Symmetry reduction to optimize a graph-based polynomial from queueing theory

TL;DR

This paper uses symmetry reduction and computer-assisted verification to prove the convexity of a polynomial from queueing theory for degrees up to 9, facilitating the analysis of its minimum over the simplex.

Contribution

It introduces a symmetry reduction technique that simplifies the convexity analysis of a family of polynomials for all n, extending previous results for degrees 2 and 3.

Findings

Proves polynomial convexity for d ≤ 9 using symmetry reduction.

Establishes convexity for all n ≥ 2 under certain positive semidefinite matrix conditions.

Provides a computational approach to verify polynomial properties in queueing models.

Abstract

For given integers $n$ and $d$, both at least 2, we consider a homogeneous multivariate polynomial $f_d$ of degree $d$ in variables indexed by the edges of the complete graph on $n$ vertices and coefficients depending on cardinalities of certain unions of edges. Cardinaels, Borst and Van Leeuwaarden (arXiv:2111.05777, 2021) asked whether $f_d$, which arises in a model of job-occupancy in redundancy scheduling, attains its minimum over the standard simplex at the uniform probability vector. Brosch, Laurent and Steenkamp [SIAM J. Optim. 31 (2021), 2227--2254] proved that $f_d$ is convex over the standard simplex if $d=2$ and $d=3$, implying the desired result for these $d$. We give a symmetry reduction to show that for fixed $d$, the polynomial is convex over the standard simplex (for all $n\geq 2$) if a constant number of constant matrices (with size and coefficients independent of $n$) are positive semidefinite. This result is then used in combination with a computer-assisted verification to show that the polynomial $f_d$ is convex for $d\leq 9$.