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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur 7fb124632ebaa9c014435ac331fe5db0
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 464 pp. Englisch. N° de réf. du vendeur 9783319332536
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Studies the approximate solutions of common fixed point problems and convex feasibility problems in the presence of computational errorsExamines the convergence of component-averaged row projections [CARP] Extends results  for a. N° de réf. du vendeur 119052161
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Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. pp. 390. N° de réf. du vendeur 26374674118
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 spaceSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 464 pp. Englisch. N° de réf. du vendeur 9783319332536
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783319332536
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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