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Solutions of fixed point problems with computational errors

Record Type:	Electronic resources : Monograph/item
Title/Author:	Solutions of fixed point problems with computational errorsby Alexander J. Zaslavski.
Author:	Zaslavski, Alexander J.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	ix, 386 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-3-031-50879-0
ISBN:	9783031508790$q(electronic bk.)

Solutions of fixed point problems with computational errors
Zaslavski, Alexander J.

Solutions of fixed point problems with computational errors[electronic resource] /by Alexander J. Zaslavski. - Cham :Springer Nature Switzerland :2024. - ix, 386 p. :ill., digital ;24 cm. - Springer optimization and its applications,v. 2101931-6836 ;. - Springer optimization and its applications ;v. 3..

1 - Introduction -- 2 - Iterative methods in a Hilbert space -- 3 - The Cimmino algorithm in a Hilbert space -- 4 - Dynamic string-averaging methods in Hilbert spaces -- 5 - Methods with remotest set control in a Hilbert space -- 6 - Algorithms based on unions of nonexpansive maps -- 7 - Inconsistent convex feasibility problems -- 8 - Split common fixed point problems.

The book is devoted to the study of approximate solutions of fixed point problems in the presence of computational errors. It begins with a study of approximate solutions of star-shaped feasibility problems in the presence of perturbations. The goal is to show the convergence of algorithms, which are known as important tools for solving convex feasibility problems and common fixed point problems. The text also presents studies of algorithms based on unions of nonexpansive maps, inconsistent convex feasibility problems, and split common fixed point problems. A number of algorithms are considered for solving convex feasibility problems and common fixed point problems. The book will be of interest for researchers and engineers working in optimization, numerical analysis, and fixed point theory. It also can be useful in preparation courses for graduate students. The main feature of the book which appeals specifically to this audience is the study of the influence of computational errors for several important algorithms used for nonconvex feasibility problems.

ISBN: 9783031508790$q(electronic bk.)

Standard No.: 10.1007/978-3-031-50879-0doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Solutions of fixed point problems with computational errors
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245 1 0 $a Solutions of fixed point problems with computational errors $h [electronic resource] / $c by Alexander J. Zaslavski.
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505 0 $a 1 - Introduction -- 2 - Iterative methods in a Hilbert space -- 3 - The Cimmino algorithm in a Hilbert space -- 4 - Dynamic string-averaging methods in Hilbert spaces -- 5 - Methods with remotest set control in a Hilbert space -- 6 - Algorithms based on unions of nonexpansive maps -- 7 - Inconsistent convex feasibility problems -- 8 - Split common fixed point problems.
520 $a The book is devoted to the study of approximate solutions of fixed point problems in the presence of computational errors. It begins with a study of approximate solutions of star-shaped feasibility problems in the presence of perturbations. The goal is to show the convergence of algorithms, which are known as important tools for solving convex feasibility problems and common fixed point problems. The text also presents studies of algorithms based on unions of nonexpansive maps, inconsistent convex feasibility problems, and split common fixed point problems. A number of algorithms are considered for solving convex feasibility problems and common fixed point problems. The book will be of interest for researchers and engineers working in optimization, numerical analysis, and fixed point theory. It also can be useful in preparation courses for graduate students. The main feature of the book which appeals specifically to this audience is the study of the influence of computational errors for several important algorithms used for nonconvex feasibility problems.
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