KYUNGPOOK Math. J. 2019; 59(4): 679-688
Statistical Approximation of Szász Type Operators Based on Charlier Polynomials
Arun Kajla
Department of Mathematics, Central University of Haryana, Haryana-123031, India
e-mail : rachitkajla47@gmail.com
Received: January 13, 2018; Revised: December 13, 2018; Accepted: December 27, 2018; Published online: December 23, 2019.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

In the present note, we study some approximation properties of the Szász type operators based on Charlier polynomials introduced by S. Varma and F. Taşdelen (Math. Comput. Modelling, 56 (5–6) (2012) 108–112). We establish the rates of A-statistical convergence of these operators. Finally, we prove a Voronovskaja type approximation theorem and local approximation theorem via the concept of A-statistical convergence.

Keywords: Szász operator, Charlier polynomials, modulus of continuity, statistical convergence, bounded variation.
1. Introduction

Szász [28] defined the following linear positive operators

$Sn(f;x)=e-nx∑k=0∞(nx)kk!f(kn),$

where x ∈ [0,∞) and f(x) is a continuous function on [0,∞) whenever the above sum converges uniformly. Many authors have studied approximation properties of these operators and generalized Szász operators involving different type of polynomials. Jakimovski and Leviatan [19] introduced a generalization of Szász operators based on the Appell polynomials and studied the approximation properties of these operators. Varma et al. [29] proposed the generalization of Szász operators based on Brenke type polynomials and gave convergence properties by means of a Korovkin type theorem and an order approximation using classical methods. Aral and Duman [4] studied a statistical asymptotic formula for the Szász-Mirkajan-Kantorovich operators via A–stastical convergence. In 2011, Örkcü and Doǧru [25] defined a Kantorovich variant of q–Szász-Mirakjan operators and obtained weighted statistical convergence theorems for the operators. Özarslan [26] proposed the Mittag-Leffler operators and obtained some direct theorems and A–statistical approximation theorems for these operators. Altomare et al. [1] considered a new kind of generalization of SMK operators and studied the rate of approximation by interms suitable moduli of continuity. Several researchers have studied various generalizations of these operators and obtained results about their approximation properties; we refer the reader to such papers as [3, 5, 6, 11, 13, 14, 15, 16, 17, 20, 22, 23, 24].

Varma and Taşdelen introduced a relation between orthogonal polynomials and linear positive operators. They considered Szász type operators depending on Charlier polynomials.

These polynomials [18] have generating functions of the form

$et(1-ta)u=∑k=0∞Ck(a)(u)tkk!, ∣t∣

where $Ck(a)(u)=∑r=0k(kr) (-u)r(1a)r$ and (m)0 = 1, (m)j = m(m+1)···(m+j−1) for j ≥ 1.

For Cγ[0,∞) := {fC[0,∞) : |f(t)| ≤ Meγt for some γ > 0,M > 0 and t ∈ [0,∞)}, Varma and Taşdelen [30] studied the following Szász type operators based on Charlier polynomials

$Ln(f;x,a)=e-1(1-1a)(a-1)nx∑k=0∞Ck(a)(-(a-1)nx)k!f(kn),$

where a > 1 and x ∈ [0,∞). If a → ∞ and $x-1n$ instead of x, these operators reduce to the operators (1.1). They studied uniform convergence of these operators by using Korovkin’s theorem on compact subsets of [0,∞) and the order of approximation by using the modulus of continuity. Very recently, Kajla and Agrawal [21] discussed some approximation properties of these operators e.g. Lipschitz type space, weighted approximation theorems and rate of approximation of functions having derivatives of bounded variation.

The aim of the present article is to study A–statistical convergence to prove a Korovkin type convergence theorem. We also obtain a Voronovskaja type approximation theorem and local approximation theorem via A–statistical convergence.

