Abstract
In this article, we propose a different generalization of -BBH operators and carry statistical approximation properties of the introduced operators towards a function which has to be approximated where -integers contains symmetric property. We establish a Korovkin approximation theorem in the statistical sense and obtain the statistical rates of convergence. Furthermore, we also introduce a bivariate extension of proposed operators and carry many statistical approximation results. The extra parameter p plays an important role to symmetrize the q-BBH operators.
Keywords:
q–Bleimann–Butzer–Hahn operators; (p,q)-integers; (p,q)-Bernstein operators; (p,q)-Bleimann–Butzer–Hahn operators; modulus of continuity; rate of approximation; K-functional MSC:
41A10; 41A25; 41A36
1. Introduction
For , the sequence of q-BBH operators (1) reduces to the classical BBH-operators [2] in which authors investigated pointwise convergence properties of the BBH-operators in a compact sub-interval of .
Let denote the space of all real-valued functions f defined on the semi-axis [3], where is the usual modulus of continuity satisfying
for any .
Gadjiev and Çakar [3] established the Korovkin type theorem which gives the convergence for the sequence of linear positive operators (LPO) to the functions in .
Now, we recollect the following theorem:
Theorem 1
-calculus, also called post-quantum calculus, is a generalization of q-calculus which has lots of applications in quantum physics. In approximation theory, the very first -type generalization of Bernstein polynomials was introduced by Mursaleen et al. [4] using -calculus and improved the said operators (see Erratum [4]). The theory of semigroups of the linear operators is used in order to prove the existence and uniqueness of a weak solutions of boundary value problems in thermoelasticity of dipolar bodies (see [5,6]).
Recently, a very nice application and usage of extra parameter p has been discussed in [7] in the computer-aided geometric design. In that paper, authors applied these -Bernstein bases to construct -Bézier curves which are further generalizations of q-Bézier curves [8]. For more results on LPOs and its -analogues, one can refer to [9,10,11,12,13,14,15].
Now, we provide some notations on -calculus.
stands for -integers defined as
and the binomial coefficients in -calculus are given by
By easy computation, we have the relation given below:
The -analogue of BBH operators was introduced by Mursaleen et al. in [20] as follows:
where , , and function f is defined on the semi axis . If we put , we get the q-BBH operators (1). In that paper, authors established different approximation properties of the sequence of operators (3).
Mursaleen and Nasiruzzaman constructed bivariate -BBH operators [21] and studied many nice properties based on that sequence of operators and also given some generalization of that sequence of bivariate operators introducing one more parameter in the operators.
The statistical convergence is another notion of convergence, which was introduced by Fast [22] nearly fifty years ago and now this is a very active area of research. The statistical limit of a sequence is an extension of the idea of limit of sequence in an ordinary sense. The natural density of is defined as:
whenever the limit exists (see [23,24]). The sequence is said to be statistically convergent to a number L means if, for every ,
and it is denoted by . It can be easily seen that every convergent sequence is statistically convergent but not conversely.
Now, we will state some preliminary results on positive linear operators:
Proposition 1
Proposition 2
([25]). (Hölder’s inequality for LPOs). Let be an operator, linear and positive, and let , where are real numbers. Then, for every
Remark 1
([25]). A particular case of Proposition 2 is the Cauchy–Schwarz’s inequality for LPOs, which is obtained from Hölder’s inequality for as:
We have organized the rest of the paper as follows. In Section 2, we have constructed -BBH operators and calculated some auxiliary results. In Section 3 and Section 4, Korovkin type results and rate of convergence are established in statistical sense, respectively. Section 5 is devoted to the construction of the bivariate -BBH operators. In Section 6, we have computed rate of statistical convergence for the bivariate -BBH operators.
2. Construction of Operators and Moment Estimation
Ersan and Doğru [26] introduced a generalization of (1) and studied different statistical approximation properties of the operators towards a function f which has to be approximated. Inspired with the work of Ersan and Doğru [26], we construct a -analogue generalization of the sequence of operators defined in [26] or, on the other hand, we generalize the operators introduced in [20] as follows:
, let us define a sequence of -BBH operators as follows:
where
It is easy to verify that, if , the operators turn into the classical BBH operators. The sequence of operators (7) is of course more generalized than (1), and it is more flexible than (1).
We need the following lemma to our main result:
Lemma 1.
Proof.
The proof is obvious with the help of the relation (8), so we skip the proof. □
Lemma 2.
Proof.
Let , then , so
By using (8), the result can be easily obtained. □
Lemma 3.
