✨
AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
In particular, we present algorithms in order to compute the dim$_q(X)$ of an arbitrary finite topological space $X$.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:We use matrices to study the dimension function dim$_q$, calling quasi covering dimension, for finite topological spaces, which is always greaterthan or equal to the classical covering dimension dim.
📊
Key Findings & Data:We use matrices to study the dimension function dim$_q$, calling quasi covering dimension, for finite topological spaces, which is always greaterthan or equal to the classical covering dimension dim.
💡
Conclusion & Impact:In particular, we present algorithms in order to compute the dim$_q(X)$ of an arbitrary finite topological space $X$.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Covering dimension · quasi covering dimension · quasi cover
Abstract
We use matrices to study the dimension function dim$_q$, calling quasi covering 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 dim$_q(X)$ of an arbitrary finite topological space $X$.
🔖
Cite This Article
GEORGİOU, D. N., MEGARİTİS, A. C., SERETİ, F. (2017). A study of the quasi covering dimension for finite spaces through the matrix theory. Hacettepe Journal of Mathematics and Statistics, Cilt: 46 - Sayı: 1, 111–125. https://ajindex.com/articles/669491