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Electronic Proceedings in Theoretical Computer Science
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Hyper-Minimization for Deterministic Weighted Tree Automata

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object , Other literature type 21 May 2014Embargo end date: 01 Jan 2014 English Publisher:Open Publishing AssociationJournal:Electronic Proceedings in Theoretical Computer Science, volume 151, pages 314-326 (eissn: 2075-2180, Copyright policy )
Authors: Andreas Maletti; Daniel Quernheim;

doi: 10.4204/eptcs.151.22 , 10.48550/arxiv.1405.5610

arXiv: 1405.5610

Hyper-Minimization for Deterministic Weighted Tree Automata

Abstract

Hyper-minimization is a state reduction technique that allows a finite change in the semantics. The theory for hyper-minimization of deterministic weighted tree automata is provided. The presence of weights slightly complicates the situation in comparison to the unweighted case. In addition, the first hyper-minimization algorithm for deterministic weighted tree automata, weighted over commutative semifields, is provided together with some implementation remarks that enable an efficient implementation. In fact, the same run-time O(m log n) as in the unweighted case is obtained, where m is the size of the deterministic weighted tree automaton and n is its number of states.

In Proceedings AFL 2014, arXiv:1405.5272

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Keywords

FOS: Computer and information sciences, Formal Languages and Automata Theory (cs.FL), Computer Science - Formal Languages and Automata Theory, QA75.5-76.95, Computational Complexity (cs.CC), Computer Science - Computational Complexity, Electronic computers. Computer science, Computer Science - Data Structures and Algorithms, QA1-939, Data Structures and Algorithms (cs.DS), Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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