Deterministic Annealing and Nonlinear Assignment

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dc.creatorSoderberg, Bo
dc.creatorJonsson, Henrik
dc.date2001-05-16
dc.date.accessioned2026-07-07T02:41:26Z
dc.date.available2026-07-07T02:41:26Z
dc.descriptionFor combinatorial optimization problems that can be formulated as Ising or Potts spin systems, the Mean Field (MF) approximation yields a versatile and simple ANN heuristic, Deterministic Annealing. For assignment problems the situation is more complex -- the natural analog of the MF approximation lacks the simplicity present in the Potts and Ising cases. In this article the difficulties associated with this issue are investigated, and the options for solving them discussed. Improvements to existing Potts-based MF-inspired heuristics are suggested, and the possibilities for defining a proper variational approach are scrutinized.
dc.description15 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0105321
dc.identifierhttp://arxiv.org/abs/cond-mat/0105321
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/17744
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.titleDeterministic Annealing and Nonlinear Assignment
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