Adaptive Hashing for Model Counting

  title={Adaptive Hashing for Model Counting},
  author={Jonathan Kuck and Tri Dao and Shenjia Zhao and Burak Bartan and Ashish Sabharwal and Stefano Ermon},
Randomized hashing algorithms have seen recent success in providing bounds on the model count of a propositional formula. These methods repeatedly check the satisfiability of a formula subject to increasingly stringent random constraints. Key to these approaches is the choice of a fixed family of hash functions that strikes a good balance between computational efficiency and statistical guarantees for a hypothetical worst case formula. In this paper we propose a scheme where the family of hash… CONTINUE READING


Publications referenced by this paper.