国产a片
学术报告[2026]058号
（高水平大学建设系列报告1317号）
报告题目：A variance reduced framework for (non)smooth nonconvex–nonconcave stochastic minimax problems with extended Kurdyka–Łojasiewicz property
报告人：徐扬扬 副教授（伦斯勒理工学院）
报告时间：2026年6月16日15:30-16:30
报告地点：粤海校区校友广场304会议室
报告摘要：In this talk, I will present an algorithm for solving stochastic constrained minimax optimization problems with nonconvex--nonconcave structure, a central problem in modern machine learning, for which reliable and efficient algorithms remain largely unexplored due to its inherent challenges.
Prior approaches for nonconvex minimax optimization often require (strong) concavity on the maximization part, or certain restrictive geometric assumptions on the joint objective to have guaranteed convergence. In contrast, our method only assumes weak convexity in the primal variable and the extended Kurdyka–Łojasiewicz (KL) property in the dual variable, significantly broadening the class of tractable problems. To this end, we propose a variance reduced algorithm that provably handles this general setting. To the best of our knowledge, this is the first unified framework that jointly accommodates weak convexity, the extended KL property, and variance-reduced stochastic updates, making it highly suitable for large-scale applications.
报告人简介：Yangyang Xu is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He received his B.S. in Computational Mathematics from Nanjing University in 2007, his M.S. in Operations Research from the Chinese Academy of Sciences in 2010, and his Ph.D. in Computational and Applied Mathematics from Rice University in 2014. His research focuses on optimization theory and algorithms and their applications in machine learning, statistics, and signal processing. His recent work centers on stochastic optimization, robust machine learning, large-scale constrained optimization, and distributed optimization. He currently serves as an Associate Editor for Mathematics of Operations Research.
邀请人：吴育洽
国产a片
2026年6月15日
