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    Optimization on Solution Sets of Common Fixed Point Problems

Optimization on Solution Sets of Common Fixed Point Problems

Alexander J. Zaslavski
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      This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the solution sets of common fixed point problems and convex feasibility problems. These optimization problems are investigated to determine good solutions obtained by different versions of the subgradient projection algorithm in the presence of sufficiently small computational errors.  The use of selected algorithms is highlighted including the Cimmino type subgradient, the iterative subgradient, and the dynamic string-averaging subgradient.  All results presented are new.  Optimization problems where the underlying constraints are the solution sets of other problems, frequently occur in applied mathematics. The reader should not miss the section in Chapter 1 which considers some examples arising in the real world applications. The problems discussed have an important impact in optimization theory as well. The book will be useful for researches interested in the optimization theory and its applications.
      Format: Paperback / softback CONTRIBUTORS: Alexander J. Zaslavski EAN: 9783030788513 COUNTRY: Switzerland PAGES: WEIGHT: 682 g HEIGHT: 235 cm
      PUBLISHED BY: Springer Nature Switzerland AG DATE PUBLISHED: 2022-08-11 CITY: GENRE: BUSINESS & ECONOMICS / Operations Research, MATHEMATICS / Applied, MATHEMATICS / Number Systems WIDTH: 155 cm SPINE:

      Book Themes:

      Operational research, Numerical analysis, Optimization

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      ​Alexander J. Zaslavski is professor in the Department of Mathematics, Technion-Israel Institute of Technology, Haifa, Israel. He has authored numerous books with Springer, the most recent of which include Turnpike Theory for the Robinson–Solow–Srinivasan Model (978-3-030-60306-9),  The Projected Subgradient Algorithm in Convex Optimization (978-3-030-60299-4),  Convex Optimization with Computational Errors (978-3-030-37821-9), Turnpike Conditions in Infinite Dimensional Optimal Control (978-3-030-20177-7).
      This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the solution sets of common fixed point problems and convex feasibility problems. These optimization problems are investigated to determine good solutions obtained by different versions of the subgradient projection algorithm in the presence of sufficiently small computational errors.  The use of selected algorithms is highlighted including the Cimmino type subgradient, the iterative subgradient, and the dynamic string-averaging subgradient.  All results presented are new.  Optimization problems where the underlying constraints are the solution sets of other problems, frequently occur in applied mathematics. The reader should not miss the section in Chapter 1 which considers some examples arising in the real world applications. The problems discussed have an important impact in optimization theory as well. The book will be useful for researches interested in the optimization theory and its applications.
      Format: Paperback / softback CONTRIBUTORS: Alexander J. Zaslavski EAN: 9783030788513 COUNTRY: Switzerland PAGES: WEIGHT: 682 g HEIGHT: 235 cm
      PUBLISHED BY: Springer Nature Switzerland AG DATE PUBLISHED: 2022-08-11 CITY: GENRE: BUSINESS & ECONOMICS / Operations Research, MATHEMATICS / Applied, MATHEMATICS / Number Systems WIDTH: 155 cm SPINE:

      Book Themes:

      Operational research, Numerical analysis, Optimization

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      ​Alexander J. Zaslavski is professor in the Department of Mathematics, Technion-Israel Institute of Technology, Haifa, Israel. He has authored numerous books with Springer, the most recent of which include Turnpike Theory for the Robinson–Solow–Srinivasan Model (978-3-030-60306-9),  The Projected Subgradient Algorithm in Convex Optimization (978-3-030-60299-4),  Convex Optimization with Computational Errors (978-3-030-37821-9), Turnpike Conditions in Infinite Dimensional Optimal Control (978-3-030-20177-7).

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