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Journal of Logic and Computation
Article . 2017 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2016
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2016
Data sources: DBLP
DBLP
Article . 2017
Data sources: DBLP
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On the likelihood of normalization in combinatory logic

Authors: Bendkowski, Maciej; Grygiel, Katarzyna; Zaionc, Marek;

On the likelihood of normalization in combinatory logic

Abstract

We present a quantitative basis-independent analysis of combinatory logic. Using a general argument regarding plane binary trees with labelled leaves, we generalise the results of David et al. and Bendkowski et al. to all Turing-complete combinator bases proving, inter alia, that asymptotically almost no combinator is strongly normalising nor typeable. We exploit the structure of recently discovered normal-order reduction grammars showing that for each positive $n$, the set of $\mathbf{S} \mathbf{K}$-combinators reducing in $n$ normal-order reduction steps has positive asymptotic density in the set of all combinators. Our approach is constructive, allowing us to systematically find new asymptotically significant fractions of normalising combinators. We show that the density of normalising combinators cannot be less than $34\%$, improving the previously best lower bound of approximately $3\%$. Finally, we present some super-computer experimental results, conjecturing that the density of normalising combinators is close to $85\%$.

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Poland
Related Organizations
Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Logic in Computer Science (cs.LO)

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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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!
3
Average
Average
Average
Green