Susceptibility Propagation for Constraint Satisfaction Problems

TL;DR

This paper introduces susceptibility propagation, a message-passing algorithm for computing correlations in constraint satisfaction problems, and proposes a susceptibility-guided decimation method that outperforms standard approaches on hard problems.

Contribution

It presents a novel susceptibility-guided decimation technique leveraging correlations, improving solution-finding in challenging constraint satisfaction problems.

Findings

Susceptibility propagation accurately computes correlations.

Susceptibility-guided decimation outperforms belief-guided decimation.

Method effectively solves locked occupation problems.

Abstract

We study the susceptibility propagation, a message-passing algorithm to compute correlation functions. It is applied to constraint satisfaction problems and its accuracy is examined. As a heuristic method to find a satisfying assignment, we propose susceptibility-guided decimation where correlations among the variables play an important role. We apply this novel decimation to locked occupation problems, a class of hard constraint satisfaction problems exhibited recently. It is shown that the present method performs better than the standard belief-guided decimation.