An FPRAS for Model Counting for Non-Deterministic Read-Once Branching Programs
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917120355336192 |
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| author | Meel, Kuldeep S. de Colnet, Alexis |
| author_facet | Meel, Kuldeep S. de Colnet, Alexis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16515 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An FPRAS for Model Counting for Non-Deterministic Read-Once Branching Programs Meel, Kuldeep S. de Colnet, Alexis Data Structures and Algorithms 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. |
| title | An FPRAS for Model Counting for Non-Deterministic Read-Once Branching Programs |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2406.16515 |