3. Korovkin Type Statistical Approximation Properties
In this section, we obtain the Korovkin type statistical approximation theorem for our sequence of operators (7). Let us give the following theorem:
Theorem 3.
[3] Let be the sequence of LPOs from into such that
Then, for any function ,
Let us take and such that
Theorem 4.
Proof.
For and using (9), we can have
By (13), the following can be easily verified, which is
For and using (10), we get
For a given , let us define the following sets:
and
It is easily perceived that , so we can write
On using (13), it is clear that
Thus,
then,
Lastly, for and using (11), we obtain
Using , the following can be easily justified that
Substituting it in (14), we can have
If we choose , , and , then by (13), we have
For any given , now we define four sets as follows:
It is obvious that . Then, we obtain
It is clear that the right-hand side of the above inequality is zero by (15); then,
Hence, the proof is completed. □
4. Rates of Statistical Convergence
This section is devoted to find rates of statistical convergence of operators (7).
The modulus of continuity for the space of functions [1] is defined by
where satisfies the following conditions:
- ,
Theorem 5.
Proof.
Thus, it is obvious that, by choosing as in (16), the theorem is proved. □
Notice that, by conditions in (13), . Then, we have
This provides us the pointwise rate of statistical convergence of the sequence of operators to .
Now, we will contribute an estimate related to the rate of approximation by means of Lipschitz type maximal functions.
Lenze [27] introduced a Lipschitz type maximal function as
The Lipschitz type maximal function space on is defined in [1] as follows:
where function f is bounded and continuous on , and M is a positive constant.
Theorem 6.
Proof.
A similar technique used in Theorem 7 in [26] will be taken to provide the proof. Letting , it is understood that
Since is a linear and positive operator, , using the previous inequality, we have
Consequently, we obtain
If the above result is substituted in (17), we will get our desired result. Hence, the theorem is proved. □
Corollary 1.
If is defined by (7) and take implies , then a special case of Theorem 6 can be obtained as the following result:
where is the same as in Theorem 6.
5. Construction of the Bivariate Operators
In this section, we define a bivariate version of operators (7) and study their approximation properties.
For and , let us define the bivariate case of the operators (7) as follows:
where and .
For , the modulus of continuity for bivariate case is defined by
for each . Details of the modulus of continuity for the bivariate case can be found in [28].
Now, we will investigate Korovkin type approximation properties by using the following test functions:
Lemma 4.
- 1.
- ,
- 2.
- ,
- 3.
- ,
- 4.
- .
Let , , and be the sequences that converge statistically to 1 but not convergent in ordinary sense, so it can be written as for ,
Now, with condition (19), let us show the statistical convergence of the sequence of bivariate operators (18).
Theorem 7.
Let , , and be the sequences satisfying the condition (19) and let be the sequence of bivariate positive linear operators acting from into . Then, for any ,
Proof.
Using Lemma 4, the proof can be achieved similarly the proof of Theorem 4. □
6. Rates of Convergence of the Bivariate Operators
For , modulus of continuity for bivariate case is defined as follows [28]:
Here, satisfies the conditions:
Now, we give the rate of the statistical convergence of the bivariate operators (18) by means of modulus of continuity in :
Theorem 8.
Proof.
By using (20), we have
In the end, we will present the rates of statistical convergence of the bivariate operators (18) by means of Lipschitz type maximal functions.
Let us give the Lipschitz type maximal function space for the bivariate case on as
Here, f is a bounded and continuous function in , M is a positive constant and , and then let us define as follows:
Theorem 9.
Proof.
Let and . Then, we can write
Applying the positive linear operators on both the sides of the above inequality and using (25), we obtain
It is known that and , so it can be written as
By using the above inequalities in (26), we have
Using Hölder’s inequality with , , , (see (5)), we obtain
If we use the above inequality in (26), we get our desired result. Thus, the proof is completed. □
7. Conclusions
In this paper, we have constructed -BBH operators and calculated some auxiliary results for these newly defined operators. We also established Korovkin type results and rate of convergence in a statistical sense. Furthermore, we constructed the bivariate -BBH operators and computed rate of statistical convergence for the bivariate -BBH operators. Our results are more general than the results for BBH and q-BBH operators.
Author Contributions
Investigation, I.A. and I.H.; Supervision, M.M.; Writing—original draft, K.J.A.
Funding
The authors extend their appreciation to “the Deanship of Scientific Research at King Khalid University” for funding this work through research groups program under Grant No. G.R.P-36-39.
Acknowledgments
We are thankful to the learned referees whose suggestions improved the paper in its present form.
Conflicts of Interest
The authors declare no conflict of interest.
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