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  • © 2018

Algorithms for Solving Common Fixed Point Problems

  • Examines approximate solutions to common fixed point problems
  • Offers a number of algorithms to solve convex feasibility problems and common fixed point problems
  • Covers theoretical achievements and applications to engineering

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 132)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-viii
  2. Introduction

    • Alexander J. Zaslavski
    Pages 1-18
  3. Iterative Methods in Metric Spaces

    • Alexander J. Zaslavski
    Pages 19-67
  4. Dynamic String-Averaging Methods in Normed Spaces

    • Alexander J. Zaslavski
    Pages 69-144
  5. Dynamic String-Maximum Methods in Metric Spaces

    • Alexander J. Zaslavski
    Pages 145-176
  6. Abstract Version of CARP Algorithm

    • Alexander J. Zaslavski
    Pages 177-235
  7. Proximal Point Algorithm

    • Alexander J. Zaslavski
    Pages 237-253
  8. Dynamic String-Averaging Proximal Point Algorithm

    • Alexander J. Zaslavski
    Pages 255-279
  9. Convex Feasibility Problems

    • Alexander J. Zaslavski
    Pages 281-306
  10. Back Matter

    Pages 307-316

About this book

This book details approximate solutions to common fixed point problems and convex feasibility problems in the presence of perturbations. Convex feasibility problems search for a common point of a finite collection of subsets in a Hilbert space; common fixed point problems pursue a common fixed point of a finite collection of self-mappings in a Hilbert space. A variety of algorithms are considered in this book for solving both types of problems,  the study of which has fueled a rapidly growing area of research. This monograph is timely and highlights the numerous applications to engineering, computed tomography, and radiation therapy planning.

Totaling eight chapters, this book begins with an introduction to foundational material and moves on to examine iterative methods in metric spaces. The dynamic string-averaging methods for common fixed point problems in normed space are analyzed in Chapter 3. Dynamic string methods, for common fixed point problemsin a metric space are introduced and discussed in Chapter 4. Chapter 5 is devoted to the convergence of an abstract version of the algorithm which has been called  component-averaged row projections (CARP). Chapter 6 studies a proximal algorithm for finding a common zero of a family of maximal monotone operators. Chapter 7 extends the results of Chapter 6 for a dynamic string-averaging version of the proximal algorithm. In Chapters 8 subgradient projections algorithms for convex feasibility problems are examined for infinite dimensional Hilbert spaces. 

Authors and Affiliations

  • Department of Mathematics, Technion:Israel Institute of Technology, Haifa, Israel

    Alexander J. Zaslavski

Bibliographic Information

Buy it now

Buying options

eBook USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access