Abstract
Boolean satisfiability (SAT) solvers are at the core of efficient approaches for solving a vast multitude of practical problems. Moreover, albeit targeting an NP-complete problem, SAT solvers are increasingly used for tackling problems beyond NP. Despite the success of SAT in practice, modeling with SAT and more importantly implementing SAT-based problem solving solutions is often a difficult and error-prone task. This paper proposes the PySAT toolkit, which enables fast Python-based prototyping using SAT oracles and SAT-related technology. PySAT provides a simple API for working with a few state-of-the-art SAT oracles and also integrates a number of cardinality constraint encodings, all aiming at simplifying the prototyping process. Experimental results presented in the paper show that PySAT-based implementations can be as efficient as those written in a low-level language.
This work was supported by FCT funding of post-doctoral grants SFRH/BPD/103609/2014, SFRH/BPD/120315/2016, FCT grant ABSOLV (028986/02/SAICT/2017), and LASIGE Research Unit, ref. UID/CEC/00408/2013.
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Notes
- 1.
The method get_model() (get_core(), resp.) can be used if a prior SAT call was made and returned True (False, resp.). The get_core() method additionally assumes the SAT call was provided with a list of assumptions.
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Ignatiev, A., Morgado, A., Marques-Silva, J. (2018). PySAT: A Python Toolkit for Prototyping with SAT Oracles. In: Beyersdorff, O., Wintersteiger, C. (eds) Theory and Applications of Satisfiability Testing – SAT 2018. SAT 2018. Lecture Notes in Computer Science(), vol 10929. Springer, Cham. https://doi.org/10.1007/978-3-319-94144-8_26
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