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

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

doi:10.1109/WSC57314.2022.10015429

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

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

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 2022 Winter Simulation Conference, WSC 2022
Editors	B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3158-3169
Number of pages	12
ISBN (Electronic)	9798350309713
DOIs	https://doi.org/10.1109/WSC57314.2022.10015429
State	Published - 2022
Event	2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: Dec 11 2022 → Dec 14 2022

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2022-December
ISSN (Print)	0891-7736

Conference

Conference	2022 Winter Simulation Conference, WSC 2022
Country/Territory	China
City	Guilin
Period	12/11/22 → 12/14/22

Funding

This work was completed prior to the affiliation of Mark Semelhago with Amazon.com and is supported by the National Science Foundation Grant Nos. DMS-1854562 and DMS-1854659.

ASJC Scopus subject areas

Software
Modeling and Simulation
Computer Science Applications

Access to Document

10.1109/WSC57314.2022.10015429

Cite this

Semelhago, M., Nelson, B. L., Song, E., & Wachter, A. (2022). Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm. In B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C. G. Corlu, L. H. Lee, E. P. Chew, T. Roeder, & P. Lendermann (Eds.), Proceedings of the 2022 Winter Simulation Conference, WSC 2022 (pp. 3158-3169). (Proceedings - Winter Simulation Conference; Vol. 2022-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC57314.2022.10015429

Semelhago, Mark ; Nelson, Barry L. ; Song, Eunhye et al. / Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm. Proceedings of the 2022 Winter Simulation Conference, WSC 2022. editor / B. Feng ; G. Pedrielli ; Y. Peng ; S. Shashaani ; E. Song ; C.G. Corlu ; L.H. Lee ; E.P. Chew ; T. Roeder ; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3158-3169 (Proceedings - Winter Simulation Conference).

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title = "Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm",

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.",

author = "Mark Semelhago and Nelson, {Barry L.} and Eunhye Song and Andreas Wachter",

note = "Funding Information: This work was completed prior to the affiliation of Mark Semelhago with Amazon.com and is supported by the National Science Foundation Grant Nos. DMS-1854562 and DMS-1854659. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/WSC57314.2022.10015429",

language = "English (US)",

series = "Proceedings - Winter Simulation Conference",

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editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",

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Semelhago, M, Nelson, BL, Song, E & Wachter, A 2022, Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm. in B Feng, G Pedrielli, Y Peng, S Shashaani, E Song, CG Corlu, LH Lee, EP Chew, T Roeder & P Lendermann (eds), Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Proceedings - Winter Simulation Conference, vol. 2022-December, Institute of Electrical and Electronics Engineers Inc., pp. 3158-3169, 2022 Winter Simulation Conference, WSC 2022, Guilin, China, 12/11/22. https://doi.org/10.1109/WSC57314.2022.10015429

Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm. / Semelhago, Mark; Nelson, Barry L.; Song, Eunhye et al.
Proceedings of the 2022 Winter Simulation Conference, WSC 2022. ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3158-3169 (Proceedings - Winter Simulation Conference; Vol. 2022-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Nelson, Barry L.

AU - Song, Eunhye

AU - Wachter, Andreas

N1 - Funding Information: This work was completed prior to the affiliation of Mark Semelhago with Amazon.com and is supported by the National Science Foundation Grant Nos. DMS-1854562 and DMS-1854659. Publisher Copyright: © 2022 IEEE.

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N2 - 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.

AB - 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.

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Semelhago M, Nelson BL, Song E, Wachter A. Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm. In Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Chew EP, Roeder T, Lendermann P, editors, Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3158-3169. (Proceedings - Winter Simulation Conference). doi: 10.1109/WSC57314.2022.10015429

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

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