Sharp inequalities for symmetric polynomials, Hunter's conjecture, and moments of exponential random variables

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Main Authors: Brazitikos, Silouanos, Pandis, Christos
Format: Preprint
Published: 2025
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author Brazitikos, Silouanos
Pandis, Christos
author_facet Brazitikos, Silouanos
Pandis, Christos
contents We prove Hunter's conjecture on complete homogeneous symmetric polynomials. For even $n$ and every integer $k\geq 1$, we show that under the constraint $\sum_{i=1}^n a_i^2=1$ the global minimum of the even-degree polynomial $h_{2k}(a_1,\dots,a_n)$ is attained precisely at the half-plus/half-minus vector and we compute the optimal value in closed form. The proof combines algebraic properties of $h_{2k}$ with the probabilistic representation $k!\,h_k(a)=\mathbb{E}(\sum_{i=1}^n a_iX_i)^k$, where $X_1,\dots,X_n$ are i.i.d. standard exponential random variables with density $e^{-x}1_{x>0}$ and a combinatorial identity. This viewpoint further yields sharp upper and lower bounds for $\mathbb{E}|\sum_{i=1}^n a_iX_i|^{q}$ under natural constraints on the coefficients, including the spherical constraint $\sum a_i^2=1$ combined with the non-negative regime $a_i\ge0$, or the centred regime $\sum a_i=0$. Moreover, we determine the exact minimum of $h_{2k}$ on the $\ell_\infty$-sphere $S_\infty = \{a \in \mathbb{R}^n : \|a\|_\infty = 1\}$, which yields sharp norm comparison inequalities between the matrix norms induced by complete homogeneous symmetric polynomials and the classical operator and Schatten norms.
format Preprint
id arxiv_https___arxiv_org_abs_2512_12254
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Sharp inequalities for symmetric polynomials, Hunter's conjecture, and moments of exponential random variables
Brazitikos, Silouanos
Pandis, Christos
Probability
Functional Analysis
Primary 05E05, 60E15, 26D15, Secondary 15A60, 52A40
We prove Hunter's conjecture on complete homogeneous symmetric polynomials. For even $n$ and every integer $k\geq 1$, we show that under the constraint $\sum_{i=1}^n a_i^2=1$ the global minimum of the even-degree polynomial $h_{2k}(a_1,\dots,a_n)$ is attained precisely at the half-plus/half-minus vector and we compute the optimal value in closed form. The proof combines algebraic properties of $h_{2k}$ with the probabilistic representation $k!\,h_k(a)=\mathbb{E}(\sum_{i=1}^n a_iX_i)^k$, where $X_1,\dots,X_n$ are i.i.d. standard exponential random variables with density $e^{-x}1_{x>0}$ and a combinatorial identity. This viewpoint further yields sharp upper and lower bounds for $\mathbb{E}|\sum_{i=1}^n a_iX_i|^{q}$ under natural constraints on the coefficients, including the spherical constraint $\sum a_i^2=1$ combined with the non-negative regime $a_i\ge0$, or the centred regime $\sum a_i=0$. Moreover, we determine the exact minimum of $h_{2k}$ on the $\ell_\infty$-sphere $S_\infty = \{a \in \mathbb{R}^n : \|a\|_\infty = 1\}$, which yields sharp norm comparison inequalities between the matrix norms induced by complete homogeneous symmetric polynomials and the classical operator and Schatten norms.
title Sharp inequalities for symmetric polynomials, Hunter's conjecture, and moments of exponential random variables
topic Probability
Functional Analysis
Primary 05E05, 60E15, 26D15, Secondary 15A60, 52A40
url https://arxiv.org/abs/2512.12254