Monte Carlo Methods in Statistical Physics (Paperback)

by M. E. J. Newman (Author), G. T. Barkema (Author) "This book is about the use of computers to solve problems in statistical physics..." (more)
Key Phrases: single histogram method, multiple histogram method, first ice rule, Monte Carlo, Swendsen Wang, Los Alamos (more...)

Editorial Reviews

Review

"This book is intended for those who are interested in the use of Monte Carlo simulations in classical statistical mechanics. Its primary goal is to explain how to perform such simulations efficiently. To this end, the authors discuss . . . some of the many interesting new algorithms designed to accelerate the simulation of particular classes of problems in statistical physics, such as cluster algorithms, multigrid methods, non-local algorithms for conserved-order-parameter models, entropic sampling, simulated tempering and continuous time Monte Carlo. The book is divided into three parts covering equilibrium (Chapters 1-8) and non-equilibrium (9-12) Monte Carlo simulations, and implementations (13-16). Each algorithm is introduced in the context of a particular model. For example, the Metropolis algorithm is illustrated by its application to the Ising model. A brief outline of the physics behind each model is always given."--Quarterly of Applied Mathematics
"In recent years there has been a flurry of activity in the development of new Monte Carlo algorithms that accelerate the dynamics of particular classes of systems in statistical physics. The present text discusses many of these algorithms . . . The book is well written and can be enjoyed at various levels. . . . [T]he primary goal of the book is to explain how to perform Monte Carlo simulations efficiently, and the authors have succeeded admirably in achieving their goal. The authors' discussion of the results of the algorithms was very helpful in understanding the algorithms. . . . In summary, this book belongs in the personal library of all researchers in statistical physics (regardless of whether they write Monte Carlo algorithms or not), computational scientists interested in Monte Carlo methods, and advanced undergraduates and graduate students wishing to learn about recent developments in statistical physics and Monte Carlo methods."--Journal of Statistical Physics
"Mark Newman and Gerard Barkema have written a remarkably clear and thorough book on the application of Monte Carlo simulations to classical statistical mechanics. Their writing is excellent throughout, and they cover a wide range of topics. Monte Carlo Methods in Statistical Physics is well suited for classroom use and could be valuable as a reference or tool for self-study for both beginning and experienced researchers. ... This book should give newcomers to Monte Carlo methods all the information and advice they need to get useful programs up and running. In addition to a basic presentation of the algorithms, Newman and Barkema discuss various implementation issues at length, give a wide range of programming advice, and discuss random number generators. This book also has an extensive treatment of data analysis techniques."--Computing in Science and Engineering
"The authors present a detailed account of Monte Carlo algorithms and techniques for the data analysis of the results of simulation. In my opinion this book can be very useful for both graduate students and experienced researchers. Problems are clearly stated, solutions are accurately discussed and there are problems to solve after every chapter. This book is surely suitable for use as a textbook for a course on simulation methods, or as a supplementary text in a course on statistical physics. Although the overall technical level is that of a graduate text, I think even experienced researchers in the field would benefit from reading the detailed accounts of the most sophisticated new simulation techniques which have appeared in recent years." -- Emilio N.M. Cirillo, Mathematical Reviews Clippings 2000m


Product Description
An up-to-date introduction to Monte Carlo simulations in classical statistical physics. Covers both equilibrium and out of equilibrium systems and discusses in detail numerous algorithms, including Metropolis and heat-bath algorithms, continuous time Monte Carlo, cluster algorithms, and entropic sampling.

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Product Details

• Paperback: 496 pages
• Publisher: Oxford University Press, USA (April 15, 1999)
• Language: English
• ISBN-10: 0198517971
• ISBN-13: 978-0198517979
• Product Dimensions: 9.2 x 6.2 x 1 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #433,245 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

11 of 11 people found the following review helpful:

5.0 out of 5 stars One of the best and up-to-date books in market, March 27, 2000

By	Nilton (Florianopolis, Brazil) - See all my reviews

This book covers a wide range of applications in Statistical Mechanics, with clear explanation, examples, tips, algorithms, and explicit programs at the end of the book. It is good for beginners and experienced alike, since it discusses "classical" and modern algorithms. It is a must for those who want to make actual numerical calculations in Statistical Physics.

2 of 2 people found the following review helpful:

5.0 out of 5 stars The best Monte Carlo book ever, September 21, 2008

By	Rohar - See all my reviews

This book is clearly written, well organized and extremely user friendly. I highly recommend this book to everyone interested in Monte Carlo methods.

Unlike many other books that focus on its applications (and those fool you around), this book spends the first three chapters to give you a clear and thorough explanation on how Monte Carlo methods work (Markov chain, detailed balance, ergodicity), and how to measure their efficiency. I say that this book is 'clear and thorough' from an average physicist's viewpoint; it may not apply to mathematicians or statistics guys.

I like this book because it is down to earth. When the authors talk about an algorithm, they don't just beat around the bush. They give you their C/C++ code (beautifully written programs), they explain underlying principles, technical details as well as common pitfalls (e.g., random number generators). They do everything to ensure you understand every damn bit of what they are talking about.

The topics in this book are clearly physics-based, by which I mean the book spends most time talking about Ising model and other related models. Many important topics are covered: cluster move, renormalization method, entropic sampling, tempering, etc.

However, I kind of feel that the derivatives of the Ising model may actually not that important and these model may not deserve so much attention (Generally, Ising model tells you the method works and it gives you a phase transition, and that's it). One reason the authors choose to do that is perhaps that those models are so simple and so much fun :-) On the other hand, applications to molecular systems are not mentioned at all. This is kind of unfair. So I should say that chemistry and biology guys may prefer some other books. But what you get from this book is so much that you can just forget my nitpicking. Besides, the book uses some nice (kind of like plastic) papers (but may be weird at first).

0 of 1 people found the following review helpful:

4.0 out of 5 stars A practical book on Monte Carlo in Stat. Phy., July 11, 2006

By	J. Ma "Big Bear" (CA, USA) - See all my reviews (REAL NAME)

Overall, it's an excellent book on the practice of Monte Carlo and the c++ code in appendix are very instructive (Random Number Generators, Solid Monte Carlo Routines, etc.). It does have certain weaknesses though.

1) Sometimes the description are trivial in principle but written in great details. For example, on Pg 58 on the exact methods (so-called 'efficient way') of calculating averaged quantities from simulation.
2) Most of the content are heuristic. The discussion of the whole book is based on practice, although you do find something looks like a rigious proof (but no in fact). By rigious, I mean the proof should be based on Markov Chain and related properties of random process and statistical physics.

But as I said in the beginning, this is a invaluable book to anyone who wants to use Monte Carlo method in his/her domain. For myself, I am using this book as a reference to tackle functional optimization - Simulated Annealing, which is a very close sibling of Monte Carlo method.