Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov

Abstract

Nonsmooth optimization (NSO) refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. These types of functions arise in many applied fields, for instance, in image denoising, optimal shape design, computational chemistry and physics, machine learning, and data mining including cluster analysis, classification and regression. Despite the considerable developments in NSO, the lack of numerically effective methods is still evident and their applications to real-world problems is somewhat limited. The aim of this Special Issue is to collect together the most recent techniques and applications in the area of NSO.