Quasiconvex Optimization and Location Theory

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.

Klappentext

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.

Inhalt
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.