报告题目：General inertial proximal gradient method with gradient extrapolation for nonconvex nonsmooth optimization problems

报告人：蔡邢菊 教授/博导，南京师范大学

报告时间：2025年5月15日（周四）下午15:30-16:30

报告地点：数理楼306会议室

主办单位：999策略手机论坛版

报告对象：全校感兴趣的老师和学生

报告摘要：The inertial strategy has been widely utilized to accelerate proximal gradient methods for nonconvex nonsmooth optimization problems. Recently, the gradient extrapolation technique has also been adopted to further enhance the acceleration of these methods. Inspired by the effectiveness of both techniques, in this paper, we propose a general inertial proximal gradient method with gradient extrapolation, named GiPMGE. Compared to existing methods, our proposed GiPMGE not only covers some classical methods, but also offers more general and flexible choices for the inertial, gradient extrapolation, and stepsize parameters. Furthermore, we present a concise counterexample to illustrate the tightness of the largest range of feasible stepsize in GiPMGE. Under the assumption that the objective function satisfies the KL property, we prove that the sequence generated by GiPMGE globally converges to a critical point of the objective function. Additionally, we conduct some numerical experiments to demonstrate the advantage of GiPMGE.

报告人简介：蔡邢菊，南京师范大学教授，博导。主要从事最优化理论与算法、变分不等式、数值优化方向研究工作。主持多项国家基金，获江苏省科技进步奖一等奖一项，发表SCI论文70余篇。担任中国运筹学会副秘书长、算法软件与应用分会常务理事兼秘书长、数学规划分会常务理事，江苏省运筹学会理事长。