Yet another look at positive linear operators, $q$-monotonicity and applications

TL;DR

This paper introduces new positive linear polynomial approximation operators that preserve $k$-monotonicity and provides sharp estimates for the approximation error in terms of a specific modulus of smoothness.

Contribution

The authors construct novel operators that preserve $k$-monotonicity for all $0 \\leq k \\leq q$ and derive explicit error bounds based on the second Ditzian-Totik modulus of smoothness.

Findings

Operators preserve $k$-monotonicity for all $k$ up to $q$.

Derived approximation error estimates in terms of the second Ditzian-Totik modulus.

Established rate of best uniform $q$-monotone polynomial approximation.

Abstract

For each $q\in{\mathbb{N}}_0$, we construct positive linear polynomial approximation operators $M_n$ that simultaneously preserve $k$-monotonicity for all $0\leq k\leq q$ and yield the estimate \[ |f(x)-M_n(f, x)| \leq c \omega_2^{\varphi^\lambda} \left(f, n^{-1} \varphi^{1-\lambda/2}(x) \left(\varphi(x) + 1/n \right)^{-\lambda/2} \right) , \] for $x\in [0,1]$ and $\lambda\in [0, 2)$, where $\varphi(x) := \sqrt{x(1-x)}$ and $\omega_2^{\psi}$ is the second Ditzian-Totik modulus of smoothness corresponding to the "step-weight function" $\psi$. In particular, this implies that the rate of best uniform $q$-monotone polynomial approximation can be estimated in terms of $\omega_2^{\varphi} \left(f, 1/n \right)$.