### Lemma 1.1.([21])

For the operatorsn(ts; x, a), s = 3, 4, we have

(i) $Ln(t3;x,a)=x3+x2n(6+3a-1)+2xn2(1(a-1)2+3a-1+5)+5n3$;

(ii) $Ln(t4;x,a)=x4+x3n(10+6a-1)+x2n2(31+30a-1+11(a-1)2)+xn3(67+31a-1+20(a-1)2+6(a-1)3)+15n4$.

### Lemma 1.2.([21])

For the operatorsn(f; x, a), we have

(i) $Ln((t-x);x,a)=1n$;

(ii) $Ln((t-x)2;x,a)=axn(a-1)+2n2$;

(iii) $Ln((t-x)4;x,a)=xn3(17+49(a-1)-20(a-1)2+6(a-1)3)+x2n2(19-46(a-1)+3(a-1)2)+15n4$.

In what follows, let B[0,∞) be the space of all real valued bounded and uniformly continuous functions f on [0,∞), endowed with the norm $‖f‖C˜B[0,∞]=supx∈[0,∞)∣f(x)∣$.

Further, let us define the following Peetre’s K-functional:

$K2(f,δ)=infg∈W2}‖f-g‖+δ‖g″‖},δ>0,$

where W2 = {gB[0,∞) : g′, g″ ∈ B[0,∞)}. By [7, p.177, Theorem 2.4], there exists an absolute constant M >0 such that

$K2(f,δ)≤M{ω2(f,δ)+min(1,δ) ‖f‖C˜B[0,∞)},$

where the second order modulus of smoothness is defined as

$ω2(f,δ)=sup0<∣h∣≤δsupx∈[0,∞)∣f(x+2h)-2f(x+h)+f(x)∣.$

We define the usual modulus of continuity of fB[0,∞) as

$ω(f,δ)=sup0<∣h∣≤δsupx∈[0,∞)∣f(x+h)-f(x)∣.$

Let Bϕ[0,∞) be the space of all real valued functions on [0,∞) satisfying the condition |f(x)| ≤ Mfϕ(x), where Mf is a positive constant depending only on f and ϕ(x) = 1 + x2 is a weight function. Let Cϕ[0,∞) be the space of all continuous functions in Bϕ[0,∞) with the norm $‖f‖ϕ:=supx∈[0,∞)∣f(x)∣ϕ(x)$ and $Cϕ*[0,∞):={f∈Cϕ[0,∞):limx→∞∣f(x)∣ϕ(x) is finite}$.

2. Main Results

### 2.1. A-statistical Convergence

First, we give some basic definitions and notations on the concept of A-statistical convergence. Let A = (ank), (n, k ∈ ℕ), be a non-negative infinite summability matrix. For a given sequence x := (xk), the A-transform of x denoted by Ax : ((Ax)n) is defined as

$(Ax)n=∑k=1∞ankxk,$

provided the series converges for each n. A is said to be regular if $limn(Ax)n=L$ whenever $limnxn=L$. The sequence x = (xn) is said to be a A-statistically convergent to L i.e. $stA-limnxn=L$ if for every ε > 0, $limn∑k:∣xk-L∣≥ɛank=0$.

Replacing A by C1, the Cesáro matrix of order one, the A-statistical convergence reduces to the statistical convergence. Similarly, if we take A = I, the identity matrix, then A-statistical convergence coincides with the ordinary convergence. Many researchers have studied the statistical convergence of different types of operators (cf. [2, 8, 9, 10, 12, 25, 27]). In the following result we prove a weighted Korovkin theorem via A-statistical convergence.

Theorem 2.1

Let A = (ank) be a non-negative regular summability matrix and x ∈ [0,∞). Then, for all$f∈Cϕ*[0,∞)$we have

$stA-limn‖Ln(f,.,a)-f‖ϕα=0,$

where ϕα(x) = 1+x2+α, α>0.

