Algorithms for Solving Common Fixed Point Problems
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The work Algorithms for Solving Common Fixed Point Problems represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Algorithms for Solving Common Fixed Point Problems
Resource Information
The work Algorithms for Solving Common Fixed Point Problems represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Algorithms for Solving Common Fixed Point Problems
 Statement of responsibility
 by Alexander J. Zaslavski
 Language
 eng
 Summary
 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 selfmappings 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 stringaveraging methods for common fixed point problems in normed space are analyzed in Chapter 3. Dynamic string methods, for common fixed point problems in 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 componentaveraged 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 stringaveraging version of the proximal algorithm. In Chapters 8 subgradient projections algorithms for convex feasibility problems are examined for infinite dimensional Hilbert spaces.
 Image bit depth
 0
 LC call number

 QA315316
 QA402.3
 QA402.5QA402.6
 Literary form
 non fiction
 Series statement
 Springer Optimization and Its Applications,
 Series volume
 132
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