Abstract
In this article, we introduce Stancu type generalization of Baskakov–Durrmeyer operators by using inverse Pólya–Eggenberger distribution. We discuss some basic results and approximation properties. Moreover, we study the statistical convergence for these operators.
1. Preliminaries and Introduction
The approximation of functions by positive linear operators is an important research area in the classical approximation theory. It provides us key tools for exploring the computer-aided geometric design, numerical analysis and the solutions of ordinary and partial differential equations that arise in the mathematical modeling of real world phenomena. In the last four decades, several operators have been modified and their approximation properties has been discussed in real and complex domain.
A fundamental result in development of functions approximation theory is known as First Weierstrass approximation theorem, given by K. Weierstrass [1] in 1885, namely: for any function and , there exists an algebraic polynomial with real coefficients, such that , for any , where is the space of all continuous functions defined on The first proof of the Weierstrass approximation theorem was long and complicated, and provoked many famous mathematicians to find simpler and more instructive proofs. In 1905, E. Borel [2] proposed determination of an interpolation process, that allows finding polynomials , which converge uniformly to the function . In 1912, S.N. Bernstein was able to give an outstanding solution for the problem proposed by E. Borel.
In [3], Bernstein polynomial was proposed to provide an easy and elegant proof of the famous Weierstrass theorem which is defined by
.
To deal with Lebesgue integrable functions, for which Bernstein operators are unsuitable, Kantorovich [4] studied the following operators:
for
For functions the Szász-Mirakjan operators
were introduced by Mirakjan [5] and Szász [6].
Durrmeyer [7] proposed
Since then a large number of such operators have been introduced to approximate functions of different classes in different settings and spaces. One of such operators are the Baskakov operators [8] defined by
The Pólya–Eggenberger distribution (P-E) and the inverse Pólya–Eggenberger (I-P-E) (see [9]) are defined by
Stancu [10] constructed new operators by using the Pólya–Eggenberger distribution (P-E) (6).
where and .
Further, in 1970, Stancu [11] introduced the following operators using (I-P-E) distribution (7)
where For operators (9) are reduced to (5).
For the operators based on (P-E) distribution, one can see the details in [12,13,14]. Gupta et al. [15] introduced Durrmeyer type modification of (9) as follows:
where
Approximation theory is very crucial subject which is used in various fields by researchers. In which of them, a part of approximation theory is linear positive operators having an important role for studying various properties. There has been an extensive study on the approximation by these operators. Many mathematicians has inspired so far from past.
The computation of the test functions by Stancu operators was done long time ago and can be found in [10]. Based on the fact that many properties of Bernstein operators can be transferred to the Stancu operators (see [16,17,18]), we define Stancu type generalization of Baskakov type Pólya-Durrmeyer operators (10) as follows:
where are any non negative real numbers such that . If , the operators (11) reduce to (10).
2. Basic Results
The following results will be needed.
Lemma 1.
For and , we get
where
and , is the Beta function of second kind.
Proof.
We use the relationship
where and are defined by
By simple calculation, we get
Hence
and it follows
By the definition of , we obtain
□
Lemma 2.
Let , . For , we have
Proof.
Using Lemma 1 for , we have
Using Equality (i) first moment can be found trivially. Also, by using Equality (ii), we get
Finally, using Equality (iii), we have
□
Lemma 3.
Using Lemma 2, we have
3. Approximation Properties
Having a sequence of operators which approximate a given function arises the question of evaluation of the committed error. This is given by the approximation order, which depends on the smoothness properties of functions. In estimates of the approximation degree a convenient tool for measuring the smoothness of functions is represented by the modulus of continuity. The next question is: how can we evaluate the committed error in the function approximation process? A convenient tool is the modulus of continuity. Another important tool for evaluating of the committed error is the modulus of smoothness of second order. Estimates using combinations of first and second order modulus of smoothness are more refined then estimates using only the modulus of continuity.
Let
Further, consider the -functional:
and . There is an absolute constant such that (see Devore and Lorentz [19], p. 177, Theorem 2.4)
where
are respectively the usual and second order modulus of continuity of .
Theorem 1.
We have
for , where
Proof.
Write
Let and . Then from the Taylor’s expansion, we get
Therefore
and hence
Since
it follows,
Taking infimum over all , we get
In view of (12),
Hence the proof. □
Now we consider some approximation properties in the weighted spaces where the constant depends only on h and Let is continuous on with and
Theorem 2.
Let as Then for we have
Proof.
It suffices to verify that
holds. Observe that these conditions hold by Lemma 2. Now using the Korovkin’s theorem [20], we have . □
Theorem 3.
Let as Then for and we have
Proof.
Let be fixed.
Since , we have
Let . Then, there exists such that
Hence
Thus
Now, let to be such that
Then,
Hence the proof. □
The Lipschitz type space is defined by
where and .
Theorem 4.
For , we have
where .
Proof.
We show the caser . For , we get
Using the Cauchy–Schwarz inequality and , we get
That is the result holds for . Now we prove for . Applying Holder’s inequality, we get
Since , we have
Therefore, we get the result. □
4. Statistical Approximation
We prove here a result on statistical approximation.
The asymptotic density of is defined by
where # denotes the cardinality of the set.
A sequence is said to be statistically convergent (see [21,22]) to the number if for each where
and we write .
Theorem 5.
Let as . Then, for all we have
where ,
Proof.
Let us consider . It is sufficient to show that
for . It is clear that
From Lemma 2, we have
For , define the sets:
Then, we obtain which implies that and hence
Again, using Lemma 2, we obtain
For , define the sets:
Then, we obtain
which implies that
and hence
Hence, the proof is completed. □
Example 1.
Consider the sequence
Then this sequence is statistically convergent but not convergent. Define Then obviously , for . Applying Theorem 2, we have
On the other hand, since and and hence
so that the sequence can not be convergent; while it is statistically convergent.
5. Conclusions
Here, Stancu type generalization of Baskakov–Durrmeyer operators are constructed which are based on inverse Pólya–Eggenberger distribution. We calculated moments and central moments for these operators and have investigated convergence properties. We have also determined the rate of convergence by using the modulus of continuity and Lipschitz type class of functions. Moreover, the statistical approximation for these operators have also been studied.
Author Contributions
The all authors contributed equally and significantly in writing the first version as well as revised version of this paper. All authors have read and agreed to publish the present version of the manuscript.
Funding
This research received no external funding.
Acknowledgments
The authors would like to thank the referees for their very valuable suggestions and remarks.
Conflicts of Interest
The authors declare no conflict of interest.
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