Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms

Review From the reviews: "The book ??? is a timely addition to the literature on optimization basics and algorithms. ??? It covers all the important topics required to teach basic concepts and methods of optimization. These topics are presented in a concise yet rigorous manner. All the basic concepts are well explained and illustrated. ??? The text is written in a very readable and teachable way. It will be useful as a text book in undergraduate senior-level and first-year graduate-level courses in engineering and other applied science fields." (Jasbir S. Arora, Structural and Multidisciplinary Optimization, Vol. 31 (3), 2006)From the reviews: "The book a ] is a timely addition to the literature on optimization basics and algorithms. a ] It covers all the important topics required to teach basic concepts and methods of optimization. These topics are presented in a concise yet rigorous manner. All the basic concepts are well explained and illustrated. a ] The text is written in a very readable and teachable way. It will be useful as a text book in undergraduate senior-level and first-year graduate-level courses in engineering and other applied science fields." (Jasbir S. Arora, Structural and Multidisciplinary Optimization, Vol. 31 (3), 2006) Product Description This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics. From the Back Cover This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form without neglecting rigour. The work should enable the professional to apply optimization theory and algorithms to his own particular practical field of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties―such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima―that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods.  AudienceIt is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, computer science, and operations research departments, and also to be useful to practising professionals in the workplace.