An aggregate subgradient method for nonsmooth and nonconvex minimization
type: Item
instance of: scholarly article
title: An aggregate subgradient method for nonsmooth and nonconvex minimization
zbMATH Open document ID: 0603.65042
DOI: 10.1016/0377-0427(86)90075-0
author: Krzysztof C. Kiwiel
published in: Journal of Computational and Applied Mathematics
publication date: 1986-01-01T00:00:00Z
review text: This paper presents an algorithm for minimizing a locally Lipschitz continuous function which is not necessarily convex. The given convergence results are not so strong especially the global convergence result which is established under the hypothesis of convexity of the function to be minimized. Concerning the proofs the reader is often referred to the bibliography. The paper ends with an appendix giving two numerical tests. The considered functions have only two variables. The author does not compare his algorithm with existing ones. In fact the nonsmooth optimization is a hard field of investigation. This explains the weakness of the obtained results.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C25
zbMATH DE Number: 3974191
zbMATH Keywords: subgradient method
zbMATH Keywords: nonsmooth optimization
zbMATH Keywords: nondifferentiable programming
zbMATH Keywords: subdifferential
zbMATH Keywords: descent method
zbMATH Keywords: global convergence
zbMATH Keywords: numerical tests
MaRDI profile type: Publication
full work available at URL: 0377-0427(86)90075-0
OpenAlex ID: W1979722146
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description: scientific article; zbMATH DE number 3974191