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Solving optimization problems with mean field methods

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  • Peterson, Carsten

Abstract

A brief review is given for the use of feed-back artificial neural networks (ANN) to obtain good approximate solutions to combinatorial optimization problems. The key element is the mean field approximation (MFT), which differs from conventional methods and “feels” its ways towards good solutions rather than fully or partly exploring different possible solutions. The methodology, which is illustrated for the graphs bisection and knapsack problems, is easily generalized to Potts systems. The latter is related to the deformable templates method, which is illustrated with the track finding problem.

Suggested Citation

  • Peterson, Carsten, 1993. "Solving optimization problems with mean field methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 200(1), pages 570-580.
  • Handle: RePEc:eee:phsmap:v:200:y:1993:i:1:p:570-580
    DOI: 10.1016/0378-4371(93)90562-I
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    1. Mario Valenzuela & José M Garrido, 1991. "Dinero y Precios en el Corto y en el Largo Plazo: Evidencia Empírica para Chile," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 28(83), pages 141-168.
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