Introduction to Stochastic Search and Optimization

James C. Spall

ISBN 10: 0471330523 / ISBN 13: 9780471330523

Edité par John Wiley & Sons, 2003

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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 p. N° de réf. du vendeur 446915970

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Titre : Introduction to Stochastic Search and ...

Éditeur : John Wiley & Sons

Date d'édition : 2003

Reliure : Gebunden

Etat : New

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Synopsis :

Book by James C Spall

Présentation de l'éditeur:

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.