Linearized Proximal Algorithms for Convex Composite Optimization with Applications
报告专家：胡耀华 教授（深圳大学）
报告时间：11月7日（星期四）下午15:30-16:30
报告地点：数学学院西202报告厅
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.
专家简介：胡耀华，先后于浙江大学获得学士与硕士学位，香港理工大学获得博士学位。现任深圳大学数学科学学院特聘教授，副院长，博士生导师，香港理工大学兼职博导，兼任中国运筹学会数学规划分会青年支部主任，中国运筹学会算法软件与应用分会常务理事，中国运筹学会科普工作委员会委员，广东省运筹学会副理事长。主要从事连续优化理论、方法与应用研究，代表性成果发表在SIAM Journal on Optimization, Mathematical Programming, Inverse Problems, Journal of Machine Learning Research, Bioinformatics等期刊，授权多项国家发明专利，开发多个生物信息学工具包，先后主持国家自然科学基金优秀青年科学基金等10余项国家与省市级科研项目。
邀请人：方亚平
