sat-solving

This paper studies parallel Continuous Local Search (CLS) for Boolean satisfiability with pseudo-Boolean constraints, revealing that redundant constraints can inhibit convergence and that CLS shows promise as a sub-solver in hybrid settings.

This paper presents Accelerated Fourier SAT (AFSAT), a GPU-accelerated solver for pseudo-Boolean satisfiability based on continuous local search. It improves upon prior proof-of-concept implementations by supporting heterogeneous constraints and leveraging JAX for parallel computation.

This paper investigates how to encode factored planning tasks (FTS) into SAT, proposing multiple encoding strategies and analyzing the impact of task transformations on SAT-based planning performance. It aims to extend SAT solving to more compact planning representations beyond heuristic search.