报告地点：腾讯会议 205-493-851
邀请人：孙海琳教授
报告摘要：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，现任深圳大学数学与统计学院副教授，博士生导师，香港理工大学兼职博导，国家优秀青年科学基金获得者，深圳市海外高层次人才，兼任中国运筹学会数学规划分会青年理事。主要从事连续优化理论、算法与应用研究，先后主持国家自然科学基金4项，省市级科研项目9项。在SIAM Journal on Optimization, Journal of Machine Learning Research, European Journal of Operational Research, Briefings in Bioinformatics等国际期刊发表论文40余篇，申请3项国家发明专利，开发多个生物信息学工具包与网页服务器。
