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    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
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Multilevel Optimization: Algorithms and Applications

Multilevel Optimization: Algorithms and Applications

Värbrand, Peter
Migdalas, A.
Pardalos, Panos M.

Paperback

Series: Nonconvex Optimization and Its Applications, Book 20

General ComputersGeneral Mathematics

ISBN10: 146137989X
ISBN13: 9781461379898
Publisher: Springer Pg
Published: Sep 17 2011
Pages: 386
Weight: 1.27
Height: 0.84 Width: 6.14 Depth: 9.21
Language: English
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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