VA & OPT: Nonsmooth DC optimization recent developments

Title: Nonsmooth DC optimization: recent developments

Speaker: Adil Bagirov (Federation University)

Abstract: In this talk we consider unconstrained optimization problems where the objective functions are represented as a difference of two convex (DC) functions. Various applications of DC optimization in machine learning are presented. We discuss two different approaches to design methods of nonsmooth DC optimization: an approach based on the extension of bundle methods and an approach based on the DCA (difference of convex algorithm). We also discuss numerical results obtained using these methods.