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A unique interdisciplinary foundation for real-world problem solving
Stochastic search and optimization techniques are used in a vast number of areas, including aerospace, medicine, transportation, and finance, to name but a few. 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 algorithms can help researchers and practitioners devise optimal solutions to countless real-world problems.
Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. 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 search
- Recursive linear estimation
- Stochastic approximation
- Simulated annealing
- Genetic and evolutionary methods
- Machine (reinforcement) learning
- Model selection
- Simulation-based optimization
- Markov chain Monte Carlo
- Optimal experimental design
The 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.
- Sales Rank: #1061007 in Books
- Published on: 2003-03
- Original language: English
- Number of items: 1
- Dimensions: 10.16" h x 1.39" w x 7.26" l, 2.85 pounds
- Binding: Hardcover
- 618 pages
Review
"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.com, January 6, 2006)
"...well written and accessible to a wide audience...a 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 stochastic methods." (Technometrics, August 2004, Vol. 46, No. 3)
Review
This book should be on the desk of anyone interested in the theory and application of stochastic search and optimization.
--Kevin Passino, Department of Electrical Engineering, The Ohio State University
From the Publisher
This book should be on the desk of anyone interested in the theory and application of stochastic search and optimization. --Kevin Passino, Department of Electrical Engineering, The Ohio State University
Most helpful customer reviews
21 of 23 people found the following review helpful.
Recommended to scholars and graduate students
By A Customer
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 carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas.
3 of 3 people found the following review helpful.
Great intro to optimization from stochastic perspective
By Keiichi Ito
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 learned about SPSA, I was most impressed to find out that it is possible to estimate the gradient by just two objective function calls (instead of finite differencing every dimension of the parameter vector to be optimized), and this is regardless of the number of dimensions of the parameter vector!
The book is aimed at rather general audiences in science and engineering. Rigorous mathematical details are avoided.
2 of 2 people found the following review helpful.
quintessential overview of what is unfortunately a dark corner of the field
By Jan Galkowski
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 notions of stochastic gradients will be seen as exploring these.
I heartily applaud this book. It was, for me, one of those which changed the way I looked at things overall.
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