Left is better than right for reducing nondeterminism of NFAs

Sang Ki Ko; Yo Sub Han

doi:10.1007/978-3-319-08846-4_18

Left is better than right for reducing nondeterminism of NFAs

Sang Ki Ko, Yo Sub Han

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Implementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings
Publisher	Springer Verlag
Pages	238-251
Number of pages	14
ISBN (Print)	9783319088457
DOIs	https://doi.org/10.1007/978-3-319-08846-4_18
Publication status	Published - 2014
Event	19th International Conference on Implementation and Application of Automata, CIAA 2014 - Giessen, Germany Duration: 2014 Jul 30 → 2014 Aug 2

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8587 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	19th International Conference on Implementation and Application of Automata, CIAA 2014
Country/Territory	Germany
City	Giessen
Period	14/7/30 → 14/8/2

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-08846-4_18

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

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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.",

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Ko, SK & Han, YS 2014, Left is better than right for reducing nondeterminism of NFAs. in Implementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8587 LNCS, Springer Verlag, pp. 238-251, 19th International Conference on Implementation and Application of Automata, CIAA 2014, Giessen, Germany, 14/7/30. https://doi.org/10.1007/978-3-319-08846-4_18

Left is better than right for reducing nondeterminism of NFAs. / Ko, Sang Ki; Han, Yo Sub.

Implementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings. Springer Verlag, 2014. p. 238-251 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8587 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Ko SK, Han YS. Left is better than right for reducing nondeterminism of NFAs. In Implementation and Application of Automata - 19th International Conference, CIAA 2014, Proceedings. Springer Verlag. 2014. p. 238-251. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-08846-4_18

Left is better than right for reducing nondeterminism of NFAs

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