Co-Certificate Learning with SAT Modulo Symmetries
Co-Certificate Learning with SAT Modulo Symmetries
Markus Kirchweger, Tomáš Peitl, Stefan Szeider
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 1944-1953.
https://doi.org/10.24963/ijcai.2023/216
We present a new SAT-based method for generating all graphs up to isomorphism that satisfy a given co-NP property. Our method extends the SAT Modulo Symmetry (SMS) framework with a technique that we call co-certificate learning. If SMS generates a candidate graph that violates the given co-NP property,
we obtain a certificate for this violation, i.e., `co-certificate' for the co-NP property. The co-certificate gives rise to a clause that the SAT solver, serving as SMS's backend, learns as part of its CDCL procedure. We demonstrate that SMS plus co-certificate learning is a powerful method that allows us to improve the best-known lower bound on the size of Kochen-Specker vector systems, a problem that is central to the foundations of quantum mechanics and has been studied for over half a century. Our approach is orders of magnitude faster and scales significantly better than a recently proposed SAT-based method.
Keywords:
Constraint Satisfaction and Optimization: CSO: Satisfiabilty
Constraint Satisfaction and Optimization: CSO: Applications
Constraint Satisfaction and Optimization: CSO: Solvers and tools