# Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control

John Wiley & Sons, Mar 11, 2005 - Mathematics - 618 pages
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.

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Auf Seite 509 und 510 gute Erklärung wann man Differenzierung und Integral tauschen kann.

### Contents

 1 Stochastic Search and Optimization Motivation and Supporting Results 1 2 Direct Methods for Stochastic Search 34 3 Recursive Estimation for Linear Models 65 4 Stochastic Approximation for Nonlinear RootFinding 95 5 Stochastic Gradient Form of Stochastic Approximation 126 6 Stochastic Approximation and the FiniteDifference Method 150 7 Simultaneous Perturbation Stochastic Approximation 176 8 AnnealingType Algorithms 208
 15 SimulationBased Optimization II Stochastic Gradient and Sample Path Methods 409 16 Markov Chain Monte Carlo 436 17 Optimal Design for Experimental Inputs 464 Appendix A Selected Results from Multivariate Analysis 505 Appendix B Some Basic Tests in Statistics 515 Appendix C Probability Theory and Convergence 526 Appendix D Random Number Generation 538 Appendix E Markov Processes 547

 9 Evolutionary Computation I Genetic Algorithms 231 10 Evolutionary Computation 11 General Methods and Theory 259 11 Reinforcement Learning via Temporal Differences 278 12 Statistical Methods for Optimization in Discrete Problems 300 13 Model Selection and Statistical Information 329 14 SimulationBased Optimization I Regeneration Common Random Numbers and Selection Methods 367
 Answers to Selected Exercises 552 References 558 Frequently Used Notation 580 Index 583 Copyright

### Popular passages

Page 561 - Adaptive identification and control algorithms for nonlinear bacterial growth systems.
Page 6 - Because of the inherent limitations of the vast majority of optimization algorithms, it is usually only possible to ensure that an algorithm will approach a local minimum with a finite amount of resources being put into the optimization process. However, since the local minimum may still yield a significantly improved solution (relative to no formal optimization process at all), the local minimum may be a fully acceptable solution for the resources available (human time, money, computer time, etc.)...

### About the author (2005)

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 Associate Editor at Large for the IEEE Transactions on Automatic Control and 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).