报告题目：AdaBB: A Parameter-Free Gradient Method for Convex Optimization

报告人： 马士谦（线上）

时间：11月20日（周三）11点      

线下地点：东区五教5207        

#腾讯会议：426-975-699 密码：5207

摘要: 

We propose AdaBB, an adaptive gradient method based on the Barzilai-Borwein stepsize. The algorithm is line-search-free and parameter-free, and essentially provides a convergent variant of the Barzilai-Borwein method for general unconstrained convex optimization. We analyze the ergodic convergence of the objective function value and the convergence of the iterates for solving general unconstrained convex optimization. Compared with existing works along this line of research, our algorithm gives the best lower bounds on the stepsize and the average of the stepsizes. Moreover, we present an extension of the proposed algorithm for solving composite optimization where the objective function is the summation of a smooth function and a nonsmooth function. Our numerical results also demonstrate very promising potential of the proposed algorithms on some representative examples.

这次报告也会介绍莱斯大学应用数学与运筹学系的PhD program的招生情况。欢迎准备申请海外博士项目的同学参加。

报告人简介: 

Shiqian Ma is a professor in Department of Computational Applied Mathematics and Operations Research and Department of Electrical and Computer Engineering at Rice University. He received his PhD in Industrial Engineering and Operations Research from Columbia University. His main research areas are optimization and machine learning. His research is currently supported by ONR and NSF Grants from the DMS, CCF, and ECCS programs. Shiqian received the 2024 INFORMS Computing Society Prize and the 2024 SIAM Review SIGEST Award, among many other awards from both academia and industry. Shiqian is an Associate Editor of Journal of Machine Learning Research, Journal of Scientific Computing, Journal of Optimization Theory and Applications, Pacific Journal of Optimization, and IISE Transactions, a Senior Area Chair of NeurIPS, an Area Chair of ICML, ICLR and AISTATS, and a Senior Program Committee of AAAI. He is a plenary speaker of the Texas Colloquium on Distributed Learning in 2023 and a semi-plenary speaker of the International Conference on Stochastic Programming in 2023. Shiqian is the elected Secretary/Treasurer of the INFORMS Optimization Society in 2023-2025, and is the General Chair of the INFORMS Optimization Society Conference 2024.