Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
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:
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.
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2 Direct Methods for Stochastic Search
3 Recursive Estimation for Linear Models
4 Stochastic Approximation for Nonlinear RootFinding
5 Stochastic Gradient Form of Stochastic Approximation
6 Stochastic Approximation and the FiniteDifference Method
7 Simultaneous Perturbation Stochastic Approximation
8 AnnealingType Algorithms
15 SimulationBased Optimization II Stochastic Gradient and Sample Path Methods
16 Markov Chain Monte Carlo
17 Optimal Design for Experimental Inputs
Appendix A Selected Results from Multivariate Analysis
Appendix B Some Basic Tests in Statistics
Appendix C Probability Theory and Convergence
Appendix D Random Number Generation
Appendix E Markov Processes
9 Evolutionary Computation I Genetic Algorithms
10 Evolutionary Computation 11 General Methods and Theory
11 Reinforcement Learning via Temporal Differences
12 Statistical Methods for Optimization in Discrete Problems
13 Model Selection and Statistical Information
14 SimulationBased Optimization I Regeneration Common Random Numbers and Selection Methods
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Introduction to Stochastic Search and Optimization: Estimation, Simulation ...
James C. Spall
No preview available - 2005