学术报告
Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
严明（香港中文大学（深圳））
报告时间：2025年1月17日星期五 10：30-11：30
报告地点：沙河主楼E404
报告摘要: We consider the decentralized optimization problem, where a network of n agents aims to collaboratively minimize the average of their individual smooth and convex objective functions through peer-to-peer communication in a directed graph. To tackle this problem, we propose two accelerated gradient tracking methods, namely APD and APD-SC, for non-strongly convex and strongly convex objective functions, respectively. We show that APD and APD-SC converge at the rates O(1/k^2) and O((1-C\sqrt{\mu/L})^k), respectively, up to constant factors depending only on the mixing matrix. APD and APD-SC are the first decentralized methods over unbalanced directed graphs that achieve the same provable acceleration as centralized methods. Numerical experiments demonstrate the effectiveness of both methods.
报告人简介：严明是香港中文大学（深圳）数据科学学院副教授，助理院长。他的研究兴趣主要集中在数学优化及其在图像处理、机器学习和其他数据科学问题中的应用。他于2005年和2008年分别获得中国科学技术大学学士和硕士学位。后于2012年获得加州大学洛杉矶分校数学博士学位。在2015年至2022年期间，他担任密歇根州立大学的助理教授和副教授。自2021年起，他持续入选“全球前2%顶尖科学家“榜单。
邀请人：崔春风
