Left is better than right for reducing nondeterminism of NFAs

Sang Ki Ko, Yo Sub Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We study the NFA reductions by invariant equivalences. It is well-known that the NFA minimization problem is PSPACE-complete. Therefore, there have been approaches to reduce the size of NFAs in low polynomial time by computing invariant equivalence and merging the states within same equivalence class. Here we consider the nondeterminism reduction of NFAs by invariant equivalences. We, in particular, show that the left-invariant equivalence is more useful than the right-invariant equivalence for reducing NFA nondeterminism. We also present experimental evidence for showing that NFA reduction by left-invariant equivalence achieves the better reduction of nondeterminism than right-invariant equivalence.

Original languageEnglish
Title of host publicationImplementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings
PublisherSpringer Verlag
Pages238-251
Number of pages14
ISBN (Print)9783319088457
DOIs
Publication statusPublished - 2014 Jan 1
Event19th International Conference on Implementation and Application of Automata, CIAA 2014 - Giessen, Germany
Duration: 2014 Jul 302014 Aug 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Implementation and Application of Automata, CIAA 2014
CountryGermany
CityGiessen
Period14/7/3014/8/2

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ko, S. K., & Han, Y. S. (2014). Left is better than right for reducing nondeterminism of NFAs. In Implementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings (pp. 238-251). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8587 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-08846-4_18