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Survey-propagation decimation through distributed local computations

Joel Chavas·Cyril Furtlehner·Marc Mezard·Riccardo Zecchina
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Abstract

We discuss the implementation of two distributed solvers of the random K-SAT problem, based on some development of the recently introduced survey-propagation (SP) algorithm. The first solver, called the "SP diffusion algorithm", diffuses as dynamical information the maximum bias over the system, so that variable nodes can decide to freeze in a self-organized way, each variable making its decision on the basis of purely local information. The second solver, called the "SP reinforcement algorithm", makes use of time-dependent external forcing messages on each variable, which let the variables get completely polarized in the direction of a solution at the end of a single convergence. Both methods allow us to find a solution of the random 3-SAT problem in a range of parameters comparable with the best previously described serialized solvers. The simulated time of convergence towards a solution (if these solvers were implemented on a distributed device) grows as log(N).

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Joel Chavas, Cyril Furtlehner, Marc Mezard, Riccardo Zecchina. 2005-11-30. Survey-propagation decimation through distributed local computations. https://doi.org/10.1088/1742-5468%2F2005%2F11%2Fp11016

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