Here, we provide a unifying treatment of the convergence of a general form of sampling-type operators, given by the so-called sampling Durrmeyer-type series. The main result consists of the study of a modular convergence theorem in the general setting of Orlicz spaces . From the latter theorem, the convergence in , in , and in the exponential spaces can be obtained as particular cases. For the completeness of the theory, we provide a pointwise and uniform convergence theorem on , in case of bounded continuous and bounded uniformly continuous functions; in this context, we also furnish a quantitative estimate for the order of approximation, using the modulus of continuity of the function to be approximated. Finally, applications and examples with graphical representations are given for several sampling series with special kernels.

On the convergence properties of sampling Durrmeyer-type operators in Orlicz spaces / Danilo Costarelli, Michele Piconi, Gianluca Vinti. - In: MATHEMATISCHE NACHRICHTEN. - ISSN 0025-584X. - ELETTRONICO. - 296:(2022), pp. 588-609. [10.1002/mana.202100117]

On the convergence properties of sampling Durrmeyer-type operators in Orlicz spaces

Michele Piconi;
2022

Abstract

Here, we provide a unifying treatment of the convergence of a general form of sampling-type operators, given by the so-called sampling Durrmeyer-type series. The main result consists of the study of a modular convergence theorem in the general setting of Orlicz spaces . From the latter theorem, the convergence in , in , and in the exponential spaces can be obtained as particular cases. For the completeness of the theory, we provide a pointwise and uniform convergence theorem on , in case of bounded continuous and bounded uniformly continuous functions; in this context, we also furnish a quantitative estimate for the order of approximation, using the modulus of continuity of the function to be approximated. Finally, applications and examples with graphical representations are given for several sampling series with special kernels.
2022
296
588
609
Danilo Costarelli, Michele Piconi, Gianluca Vinti
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1253897
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