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 Fifth Edition includes a new chapter on Lagrangian (nonlinear) duality, expanded coverage on matrix games and linear-programming duality, projected gradient algorithms, and sparsity methods, and numerous new exercises at the end of each chapter. The book begins with a review of basic definitions and notations while 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. In addition, the book includes an 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.

Edwin K. P. Chong, PhD, is Professor and Head of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. He is a Fellow of the IEEE and AAAS and was Senior Editor of the IEEE Transactions on Automatic Control.

Wu-Sheng Lu, PhD, is Professor Emeritus of Electrical and Computer Engineering at University of Victoria, Canada. He is a Fellow of the IEEE and former Associate Editor of the IEEE Transactions on Circuits and Systems.

Stanislaw H. Żak, PhD, is Professor in the School of Electrical and Computer Engineering at Purdue University. He is former Associate Editor of Dynamics and Control and the IEEE Transactions on Neural Networks.