Abstract
We introduce a tensor-product kind bivariate operator of a new generalization of Bernstein-type rational functions and its GBS (generalized Boolean sum) operator, and we investigate their approximation properties by obtaining their rates of convergence. Moreover, we present some graphical comparisons visualizing the convergence of tensor-product kind bivariate operator and its GBS operator.
MSC:
41A25; 41A36
1. Introduction
Bernstein-type rational functions were defined by Balázs in [] as follows:
where and are suitably chosen non-negative real sequences such that for each and f is a real-valued function on .
In [], Atakut and İspir introduced the bivariate operator of the Bernstein-type rational functions defined by (1) as follows:
where , , and are suitably chosen non-negative sequences such that for , and f is a real-valued function on . They obtained an estimate by means of the usual first modulus of continuity and proved an asymptotic approximation theorem with the classical methods. Moreover, Atakut [] presented some convergence results associated with the derivatives of the operator defined by (2).
Recently, a new generalization of Bernstein-type rational functions has been defined in [] by:
where f is a real-valued continuous function on , and , and are non-negative real sequences such that satisfying the following properties:
The operator is a linear and positive operator. When , and , it is reduced to the Bernstein-type rational functions given by (1). Therefore, it is a generalization of the Bernstein-type rational functions. Its Korovkin-type approximation results have been investigated in [].
Recently, the approximation properties of lots of bivariate operators have been investigated. Readers can see the following references for details [,,,,,,,,,,].
In this study, we introduce a tensor-product kind bivariate operator and its associated GBS (generalized Boolean sum) operator of the generalized Bernstein-type rational function defined by (3), which is a generalization of the bivariate operator defined by (2). Moreover, we investigate their approximation properties on rectangular region such that . Lastly, we present an application including illustrative graphics visualizing the convergence of the tensor-product kind bivariate operator and its GBS operator, which also compare their convergence with the bivariate operator defined by (2).
2. Construction of Tensor-Product Kind Bivariate Operator
In this part, we introduce a tensor-product kind bivariate operator of the generalized Bernstein-type rational function defined by (3) and investigate its approximation properties.
Let , , , , and be non-negative real sequences such that for , fulfilling the following conditions:
Let f be a real-valued continuous function on . We define the following tensor-product kind bivariate operator:
where , . For any and any real-valued continuous functions on , we have the following relation:
and if f is non-negative, then is non-negative. Therefore, the bivariate operator is linear and positive. By denoting:
the bivariate operator is the tensorial product of and such that:
Indeed, by denoting , we obtain:
Similarly, we have the following relation:
If , and for , then the tensor-product kind operator is reduced to the bivariate operator defined by (2). Therefore, the tensor-product kind operator is a generalization of the bivariate operator defined by (2)
Now, we give some auxilary results:
Lemma 1.
Let be the operator defined by (6) and , , be the bivariate test functions. Then, we have the following equalities:
Proof.
By the proof of Lemma 1 of [], we can write:
Similarly, and can be easily calculated by interchanging the roles of the components n and u of with k, m and v and the components k, m and v of with n and u, respectively. □
Remark 1.
From Lemma 1, we obtain:
3. Approximation Results
In this part, we firstly present a Volkov-type result for the tensor-product kind bivariate operator .
Let be a compact set of , and be the space of all real-valued continuous functions f on A with the supremum norm
Theorem 1.
Proof.
By Lemma 1, the theorem can be proved by considering Volkov’s theorem in [] with similar methods to the proof of Theorem 1 of []; therefore, we omit its proof. □
Now, we obtain inequalities estimating the error of the approximation by the tensor-product kind bivariate operator defined by (6).
The complete modulus of continuity for bivariate functions is defined as follows:
where .
Moreover, the partial modulus of continuity according to x and y are defined by:
which fulfill the properties of the classical modulus of continuity. The details of the modulus of continuity for the bivariate functions can be found in [].
Secondly, we estimate the rate of convergence of the tensor-product kind bivariate operator defined in (6) by using the complete modulus of continuity.
Theorem 2.
Let , . Then, the following inequality holds:
where and .
Proof.
We present in the following theorem the estimation of the rate of the convergence by the tensor-product kind bivariate operator defined in (6) by means of the partial modulus of continuities.
Theorem 3.
Let , . Then, the following inequality is valid:
Proof.
Considering the definition of the partial modulus of continuity and using the Cauchy–Schwarz inequality, we can write:
Choosing and , we complete the proof. □
Now, we investigate the rate of convergence of the operator defined in (6) with the help of functions of the Lipschitz type.
Any function is called a function of Lipschitz type and denoted by if there exists an such that:
where are arbitrary and
Theorem 4.
