SPS Webinar: Fractional Programming for Discrete Optimization in Signal Processing and Machine Learning
Date: 16 January 2025
Time: 7:30 AM ET (New York Time)
Presenter(s): Dr. Kaiming Sehen
Based on the IEEE Xplore® article:
Fractional Programming for Communication Systems-Part II: Uplink Scheduling via Matching, published in the IEEE Transactions on Signal Processing, March 2018.
Download article: Original article will be made publicly available for download on the day of the webinar for 48 hours.
Abstract
Fractional programming (FP) refers to optimization problems involving functions of ratios. FP plays an important role in signal processing and machine learning, because many problems in these application areas are fractionally structured. This talk focuses on a state-of-the-art FP method called the quadratic transform and illustrates the use of quadratic transform for discrete optimization problems through two application examples. The first example is the joint optimization of beamforming and user scheduling for uplink wireless cellular networks, which is a mixed discrete and continuous nonlinear optimization problem. In this domain, the weighted minimum mean square error (WMMSE) algorithm has been extensively used. We connect WMMSE to FP, and further improve upon WMMSE by using the quadratic transform. The second example is the 0-1 normalized-cut (NCut) problem for data clustering and image segmentation. Unlike the previous relaxation-based methods, the proposed FP method recasts the NCut problem into a sequence of weighted bipartite matching problems, which can be solved efficiently without relaxing the discrete variables.
Biography
Kaiming Shen received the B.Eng. degree in information security and the B.Sc. degree in mathematics from Shanghai Jiao Tong University, Shanghai, China in 2011, and the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Toronto, Toronto, Canada in 2013 and 2020 respectively.
He has been a tenure-track assistant professor with the School of Science and Engineering at The Chinese University of Hong Kong (CUHK), Shenzhen, China since 2020. His research interests include optimization, wireless communications, information theory, and machine learning.
Dr. Shen received the IEEE Signal Processing Society Young Author Best Paper Award in 2021, the CUHK Teaching Achievement Award in 2023, and the Frontiers of Science Award in 2024. Dr. Shen is a Senior Member of IEEE and currently serves as an Editor for IEEE Transactions on Wireless Communications.
