Proof Complexity of Propositional Model Counting

Recently, the proof system MICE for the model counting problem #SAT was introduced by Fichte, Hecher and Roland (SAT’22). As demonstrated by Fichte et al., the system MICE can be used for proof logging for state-of-the-art #SAT solvers. We perform a proof-complexity study of MICE. For this we first simplify the rules of MICE and obtain a calculus MIC E ′ that is polynomially equivalent to MICE. We then establish an exponential lower bound for the number of proof steps in MIC E ′ (and hence also in MICE) for a specific family of CNFs. We also explain a tight connection between MIC E ′ proofs and decision DNNFs.

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Publication Details

Journal
Journal on Satisfiability Boolean Modeling and Computation
Published
2024-11-05
DOI
https://doi.org/10.3233/sat-231507
Citations
1
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
0.35

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article

Proof Complexity of Propositional Model Counting

Olaf Beyersdorff, Tim Hoffmann, Luc N. Spachmann
1 citations
Journal on Satisfiability Boolean Modeling and Computation
Bayesian Modeling and Causal Inference
0.35
article

Proof Complexity of Propositional Model Counting

Olaf Beyersdorff, Tim Hoffmann, Luc N. Spachmann
article en
1 citations

Abstract

Recently, the proof system MICE for the model counting problem #SAT was introduced by Fichte, Hecher and Roland (SAT’22). As demonstrated by Fichte et al., the system MICE can be used for proof logging for state-of-the-art #SAT solvers. We perform a proof-complexity study of MICE. For this we first simplify the rules of MICE and obtain a calculus MIC E ′ that is polynomially equivalent to MICE. We then establish an exponential lower bound for the number of proof steps in MIC E ′ (and hence also in MICE) for a specific family of CNFs. We also explain a tight connection between MIC E ′ proofs and decision DNNFs.

Journal on Satisfiability Boolean Modeling and ComputationVol. 15(1)
Friedrich Schiller University Jena (DE)
Deutsche Forschungsgemeinschaft
Peace, Justice and strong institutions
Openalex Percentile: Top 32%
Bayesian Modeling and Causal Inference
0.35
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