Globalized inexact proximal Newton-type methods for nonconvex composite functions - Christian Kanzow; Theresa Lechner (Computational Optimization and Applications, 2020)
Optimization problems with composite functions consist of an objective function which is the sum of a smooth and a (convex) nonsmooth term. This particular structure is exploited by the class of proximal gradient methods and some of their generalizations like proximal Newton and quasi-Newton methods. The current literature on these classes of methods almost exclusively considers the case where also the smooth term is convex. Here we present a globalized proximal Newton-type method which allows the smooth term to be nonconvex. The method is shown to have nice global and local convergence properties, and some numerical results indicate that this method is very promising also from a practical point of view.
Keywords: 510; Optimization; Operations Research; Management Science; Operations Research/Decision Theory; Statistics; general; Convex and Discrete Geometry