Multilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications (Closed)) von Migdalas, A. (Editor) / Pardalos, Panos M. (Editor) / V�rbrand, Peter (Editor): Brand New Paperback (2012)

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reprint edition. 412 pages. 9.45x6.30x0.93 inches. In Stock.

Bestandsnummer des Verk�ufers x-146137989X

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Bibliografische Details

Titel: Multilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications (Closed))
Verlag: Springer
Erscheinungsjahr: 2012
Sprache: Englisch
ISBN-10: 146137989X
ISBN-13: 9781461379898
Einband: Paperback
Zustand: Brand New

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Inhaltsangabe

Researchers working with nonlinear programming often claim "the word is non� linear" indicating that real applications require nonlinear modeling. The same is true for other areas such as multi-objective programming (there are always several goals in a real application), stochastic programming (all data is uncer� tain and therefore stochastic models should be used), and so forth. In this spirit we claim: The word is multilevel. In many decision processes there is a hierarchy of decision makers, and decisions are made at different levels in this hierarchy. One way to handle such hierar� chies is to focus on one level and include other levels' behaviors as assumptions. Multilevel programming is the research area that focuses on the whole hierar� chy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of opti� mization problems which must be solved in a predetermined sequence. If only two levels are considered, we have one leader (associated with the upper level) and one follower (associated with the lower level).

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Researchers working with nonlinear programming often claim "the word is non� linear" indicating that real applications require nonlinear modeling. The same is true for other areas such as multi-objective programming (there are always several goals in a real application), stochastic programming (all data is uncer� tain and therefore stochastic models should be used), and so forth. In this spirit we claim: The word is multilevel. In many decision processes there is a hierarchy of decision makers, and decisions are made at different levels in this hierarchy. One way to handle such hierar� chies is to focus on one level and include other levels' behaviors as assumptions. Multilevel programming is the research area that focuses on the whole hierar� chy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of opti� mization problems which must be solved in a predetermined sequence. If only two levels are considered, we have one leader (associated with the upper level) and one follower (associated with the lower level).

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