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On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms

Federico Ricci-Tersenghi1 and Guilhem Semerjian2

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We introduce a version of the cavity method for diluted mean-field spin models that allows the computation of thermodynamic quantities similar to the Franz–Parisi quenched potential in sparse random graph models. This method is developed in the particular case of partially decimated random constraint satisfaction problems. This allows us to develop a theoretical understanding of a class of algorithms for solving constraint satisfaction problems, in which elementary degrees of freedom are sequentially assigned according to the results of a message passing procedure (belief propagation). We confront this theoretical analysis with the results of extensive numerical simulations.


Keywords

analysis of algorithms

cavity and replica method

message-passing algorithms

 

E-print Number: 0904.3395

Cited: by |

Refers: to

PACS

05.40.-a Fluctuation phenomena, random processes, noise, and Brownian motion

02.50.Cw Probability theory

02.10.Ox Combinatorics; graph theory

MSC

60G50 Sums of independent random variables; random walks

05C80 Random graphs

Subjects

Mathematical physics

Computational physics

Statistical physics and nonlinear systems

Dates

Issue 09 (September 2009)

Received 10 June 2009, accepted for publication 1 August 2009

Published 9 September 2009



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