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Moreover, the underlying operator T is extended to a bivariate operator, and the property defined on T is more general than [12] in convergence analysis.
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We now introduce the concept of demicompactness at a point for a bivariate operator (adapted from the original definition of Petryshyn [34]).
We obtain the rate of approximation of the bivariate operators by using the complete and partial moduli of continuity and the degree of approximation with the aid of a Lipschitz-type space and the Peetre K-functional.
In our next result, we study the degree of approximation for the bivariate operators by means of the Lipschitz class.
Now, the rate of statistical convergence of bivariate operator (6.1) by means ofmodulus of continuity in f ∈ C B ( K ) will be given in the following theorem.
The purpose of this part is to give a representation for the bivariate operators ofKantorovich type (1.6), introduce the statistical convergence of the operators tothe function f and show the rate of statistical convergence of theseoperators.
CADBIOM borrows the default operator and the when operator from the Signal language [ 53].
In terms of destroy operators these are the random removal operator, the worst removal operator, and the related removal operator; in terms of repair operators, these are a greedy insertion heuristic, and k-regret insertion heuristics.
Now, we obtain the rate of convergence of the approximation of the bivariate operators defined in (3) by means of modulus of continuity of functions.
In the following example, the rate of convergence of the bivariate operators given by (3.1) to a certain function is shown by illustrative graphics.
Now, we give an estimate of the rate of convergence of the bivariate operators.
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