Hyper-Minimization for Deterministic Weighted Tree Automata

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...

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Main Authors: Andreas Maletti, Daniel Quernheim
Format: Article
Language:English
Published: Open Publishing Association 2014-05-01
Series:Electronic Proceedings in Theoretical Computer Science
Online Access:http://arxiv.org/pdf/1405.5610v1
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author Andreas Maletti
Daniel Quernheim
author_facet Andreas Maletti
Daniel Quernheim
author_sort Andreas Maletti
collection DOAJ
description 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.
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spelling doaj.art-28c1277472544e2f95479dfdb917acfa2022-12-22T00:18:46ZengOpen Publishing AssociationElectronic Proceedings in Theoretical Computer Science2075-21802014-05-01151Proc. AFL 201431432610.4204/EPTCS.151.22:33Hyper-Minimization for Deterministic Weighted Tree AutomataAndreas Maletti0Daniel Quernheim1 Universität Leipzig Universität Stuttgart 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.http://arxiv.org/pdf/1405.5610v1
spellingShingle Andreas Maletti
Daniel Quernheim
Hyper-Minimization for Deterministic Weighted Tree Automata
Electronic Proceedings in Theoretical Computer Science
title Hyper-Minimization for Deterministic Weighted Tree Automata
title_full Hyper-Minimization for Deterministic Weighted Tree Automata
title_fullStr Hyper-Minimization for Deterministic Weighted Tree Automata
title_full_unstemmed Hyper-Minimization for Deterministic Weighted Tree Automata
title_short Hyper-Minimization for Deterministic Weighted Tree Automata
title_sort hyper minimization for deterministic weighted tree automata
url http://arxiv.org/pdf/1405.5610v1
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AT danielquernheim hyperminimizationfordeterministicweightedtreeautomata