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

Physics – Condensed Matter – Disordered Systems and Neural Networks

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

32 pages, 24 figures

Scientific paper

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-274863

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.