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报告题目：Linearized Proximal Algorithms for Convex Composite Optimization with Applications

报告人：胡耀华（深圳大学）

报告时间：2022年7月23日 9:00~10:00

报告地点：腾讯会议  会议号：554-976-5868

报告内容简介：In this talk, we consider the convex composite optimization (CCO) problem that provides a unified framework of a wide variety of important optimization problems, such as convex inclusions, penalty methods for nonlinear programming, and regularized minimization problems. We will introduce a linearized proximal algorithm (LPA) to solve the CCO. The LPA has the attractive computational advantages of simple implementation and fast convergence rate. Under the assumptions of local weak sharp minima of Holderian order and a quasi-regularity condition, we establish a local/semi-local/global superlinear convergence rate for the LPA-type algorithms. We further apply the LPA to solve a (possibly nonconvex) feasibility problem, as well as a sensor network localization problem. Our numerical results illustrate that the LPA meets the demand for an efficient and robust algorithm for the sensor network localization problem.

报告人简介：胡耀华，1984年，江西吉安人。先后获得浙江大学学士和硕士学位，香港理工大学博士学位（师从杨晓琪教授），现任深圳大学数学与统计学院副教授，硕士生导师，香港理工大学兼职博士生导师，兼任中国运筹学会一数学规划分会青年理事，广东省工业与应用数学学会理事，广东省运筹学会理事。 主要从事连续优化理论与应用研究，主持国家自然科学基金3项，省市级科研项目多项。在SIAM Journal on Optimization, Journal of Machine Leaning Research, Inverse Problems, European Joumal of Operational Research等国际学术期刊发表40余篇论文，授权3项发明专利，开发多个生物信息学工具包/网页服务器。个人主页：https://mayhhu.github.io/ch/index.html

邀请人：矫立国 (助理研究员  jiaolg356@nenu.edu.cn)