Introduction to Stochastic Search and Optimization

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Condensed Table of Contents

Additional information is available at the publisher’s web site
ISBN 0-471-33052-3

Further Information
James C. Spall
Johns Hopkins University
Applied Physics Laboratory
11100 Johns Hopkins Rd.
Laurel, MD 20723-6099


From the back cover...

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

Machine (reinforcement) learning

Recursive linear estimation

Model selection

Stochastic approximation

Simulation-based optimization

Simulated annealing

Markov chain Monte Carlo

Genetic and evolutionary algorithms

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.  

James C. Spall is a member of the Principal Professional Staff at The Johns Hopkins University, Applied Physics Laboratory, and is the Chair of the Applied and Computational Mathematics Program within the Johns Hopkins School of Engineering.  Dr. Spall has published extensively in the areas of control and statistics and holds two U.S. patents. Among other appointments, he is a Senior Editor for the IEEE Transactions on Automatic Control and a Contributing Editor for the Current Index to Statistics. Dr. Spall has received numerous research and publications awards and is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE).