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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
So, we obtain more powerful results than the classical ones in complex analysis.
📌 3 Key Takeaways
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Objective & Hypothesis:In this article, using the notion of statistical convergence, we relax the hypotheses of the well-known theorems from classical complex analysis, such as Weierstrass’ Theorem, Montel’s Theorem and Hurwitz’s Theorem.
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Key Findings & Data:In this article, using the notion of statistical convergence, we relax the hypotheses of the well-known theorems from classical complex analysis, such as Weierstrass’ Theorem, Montel’s Theorem and Hurwitz’s Theorem.
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Conclusion & Impact:So, we obtain more powerful results than the classical ones in complex analysis.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Statistical convergence · Weierstrass’ theorem · Montel’s theorem
Abstract
In this article, using the notion of statistical convergence, we relax the hypotheses of the well-known theorems from classical complex analysis, such as Weierstrass’ Theorem, Montel’s Theorem and Hurwitz’s Theorem. So, we obtain more powerful results than the classical ones in complex analysis.
Keywords
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Cite This Article
DUMAN, O. (2022). Strong Versions of the Theorems of Weierstrass, Montel and Hurwitz. Hacettepe Journal of Mathematics and Statistics, Cilt: 38 - Sayı: 2, 153–159. https://ajindex.com/articles/530688