Quasiconvex Optimization and Location Theory (Applied Optimization, 9)

Dos Santos Gromicho, J.A.

ISBN 10: 0792346947 / ISBN 13: 9780792346944

Published by Springer, 1998

About this Item

In.

Seller Inventory # ria9780792346944_new

Bibliographic Details

• Title: Quasiconvex Optimization and Location Theory (Applied Optimization, 9)
• Publisher: Springer
• Publication Date: 1998
• Language: English
• ISBN 10: 0792346947
• ISBN 13: 9780792346944
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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 fractional pro­ grams could be found in other programs, given that the objective function could be written as a particular composition of functions. He called this new field C­ programming, standing for composite concave programming. In his seminal book on dynamic programming (Ref. [104]), Sniedovich shows how the study of such com­ positions can help tackling non-separable dynamic programs that otherwise would defeat solution. Barros and Frenk (Ref. [9]) developed a cutting plane algorithm capable of optimizing C-programs. More recently, this algorithm has been used by Carrizosa and Plastria to solve a global optimization problem in facility location (Ref. [16]). The distinction between global optimization problems (Ref. [54]) and generalized convex problems can sometimes be hard to establish. That is exactly the reason why so much effort has been placed into finding an exhaustive classification of the different weak forms of convexity, establishing a new definition just to satisfy some desirable property in the most general way possible. This book does not aim at all the subtleties of the different generalizations of convexity, but concentrates on the most general of them all, quasiconvex programming. Chapter 5 shows clearly where the real difficulties appear.

Synopsis

This text 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. 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 text provides a clear and direct interpretation of the main theoretical results.