胡耀华副教授受邀进行讲座

发布时间: 2021-11-16 10:07:00

题       目：Linearized Proximal Algorithms for Convex Composite Optimization with Applications

报  告 人：胡耀华副教授    深圳大学

时       间：2021-11-15  16:00--17:00

地       点：学院401

报告人简介:

        胡耀华，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，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 Learning Research, Inverse Problems, European Journal of Operational Research上发表多篇论文，参与开发多个生物信息学工具包/网页服务器。

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.