An FPRAS for Model Counting for Non-Deterministic Read-Once Branching Programs

Authors Kuldeep S. Meel , Alexis de Colnet



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Author Details

Kuldeep S. Meel
  • University of Toronto, Canada
Alexis de Colnet
  • TU Wien, Austria

Acknowledgements

This research was initiated at Dagstuhl Seminar 24171 on "Automated Synthesis: Functional, Reactive and Beyond" (https://www.dagstuhl.de/24171). We gratefully acknowledge the Schloss Dagstuhl - Leibniz Center for Informatics for providing an excellent environment and support for scientific collaboration.

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Kuldeep S. Meel and Alexis de Colnet. An FPRAS for Model Counting for Non-Deterministic Read-Once Branching Programs. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 30:1-30:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.30

Abstract

Non-deterministic read-once branching programs, also known as non-deterministic free binary decision diagrams (nFBDD), are a fundamental data structure in computer science for representing Boolean functions. In this paper, we focus on #nFBDD, the problem of model counting for non-deterministic read-once branching programs. The #nFBDD problem is #P-hard, and it is known that there exists a quasi-polynomial randomized approximation scheme for #nFBDD. In this paper, we provide the first FPRAS for #nFBDD. Our result relies on the introduction of new analysis techniques that focus on bounding the dependence of samples.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
Keywords
  • Approximate model counting
  • FPRAS
  • Knowledge compilation
  • nFBDD

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References

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