Matches in SemOpenAlex for { <https://semopenalex.org/work/W2270441432> ?p ?o ?g. }
- W2270441432 abstract "Any performance analysis based on stochastic simulation is subject to the errors inherent in misspecifying the modeling assumptions, particularly the input distributions. One way to quantify such errors, especially in situations with little support from data, is to carry out a worst-case analysis, with the partial, nonparametric knowledge of the input model represented via optimization constraints. We design and analyze a numerical scheme for both a general class of simulation objectives and of uncertainty specifications, based on a Frank-Wolfe (FW) variant of the stochastic approximation (SA) method (which we call FWSA). A key step involves the construction of simulable unbiased gradient estimators using a suitable nonparametric analog of the classical likelihood ratio method. A convergence analysis for FWSA on non-convex problems is provided. We illustrate our approach numerically with robustness analyses for a canonical M/GI/1 queue under uncertain service time distribution specification, and for an energy storage facility acting as a hedge against uncertainty in local renewable generation." @default.
- W2270441432 created "2016-06-24" @default.
- W2270441432 creator A5025334598 @default.
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- W2270441432 date "2015-07-19" @default.
- W2270441432 modified "2023-09-27" @default.
- W2270441432 title "Computing Worst-case Input Models in Stochastic Simulation" @default.
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