Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	November 2012
Genre:	Schulbücher
	addition;calculus;optimization;scheduling;sets / Algebraic Geometry / C / Calculus of variations / Calculus of Variations and Optimal Control; Optimization / Calculus of Variations and Optimization / Convex and Discrete Geometry / Convex geometry / Decision Making / Discrete geometry / Discrete Mathematics / Management decision making / Mathematical foundations / Mathematical logic / Mathematical Logic and Foundations / Mathematical optimization / Mathematics and Statistics / Operational research / Operations Research / Operations Research and Decision Theory / Operations Research/Decision Theory / Optimization
ISBN:	9781461355779
EAN-Code:	9781461355779
Verlag:	Springer Nature EN
Einband:	Kartoniert
Sprache:	English
Serie:	#41 - International Series in Operations Research & Management Science
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	486 gr
Seiten:	296
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Convexity of sets in linear spaces, and concavity and convexity of functions, lie at the root of beautiful theoretical results that are at the same time extremely useful in the analysis and solution of optimization problems, including problems of either single objective or multiple objectives. Not all of these results rely necessarily on convexity and concavity; some of the results can guarantee that each local optimum is also a global optimum, giving these methods broader application to a wider class of problems. Hence, the focus of the first part of the book is concerned with several types of generalized convex sets and generalized concave functions. In addition to their applicability to nonconvex optimization, these convex sets and generalized concave functions are used in the book's second part, where decision-making and optimization problems under uncertainty are investigated. Uncertainty in the problem data often cannot be avoided when dealing with practical problems. Errors occur in real-world data for a host of reasons. However, over the last thirty years, the fuzzy set approach has proved to be useful in these situations. It is this approach to optimization under uncertainty that is extensively used and studied in the second part of this book. Typically, the membership functions of fuzzy sets involved in such problems are neither concave nor convex. They are, however, often quasiconcave or concave in some generalized sense. This opens possibilities for application of results on generalized concavity to fuzzy optimization. Despite this obvious relation, applying the interface of these two areas has been limited to date. It is hoped that the combination of ideas and results from the field of generalized concavity on the one hand and fuzzy optimization on the other hand outlined and discussed in Generalized Concavity in Fuzzy Optimization and Decision Analysis will be of interest to both communities. Our aimis to broaden the classes of problems that the combination of these two areas can satisfactorily address and solve.