Title:  Second-Order Type Optimization Methods For Data Analysis

Speaker: Prof. Wen Zaiwen, Beijing International Center for Mathematical Research (BICMR), Peking University

Abstract: Optimization models are ubiquitous in data analysis. In this talk, we will review Gauss-Newton methods for phase retrieval, semi-smooth Newton methods for composite convex programs and its application to large-scale semi-definite program problems, sub-sampled semismooth Newton method for nonsmooth composite minimization problems from large-scale machine learning, as well as an adaptive regularized Newton method for Riemannian Optimization.