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Space Complexity of Stack Automata Models

This paper examines several measures of space complexity on variants of stack automata: non-erasing stack automata and checking stack automata. These measures capture the minimum stack size required to accept any word in a language (weak measure), the maximum stack size used in any accepting computa...

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Detalles Bibliográficos
Autores principales: Ibarra, Oscar H., Jirásek, Jozef, McQuillan, Ian, Prigioniero, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247887/
http://dx.doi.org/10.1007/978-3-030-48516-0_11
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author Ibarra, Oscar H.
Jirásek, Jozef
McQuillan, Ian
Prigioniero, Luca
author_facet Ibarra, Oscar H.
Jirásek, Jozef
McQuillan, Ian
Prigioniero, Luca
author_sort Ibarra, Oscar H.
collection PubMed
description This paper examines several measures of space complexity on variants of stack automata: non-erasing stack automata and checking stack automata. These measures capture the minimum stack size required to accept any word in a language (weak measure), the maximum stack size used in any accepting computation on any accepted word (accept measure), and the maximum stack size used in any computation (strong measure). We give a detailed characterization of the accept and strong space complexity measures for checking stack automata. Exactly one of three cases can occur: the complexity is either bounded by a constant, behaves (up to small technicalities explained in the paper) like a linear function, or it grows arbitrarily larger than the length of the input word. However, this result does not hold for non-erasing stack automata; we provide an example when the space complexity grows with the square root of the input length. Furthermore, an investigation is done regarding the best complexity of any machine accepting a given language, and on decidability of space complexity properties.
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spelling pubmed-72478872020-05-26 Space Complexity of Stack Automata Models Ibarra, Oscar H. Jirásek, Jozef McQuillan, Ian Prigioniero, Luca Developments in Language Theory Article This paper examines several measures of space complexity on variants of stack automata: non-erasing stack automata and checking stack automata. These measures capture the minimum stack size required to accept any word in a language (weak measure), the maximum stack size used in any accepting computation on any accepted word (accept measure), and the maximum stack size used in any computation (strong measure). We give a detailed characterization of the accept and strong space complexity measures for checking stack automata. Exactly one of three cases can occur: the complexity is either bounded by a constant, behaves (up to small technicalities explained in the paper) like a linear function, or it grows arbitrarily larger than the length of the input word. However, this result does not hold for non-erasing stack automata; we provide an example when the space complexity grows with the square root of the input length. Furthermore, an investigation is done regarding the best complexity of any machine accepting a given language, and on decidability of space complexity properties. 2020-05-26 /pmc/articles/PMC7247887/ http://dx.doi.org/10.1007/978-3-030-48516-0_11 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Ibarra, Oscar H.
Jirásek, Jozef
McQuillan, Ian
Prigioniero, Luca
Space Complexity of Stack Automata Models
title Space Complexity of Stack Automata Models
title_full Space Complexity of Stack Automata Models
title_fullStr Space Complexity of Stack Automata Models
title_full_unstemmed Space Complexity of Stack Automata Models
title_short Space Complexity of Stack Automata Models
title_sort space complexity of stack automata models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247887/
http://dx.doi.org/10.1007/978-3-030-48516-0_11
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