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Quasiconvex Optimization and Location Theory by J.a. dos Santos Gromicho Hardcover | Indigo Chapters - neues Buch

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This book includes variants of the ellipsoid method for convex and quasiconvex problems and applies them to very general convex and quasiconvex models in location theory. It starts by des… Mehr…

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ISBN: 9780792346944

grams of which the objective is given by the ratio of a convex by a positive (over a convex domain) concave function. As observed by Sniedovich (Ref. [102, 103]) most of the properties of… Mehr…

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dos Santos Gromicho, J.A.:
Quasiconvex Optimization and Location Theory (Applied Optimization, 9) - gebunden oder broschiert

1998

ISBN: 9780792346944

Springer, Hardcover, Auflage: 1998, 241 Seiten, Publiziert: 1998-01-31T00:00:01Z, Produktgruppe: Book, 1.16 kg, Econometrics & Statistics, Economics, Business & Money, Subjects, Books, Co… Mehr…

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dos Santos Gromicho, J.A.:
Quasiconvex Optimization and Location Theory (Applied Optimization, 9) - gebunden oder broschiert

1998, ISBN: 0792346947

[EAN: 9780792346944], Used, as new, [PU: Springer], Like New, Books

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Quasiconvex Optimization and Location Theory - gebunden oder broschiert

ISBN: 9780792346944

Springer , pp. 246 . Hardback. New., Springer, 6

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Quasiconvex Optimization and Location Theory (Applied Optimization, 9)

This book includes variants of the ellipsoid method for convex and quasiconvex problems and applies them to very general convex and quasiconvex models in location theory. It starts by describing the adopted notation and provides basic details of convexity and convex optimization. Without aiming at replacing classical references, it manages to bring the required concepts into an easily tractable form and to focus the reader on the more elaborate developments that follow. Many techniques in convex optimization rely on the use of separation hyperplanes. The book uses the ellipsoid method as an illustration of such a technique and provides a new and more stable version of this method. The new algorithm receives a clear and concise treatment, starting with its derivation and ending with its convergence analysis. Both the derivation and the analysis use a simpler approach than previously found in the literature. The second part of the book generalizes the new algorithm to solve quasiconvex programs. Although the techniques required by the quasiconvex case are more complex, the book provides a clear and direct interpretation of the main theoretical results. Audience: This book will be of great value to graduate students and researchers working in continuous optimization using separation techniques and for those dealing with general continuous location models.

Detailangaben zum Buch - Quasiconvex Optimization and Location Theory (Applied Optimization, 9)


EAN (ISBN-13): 9780792346944
ISBN (ISBN-10): 0792346947
Gebundene Ausgabe
Erscheinungsjahr: 1998
Herausgeber: Springer
240 Seiten
Gewicht: 0,522 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-04-13T12:00:22+02:00 (Vienna)
Detailseite zuletzt geändert am 2023-12-29T13:16:20+01:00 (Vienna)
ISBN/EAN: 9780792346944

ISBN - alternative Schreibweisen:
0-7923-4694-7, 978-0-7923-4694-4
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: dos santos, san antonio
Titel des Buches: location theory, theory wants


Daten vom Verlag:

Autor/in: J.A. dos Santos Gromicho
Titel: Applied Optimization; Quasiconvex Optimization and Location Theory
Verlag: Springer; Springer US
219 Seiten
Erscheinungsjahr: 1998-01-31
New York; NY; US
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XXII, 219 p.

BB; Hardcover, Softcover / Mathematik/Sonstiges; Optimierung; Verstehen; algorithms; classification; complexity; computation; derivative; derivatives; dynamic programming; Facility Location; geometry; optimization; programming; sets; Subdifferential; Optimization; Algorithms; Computational Mathematics and Numerical Analysis; Theory of Computation; Econometrics; Algorithmen und Datenstrukturen; Numerische Mathematik; Theoretische Informatik; Ökonometrie und Wirtschaftsstatistik; EA; BC

1 Introduction.- 2 Elements of Convexity.- 2.1 Generalities.- 2.2 Convex sets.- 2.3 Convex functions.- 2.4 Quasiconvex functions.- 2.5 Other directional derivatives.- 3 Convex Programming.- 3.1 Introduction.- 3.2 The ellipsoid method.- 3.3 Stopping criteria.- 3.4 Computational experience.- 4 Convexity in Location.- 4.1 Introduction.- 4.2 Measuring convex distances.- 4.3 A general model.- 4.4 A convex location model.- 4.5 Characterizing optimality.- 4.6 Checking optimality in the planar case.- 4.7 Computational results.- 5 Quasiconvex Programming.- 5.1 Introduction.- 5.2 A separation oracle for quasiconvex functions.- 5.3 Easy cases.- 5.4 When we meet a “bad” point.- 5.5 Convergence proof.- 5.6 An ellipsoid algorithm for quasiconvex programming.- 5.7 Improving the stopping criteria.- 6 Quasiconvexity in Location.- 6.1 Introduction.- 6.2 A quasiconvex location model.- 6.3 Computational results.- 7 Conclusions.

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