This book has been used in courses at the Johns Hopkins University, Morgan State University, and the University of Maryland, among other institutions. At Johns Hopkins, the author taught two graduate courses from this book, one in stochastic optimization and one in simulation and Monte Carlo methods. These were offered in the Department of Applied Mathematics and Statistics as part of the full-time graduate program and in the Applied and Computational Mathematics and Electrical and Computer Engineering Programs, which are divisions of the Johns Hopkins Part-Time Engineering Program (catering largely to people employed full-time and pursuing a master’s degree by taking courses in the evening).

As a potential guide to faculty at other universities considering similar courses, the syllabi for courses in stochastic optimization and simulation and Monte Carlo list the subjects in the above-mentioned two courses. The prerequisites for these courses are the same as the prerequisites for the book (see excerpts from preface). The courses cover a blend of theory and practical algorithm descriptions.

Each chapter and appendix contains examples and exercises for the reader to use in practicing with the methods being described. A partial set of solutions to the exercises is included at the back of the book and at answers to selected exercises. Faculty using this book as a text for a course may request a more complete set of solutions by sending a letter to the author on institutional letterhead. The request may be sent to:

James C. Spall
The Johns Hopkins University
Applied Physics Laboratory
11100 Johns Hopkins Road
Laurel, Maryland 20723-6099
USA

For the most part, the two courses in the table cover distinct material. However, because neither course is a prerequisite for the other, there is a small amount of overlap. Appendices A and B review material that most students should have encountered prior to these classes, but since “encountered” and “retained” are different things, it may be useful to spend some time on these subjects. Appendices C, D, and E, on the other hand, include material that may not be familiar to some students, suggesting that these appendices might be covered more slowly. In the simulation course, some material is drawn from supplementary sources, as indicated in the table at syllabi for courses in stochastic optimization and simulation and Monte Carlo.

Aside from the graduate courses above, the material here has also been used in short courses at conferences sponsored by the Institute of Electrical and Electronics Engineers (IEEE), the American Statistical Association, the U. S. Department of Defense, and the Society for Computer Simulation.

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Syllabi for courses in stochastic optimization and simulation and Monte Carlo