Computational methods for optimization via simulation using Gaussian Markov Random Fields

Mark Semelhago, Barry L. Nelson, Andreas Wachter, Eunhye Song

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

6 Scopus citations


There has been recent interest, and significant success, in adapting and extending ideas from statistical learning via Gaussian process (GP) regression to optimization via simulation (OvS) problems. At the heart of all such methods is a GP representing knowledge about the objective function whose conditional distribution is updated as more of the feasible region is explored. Calculating the conditional distribution requires inverting a large, dense covariance matrix, and this is the primary bottleneck for applying GP learning to large-scale OvS problems. If the GP is a Gaussian Markov Random Field (GMRF), then the precision matrix (inverse of the covariance matrix) can be constructed to be sparse. In this paper we show how to exploit this sparse-matrix structure to extend the reach of OvS based on GMRF learning for discrete-decision-variable problems.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538634288
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

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


Other2017 Winter Simulation Conference, WSC 2017
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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