主 講 人：胡耀華

主辦單位：數學與大數據學院

講座時間：2020年11月3日（周二）上午10:00-11:00

講座地點：知津樓C303

內容簡介：

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.

主講人簡介：

胡耀華博士，深圳大學數學與統計學院副教授，碩士生導師，本科和碩士畢業于浙江大學，博士畢業于香港理工大學，從事最優化理論，算法和應用方面的研究工作。目前在最優化領域的權威期刊SIAM Journal on Optimization，Journal of Machine Learning Research，European Journal of Operational Research，Journal of Global Optimization及Numerical Algorithms和Inverse Problems等期刊上發表了多篇學術論文。主持國家自然科學基金青年項目和面上項目各1項。