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020 _a9783031508790
_9978-3-031-50879-0
024 7 _a10.1007/978-3-031-50879-0
_2doi
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT042000
_2bisacsh
072 7 _aPBU
_2thema
100 1 _aZaslavski, Alexander J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_973210
245 1 0 _aSolutions of Fixed Point Problems with Computational Errors
_h[electronic resource] /
_cby Alexander J. Zaslavski.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aIX, 386 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Optimization and Its Applications,
_x1931-6836 ;
_v210
505 0 _a1 - Introduction -- 2 - Iterative methods in a Hilbert space -- 3 - The Cimmino algorithm in a Hilbert space -- 4 - Dynamic string-averaging methods in Hilbert spaces -- 5 - Methods with remotest set control in a Hilbert space -- 6 - Algorithms based on unions of nonexpansive maps -- 7 - Inconsistent convex feasibility problems -- 8 - Split common fixed point problems.
520 _aThe book is devoted to the study of approximate solutions of fixed point problems in the presence of computational errors. It begins with a study of approximate solutions of star-shaped feasibility problems in the presence of perturbations. The goal is to show the convergence of algorithms, which are known as important tools for solving convex feasibility problems and common fixed point problems. The text also presents studies of algorithms based on unions of nonexpansive maps, inconsistent convex feasibility problems, and split common fixed point problems. A number of algorithms are considered for solving convex feasibility problems and common fixed point problems. The book will be of interest for researchers and engineers working in optimization, numerical analysis, and fixed point theory. It also can be useful in preparation courses for graduate students. The main feature of the book which appeals specifically to this audience is the study of the influence of computational errors for several important algorithms used for nonconvex feasibility problems.
650 0 _aMathematical optimization.
_965522
650 0 _aOperator theory.
650 0 _aMathematics
_xData processing.
_965715
650 1 4 _aOptimization.
_965528
650 2 4 _aOperator Theory.
650 2 4 _aComputational Mathematics and Numerical Analysis.
_966073
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031508783
776 0 8 _iPrinted edition:
_z9783031508806
776 0 8 _iPrinted edition:
_z9783031508813
830 0 _aSpringer Optimization and Its Applications,
_x1931-6836 ;
_v210
_973211
856 4 0 _uhttps://doi.org/10.1007/978-3-031-50879-0
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c58395
_d58395