Enumerating models of DNF faster: breaking the dependency on the formula size

Fuente: arXiv
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Autores principales: Capelli, Florent, Strozecki, Yann
Formato: Preprint
Publicado: 2018
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author Capelli, Florent
Strozecki, Yann
author_facet Capelli, Florent
Strozecki, Yann
contents In this article, we study the problem of enumerating the models of DNF formulas. The aim is to provide enumeration algorithms with a delay that depends polynomially on the size of each model and not on the size of the formula, which can be exponentially larger. We succeed for two subclasses of DNF formulas: we provide a constant delay algorithm for $k$-DNF with fixed $k$ by an appropriate amortization method and we give a quadratic delay algorithm for monotone formulas. We then focus on the \emph{average delay} of enumeration algorithms and show how to obtain a sublinear delay in the formula size.
format Preprint
id arxiv_https___arxiv_org_abs_1810_04006
institution arXiv
publishDate 2018
record_format arxiv
spellingShingle Enumerating models of DNF faster: breaking the dependency on the formula size
Capelli, Florent
Strozecki, Yann
Computational Complexity
Data Structures and Algorithms
In this article, we study the problem of enumerating the models of DNF formulas. The aim is to provide enumeration algorithms with a delay that depends polynomially on the size of each model and not on the size of the formula, which can be exponentially larger. We succeed for two subclasses of DNF formulas: we provide a constant delay algorithm for $k$-DNF with fixed $k$ by an appropriate amortization method and we give a quadratic delay algorithm for monotone formulas. We then focus on the \emph{average delay} of enumeration algorithms and show how to obtain a sublinear delay in the formula size.
title Enumerating models of DNF faster: breaking the dependency on the formula size
topic Computational Complexity
Data Structures and Algorithms
url https://arxiv.org/abs/1810.04006