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Article . 2007 . Peer-reviewed
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Computing the topological entropy of shifts

Authors: Christoph Spandl;

Computing the topological entropy of shifts

Abstract

AbstractDifferent characterizations of classes of shift dynamical systems via labeled digraphs, languages, and sets of forbidden words are investigated. The corresponding naming systems are analyzed according to reducibility and particularly with regard to the computability of the topological entropy relative to the presented naming systems. It turns out that all examined natural representations separate into two equivalence classes and that the topological entropy is not computable in general with respect to the defined natural representations. However, if a specific labeled digraph representation – namely primitive, right‐resolving labeled digraphs – of some class of shifts is considered, namely the shifts having the specification property, then the topological entropy gets computable. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Keywords

labeled digraphs, Topological entropy, Type-2 computability, Symbolic dynamics, shift dynamical systems, Theoretical Computer Science, Shift dynamical systems, topological entropy, type-2 computability, Constructive and recursive analysis, Computer Science(all)

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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!
20
Top 10%
Top 10%
Average
gold