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Optimization by simulated annealing.

Optimization by simulated annealing. There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Science (New York, N.Y.) Pubmed

Optimization by simulated annealing.

Science (New York, N.Y.) , Volume 220 (4598): 10 – Jul 2, 2010

Optimization by simulated annealing.


Abstract

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

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ISSN
0036-8075
DOI
10.1126/science.220.4598.671
pmid
17813860

Abstract

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Journal

Science (New York, N.Y.)Pubmed

Published: Jul 2, 2010

There are no references for this article.