Engineering an Efficient Approximate DNF-Counter

TL;DR

This paper introduces pepin, an innovative approximate #DNF counter that leverages recent streaming set union techniques to significantly improve efficiency and accuracy over existing methods.

Contribution

The paper presents pepin, a novel approximate #DNF counting algorithm that outperforms previous approaches by applying recent advances in streaming set union models.

Findings

pepin achieves higher accuracy than prior methods

pepin runs faster on large DNF formulas

Extensive experiments validate pepin's efficiency

Abstract

Model counting is a fundamental problem in many practical applications, including query evaluation in probabilistic databases and failure-probability estimation of networks. In this work, we focus on a variant of this problem where the underlying formula is expressed in the Disjunctive Normal Form (DNF), also known as #DNF. This problem has been shown to be #P-complete, making it often intractable to solve exactly. Much research has therefore focused on obtaining approximate solutions, particularly in the form of $(\varepsilon, \delta)$ approximations. The primary contribution of this paper is a new approach, called pepin, an approximate #DNF counter that significantly outperforms prior state-of-the-art approaches. Our work is based on the recent breakthrough in the context of the union of sets in the streaming model. We demonstrate the effectiveness of our approach through extensive experiments and show that it provides an affirmative answer to the challenge of efficiently computing #DNF.