A new class of positive linear operators preserving logarithmic functions

In this paper, we introduce a new class of positive linear operators that generalize the classical Bernstein operators. Specifically, we construct a sequence of operators that reproduce the logarithmic function $\ln(1+μ+x)$, with $μ> 0$ and $x \in [0,1]$. We prove pointwise and uniform convergence and we derive a quantitative estimate of the approximation error in terms of the modulus of continuity. We also obtain a Voronovskaja-type asymptotic formula, that is used to establish saturation results and inverse theorems. In particular, the saturation class of the considered approximation process is characterized by solving a second order differential equation. Shape-preserving properties, such as monotonicity, concavity and variation diminishing, are also investigated. Finally, a simple application to signal denoising is addressed.

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Publication Details

Journal
Pure and Applied Analysis
Published
2026-09-12
DOI
https://doi.org/10.2140/paa.2026.8.755
Primary Topic
Approximation Theory and Sequence Spaces
Type
article
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article

A new class of positive linear operators preserving logarithmic functions

Laura Angelonı, Danilo Costarellı, Chiara Darielli
Pure and Applied Analysis
Approximation Theory and Sequence Spaces
article

A new class of positive linear operators preserving logarithmic functions

Laura Angelonı, Danilo Costarellı, Chiara Darielli
article en

Abstract

In this paper, we introduce a new class of positive linear operators that generalize the classical Bernstein operators. Specifically, we construct a sequence of operators that reproduce the logarithmic function $\ln(1+μ+x)$, with $μ> 0$ and $x \in [0,1]$. We prove pointwise and uniform convergence and we derive a quantitative estimate of the approximation error in terms of the modulus of continuity. We also obtain a Voronovskaja-type asymptotic formula, that is used to establish saturation results and inverse theorems. In particular, the saturation class of the considered approximation process is characterized by solving a second order differential equation. Shape-preserving properties, such as monotonicity, concavity and variation diminishing, are also investigated. Finally, a simple application to signal denoising is addressed.

Pure and Applied AnalysisVol. 8(3)
University of Perugia (IT), University of Florence (IT)
Reduced inequalities
Openalex Percentile: Top 87%
Approximation Theory and Sequence Spaces
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