Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm

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

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

1 Scopus citations

Abstract

The Rapid Gaussian Markov Improvement Algorithm (rGMIA) solves discrete optimization via simulation problems by using a Gaussian Markov random field and complete expected improvement as the sampling and stopping criterion. rGMIA has been created as a sequential sampling procedure run on a single processor. In this paper, we extend rGMIA to a parallel computing environment when q + 1 solutions can be simulated in parallel. To this end, we introduce the q-point complete expected improvement criterion to determine a batch of q + 1 solutions to simulate. This new criterion is implemented in a new object-oriented rGMIA package.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 Winter Simulation Conference, WSC 2022
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3158-3169
Number of pages12
ISBN (Electronic)9798350309713
DOIs
StatePublished - 2022
Event2022 Winter Simulation Conference, WSC 2022 - Guilin, China
Duration: Dec 11 2022Dec 14 2022

Publication series

NameProceedings - Winter Simulation Conference
Volume2022-December
ISSN (Print)0891-7736

Conference

Conference2022 Winter Simulation Conference, WSC 2022
Country/TerritoryChina
CityGuilin
Period12/11/2212/14/22

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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