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Approximate Solutions of Common Fixed-Point Problems

  • Alexander J. Zaslavski

Part of the Springer Optimization and Its Applications book series (SOIA, volume 112)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Alexander J. Zaslavski
    Pages 1-11
  3. Alexander J. Zaslavski
    Pages 13-48
  4. Alexander J. Zaslavski
    Pages 49-97
  5. Alexander J. Zaslavski
    Pages 99-151
  6. Alexander J. Zaslavski
    Pages 153-197
  7. Alexander J. Zaslavski
    Pages 199-250
  8. Alexander J. Zaslavski
    Pages 251-288
  9. Alexander J. Zaslavski
    Pages 289-318
  10. Alexander J. Zaslavski
    Pages 319-339
  11. Alexander J. Zaslavski
    Pages 341-384
  12. Alexander J. Zaslavski
    Pages 385-409
  13. Alexander J. Zaslavski
    Pages 411-446
  14. Back Matter
    Pages 447-454

About this book

Introduction

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 

Keywords

Bregman type Hilbert space approximate solutions behavior of algorithms computational errors computed tomography convergence solution dynamic string-averaging fixed point problems iterative methods radiation therapy planning string-averaging methods

Authors and affiliations

  • Alexander J. Zaslavski
    • 1
  1. 1.Department of MathematicsThe Technion – Israel Institute of TechnRishon LeZionIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-33255-0
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-33253-6
  • Online ISBN 978-3-319-33255-0
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • Buy this book on publisher's site
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