„This volume deserves a prominent role not only as a textbook, butalso as a desk reference for anyone who must cope with noisydata...“ (Computing Reviews. com, January 6, 2006)
„... well written and accessible to a wide audience... a welcomeaddition to the control and optimization community.“ (IEEEControl Systems Magazine, June 2005)
"... a step toward learning more about optimizationtechniques that often are not part of a statistician's training.„(Journal of the American Statistical Association, December2004)
“... provides easy access to a very broad, but related, collection of topics...„ (Short Book Reviews, August 2004)
“Rather than simply present various stochastic search andoptimization algorithms as a collection of distinct techniques, thebook compares and contrasts the algorithms within a broader contextof stochastic methods." (Technometrics, August 2004, Vol.46, No. 3)This book should be on the desk of anyone interested in the theoryand application of stochastic search and optimization.
--Kevin Passino, Department of Electrical Engineering, The OhioState University
Introduction to Stochastic Search and Optimization
Estimation, Simulation, and Control
von James C. SpallStochastic search and optimization techniques are used in a vastnumber of areas, including aerospace, medicine, transportation, andfinance, to name but a few. Whether the goal is refining the designof a missile or aircraft, determining the effectiveness of a newdrug, developing the most efficient timing strategies for trafficsignals, or making investment decisions in order to increaseprofits, stochastic algorithms can help researchers andpractitioners devise optimal solutions to countless real-worldproblems.
Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to theprinciples, algorithms, and practical aspects of stochasticoptimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorousand broadly accessible, distinguishing this text from much of thecurrent literature and providing students, researchers, andpractitioners with a strong foundation for the often-daunting taskof solving real-world problems.
The text covers a broad range of today's most widely usedstochastic 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 anddata sets, more than 250 exercises for the reader, and an extensivelist of references. These features help make the text an invaluableresource for those interested in the theory or practice ofstochastic search and optimization.
