title: CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
abstract: Boolean satisfiability (SAT) is one of the most well-known NP-complete
  problems and has been extensively studied.  State-of-the-art solvers
  exist and have found a wide range of applications. However, they still
  do not scale well to formulas with hundreds of variables. To tackle
  this fundamental scalability challenge, we introduce CNNSAT, a fast
  and accurate statistical decision procedure for SAT based on
  convolutional neural networks. CNNSAT's effectiveness is due to a
  precise and compact representation of Boolean
  formulas. On both real and synthetic formulas, CNNSAT is highly
    accurate and orders of magnitude faster than the
  state-of-the-art solver Z3.  We also describe how to extend CNNSAT to
  predict satisfying assignments when it predicts a formula to be
  satisfiable.
authors: Yu Wang Fengjuan Gao Amin Alipour Linzhang Wang Xuandong Li Zhendong Su
hIndex: Zhendong Su 52