Discrete entropies of orthogonal polynomials

TL;DR

This paper investigates the discrete Shannon entropies associated with orthogonal polynomials, providing explicit formulas for Chebyshev polynomials and numerical insights for other families, revealing connections to number theory.

Contribution

It derives explicit formulas for the discrete entropies of Chebyshev polynomials and explores their properties, offering new insights into the entropy behavior of orthogonal polynomial sequences.

Findings

Explicit formulas for Chebyshev polynomial entropies

Numerical analysis of entropy for various polynomial families

Connections between entropy and number theory

Abstract

Let $p_n$ be the $n$-th orthonormal polynomial on the real line, whose zeros are $\lambda_j^{(n)}$, $j=1, ..., n$. Then for each $j=1, ..., n$, $$ \vec \Psi_j^2 = (\Psi_{1j}^2, ..., \Psi_{nj}^2) $$ with $$ \Psi_{ij}^2= p_{i-1}^2 (\lambda_j^{(n)}) (\sum_{k=0}^{n-1} p_k^2(\lambda_j^{(n)}))^{-1}, \quad i=1, >..., n, $$ defines a discrete probability distribution. The Shannon entropy of the sequence $\{p_n\}$ is consequently defined as $$ \mathcal S_{n,j} = -\sum_{i=1}^n \Psi_{ij}^{2} \log (\Psi_{ij}^{2}) . $$ In the case of Chebyshev polynomials of the first and second kinds an explicit and closed formula for $\mathcal S_{n,j}$ is obtained, revealing interesting connections with the number theory. Besides, several results of numerical computations exemplifying the behavior of $\mathcal S_{n,j}$ for other families are also presented.