03870nam 22005295 450 991030010650332120200706100929.03-319-77437-910.1007/978-3-319-77437-4(CKB)4100000004243798(DE-He213)978-3-319-77437-4(MiAaPQ)EBC5379798(PPN)227402677(EXLCZ)99410000000424379820180502d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierAlgorithms for Solving Common Fixed Point Problems /by Alexander J. Zaslavski1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (VIII, 316 p.) Springer Optimization and Its Applications,1931-6828 ;1323-319-77436-0 1. Introduction -- 2. Iterative methods in metric spaces -- 3. Dynamic string-averaging methods in normed spaces -- 4. Dynamic string-maximum methods in metric spaces -- 5. Abstract version of CARP algorithm -- 6. Proximal point algorithm -- 7. Dynamic string-averaging proximal point algorithm -- 8. Convex feasibility problems.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 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 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. .Springer Optimization and Its Applications,1931-6828 ;132Calculus of variationsOperator theoryNumerical analysisCalculus of Variations and Optimal Control; Optimizationhttps://scigraph.springernature.com/ontologies/product-market-codes/M26016Operator Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/M12139Numerical Analysishttps://scigraph.springernature.com/ontologies/product-market-codes/M14050Calculus of variations.Operator theory.Numerical analysis.Calculus of Variations and Optimal Control; Optimization.Operator Theory.Numerical Analysis.515.64Zaslavski Alexander Jauthttp://id.loc.gov/vocabulary/relators/aut721713BOOK9910300106503321Algorithms for Solving Common Fixed Point Problems1564647UNINA