Global optimization concerns the computation and characterization of global optima of nonlinear functions. Such problems are widespread in the mathematical modelling of real systems in a very wide range of applications, and recent years have seen the development of many new theoretical, algorithmic and computational contributions which have helped to solve globally multi-extreme problems in important practical applications. Most of the existing books on optimization focus on the problem of computing locally optimal solutions. This is a textbook on constrained global optimization that covers the fundamentals of the subject, presenting material including algorithms, applications and complexity results for quadratic programming, concave minimization, DC and Lipschitz problems, and nonlinear network flow. Each chapter contains examples and ends with selected exercises, designed to help students grasp the material and enhance their knowledge of the methods involved.
It is designed for students of mathematical programming, and all scientists, from whatever discipline, who need global optimization methods in such diverse areas as economic modelling, fixed charges, finance, networks and transportation, databases, chip design, image processing, nuclear and mechanical design, chemical engineering design and control, molecular biology, and environmental engineering.