SharpSMT: a scalable toolkit for measuring solution spaces of SMT(LA) formulas

Abstract

In this paper, we present SHARPSMT, a toolkit for measuring solution spaces of SMT(LA) formulas which are Boolean combinations of linear arithmetic constraints, i.e., #SMT(LA) problems. It integrates SMT satisfiability solving algorithm with various polytope subroutines: volume computation, volume estimation, lattice counting, and approximate lattice counting. We propose a series of new polytope preprocessing techniques which have been implemented in SHARPSMT. Experimental results show that the new polytope preprocessing techniques are very effective, especially on application instances. We believe that SHARPSMT will be useful in a number of areas.

Graphical abstract

Keywords

#SMT(LA) problems / DPLL(T) algorithm / polytope preprocessing techniques / volume computation / lattice counting

Cunjing Ge is a PostDoc in School of Artificial Intelligence, Nanjing University, China. He received his PhD degree in Computer Software and Theory from Institute of Software, Chinese Academy of Sciences, China. His research interests including constraint satisfaction problem, model counting, and abductive learning

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Acknowledgements

Cunjing Ge is supported by the National Natural Science Foundation of China (Grant No. 62202218), and is sponsored by CCF-Huawei Populus Grove Fund (CCF-HuaweiFM202309).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2025 Higher Education Press