Let . Then, there exists an such that:
for all , where and .
Proof.
By the hypothesis of the theorem, we can write:
Respectively, applying the Hölder’s inequality to the last inequality for , , and such that , we obtain:
which completes the proof of the theorem. □
4. GBS Operator
In this part, we construct the GBS operator associated with the tensor-product kind bivariate operator
We recall some basic notations given by Bögel. The details of which can be found in references [,,].
Let A be a compact subset of . A real-valued function on A is called Bögel-continuous function at if:
where denotes the mixed difference defined by:
Let A be a subset of . A real-valued function on A is Bögel-bounded function if there exists an such that:
for all .
Let A be a compact subset of . Then, each Bögel-continuous function is a Bögel-bounded function. Let denote the space of all the real-valued Bögel-continuous functions defined on A endowed with the following norm:
It is obvious that .
For , , we introduce the GBS (generalized Boolean sum) operator associated with the operator by:
for all and We can definitely write:
where , and ,,, , and are non-negative real sequences such that for fulfilling the condition of (5).
It is clear that maps into itself, and it is linear and positive.
The mixed modulus of smoothness of is defined in [] by:
Theorem 5.
For any , the following inequality is valid:
where and .
Proof.
By (11) and for , the mixed modulus of smoothness possesses the following property:
by which for all we obtain:
By (9), we can write:
Considering the definition of and , we obtain:
By (12) and taking the Cauchy–Schwarz inequality into account, we obtain:
Choosing and , we obtain the desired result. □
Now, we recall the Bögel-continuous functions of Lipschitz type. For , and , if there exists an such that:
then f is called a Bögel-continuous function of Lipschitz type and denoted by .
Theorem 6.
Let . Then, for all , we have the following inequality:
where and .
Proof.
Since:
we can write:
Applying the Hölder’s inequality to the last inequality by choosing , , and such that , we obtain:
which is the desired result. □
5. Graphical Comparisons
Let such that
1. Let us choose , , and such that ,
Figure 1 compares the approximation of (red), (yellow) and (green) to (blue) on . For increasing value of n, the approximation of to becomes better.
Figure 1.
Approximation of to (blue) on for (red), (yellow), (green).
Figure 2 compares the approximation of (red), (yellow) and (green) to (blue) on . Similarly, for increasing value of n, the approximation of to becomes better.
Figure 2.
Approximation of to (blue) on for (red), (yellow), (green).
2. Let us choose , , and , and such that ,
Figure 3 compares the approximation of (red), (yellow) and (green) to (blue) on . One can see that the approximation of (green) to (blue) is better than others.
Figure 3.
Comparison by Approximation of to (blue) on for (red), (yellow) and (green).
Figure 4 compares the approximation of (red), (yellow) and (green) to (blue) on . One can see that the approximations of (red) and (green) are better in places of the sub-region of the region than (yellow).
Figure 4.
Comparison by Approximation of to (blue) on for (red), (yellow) and (green).
3. Let us choose , , and such that ,
Figure 5, compares the approximation of the operators (green), (red) and (yellow) to (blue) on . One can see that the approximation of (yellow) to (blue) is the best. (yellow) is so close to (blue) that it almost coincides.
Figure 5.
Comparison by Approximation of (green), (red) and (yellow) to (blue) on .
6. Conclusions
In this paper, we have introduced the tensor-product kind bivariate operator of the generalized Bernstein-type rational function defined in [] and its GBS (generalized Boolean sum) operator , and we have investigated their approximation properties on rectangular region such that . Moreover, we have given some graphical comparisons visualizing the convergence of the tensor-product kind bivariate operator and its GBS operator, which also compare their convergence with the bivariate operator defined in [].
The results of this paper demonstrate that the GBS operator possesses at least a better approximation than the tensor-product kind bivariate operator , while the tensor-product kind bivariate operator has at least a better approximation than the bivariate operator defined in [].
Author Contributions
Conceptualization, E.Y.Ö.; methodology, E.Y.Ö. and G.A.; software, E.Y.Ö.; validation, E.Y.Ö. and G.A.; investigation, E.Y.Ö. and G.A.; resources, E.Y.Ö. and G.A.; data curation, E.Y.Ö.; writing—original draft preparation, E.Y.Ö. and G.A.; writing—review and editing, E.Y.Ö.; visualization, E.Y.Ö.; supervision, E.Y.Ö. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
All data of this article are included in the text.
Acknowledgments
The authors are grateful to all the referees contributed to the best presentation of the paper with their valuable comments.
Conflicts of Interest
The authors declare no conflict of interest.
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