Special Issue “Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov”: Foreword by Guest Editors

Napsu Karmitsa,Sona Taheri

doi:10.3390/a13110282

Abstract

Nonsmooth optimization refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. This Special Issue contains six research articles that collect together the most recent techniques and applications in the area of nonsmooth optimization. These include novel techniques utilizing some decomposable structures in nonsmooth problems—for instance, the difference-of-convex (DC) structure—and interesting important practical problems, like multiple instance learning, hydrothermal unit-commitment problem, and scheduling the disposal of nuclear waste.

Highlights

We take the opportunity to acknowledge the outstanding contributions of Professor Adil Bagirov (Figure 1) to nonsmooth optimization (NSO) in both theoretical foundations and its practical aspects during his 35 year long research career
The Guest Editors are grateful to Professor Adil Bagirov, with whom they have had the privilege to do research in the area of NSO and its real-life applications
Optimization Problems” by Angel Alejandro Juan, Canan Gunes Corlu, Rafael David Tordecilla, Rocio de la Torre and Albert Ferrer [16], introduces the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and NSO problems in many practical applications, in particular, those including so called soft constraints

Summary

Introduction

We take the opportunity to acknowledge the outstanding contributions of Professor Adil Bagirov (Figure 1) to nonsmooth optimization (NSO) in both theoretical foundations and its practical aspects during his 35 year long research career. This Special Issue collects together the most recent techniques and applications in the area of NSO. Professor Bagirov has contributed exceptionally to NSO and its applications to real-life problems.

Results

Conclusion