Abstract:

 

Nonconvex constrained optimization is a vital research area within the optimization community, encompassing a wide range of applications across various fields. However, addressing nonconvex constrained optimization presents significant challenges due to the large-scale data and inherent uncertainties as well as potentially nonconvex functional constraints in optimization models. In this talk, I will report our recent progress on stochastic approximation methods for nonconvex constrained optimization that include established complexity bounds and/or convergence properties.

 

Biography:

 

Dr. Xiao Wang is currently a professor of Computational Mathematics in the School of Computer Science and Engineering at Sun Yat-sen University. Before joining SYSU, she was an associate professor at University of Chinese Academy of Sciences (2017-2021) and Pengcheng National Laboratory (2021-2025). She obtained PhD in Computational Mathematics from Academy of Mathematics and System Science, Chinese Academy of Sciences and BSc in Information and Computational Science from Shandong University. Her current research interests include theory and algorithms for nonlinear optimization, and their applications in machine learning, scientific computing, etc. She has published more than 30 papers in journals including SIAM Journal on Optimization, SIAM Journal on Numerical Analysis, Mathematics of Operations Research, Mathematics of Computation, Journal of Machine Learning Research. 

 

Everyone is welcome to attend the talk!

SEEM-5202 Website: http://seminar.se.cuhk.edu.hk