || Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave., Ann Arbor, 48109, USA (Lam, Henry) |
| Abstract | Performance analysis via stochastic simulation is often subject to input model uncertainty, meaning that the input model is unknown and needs to be inferred from data. Motivated especially from situations with limited data, we consider a worst-case analysis to handle input uncertainty by representing the partially available input information as constraints and solving a worst-case optimization problem to obtain a conservative bound for the output. In the context of i.i.d. input processes, such approach involves simulation-based nonlinear optimizations with decision variables being probability distributions. We explore the use of a specialized class of mirror descent stochastic approximation (MDSA) known as the entropic descent algorithm, particularly effective for handling probability simplex constraints, to iteratively solve for the local optima. We show how the mathematical program associated with each iteration of the MDSA algorithm can be efficiently computed, and carry out numerical experiments to illustrate the performance of the algorithm. |
| Starting Page | 425 |
| Ending Page | 436 |
| File Size | 522567 |
| Page Count | 12 |
| File Format | |
| ISSN | 15584305 |
| e-ISBN | 9781467397438 |
| DOI | 10.1109/WSC.2015.7408184 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-12-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational modeling Stochastic processes Optimization Approximation algorithms Mirrors Data models Uncertainty |
| Content Type | Text |
| Resource Type | Article |
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