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

报告人：胡耀华 教授 深圳大学

邀请人：赵志华

报告时间：2023年6月6日（周二）下午3:00-5:20

腾讯会议ID：407-857-026

报告人简介：胡耀华，深圳大学澳门所有的游戏网站大全教授，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。主要从事数学优化理论、算法和应用研究。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等国际期刊上发表四十余篇学术论文，授权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。

报告摘要：Abstract:In this talk, we consider the convex composite optimization (CCO) problem that provides a unified framework ofa 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 theCCO. The LPA has the attractive computational advantagesof simple implementation and fast convergence rate. Under the assumptions of local weak sharp minima of Holderian orderand a quasi-regularity condition, we establish a local/semi-local/global superlinear convergence rate for theLPA-type algorithms. We further apply the LPA to solve a (possibly nonconvex) feasibilityproblem, as well as a sensor network localization problem. Our numerical results illustrate thatthe LPA meets the demand for an efficient and robust algorithm for the sensor network localizationproblem.

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