Multilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications (Closed))

Migdalas, A. (Editor) / Pardalos, Panos M. (Editor) / Värbrand, Peter (Editor)

ISBN 10: 146137989X / ISBN 13: 9781461379898

Edité par Springer, 2012

A propos de cet article

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

N° de réf. du vendeur x-146137989X

Détails bibliographiques

• Titre: Multilevel Optimization: Algorithms and Applications (Nonconvex Optimization and Its Applications (Closed))
• Éditeur: Springer
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 146137989X
• ISBN à 13 chiffres: 9781461379898
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

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).

Présentation de l'éditeur

In many decision processes there is an hierarchy of decision-makers and decisions are taken at different levels in this hierarchy. Multilevel programming focuses on the whole hierarchy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of optimization problems which must be solved in a predetermined sequence. The field of multilevel optimization has become a well-known and important research field. Hierarchical structures can be found in scientific disciplines such as environment, ecology, biology, chemical engineering, mechanics, classification theory, databases, network design, transportation, game theory and economics. Moreover, new applications are constantly being introduced. This has stimulated the development of new theory and efficient algorithms. This volume contains 16 chapters written by various leading researchers and presents a cohesive authoritative overview of developments and applications in their emerging field of optimization. Audience: Researchers whose work involves the application of mathematical programming and optimization to hierarchical structures.