
Reviews available as
of March 2012 
This is a great book. I wish it would
have been available to me years ago. This book should be on the
desk of anyone interested in the theory and application of stochastic
search and optimization. It is written by an experienced expert
who has made fundamental contributions to the field. It has both
a nice treatment of the theory and excellent examples to provide
key insights.
It is fully modern, nicely highlights key principles of the topics
studied, and identifies relationships between many different methods.
It will be very useful for the student, researcher, scientist, or
engineer. 
— 
Prepublication review
Professor Kevin Passino
Department of Electrical Engineering, The Ohio State University
Director of the Collaborative Center of Control Science


Recommended to scholars and graduate students: Introduction
to Stochastic Search and Optimization provides comprehensive,
current information on methods for realworld problem solving, including
stochastic gradient and nongradient techniques, as well as relatively
recent innovations such as simulated annealing, genetic algorithms,
and MCMC. It is written to be read and understood by graduate students,
industrial practitioners, and experienced researchers in the field.
Web links to software and data sets, and an extensive list of references
of the book allows the reader to explore deeper into certain topic
areas. I also found the index to be very comprehensive and carefully
done. The appendices are as a refresher and summary of much of the
prerequisite material. The book is somewhat unique in providing
a balanced discussion of algorithms, including both their strengths
and weaknesses. The book is among very few books that have integrated
essential parts of statistical fields with optimization and decision
making. The book's inclusion of a chapter on optimal experimental
design is an example of such integration. The approaches discussed
in the book could be used for financial decision making, forecasting,
and quality improvement, among many other areas. 
— 
Review on Amazon.com ()
(anonymous reader)
September 23, 2003 

Stochastic search and optimization concerns algorithms for decision making in industry, academia and government, in circumstances when the measurements available to the algorithms are corrupted by noise and/or randomness is artificially inserted into the algorithms for better performance. The book, which is largely selfcontained, provides easy access to a very broad, but related, collection of topics, from basic stochastic approximation algorithms for root finding and optimization to powerful Markov Chain Monte Carlo (MCMC) methods for computing statistical estimates of expectations, where conventional, analytical or multidimensional numerical integration techniques fail. Along the way, material is included on simulated annealing, evolutionary computation, reinforcement learning and experiment design. The care taken by the author to motivate the analysis and provide perspective, and also the extensive literature review, are distinguishing features of this book and ones that will help greatly to extend its readership. 
— 
International Statistical Institute (ISI) Short Book Reviews, 2004 (reviewer: R.B. Vinter) 

....if one has some extra interests and likes to
pursue the issue [optimization of the atmospheric datamodel] along
this new line of stochastic search algorithms one can read a very
good textbook Introduction to Stochastic Search and Optimization
(by J. C. Spall) that gives a just right balance between the necessary
math background and many useful application algorithms, and also
a good balance between the classic wellknown approaches and modern
frontier new tools. 
— 
Anonymous review at Chinese Atmospheric
Sciences Forum
(www.lasg.ac.cn/cgibin/forum/view.cgi?forum=2&topic=557#top)
(excerpt) 

.....In summary, I recommend this book for a graduate
course or self study in optimization and equation solving for functions
evaluated with noise....I appreciated the informative discussions
of issues and relative merits of procedures. 
— 
Technometrics,
August 2004 (reviewer: Tim Hesterberg) (excerpt) 

Great book!! A must have for anyone interested in
optimization! Extremely well written and objective. 
— 
Review on Amazon.com ()
Wagner F. Sacco
December 7, 2004 

In statistics, we spend no small amount of time trying
to do things in an optimal way. In testing and estimation, we try
to make optimal use of the information in our data, and in experimental
design, we try to do the experiment that is in some sense expected
to yield the optimal amount of information given the budgetary and
other constraints that we face. However, many of us (or maybe just
me) do not necessarily know a lot about numerical optimization beyond
a few deterministic favorites....For anyone in that situation, reading
all or parts of Introduction to Stochastic Search and Optimization
would be a step toward learning more about optimization techniques
that are often not a part of a statistician’s training....
Another recurring idea in the book is the idea that simpler may
be better. Highly specialized methods are given only minimal treatment,
with the focus put on methods that the author considers to have
strong records of success in a variety of applications....
The approach taken in the book is very much a comparative one. Every
chapter features discussion of the relative merits of the methods
being presented....
The book is written at a level accessible for any graduate student
in statistics....most of the statistical theory needed is reviewed
in the 50 or so pages of appendixes near the end of the book. 
— 
Journal of the American
Statistical Association, December 2004 (reviewer: Jesse
Frey) (excerpt) 

