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

The PowerPoint slides here have been used as an aid in teaching from the book Introduction to Stochastic Search and Optimization. These slides are a complement to the book and are not intended to be a stand-alone treatment of the material in the text. The coverage of the chapters is not even; the slides for some chapters are more thoroughly developed than for other chapters. Development and modification of these slides is an ongoing process. Many fundamental topics in the book are not covered in these slides. Nonetheless, students and instructors are free to use these slides as appropriate. This use is restricted to noncommercial applications for self-study or for courses at educational institutions.

© 2012 The Johns Hopkins University
Applied Physics Laboratory (JHU/APL)
All rights reserved.

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CHAPTERS 1 – 17

Chapter

1.

Stochastic Search and Optimization: Motivation and Supporting Results

Chapter

2.

Direct Methods for Stochastic Search

Chapter

3.

Recursive Estimation for Linear Models

Chapter

4.

Stochastic Approximation for Nonlinear Root-Finding

Chapter

5.

Stochastic Gradient Form of Stochastic Approximation

Chapter

6.

Stochastic Approximation and the Finite-Difference Method

Chapter

7.

Simultaneous Perturbation Stochastic Approximation

Chapter

8.

Annealing-Type Algorithms

Chapter

9.

Evolutionary Computation I: Genetic Algorithms

Chapter

10.

Evolutionary Computation II: General Methods and Theory

Chapter

11.

Reinforcement Learning via Temporal Differences

Chapter

12.

Statistical Methods for Optimization in Discrete Problems

Chapter

13.

Model Selection and Statistical Information

Chapter

14.

Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods

Chapter

15.

Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods

Chapter

16.

Markov Chain Monte Carlo

Chapter

17.

Optimal Design for Experimental Inputs


APPENDICES A – E

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


SUPPLEMENTAL SLIDES FOR COURSE IN SIMULATION AND MONTE CARLO METHODS

Simulation and Monte Carlo: Some Introductory Remarks