报告地点：行健楼学术活动室526
邀请人：蔡邢菊教授
报告摘要：We consider stochastic and finite-sum optimization problems with deterministic constraints. Existing methods typically focus on finding an approximate stochastic solution that ensures the expected constraint violations and optimality conditions meet a prescribed accuracy. However, such an approximate solution can possibly lead to significant constraint violations. To address this issue, we propose variance-reduced first-order methods that treat the objective and constraints differently. Under suitable assumptions, our proposed methods achieve stronger approximate stochastic solutions with complexity guarantees that more reliably satisfy the constraints compared to existing methods. This is joint work with Sanyou Mei (HKUST) and Yifeng Xiao (UMN).
报告人简介：
Zhaosong Lu is a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received his Ph.D. in Operations Research from Georgia Institute of Technology. His research focuses on the theory and algorithms of continuous optimization, with applications in data science and machine learning. Dr. Lu has published extensively in leading journals such as Mathematical Programming, Mathematics of Operations Research, and SIAM Journal on Optimization. His work has been supported by funding agencies including AFOSR, NSF, and ONR. He has served on several prize committees, such as the INFORMS George Nicholson Prize and the ICCOPT Best Paper Award. In addition, he has served as an Associate Editor for journals including SIAM Journal on Optimization, Computational Optimization and Applications, and Journal of Global Optimization.
