Low-Rank Semidefinite Programming for the MAX2SAT Problem


  • Po-Wei Wang Carnegie Mellon University
  • J. Zico Kolter Carnegie Mellon University




This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches. Semidefinite programming techniques are well-known as a theoretical tool for approximating maximum satisfiability problems, but their application has traditionally been very limited by their speed and randomized nature. Our approach overcomes this difficult by using a recent approach to low-rank semidefinite programming, specialized to work in an incremental fashion suitable for use in an exact search algorithm. The method can be used both within complete or incomplete solver, and we demonstrate on a variety of problems from recent competitions. Our experiments show that the approach is faster (sometimes by orders of magnitude) than existing state-of-the-art complete and incomplete solvers, representing a substantial advance in search methods specialized for MAX2SAT problems.




How to Cite

Wang, P.-W., & Kolter, J. Z. (2019). Low-Rank Semidefinite Programming for the MAX2SAT Problem. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1641-1649. https://doi.org/10.1609/aaai.v33i01.33011641



AAAI Technical Track: Constraint Satisfaction and Optimization