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Optimization on solution sets of common fixed point problems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization on solution sets of common fixed point problemsby Alexander J. Zaslavski.
Author:	Zaslavski, Alexander J.
Published:	Cham :Springer International Publishing :2021.
Description:	xi, 434 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-3-030-78849-0
ISBN:	9783030788490

Optimization on solution sets of common fixed point problems
Zaslavski, Alexander J.

Optimization on solution sets of common fixed point problems[electronic resource] /by Alexander J. Zaslavski. - Cham :Springer International Publishing :2021. - xi, 434 p. :ill., digital ;24 cm. - Springer optimization and its applications,v.1781931-6836 ;. - Springer optimization and its applications ;v. 3..

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 useful for researches interested in the optimization theory and its applications.

ISBN: 9783030788490

Standard No.: 10.1007/978-3-030-78849-0doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5 / .Z37 2021

Dewey Class. No.: 519.6

Optimization on solution sets of common fixed point problems
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505 0 $a 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.
520 $a 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 useful for researches interested in the optimization theory and its applications.
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