Convexity and its Applications in Discrete and Continuous Optimization

A unified introduction to continuous and discrete aspects of convexity, including recent developments in optimization and 300+ exercises.

Amitabh Basu (Author)

9781108837590, Cambridge University Press

Hardback, published 30 January 2025

328 pages
23.4 x 15.7 x 2.4 cm, 0.59 kg

'Convexity is central to most optimization algorithms. This book brings together classical and new developments at the interface between these two vibrant areas of mathematics. It is an essential reference for scholars in optimization. The numerous exercises make it an ideal textbook at the graduate and upper undergraduate levels.' Gérard Cornuéjols, Carnegie Mellon University

Using a pedagogical, unified approach, this book presents both the analytic and combinatorial aspects of convexity and its applications in optimization. On the structural side, this is done via an exposition of classical convex analysis and geometry, along with polyhedral theory and geometry of numbers. On the algorithmic/optimization side, this is done by the first ever exposition of the theory of general mixed-integer convex optimization in a textbook setting. Classical continuous convex optimization and pure integer convex optimization are presented as special cases, without compromising on the depth of either of these areas. For this purpose, several new developments from the past decade are presented for the first time outside technical research articles: discrete Helly numbers, new insights into sublinear functions, and best known bounds on the information and algorithmic complexity of mixed-integer convex optimization. Pedagogical explanations and more than 300 exercises make this book ideal for students and researchers.

1. Preliminaries
Part I. Structural Aspects: 2. Convex sets
3. Convex functions
4. Geometry of numbers
Part II. Optimization: 5. Ingredients of mathematical optimization
6. Complexity of convex optimization with integer variables
7. Certificates and duality
Hints to selected exercises
References
Index.

Subject Areas: Optimization [PBU]