Extension of Saturation Theorems for the Sampling Kantorovich Operators

被引:0
作者
Benedetta Bartoccini
Danilo Costarelli
Gianluca Vinti
机构
[1] University of Perugia,Department of Mathematics and Computer Science
来源
Complex Analysis and Operator Theory | 2019年 / 13卷
关键词
Inverse results; Sampling Kantorovich series; Order of approximation; Generalized sampling operators; Saturation order; 41A25; 41A05; 41A30; 47A58;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, we extend the saturation results for the sampling Kantorovich operators proved in a previous paper, to more general settings. In particular, exploiting certain Voronovskaja-formulas for the well-known generalized sampling series, we are able to extend a previous result from the space of C2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C^2$$\end{document}-functions to the space of C1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C^1$$\end{document}-functions. Further, requiring an additional assumption, we are able to reach a saturation result even in the space of the uniformly continuous and bounded functions. In both the above cases, the assumptions required on the kernels, which define the sampling Kantorovich operators, have been weakened with respect to those assumed previously. On this respect, some examples have been discussed at the end of the paper.
引用
收藏
页码:1161 / 1175
页数:14
相关论文
共 82 条
  • [1] Agrawal PN(2017)Degree of approximation for bivariate extension of Chlodowsky-type q-Bernstein–Stancu–Kantorovich operators Appl. Math. Comput. 306 56-72
  • [2] Baxhaku B(1996)Jackson and Jackson–Vallee Poussin-type kernels and their probability applications Theory Probab. Appl. 41 137-195
  • [3] Aleskeev VG(2012)A class of spline functions for landmark-based image registration Math. Methods Appl. Sci. 35 923-934
  • [4] Allasia G(2013)Lobachevsky spline functions and interpolation to scattered data Comput. Appl. Math. 32 71-87
  • [5] Cavoretto R(2018)A characterization of the convergence in variation for the generalized sampling series Ann. Acad. Sci. Fennicae Math. 43 755-767
  • [6] De Rossi A(2010)Approximation with respect to Goffman–Serrin variation by means of non-convolution type integral operators Numer. Funct. Anal. Optim. 31 519-548
  • [7] Allasia G(2013)Approximation in variation by homothetic operators in multidimensional setting Differ. Integr. Equ. 26 655-674
  • [8] Cavoretto R(2018)Detection of thermal bridges from thermographic images by means of image processing approximation algorithms Appl. Math. Comput. 317 160-171
  • [9] De Rossi A(2018)A model for the improvement of thermal bridges quantitative assessment by infrared thermography Appl. Energy 211 854-864
  • [10] Angeloni L(2007)Kantorovich-type generalized sampling series in the setting of Orlicz spaces Sampl. Theory Signal Image Process. 6 29-52