Mean field annealing

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Mean field annealing is a deterministic approximation to the simulated annealing technique of solving optimization problems. [1] This method uses mean field theory and is based on Peierls' inequality.

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In physics and probability theory, mean field theory studies the behavior of large and complex stochastic models by studying a simpler model. Such models consider a large number of small individual components that interact with each other. The effect of all the other individuals on any given individual is approximated by a single averaged effect, thus reducing a many-body problem to a one-body problem.

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References

  1. Bilbro, G.L.; Snyder, W.E.; Garnier, S.J.; Gault, J.W. (Jan 1992). "Mean field annealing: a formalism for constructing GNC-like algorithms" (PDF). IEEE Transactions on Neural Networks. 3 (1): 131–138. doi:10.1109/72.105426.