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

Front Cover
John Wiley & Sons, Mar 11, 2005 - Mathematics - 618 pages
1 Review
A unique interdisciplinary foundation for real-world problemsolving

Stochastic 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.


What people are saying - Write a review

User Review - Flag as inappropriate

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


1 Stochastic Search and Optimization Motivation and Supporting Results
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
Answers to Selected Exercises
Frequently Used Notation

Other editions - View all

Common terms and phrases

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.)...

References to this book

All Book Search results »

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).

Bibliographic information