APPROXIMATING MINIMUM REPRESENTATIONS OF KEY HORN FUNCTIONS

Kristóf Bérczi, Endre Boros, Ondřej Čepek, Petr Kučera, Kazuhisa Makino

Research output: Contribution to journalArticlepeer-review

Abstract

Horn functions form an important subclass of Boolean functions and appear in many different areas of computer science and mathematics as a general tool to describe implications and dependencies. Finding minimum sized representations for such functions with respect to most commonly used measures is a computationally hard problem admitting a 2log1-o(1) n inapproximability bound. In this paper we consider the natural class of key Horn functions representing keys of relational databases. For this class, the minimization problems for most measures remain NP-hard. In this paper we provide logarithmic factor approximation algorithms for key Horn functions with respect to all such measures.

Original languageAmerican English
Pages (from-to)116-138
Number of pages23
JournalSIAM Journal on Computing
Volume51
Issue number1
DOIs
StatePublished - 2022

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Keywords

  • Horn minimization
  • approximation algorithms
  • directed hypergraphs
  • implicational systems

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