TY - GEN
T1 - A Computational Study of Probabilistic Branch and Bound with Multilevel Importance Sampling
AU - Huang, Hao
AU - Maneekul, Pariyakorn
AU - Morey, Danielle F.
AU - Zabinsky, Zelda B.
AU - Pedrielli, Giulia
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Probabilistic branch and bound (PBnB) is a partition-based algorithm developed for level set approximation, where investigating all subregions simultaneously is a computational costly sampling scheme. In this study, we hypothesize that focusing branching and sampling on promising subregions will improve the efficiency of the PBnB algorithm. Two variations to Original PBnB are proposed: Multilevel PBnB and Multilevel PBnB with Importance Sampling. Multilevel PBnB focuses its branching on promising subregions that are likely to be maintained or pruned, as opposed to Original PBnB that branches more subregions. Multilevel PBnB with Importance Sampling attempts to further improve this efficiently by combining focused branching with a posterior distribution that updates iteratively. We present numerical experiments using benchmark functions to compare the performance of each PBnB variation.
AB - Probabilistic branch and bound (PBnB) is a partition-based algorithm developed for level set approximation, where investigating all subregions simultaneously is a computational costly sampling scheme. In this study, we hypothesize that focusing branching and sampling on promising subregions will improve the efficiency of the PBnB algorithm. Two variations to Original PBnB are proposed: Multilevel PBnB and Multilevel PBnB with Importance Sampling. Multilevel PBnB focuses its branching on promising subregions that are likely to be maintained or pruned, as opposed to Original PBnB that branches more subregions. Multilevel PBnB with Importance Sampling attempts to further improve this efficiently by combining focused branching with a posterior distribution that updates iteratively. We present numerical experiments using benchmark functions to compare the performance of each PBnB variation.
UR - http://www.scopus.com/inward/record.url?scp=85147444441&partnerID=8YFLogxK
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U2 - 10.1109/WSC57314.2022.10015267
DO - 10.1109/WSC57314.2022.10015267
M3 - Conference contribution
AN - SCOPUS:85147444441
T3 - Proceedings - Winter Simulation Conference
SP - 3251
EP - 3262
BT - Proceedings of the 2022 Winter Simulation Conference, WSC 2022
A2 - Feng, B.
A2 - Pedrielli, G.
A2 - Peng, Y.
A2 - Shashaani, S.
A2 - Song, E.
A2 - Corlu, C.G.
A2 - Lee, L.H.
A2 - Chew, E.P.
A2 - Roeder, T.
A2 - Lendermann, P.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Winter Simulation Conference, WSC 2022
Y2 - 11 December 2022 through 14 December 2022
ER -