On the solution of stochastic optimization problems in imperfect information regimes

Hao Jiang, Uday V. Shanbhag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We consider the solution of a stochastic convex optimization problem E[f(x;θlowast,ξ)] in x over a closed and convex set X in a regime where θlowast is unavailable. Instead, θlowast may be learnt by minimizing a suitable metric E[g(θη)] in θ over a closed and convex set Θ. We present a coupled stochastic approximation scheme for the associated stochastic optimization problem with imperfect information. The schemes are shown to be equipped with almost sure convergence properties in regimes where the function f is both strongly convex as well as merely convex. Rate estimates are provided in both a strongly convex as well as a merely convex regime, where the use of averaging facilitates the development of a bound.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 Winter Simulation Conference - Simulation
Subtitle of host publicationMaking Decisions in a Complex World, WSC 2013
Pages821-832
Number of pages12
DOIs
StatePublished - 2013
Event2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013 - Washington, DC, United States
Duration: Dec 8 2013Dec 11 2013

Publication series

NameProceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

Other

Other2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
CountryUnited States
CityWashington, DC
Period12/8/1312/11/13

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation

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