Perturbed Message Passing for Constraint Satisfaction Problems
This work addresses the efficiency and success rate of solving CSPs, which is a domain-specific problem in computational optimization and AI, with incremental improvements over existing message-passing methods.
The authors tackled the problem of solving Constraint Satisfaction Problems (CSPs) by introducing Perturbed Belief Propagation and Perturbed Survey Propagation, which use stochastic perturbation to bypass decimation and directly produce satisfying assignments, resulting in methods that are tens to hundreds of times more efficient than standard BP-guided decimation and outperform state-of-the-art SP-guided decimation in difficult regimes.
We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly produce a single satisfying assignment. Our first CSP solver, called Perturbed Blief Propagation, smoothly interpolates two well-known inference procedures; it starts as BP and ends as a Gibbs sampler, which produces a single sample from the set of solutions. Moreover we apply a similar perturbation scheme to SP to produce another CSP solver, Perturbed Survey Propagation. Experimental results on random and real-world CSPs show that Perturbed BP is often more successful and at the same time tens to hundreds of times more efficient than standard BP guided decimation. Perturbed BP also compares favorably with state-of-the-art SP-guided decimation, which has a computational complexity that generally scales exponentially worse than our method (wrt the cardinality of variable domains and constraints). Furthermore, our experiments with random satisfiability and coloring problems demonstrate that Perturbed SP can outperform SP-guided decimation, making it the best incomplete random CSP-solver in difficult regimes.