Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing [Hardcover]

Emile Aarts (Author), Jan Korst (Author)

Book Description

ISBN-10: 0471921467 | ISBN-13: 978-0471921462 | Publication Date: January 1989 | Edition: 1

Wiley-Interscience Series in Discrete Mathematics and Optimization Advisory Editors Ronald L. Graham Jan Karel Lenstra Robert E. Tarjan Discrete Mathematics and Optimization involves the study of finite structures. It is one of the fastest growing areas in mathematics today. The level and depth of recent advances in the area and the wide applicability of its evolving techniques point to the rapidity with which the field is moving from its beginnings to maturity and presage the ever-increasing interaction between it and computer science. The Series provides a broad coverage of discrete mathematics and optimization, ranging over such fields as combinatorics, graph theory, enumeration, mathematical programming and the analysis of algorithms, and including such topics as Ramsey theory, transversal theory, block designs, finite geometries, Polya theory, graph and matroid algorithms, network flows, polyhedral combinatorics and computational complexity. The Wiley - Interscience Series in Discrete Mathematics and Optimization will be a substantial part of the record of this extraordinary development. Recent titles in the Series: Search Problems Rudolf Ahlswede, University of Bielefeld, Federal Republic of Germany Ingo Wegener, Johann Wolfgang Goethe University, Frankfurt, Federal Republic of Germany The problems of search, exploration, discovery and identification are of key importance in a wide variety of applications. This book will be of great interest to all those concerned with searching, sorting, information processing, design of experiments and optimal allocation of resources. 1987 Introduction to Optimization E. M. L. Beale FRS, Scicon Ltd, Milton Keynes, and Imperial College, London This book is intended as an introduction to the many topics covered by the term 'optimization', with special emphasis on applications in industry. It is divided into three parts. The first part covers unconstrained optimization, the second describes the methods used to solve linear programming problems, and the third covers nonlinear programming, integer programming and dynamic programming. The book is intended for senior undergraduate and graduate students studying optimization as part of a course in mathematics, computer science or engineering. 1988

Editorial Reviews

From the Publisher

Introduces a method of solution for maximizing annealing, while minimizing cost, using massively parallel processing for quick execution. Establishes a correspondence between the free energy of the material being annealed and the cost function, and between the solutions and the physical states--the result is a solution method of combinatorial optimization based on a simulation of the annealing process. This method features general applicability and the ability to produce solutions arbitrarily close to an optimum. Part I treats the simulated annealing algorithm in detail. Part II addresses the problem of designing parallel annealing algorithms on the basis of Boltzmann machines.

From the Inside Flap

Simulated Annealing and Boltzmann Machines A Stochastic Approach to Combinatorial Optimization and Neural Computing Emile Aarts, Philips Research Laboratories, Eindhoven, and Eindhoven University of Technology, The Netherlands Jan Korst, Philips Research Laboratories, Eindhoven, The Netherlands Simulated annealing is a solution method in the field of combinatorial optimization based on an analogy with the physical process of annealing. The method is generally applicable, and can obtain solutions arbitrarily close to an optimum. However, finding high quality solutions can require large computational effort. The computational effort required can be greatly reduced by using the computational model of the Boltzmann machine. This is a neural network model which belongs to the class of connectionist models. It is characterized by massive parallelism and distributed representations. These features lead to a conceptually simple yet powerful model, which can be seen as an architectural blueprint for future parallel computers which can cope with higher order optimization problems such as learning. This book brings together in one volume the theory of simulated annealing and the model of the Boltzmann machine. It combines a mathematical treatment with a clear view of the applications which are already possible and the exciting developments which are beginning. It will be of great interest to graduate students and researchers in combinatorial optimization, numerical optimization, parallel processing, neural networks, computer science, artificial intelligence and automaton theory. Contents Preface

1. Simulated Annealing
   1. Combinatorial Optimization
   2. Simulated Annealing
   3. Asymptotic Convergence
   4. Finite-Time Approximation
   5. Simulated Annealing in Practice
   6. Parallel Simulated Annealing Algorithms
2. Boltzmann Machines
   7. Neural Computing
   8. Boltzmann Machines
   9. Combinatorial Optimization and Boltzmann Machines
   10. Classification and Boltzmann Machines
   11. Learning and Boltzmann Machines

Appendix A: The EUR100 Instance Bibliography

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Product Details

• Hardcover: 284 pages
• Publisher: Wiley; 1 edition (January 1989)
• Language: English
• ISBN-10: 0471921467
• ISBN-13: 978-0471921462
• Product Dimensions: 9.5 x 6.6 x 0.9 inches
• Shipping Weight: 1.3 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #2,073,389 in Books (See Top 100 in Books)

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3 of 3 people found the following review helpful:

5.0 out of 5 stars Helpful handbook, December 15, 2008

By 

William A. Huber (Rosemont, PA USA) - See all my reviews
(REAL NAME)   

This review is from: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing (Hardcover)

I bought this book many years ago for the first half on simulated annealing because it was showing up in so many references on the subject. In about 90 pages it covers the theory (Metropolis algorithm, the Markov chain theory), shows how to apply it, suggests an algorithm to determine a good cooling schedule, and gives some example applications to combinatorial problems. The exposition is clear, the theory leads naturally into the practical material, and the results readily translate to computer algorithms. Consider this as a point of departure, though: in practice I have had to modify the suggested cooling schedule according to the problem being solved.

This is the book I go to when developing a new simulated annealing solution to any problem. The price is exorbitant, though. If you can't find it in a library, look for cheaper copies overseas.

0 of 17 people found the following review helpful:

5.0 out of 5 stars Many complex notation use in this book but good reading one, May 15, 1997

By A Customer

This review is from: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing (Hardcover)

R.H.J.M Otten and L.P.P.P van Ginneken The Annealing Algorithm Kluwer Academic Publisher