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International Workshop on Logic, Language, Information, and Computation

WoLLIC 2017: Logic, Language, Information, and Computation pp 68–79Cite as

On the Length of Medial-Switch-Mix Derivations

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10388))

Abstract

Switch and medial are two inference rules that play a central role in many deep inference proof systems. In specific proof systems, the mix rule may also be present. In this paper we show that the maximal length of a derivation using only the inference rules for switch, medial, and mix, modulo associativity and commutativity of the two binary connectives involved, is quadratic in the size of the formula at the conclusion of the derivation. This shows, at the same time, the termination of the rewrite system.

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Notes

  1. 1.

    The relation web of a formula provides a graph-based representation of a formula in deep inference contextual rewriting, as in [12]. An equivalent definition of the relweb-or-number is the number of edges in the cograph for .

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Acknowledgements

We are grateful to Alessio Guglielmi and Tom Gundersen, for their encouragements and checks, and to the anonymous referees. Paola Bruscoli was funded by EPSRC Project EP/K018868/1 “Efficient and Natural Proof Systems”. This research is also supported by ANR Project FISP – “The Fine Structure of Formal Proof Systems and their Computational Interpretations.”

Author information

Authors and Affiliations

  1. University of Bath, Bath, UK

    Paola Bruscoli

  2. Inria, Palaiseau, France

    Lutz Straßburger

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  1. Paola Bruscoli
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  2. Lutz Straßburger
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Correspondence to Paola Bruscoli .

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Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Juliette Kennedy

  2. Centro de Informática, Recife, Pernambuco, Brazil

    Ruy J.G.B. de Queiroz

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Bruscoli, P., Straßburger, L. (2017). On the Length of Medial-Switch-Mix Derivations. In: Kennedy, J., de Queiroz, R. (eds) Logic, Language, Information, and Computation. WoLLIC 2017. Lecture Notes in Computer Science(), vol 10388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55386-2_5

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  • DOI: https://doi.org/10.1007/978-3-662-55386-2_5

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  • Print ISBN: 978-3-662-55385-5

  • Online ISBN: 978-3-662-55386-2

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