Practical Optimization: Algorithms and Engineering Applications
Andreas Antoniou, Wu-Sheng Lu
Practical Optimization: Algorithms and Engineering Applications provides a hands-on treatment of the subject of optimization. A comprehensive set of problems and exercises makes the book suitable for use in one or two semesters of a first-year graduate course or an advanced undergraduate course. Each half of the book contains a full semester’s worth of complementary yet stand-alone material. The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable as a reference work for practitioners in the field.
Advancements in the efficiency of digital computers and the evolution of reliable software for numerical computation during the past three decades have led to a rapid growth in the theory, methods, and algorithms of numerical optimization. This body of knowledge has motivated widespread applications of optimization methods in many disciplines, e.g., engineering, business, and science, and has subsequently led to problem solutions that were considered intractable not too long ago.
- extensively class-tested
- provides a complete teaching package with MATLAB exercises and online solutions to end-of-chapter problems
- includes recent methods of emerging interest such as semidefinite programming and second-order cone programming
- presents a unified treatment of unconstrained and constrained optimization
- uses a practical treatment of optimization accessible to broad audience, from college students to scientists and industry professionals
- provides a thorough appendix with background theory so non-experts can understand how applications are solved from point of view of optimization
Table of contents
The Optimization Problem.- Basic Principles.- General Properties of Algorithms.- One-Dimensional Optimization.- Basic Multidimensional Gradient Methods.- Conjugate-Direction Methods.- Quasi-Newton Methods.- Minimax Methods.- Applications of Unconstrained Optimization.- Fundamentals of Constrained Optimization.- Linear Programming Part I: The Simplex Method.- Linear Programming Part II: Interior-Point Methods.- Quadratic and Convex Programming.- Semidefinite and Second-Order Cone Programming.- General Nonlinear Optimization Problems.- Applications of Constrained Optimization.