Proof

From [10, p. 191, Th. 3], it is sufficient to show that $stA-limn‖Ln(ej,.,a)-ej‖ϕ=0$, where ej(x) = xj, j = 0, 1, 2.

In view of [30, Lemma 1], it follows that

$stA-limn‖Ln(e0,.,a)-e0‖ϕ=0.$

Again, using [30, Lemma 1], we find that

$‖Ln(e1,.,a)-e1‖ϕ=supx≥0|x+1n-x|(1+x2)≤1n.$

For ε > 0, let us define the following sets:

$ℱ:={n∈ℕ:‖Ln(e1;.,a)-e1‖ϕ≥ɛ}$

and

$ℱ1:={n∈ℕ:1n≥ɛ}.$

Then, we obtain ℱ ⊆ ℱ1 which implies that $∑k∈ℱank≤∑k∈ℱ1ank$ and hence

$stA-limn‖Ln(e1;.,a)-e1‖ϕ=0.$

Next, we can write

$‖Ln(e2;.,a)-e2‖ϕ=supx≥011+x2|x2+xn(3+1a-1)+2n2-x2|≤1n(3a-2)(a-1)+2n2.$

Now, we define the following sets:

$G:={n∈ℕ:‖Ln(e2;.,a)-e2‖ϕ≥ɛ},G1:={n∈ℕ:1n(3a-2)(a-1)≥ɛ2}andG2:={n∈ℕ:2n2≥ɛ2}.$

Then, we get , which implies that

$∑k∈Gank≤∑k∈G1ank+∑k∈G2ank$

and hence

$stA-limn‖Ln(e2;.,a)-e2‖ϕ=0.$

This completes the proof of the theorem.

Similarly, from Lemma 1.2, we have

$stA-limn‖Ln((e1-xe0)j;x,a‖ϕ=0, j=0,1,2,3,4.$

Next, we prove a Voronovskaja type theorem for the operators ℒn.

### Theorem 2.2

Let A = (ank) be a nonnegative regular summability matrix. Then, for every$f∈Cϕ*[0,∞)$such that f′, $f″∈Cϕ*[0,∞)$, we have

$stA-limn→∞n(Ln(f;x,a)-f(x))=f′(x)+12ax(a-1)f″(x),$

uniformly with respect to x ∈ [0,E], (E > 0).

Proof

Let f, f′, $f″∈Cϕ*[0,∞)$. For each x ≥ 0, define a function

$Θ(t,x)={f(t)-f(x)-(t-x)f′(x)-12(t-x)2f″(x)(t-x2)if t≠x0,if t=x.$

Then

$Θ(x,x)=0 and Θ(·,x)∈Cϕ*[0,∞).$

Thus, we have

$f(t)=f(x)+(t-x)f′(x)+12(t-x)2f″(x)+(t-x)2Θ(t,x).$

Operating by ℒn on the above equality, we obtain

$n(Ln(f;x,a)-f(x))=f′(x)nLn((t-x);x,a)+12f″(x)nLn((t-x2);x,a)+nLn((t-x)2Θ(t,x);x,a).$

In view of Lemma 1.2, we get

$stA-limn→∞nLn((t-x);x,a)=1,$$stA-limn→∞nLn((t-x)2;x,a)=ax(a-1),$

and

$stA-limn→∞n2Ln((t-x)4;x,a)=x2(19-46(a-1)+3(a-1)2),$

uniformly with respect to x ∈ [0,E].

Applying Cauchy-Schwarz inequality, we have

$nLn((t-x)2Θ(t,x);x,a)≤n2Ln((t-x)4;x,a)Ln(Θ2(t,x);x,a).$

Let η(t, x) = Θ2(t, x), we observe that η(x, x) = 0 and $η(·,x)∈Cϕ*[0,∞)$. It follows from the proof of Theorem 2.1 that

$stA-limn→∞Ln(Θ2(t,x);x,a)=stA-limn→∞Ln(η(t,x);x,a)=η(x,x)=0,$

uniformly with respect to x ∈ [0,E]. Now, using (2.7), we obtain

$stA-limn→∞nLn((t-x)2Θ(t,x);x,a)=0.$

Combining (2.5), (2.6) and (2.8), we get the desired result.

