Equi-Statistical Relative Convergence and Korovkin-Type Approximation

S. Yildiz, K. Demirci, F. Dirik
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Abstract

Classical approximation theory has started with the proof of Weierstrass approximation theorem and after that Korovkin [Linear operators and approximation theory, Hindustan Publ. Corp, Delhi, 1960] first established the necessary and sufficient conditions for uniform convergence of a sequence of positive linear operators to a function f . In classical Korovkin theorem, most of the classical operators tend to converge to the value of the function being approximated. Also, the attention of researchers has been attracted to the notion of statistical convergence because of the fact that it is stronger than the classical convergence method. Furthermore, the concept of equi-statistical convergence is more general than the statistical uniform convergence. In this work, we introduce our new convergence method named equi-statistical relative convergence to demonstrate a Korovkin type approximation theorems which were proven by earlier authors. Then, we present an example in support of our definition and result presented in this paper. Finally, we compute the rate of the convergence.
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等统计相对收敛与korovkin型近似
经典逼近理论从Weierstrass逼近定理的证明开始,之后Korovkin[线性算子与逼近理论,Hindustan Publ]。Corp, Delhi, 1960]首先建立了正线性算子序列一致收敛于函数f的充分必要条件。在经典的柯洛夫金定理中,大多数经典算子都趋向于收敛于被逼近函数的值。此外,统计收敛的概念也因其比经典的收敛方法更强而引起了研究人员的注意。此外,等统计收敛的概念比统计一致收敛的概念更具有普遍性。在这项工作中,我们引入了一种新的收敛方法,称为等统计相对收敛,以证明先前作者证明的一个Korovkin型近似定理。然后,我们给出了一个例子来支持我们的定义和本文的结果。最后,我们计算了收敛速度。
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