Numerical Optimization with Computational Errors (Springer Optimization and Its Applications) von Alexander J Zaslavski: Brand New Paperback (2018)

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reprint edition. 316 pages. 9.25x6.10x0.72 inches. In Stock.

Bestandsnummer des Verk�ufers 3319809172

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Bibliografische Details

Titel: Numerical Optimization with Computational Errors (Springer Optimization and Its Applications)
Verlag: Springer
Erscheinungsjahr: 2018
Sprache: Englisch
ISBN-10: 3319809172
ISBN-13: 9783319809175
Einband: Paperback
Zustand: Brand New

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Inhaltsangabe

This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors are examined with the aim of determining an approximate solution. Researchers and students interested in the optimization theory and its applications will find this book instructive and informative.

This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton’s method.

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This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors are examined with the aim of determining an approximate solution. Researchers and students interested in the optimization theory and its applications will find this book instructive and informative.

This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton s method.

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