Introduction; Problem Formulations; Convex and Lagrangian Relaxations; Decomposition Methods; Semidefinite Relaxations; Convex Underestimators; Cuts, Lower Bounds and Box Reduction; Local and Global Optimality Criteria; Adaptive Discretization of Infinite Dimensional MINLPs; Overview of Global Optimization Methods; Deformation Heuristics; Rounding, Partitioning and Lagrangian Heuristics; Branch-Cut-and-Price Algorithms; LaGO -- An Object-Oriented Library for Solving MINLPs
Summary
This book presents a comprehensive description of efficient methods for solving nonconvex mixed integer nonlinear programs, including several numerical and theoretical results, which are presented here for the first time. It contains many illustrations and an up-to-date bibliography. Because on the emphasis on practical methods, as well as the introduction into the basic theory, the book is accessible to a wide audience. It can be used both as a research and as a graduate text
Bibliography
Includes bibliographical references (pages 195-209) and index
