Plausible optima

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

4 Scopus citations


We propose a framework and specific algorithms for screening a large (perhaps countably infinite) space of feasible solutions to generate a subset containing the optimal solution with high confidence. We attain this goal even when only a small fraction of the feasible solutions are simulated. To accomplish it we exploit structural information about the space of functions within which the true objective function lies, and then assess how compatible optimality is for each feasible solution with respect to the observed simulation outputs and the assumed function space. The result is a set of plausible optima. This approach can be viewed as a way to avoid slow simulation by leveraging fast optimization. Explicit formulations of the general approach are provided when the space of functions is either Lipschitz or convex. We establish both small- and large-sample properties of the approach, and provide two numerical examples.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538665725
StatePublished - Jan 31 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2018 Winter Simulation Conference, WSC 2018

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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