Dr. L. Jeff Hong, Large-scale ranking and selection in parallel computing environments,14:00 – 15:00, March 19 (Thursday), 2020, Online Lotus Pond Rain Classroom: W141WB ZOOM ID: 320 388 996 Tecent ID:984 027 9632020.03.14 281

[Time] 14:00 – 15:00, March 19 (Thursday), 2020
 
[Location] Online
Lotus Pond Rain Classroom: W141WB
ZOOM ID: 320 388 996
Tecent ID:984 027 963
 
[Speaker]  Dr. L. Jeff Hong

[Host] Dr. Lei Zhao
 
[Title] Large-scale ranking and selection in parallel computing environments

Note: We will register the seminar for the attendees by the record in Lotus Pond Rain Classroom.

[Abstract] On one hand, large-scale ranking and selection (R&S) problems require a large amount of computation. On the other hand, parallel computing environments that provide a large capacity for computation are becoming prevalent today and they are accessible by ordinary users. Therefore, solving large-scale R&S problems in parallel computing environments has emerged as an important research topic in recent years. However, directly implementing traditional stage-wise procedures and fully-sequential procedures in parallel computing environments may encounter problems, because either the procedures require too many simulation observations or the procedures’ selection structures induce too many comparisons and too frequent communications among the processors. In this paper, inspired by the knockout-tournament arrangement of tennis Grand Slam tournaments, we develop new R&S procedures to solve large-scale problems in parallel computing environments. We show that no matter whether the variances of the alternatives are known or not, our procedures can theoretically achieve the lowest growth rate on the expected total sample size with respect to the number of alternatives and thus are optimal in rate. Moreover, common random numbers (CRNs) can be easily adopted in our procedures to further reduce the total sample size. Meanwhile, the comparison time in our procedures is negligible compared to the simulation time, and our procedures barely request for communications among the processors.


[Bio] Prof. Jeff Hong received his bachelor’s degree from Tsinghua University and Ph.D. from Northwestern University. He is currently with School of Management and School of Data Science at Fudan University, holding the positions of Fudan Distinguished Professor and Hongyi Chair Professor. He was Chair Professor of Management Sciences at City University of Hong Kong, and Professor and Director of Financial Engineering Laboratory at the Hong Kong University of Science and Technology. Prof. Hong’s research focuses on the areas of operations research, data science, and financial engineering and risk management. He has published over 60 papers on leading academic journals and conference proceedings, including 20 papers on the UTD journals. He has won numerous awards, including Outstanding Research Award from Operations Research Society of China, Outstanding Simulation Publication Award from INFORMS Simulation Society, Operations Best Paper Award from Institute of Industrial Engineers. Prof. Hong is currently an Area Editor of Operations Research, an Associate Editor of Management Science and ACM Transactions on Modeling and Computer Simulation, and the President-Elect of INFORMS Simulation Society.


All interested are welcome!

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