A Computational Study of Probabilistic Branch and Bound with Multilevel Importance Sampling-Reference-Cited by-同舟云学术

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A Computational Study of Probabilistic Branch and Bound with Multilevel Importance Sampling

Published:2022-12-11 Issue: Volume: Page:
ISSN:
Container-title:2022 Winter Simulation Conference (WSC)
language:
Short-container-title:

Author:

Huang Hao¹,Maneekul Pariyakorn²,Morey Danielle F.²,Zabinsky Zelda B.²,Pedrielli Giulia³

Affiliation:

1. Yuan Ze University,Department of Industrial Engineering and Management,Taoyuan,Taiwan

2. University of Washington,Department of Industrial and Systems Engineering,Seattle,WA,USA

3. School of Computing and Augmented Intelligence, Arizona State University,Tempe,AZ,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10015196/10015230/10015267.pdf?arnumber=10015267

Reference11 articles.

1. Adaptive probabilistic branch and bound with confidence intervals for level set approximation

2. High Dimensional Level Set Estimation with Bayesian Neural Network;ha;Proceedings of the AAAI Conference on Artificial Intelligence,2020

3. STOCHASTIC OPTIMIZATION FOR FEASIBILITY DETERMINATION: AN APPLICATION TO WATER PUMP OPERATION IN WATER DISTRIBUTION NETWORK

4. A Partition-Based Optimization Approach for Level Set Approximation: Probabilistic Branch and Bound

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