On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms
Disordered Systems and Neural Networks
2015-05-13 v1 Statistical Mechanics
Discrete Mathematics
Abstract
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 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 to the results of extensive numerical simulations.
Keywords
Cite
@article{arxiv.0904.3395,
title = {On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms},
author = {Federico Ricci-Tersenghi and Guilhem Semerjian},
journal= {arXiv preprint arXiv:0904.3395},
year = {2015}
}
Comments
32 pages, 24 figures