Now, we obtain the rate of A-statistical convergence for the operators ℒn with the help of Peetre’s K-functional.

### Theorem 2.3

Let fW2. Then, we have

$stA-limn‖Ln(f;x,a)-f(x)‖C˜B[0,∞)=0.$
Proof

By our hypothesis, from Taylor’s expansion we find that

$Ln(f;x,a)-f(x)=f′(x)Ln((e1-x);x,a)+12f″(χ)Ln((e1-x)2;x,a);$

where χ lies between t and x.

Thus, we get

$‖Ln(f;x,a)-f(x)‖C˜B[0,∞)≤‖f′‖C˜B[0,∞)‖Ln((e1-x);x,a)‖C[0,b]+‖f″‖C˜B[0,∞)‖Ln((e1-x)2;x,a)‖C[0,b]$

Using (2.4) for ε > 0, we have

$limn∑k∈ℕ:‖f′‖C˜B[0,∞)‖Lk((e1-x);x,a)‖C[0,b]≥ɛ2ank=0,limn∑k∈ℕ:‖f″‖C˜B[0,∞)‖Lk((e1-x)2;x,a)‖C[0,b]≥ɛ2ank=0.$

From (2.10), we may write

$∑k∈ℕ:‖Lk(f;x,a)-f(x)‖C˜B[0,∞)≥ɛank≤∑k∈ℕ:‖f′‖C˜B[0,∞)‖Lk((e1-x);x,a)‖C[0,b]≥ɛ2ank+∑k∈ℕ:‖f″‖C˜B[0,∞)‖Lk((e1-x)2;x,a)‖C[0,b]≥ɛ2ank.$

Hence taking limit as n→∞, we get the desired result.

### Theorem 2.4

Let fB[0,∞), we have

$‖Ln(f;x,a)-f(x)‖C˜B[0,∞)≤Mω2(f,δna),$

wher$δna=‖Ln((e1-x);x,a)‖C˜B[0,∞]+‖Ln((e1-x)2;x,a)‖C˜B[0,∞]$e.

Proof

Let gW2, by (2.10), we have

$‖Ln(g;x,a)-g(x)‖C˜B[0,∞)≤‖Ln((e1-x);x,a)‖C˜B[0,∞)‖g′‖C˜B[0,∞)+12‖Ln((e1-x)2;x,a)‖C˜B[0,∞)‖g″‖C˜B[0,∞)≤δna‖g‖W2.$

For every fB[0,∞) and gW2, we get

$‖Ln(f;x,a)-f(x)‖C˜B[0,∞)≤‖Ln(f;x,a)-Ln(g;x,a)‖C˜B[0,∞)+‖Ln(g;x,a)-g(x)‖C˜B[0,∞)+‖g-f‖C˜B[0,∞)≤2‖g-f‖C˜B[0,∞)+‖Ln(g;x,a)-g(x)‖C˜B[0,∞)≤2‖g-f‖C˜B[0,∞)+δna‖g‖W2.$

Taking the infimum on the right hand side over all gW2, we obtain

$‖Ln(f;x,a)-f(x)‖C˜B[0,∞)≤2K2(f,δna).$

Using (1.4), we have

$‖Ln(f;x,a)-f(x)‖C˜B[0,∞)≤M{ω2(f,δna)+min(1,δna)‖f‖C˜B[0,∞)}$

From (2.4), we get $stA-limnδna=0$, hence $stA-ω2(f,δna)=0$. Therefore we get the rate of A-statistical convergence of the sequence of the operators ℒn(f; x, a) defined by (1.3) to f(x) in the space B[0,∞). If we take A = I, we obtain the ordinary rate of convergence of these operators.

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