| Stochastic Adaptive Search for Global Optimization Softcover Repri Edition Contributor(s): Zabinsky, Z. B. (Author) |
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ISBN: 1461348269 ISBN-13: 9781461348269 Publisher: Springer OUR PRICE: $104.49 Product Type: Paperback - Other Formats Published: November 2013 |
| Additional Information |
| BISAC Categories: - Mathematics | Applied - Mathematics | Calculus - Computers | Computer Science |
| Dewey: 004.015 |
| Series: Nonconvex Optimization and Its Applications |
| Physical Information: 0.51" H x 6.14" W x 9.21" (0.76 lbs) 224 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algo- rithms, are gaining in popularity among practitioners and engineers be- they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well under- stood. In this book, an attempt is made to describe the theoretical prop- erties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and de- velopment of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical anal- ysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods. |