Multilevel Optimization: Algorithms and Applications

A. Migdalas

ISBN 10: 146137989X / ISBN 13: 9781461379898

Published by Springer US, Springer New York Sep 2011, 2011

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 412 pp. Englisch.

Seller Inventory # 9781461379898

Bibliographic Details

• Title: Multilevel Optimization: Algorithms and Applications
• Publisher: Springer US, Springer New York Sep 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 146137989X
• ISBN 13: 9781461379898
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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).