SharpSAT-TD in Model Counting Competitions 2021-2023
This work addresses the problem of efficient model counting for SAT solvers in competitions, representing an incremental improvement over existing methods.
The paper presents SharpSAT-TD, a model counting solver that won 6 first places in competitions from 2021-2023, by enhancing SharpSAT with tree decompositions for variable selection and other modifications like a new preprocessor.
We describe SharpSAT-TD, our submission to the unweighted and weighted tracks of the Model Counting Competition in 2021-2023, which has won in total $6$ first places in different tracks of the competition. SharpSAT-TD is based on SharpSAT [Thurley, SAT 2006], with the primary novel modification being the use of tree decompositions in the variable selection heuristic as introduced by the authors in [CP 2021]. Unlike the version of SharpSAT-TD evaluated in [CP 2021], the current version that is available in https://github.com/Laakeri/sharpsat-td features also other significant modifications compared to the original SharpSAT, for example, a new preprocessor.