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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In particular,we present algorithms in order to compute the dimq(X) of an arbitraryfinite topological space X.
📌 3 Key Takeaways
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Objective & Hypothesis:We use matrices to study the dimension function dimq, calling quasicovering dimension, for finite topological spaces, which is always greaterthan or equal to the classical covering dimension dim.
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Key Findings & Data:We use matrices to study the dimension function dimq, calling quasicovering dimension, for finite topological spaces, which is always greaterthan or equal to the classical covering dimension dim.
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Conclusion & Impact:In particular,we present algorithms in order to compute the dimq(X) of an arbitraryfinite topological space X.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
We use matrices to study the dimension function dimq, calling quasicovering dimension, for finite topological spaces, which is always greaterthan or equal to the classical covering dimension dim. In particular,we present algorithms in order to compute the dimq(X) of an arbitraryfinite topological space X.
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Cite This Article
GEORGIOU, D. N., MEGARİTİS, A. C., SERETİ, F. (2022). A study of the quasi covering dimension for nite spaces through the matrix theory. Hacettepe Journal of Mathematics and Statistics, Cilt: 46 - Sayı: 1, 111–125. https://ajindex.com/articles/530007