The smoothest average and some extremal problems for polynomials

TL;DR

This paper investigates the problem of finding the smoothest local average of functions on integers through convolution with kernels, establishing sharp constants and optimal kernels for higher derivatives using complex analysis.

Contribution

It provides new proofs for known cases and introduces the first results for higher derivatives, including cases with non-negative Fourier transform restrictions.

Findings

Established sharp constants and optimal kernels for k=3.

Extended results to cases k=4 and k=6 with non-negative Fourier transform.

Derived a relation between constants and kernels for different derivatives.

Abstract

We study the problem of finding the "smoothest'' local average of a function $f \in \ell^2(\mathbb{Z})$ when we consider its convolution with suitable kernels $u$. The measurement of smoothness is as follows: Given a positive integer $k$, we aim to minimize the constant \begin{equation*} \sup_{0 \neq f \in \ell^2(\mathbb{Z})} \frac{\|\nabla^{k}(u\ast f)\|_{\ell^2(\mathbb{Z})}}{\|f\|_{\ell^2(\mathbb{Z})}} \end{equation*} among all symmetric kernels $u : \{-n,\dots,n\} \to \mathbb{R}$ with normalization $\sum_{j=-n}^{n}u(j) = 1$. We are also interested in finding the kernel for which the least constant is attained. For $k=1$ and $k=2$, the sharp constants and optimal kernels were obtained by Kravitz-Steinerberger, and Richardson. In this paper, we provide alternative proofs for $k\in \{1,2\}$ by using complex analysis tools. Moreover, we establish the case $k=3$, and also the cases $k\in \{4,6\}$ when the kernels are restricted to have non-negative Fourier transform. These are the first results in the literature for $k>2$. Finally, we deduce a general relation between the sharp constants and optimal kernels corresponding to $\nabla^k$ and $\nabla^{2k}$.