Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/1946
Title: Weak subgradient method for solving nonsmooth nonconvex optimization problems
Authors: Yalçın, Gülçin Dinç
Kasımbeyli, Refail
Keywords: Subgradient
weak subgradient
nonconvex optimization
nonsmooth optimization
nonlinear optimization
solution method
Issue Date: 2021
Publisher: Taylor & Francis Ltd
Abstract: This paper presents a weak subgradient based method for solving nonconvex optimization problems. The method uses a weak subgradient of the objective function at a current point to generate a new one at every iteration. The concept of the weak subgradient is based on the idea of using supporting cones to the graph of a function under consideration which replaces in some sense the supporting hyperplanes used for subgradient notion of convex analysis. Because of this reason, the method developed in this paper does not require convexity assumption neither on the objective function nor on the set of feasible solutions. The new method is similar to subgradient methods of convex analysis and can be considered as a generalization of those methods. The paper investigates different stepsize parameters and provides convergence theorems for all cases. The significant difficulty of subgradient methods is an estimation of subgradients at every iteration. In this paper, a method for estimating the weak subgradients is also presented. The new method is tested on well-known test problems from the literature and computational results are reported and interpreted.
URI: https://doi.org/10.1080/02331934.2020.1745205
https://hdl.handle.net/20.500.13087/1946
ISSN: 0233-1934
1029-4945
Appears in Collections:İnşaat Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

Show full item record

CORE Recommender

Page view(s)

24
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.