Introduction to Stochastic Search and Optimization
| Author | : | |
| Rating | : | 4.74 (829 Votes) |
| Asin | : | 0471330523 |
| Format Type | : | paperback |
| Number of Pages | : | 618 Pages |
| Publish Date | : | 0000-00-00 |
| Language | : | English |
DESCRIPTION:
3). 46, No. "This volume deserves a prominent role not only as a textbook, but also as a desk reference for anyone who must cope with noisy data…" (Computing Reviews, January 6, 2006) "well written and accessible to a wide audiencea welcome addition to the control and optimization community." (IEEE Control Systems Magazine, June 2005)"…a step toward learning more about optimization techniques that often are not part of a statistician's training." (Journal of the American Statistical Association, December 2004)“…provides easy access to a very broad, but related, collection of topics…” (Short Book Reviews, August 2004)"Rather than simply present various stochastic search and optimization algorithms as a collection of distinct techniques, the book compares and contrasts the algorithms within a broader context of sto
Great intro to optimization from stochastic perspective I stumbled upon this book searching for a Genetic Algorithm book. The coverage of topics are unique and very interesting. This is the first book I came across that treats both the evolutionary algorithms (GA) and the stochastic search methods. Recursive Linear Estimator (e.g. Kalman Filter), Markov Chain Monte Carlo (e.g. Metropolis-Hastings, Gibbs), and Reinforcement Learning, are some of the stochastic material discussed. Continuous and discrete parameters are treated as well as noisy data, but not so much on constrained optimization.The algorithms presented are very practical and theoretically well founded. When I . A Customer said Recommended to scholars and graduate students. Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and. quintessential overview of what is unfortunately a dark corner of the field Stochastic optimization seems to be a "dark corner" for the fields of optimization and of Monte Carlo methods. Spall brings a quantitative engineering perspective to the problems, yet gives theory its proper dues. Moreover, he weaves a consistent interpretation among these algorithms which deserve greater attention and focus. While he clearly is a practitioner, his original work in algorithmic stochastic search and that of those he's inspired will, in my opinion, enable new theory to arise, whereby problems with horrible violations of continuity and the like will be embedded in some kind of mathematical manifold, and
The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems.The text covers a broad range of today’s most widely used stochastic algorithms, including:Random searchRecursive linear estimationStochastic approximationSimulated annealingGenetic and evolutionary methodsMachine (reinforcement) learningModel selectionSimulation-based optimizationMarkov chain Monte CarloOptimal experimental designThe book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization.. Whether the goal is refining the design of a missile or aircraft, determining the effectiveness of a new drug, developing the most efficient timing strategies for traffic signals, or making investment decisions in order to increase profits, stochastic alg