Sum-of-squares hierarchies for binary polynomial optimization

TL;DR

This paper analyzes the sum-of-squares hierarchy for binary polynomial optimization, providing worst-case error bounds related to roots of Krawtchouk polynomials, and extends results to q-ary cubes.

Contribution

It offers a detailed error analysis of the sum-of-squares hierarchy for binary polynomial optimization using orthogonal polynomial roots, extending to q-ary cases.

Findings

Worst-case error scales as 1/2 - sqrt(t(1-t)) for r ≈ t·n

Bounds are derived using Fourier analysis and orthogonal polynomial roots

Results extend to q-ary cube optimization

Abstract

We consider the sum-of-squares hierarchy of approximations for the problem of minimizing a polynomial $f$ over the boolean hypercube $\mathbb{B}^{n}=\{0,1\}^n$. This hierarchy provides for each integer $r \in \mathbb{N}$ a lower bound $f_{(r)}$ on the minimum $f_{\min}$ of $f$, given by the largest scalar $\lambda$ for which the polynomial $f - \lambda$ is a sum-of-squares on $\mathbb{B}^{n}$ with degree at most $2r$. We analyze the quality of these bounds by estimating the worst-case error $f_{\min} - f_{(r)}$ in terms of the least roots of the Krawtchouk polynomials. As a consequence, for fixed $t \in [0, 1/2]$, we can show that this worst-case error in the regime $r \approx t \cdot n$ is of the order $1/2 - \sqrt{t(1-t)}$ as $n$ tends to $\infty$. Our proof combines classical Fourier analysis on $\mathbb{B}^{n}$ with the polynomial kernel technique and existing results on the extremal roots of Krawtchouk polynomials. This link to roots of orthogonal polynomials relies on a connection between the hierarchy of lower bounds $f_{(r)}$ and another hierarchy of upper bounds $f^{(r)}$, for which we are also able to establish the same error analysis. Our analysis extends to the minimization of a polynomial over the $q$-ary cube $(\mathbb{Z}/q\mathbb{Z})^{n}$.