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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Since statistical convergence and almost convergence methods are incompatible, we conclude that these methods can be used alternatively to get some approximation results.
📌 3 Key Takeaways
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Objective & Hypothesis:The aim of this paper is to present Korovkin type theorems on approximation of continuous functions with the use of A−statistical convergence and matrix summability method which includes both convergence and almost convergence.
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Key Findings & Data:The aim of this paper is to present Korovkin type theorems on approximation of continuous functions with the use of A−statistical convergence and matrix summability method which includes both convergence and almost convergence.
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Conclusion & Impact:Since statistical convergence and almost convergence methods are incompatible, we conclude that these methods can be used alternatively to get some approximation results.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Matrix summability method · sequence of positive linear operators · the Korovkin approximation theorem
Abstract
The aim of this paper is to present Korovkin type theorems on approximation of continuous functions with the use of A−statistical convergence and matrix summability method which includes both convergence and almost convergence. Since statistical convergence and almost convergence methods are incompatible, we conclude that these methods can be used alternatively to get some approximation results.
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Cite This Article
ATLIHAN, Ö. G. (2022). Convergence processes of approximating operators. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 4, 813–821. https://ajindex.com/articles/530246