Systematic Parameter Decision in Approximate Model Counting

This paper proposes a novel approach to determining the internal parameters of the hashing-based approximate model counting algorithm $\mathsf{ApproxMC}$. In this problem, the chosen parameter values must ensure that $\mathsf{ApproxMC}$ is Probably Approximately Correct (PAC), while also making it as efficient as possible. The existing approach to this problem relies on heuristics; in this paper, we solve this problem by formulating it as an optimization problem that arises from generalizing $\mathsf{ApproxMC}$'s correctness proof to arbitrary parameter values.

@article{lei2025_2504.05874,
  title={ Systematic Parameter Decision in Approximate Model Counting },
  author={ Jinping Lei and Toru Takisaka and Junqiang Peng and Mingyu Xiao },
  journal={arXiv preprint arXiv:2504.05874},
  year={ 2025 }
}