James Spall’s book provides a survey of random
search (called stochastic search by the author) and optimization
methods, including stochastic approximation algorithms, evolutionary
computation, Monte Carlo simulation, and related statistical methods.
Stochastic approximation algorithms include recursive least squares,
adaptive algorithms, and reinforcement learning....
The book consists of 17 chapters and five appendices. The chapters,
as the author points out, can be divided into two main parts: Part
1 (comprised of Chapters 1–12) focuses on stochastic search
and optimization, whereas Part 2 (comprised of chapters 13–17)
studies modeling, simulation, and estimation. Each chapter ends
with a conclusion section that provides some historical perspective.
Related problems of interest and appropriate references are also
given....
The prerequisite background for reading this book is modest. The
book includes many exercises and selected solutions. It can be used
either as a graduate textbook or as a reference for people working
in the area of optimization and design of experiments. A onesemester
course can cover Chapters 1–8 and Chapters 11 and 12. A second
course can include Chapters 9, 10, and 13–17.
One of the main features of Spall’s book is the description
of the use of recursive stochastic approximation algorithms. This
book does a good job of comparing various algorithms in the context
of stochastic optimization. It is well written and accessible to
a wide audience. Although the book presents theoretical issues in
algorithms and methods, it does not include detailed derivations.
In summary, the book is a welcome addition to the control and optimization
community. 
— 
IEEE Control Systems Magazine,
June 2005 (reviewer: George Yin) (excerpt) 

Stochasticity can play two different roles in search and optimization. It can be an adversary, in the
form of noise in measurements of the system’s state, or it can be a friend, in the form of stochastic
choices that enable the search process to escape local optima. Taming its adversity and harnessing
its power require a wide range of techniques drawn from many different specialties, including
classical optimization and search, artificial intelligence, simulation, statistics, analysis,
mathematical biology, and physics. Spall seeks to bring these disciplines together in a consistent
treatment. His orientation is practical, mathematical, and encyclopedic. He writes as a practitioner,
willing to sacrifice methodological purity in favor of hybrid methods that can get the job done. Yet he
is a mathematician, insisting on understanding the formal foundations of the methods he presents,
and citing the key theorems on which the methods rest. For the proofs of the theorems and more, he
provides the reader with copious pointers to the literature (over 400 citations through 2002), closely
integrated with his exposition. He integrates this wide range of material under a common notation,
providing readers with an invaluable reference book for this complex multidisciplinary field....
The book embodies the experience of a seasoned teacher. Each chapter has a summary introduction,
a conclusion that highlights its main points, and an extensive set of exercises....The volume deserves a prominent role not only as a textbook, but also as a desk reference for anyone
who must cope with noisy data or who wishes to use stochasticity to guide search. 
— 
Computing Reviews, January 2006 (reviewer: H. Van Dyke Parunak) (excerpt) 

It is hard to name any field of science,
medicine, industry, or economy that does not build on extensive use of
stochastic search and optimization algorithms. The reviewed book by
J. C. Spall is an excellent graduate level introduction to this important
part of applied mathematics and computer science. I was impressed
by the truly general and interdisciplinary approach taken by the author....
The book is comprehensive—the main text has 504 pages divided
into 17 chapters. In addition, five very useful (especially for newcomers
in the field) and clearly written appendices are provided, on
multivariate analysis, basic tests in statistics, probability theory and
convergence, random number generators, and Markov processes....
The text is quite readable, despite the difficult topics covered.
The majority of important theorems, ideas of algorithms, methods,
and issues are well illustrated by over 130 examples. The author puts
a lot of wellbalanced comments in the text and this type of lecturing
definitively helps with understanding the presented stochastic methods....the presentations
of algorithms and theorems are rigorous, and, in many instances, the references to original papers or relevant monographs are given. The
list of 415 references covers all major literature related to stochastic
search, optimization, control, and analysis....
The book may serve as either a reference for researchers and practitioners
in many fields or as a textbook. It requires previous knowledge
of basic probability and statistics, multivariate calculus, and some
matrix algebra. Thus, students of virtually all graduate courses in science
and engineering departments may profit from this useful book.
Exploiting knowledge on stochastic search and optimization presented
in this book should contribute to solid advancements of many areas of
experimental and practical activity. 
— 
IEEE Transactions on Neural Networks, May 2007 (reviewer: W. Nowak) (excerpt) 

This book presents the notes of graduate courses taught by the author on the title theme. His experience is transmitted also in some recommendations for the possible structuring of courses on Stochastic Optimization and on Simulation and Monte Carlo Methods .... The book discusses the theoretical basis of the algorithms avoiding large proofs and placing the coordinates of the references. A large set of worked out examples is included for illustrating the results. More than 300 references are listed and a web site is disposable for obtaining extra information and computation codes. I consider that an outstanding feature of the book is its successful synthesis and the direct applicability of computer algorithms. I warmly recommend it for those specialists involved with work on optimization. 
— 
Zentralblatt MATH,
Zbl 1088.90002,accessed from Zentralblatt MATH site on 30 January 2012 (reviewer: Carlos Narciso Bouza Herrera [Habana] ) (excerpt) 

