A Non-monotone Conjugate Subgradient Type Method for Minimization of Convex Functions

dc.contributor.author	Konnov I.
dc.date.accessioned	2020-01-21T20:32:01Z
dc.date.available	2020-01-21T20:32:01Z
dc.date.issued	2019
dc.identifier.issn	0022-3239
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/157354
dc.description.abstract	© 2019, Springer Science+Business Media, LLC, part of Springer Nature. We suggest a conjugate subgradient type method without any line search for minimization of convex non-differentiable functions. Unlike the custom methods of this class, it does not require monotone decrease in the goal function and reduces the implementation cost of each iteration essentially. At the same time, its step-size procedure takes into account behavior of the method along the iteration points. The preliminary results of computational experiments confirm the efficiency of the proposed modification.
dc.relation.ispartofseries	Journal of Optimization Theory and Applications
dc.subject	Conjugate subgradient method
dc.subject	Convergence properties
dc.subject	Convex minimization problems
dc.subject	Non-differentiable functions
dc.subject	Simple step-size choice
dc.title	A Non-monotone Conjugate Subgradient Type Method for Minimization of Convex Functions
dc.type	Article
dc.collection	Публикации сотрудников КФУ
dc.source.id	SCOPUS00223239-2019-SID85074041175

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Публикации сотрудников КФУ Scopus [24551]
Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.