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Electronic Notes in Theoretical Computer Science
Article . 2007 . Peer-reviewed
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On Computable Metrization

Authors: Tanja Grubba; Klaus Weihrauch;

On Computable Metrization

Abstract

AbstractEvery second-countable regular topological space X is metrizable. For a given “computable” topological space satisfying an axiom of computable regularity M. Schröder [M. Schröder, Effective metrization of regular spaces, in: K.-I. Ko, A. Nerode, M. B. Pour-El, K. Weihrauch and J. Wiedermann, editors, Computability and Complexity in Analysis, Informatik Berichte 235 (1998), pp. 63–80, cCA Workshop, Brno, Czech Republic, August, 1998.] has constructed a computable metric. In this article we study whether this metric space (X,d) can be considered computationally as a subspace of some computable metric space [K. Weihrauch, Computable Analysis, Springer, Berlin, 2000]. While Schröder's construction is “pointless”, i.e., only sets of a countable base but no concrete points are known, for a computable metric space a concrete dense set of computable points is needed. By partial completion we extend (X,d) to a metric space (X˜,d˜) with computable metric and canonical representation. We construct a computable sequence (xi)i∈N of points which is dense in (X˜,d˜). The isometric embedding of X into X˜ is computable. Its inverse is computable if some further computability axiom holds true. The space (X˜,d˜) can be embedded computationally into the computable metric space generated by the sequence (xi)i∈N of points. The inverse of this embedding is continuous.

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Keywords

computable metrization, computable metric space, Computable Analysis, computable embedding, TTE, Theoretical Computer Science, 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!
12
Top 10%
Top 10%
Average
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