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Añadir al carritoTaschenbuch. Condición: Neu. Neu Neuware; original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications. 248 pp. Englisch.
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Añadir al carritoTaschenbuch. Condición: Neu. Neu Neuware; original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications. 248 pp. Englisch.
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Añadir al carritoHardcover. Condición: Fine. 0792338405 Fine/As New; Hardcover; Covers are still glossy with "sharp" edge-corners; Unblemished textblock edges; The endpapers and all text pages are bright and unmarked; Binding is tight with a straight spine; This book will be stored and delivered in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); Blue covers with title in black lettering; 1996, Springer-Verlag Publishing; 222 pages; "Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)," by Julia L. Higle & S. Sen.
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Publicado por Kluwer Academic Publishers, 1996
ISBN 10: 0792338405 ISBN 13: 9780792338406
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Añadir al carritoCondición: New. Summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. This title is suitable for researchers in mathematical optimization, including those working in telecommunications and electric power generation. Series: Nonconvex Optimization and Its Applications. Num Pages: 246 pages, biography. BIC Classification: PBT; PBW; UM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 15. Weight in Grams: 1160. . 1996. Hardback. . . . .
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Añadir al carritoCondición: New. Summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. This title is suitable for researchers in mathematical optimization, including those working in telecommunications and electric power generation. Series: Nonconvex Optimization and Its Applications. Num Pages: 246 pages, biography. BIC Classification: PBT; PBW; UM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 15. Weight in Grams: 1160. . 1996. Hardback. . . . . Books ship from the US and Ireland.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found a.
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Añadir al carritoGebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found a.
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Añadir al carritoTaschenbuch. Condición: Neu. Stochastic Decomposition | A Statistical Method for Large Scale Stochastic Linear Programming | Julia L. Higle (u. a.) | Taschenbuch | Nonconvex Optimization and Its Applications | xxiv | Englisch | 2013 | Springer | EAN 9781461368458 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Idioma: Inglés
Publicado por Springer, Springer Nov 2013, 2013
ISBN 10: 1461368456 ISBN 13: 9781461368458
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 248 pp. Englisch.