9783030685164 - a derivative-free two level random search method for unconstrained optimization (springerbriefs in optimization) de andrei, neculai (15 resultados)
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Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book isintended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in specified domain…s and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust.Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: thereduction phaseand thestallingone. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities.There are a number ofopen problemswhich refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search.
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Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Taschenbuch. Condición: Neu. A Derivative-free Two Level Random Search Method for Unconstrained Optimization | Neculai Andrei | Taschenbuch | SpringerBriefs in Optimization | xi | Englisch | 2021 | Springer | EAN 9783030685164 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen…[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Condición: new. Questo è un articolo print on demand.
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Editorial: Springer International Publishing Apr 2021, 2021
Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book isintended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in…specified domains and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust.Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: thereduction phaseand thestallingone. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities.There are a number ofopen problemswhich refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search. 132 pp. Englisch.
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Editorial: Springer, 2021
Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Editorial: Springer, 2021
Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Librería: moluna, Greven, Alemaniamoluna
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Clarity of presentation as well as discussion of open problems are an attractive feature for instructors and potential practitioners in derivative-free methods for optimizationHighlights a new… and simple derivative-free optimization algo.
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Editorial: Springer, Palgrave Macmillan Apr 2021, 2021
Serie: SpringerBriefs in Optimization, Libro 43 de 43. Libro 43 de 43 - SpringerBriefs in Optimization
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book is intended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in spe…cified domains and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust.Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: the reduction phase and the stalling one. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities.There are a number of open problems which refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.
