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Approximate Solutions of Common Fixed-Point Problems

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This book presents results on the convergence behavior of algorithms which are known as vital tools for solving convex feasibility problems and common fixed point problems. The main goal for us in dealing with a known computational error is to find what approximate solution can be obtained and how many iterates one needs to find it. According to know results, these algorithms should converge to a solution. In this exposition, these algorithms are studied, taking into account computational errors which remain consistent in practice. In this case the convergence to a solution does not take place. We show that our algorithms generate a good approximate solution if computational errors are bounded from above by a small positive constant.

Beginning with an introduction, this monograph moves on to study:

· dynamic string-averaging methods for common fixed point problems in a Hilbert space

· dynamic string methods for common fixed point problems in a metric space<

· dynamic string-averaging version of the proximal algorithm

· common fixed point problems in metric spaces
· common fixed point problems in the spaces with distances of the Bregman type

· a proximal algorithm for finding a common zero of a family of maximal monotone operators

· subgradient projections algorithms for convex feasibility problems in Hilbert spaces

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Detail Information

Series Title

Springer Optimization and Its Applications

Call Number

518 ZAS a

Publisher

Cham, Switzerland : Springer Cham., 2016

Collation

IX, 454

Language

English

ISBN/ISSN

978-3-319-33255-0

Classification

518

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

Ed. 1

Subject(s)

Numerical Analysis
Topics Calculus of Variations and Optimal Control

Specific Detail Info

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Statement of Responsibility

Alexander J. Zaslavski

Other Information

Cataloger

umi

Source

https://link.springer.com/book/10.1007/978-3-319-33255-0

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