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＜電子ブック＞
Optimization on Solution Sets of Common Fixed Point Problems / by Alexander J. Zaslavski
(Springer Optimization and Its Applications. ISSN:19316836 ; 178)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XI, 434 p : online resource
著者標目	*Zaslavski, Alexander J author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Calculus of variations LCSH:Operations research LCSH:Management science LCSH:Numerical analysis FREE:Calculus of Variations and Optimization FREE:Operations Research, Management Science FREE:Numerical Analysis
一般注記	Preface -- Introduction -- Fixed Point Subgradient Algorithm -- Proximal Point Subgradient Algorithm -- Cimmino Subgradient Projection Algorithm -- Iterative Subgradient Projection Algorithm -- Dynamic Strong-Averaging Subgradient Algorithm -- Fixed Point Gradient Projection Algorithm -- Cimmino Gradient Projection Algorithm -- A Class of Nonsmooth Convex Optimization Problems -- Zero-Sum Games with Two Players -- References -- Index This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the solution sets of common fixed point problems and convex feasibility problems. These optimization problems are investigated to determine good solutions obtained by different versions of the subgradient projection algorithm in the presence of sufficiently small computational errors. The use of selected algorithms is highlighted including the Cimmino type subgradient, the iterative subgradient, and the dynamic string-averaging subgradient. All results presented are new. Optimization problems where the underlying constraints are the solution sets of other problems, frequently occur in applied mathematics. The reader should not miss the section in Chapter 1 which considers some examples arising in the real world applications. The problems discussed have an important impact in optimization theory as well. The book will be usefulfor researches interested in the optimization theory and its applications HTTP:URL=https://doi.org/10.1007/978-3-030-78849-0

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