Scalable Algorithms for Approximate DNF Model Counting

The paper introduces a new Monte Carlo algorithm for approximate counting of Disjunctive Normal Form (DNF) formulas, featuring an adaptive stopping rule and short‑circuit evaluation. It achieves PAC learning bounds and is asymptotically more efficient than existing methods, including classical Monte Carlo, hashing‑based, and neural‑network approaches. Experiments demonstrate that the algorithm outperforms prior techniques by orders of magnitude and scales to problems with millions of variables.

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