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Perturbed Message Passing for Constraint Satisfaction Problems

• Siamak Ravanbakhsh, Dept of Computing Science, University of Alberta
• Russ Greiner, Dept of Computing Science; PI of AICML

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 Belief 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 (w.r.t. 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.

Citation

BibTeX

@article{Ravanbakhsh+Greiner:JMLR15,
  author = {Siamak Ravanbakhsh and Russ Greiner},
  title = {Perturbed Message Passing for Constraint Satisfaction Problems},
  Editor = {Alexander Ihler},
  Volume = "16",
  Number = "37",
  Pages = {1249−1274},
  journal = {Journal of Machine Learning Research (JMLR)},
  year = 2015,
}

Last Updated: February 10, 2020
Submitted by Sabina P

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