摘要：Although we have witnessed growing interests from the continuous optimization community in the nonconvex and nonsmooth optimization problems, most of the existing work focus on the Clarke regular objective or constraints so that many computationally-friendly properties hold. In this talk, we will discuss the pervasiveness of the non-(Clarke) regularity in the modern operations research and statistical estimation problems due to the complicated composition of nonconvex and nonsmooth functions. Emphasis will be put on the difficulties brought by the non-regularity both in terms of the computation and the statistical inference, and our initial attempts to overcome them.

个人简介：Ying Cui is currently an assistant professor in the Department of Industrial and Systems Engineering at the University of Minnesota. Prior to that appointment, she was a postdoc research associate in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California working with Professor Jong-Shi Pang. Cui completed her undergraduate study in Mathematics at Zhejiang University and PhD in Mathematics at the National University of Singapore. Her research focuses on the mathematical foundation of data science with emphasis on optimization techniques for operations research, machine learning and statistical estimations. She is particularly interested in leveraging nonsmoothness to design efficient algorithms for large scale nonlinear optimization problems. She is the co-author of the recently published monograph ``Modern Nonconvex Nondifferenable Optimization‘’.