An Introduction to Optimization

Synopsis

Fully updated to reflect modern developments in the field, this new edition fills the need for an accessible, yet rigorous, introduction to optimization theory and methods.  Featuring innovative coverage and a straightforward approach, the Fourth Edition includes a new chapter on integer programming, expanded coverage on one-dimensional methods, improvements to the sections on linear matrix inequalities, and numerous new exercises at the end of all chapters.  The book begins with a review of basic definitions and notations and also provides the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization.  An optimization perspective on global search methods is featured and includes discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm.  In addition, the book includes an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, all of which are of tremendous interest to students, researchers, and practitioners.  Numerous diagrams and figures found throughout the book complement the written presentation of key concepts, and each chapter is followed by MATLAB® exercises and drill problems that reinforce the discussed theory and algorithms.