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

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 Simulated AnnealingCombinatorial OptimizationSimulated AnnealingAsymptotic ConvergenceFinite-Time ApproximationSimulated Annealing in PracticeParallel Simulated Annealing AlgorithmsBoltzmann Machines Neural ComputingBoltzmann MachinesCombinatorial Optimization and Boltzmann MachinesClassification and Boltzmann MachinesLearning and Boltzmann MachinesAppendix A: The EUR100 Instance BibliographyJan Korst is the author of 'Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing' with ISBN 9780471921462 and ISBN 0471921467.