# Sum-of-squares hierarchies for binary polynomial optimization

arXiv: https://arxiv.org/abs/2011.04027 · HTML: https://mlsanity.com/paper/2011.04027/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Sum-of-Squares Relaxations for Information Theory and Variational Inference](https://mlsanity.com/paper/2206.13285/) (2022)
- [Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube](https://mlsanity.com/paper/2610.12358/) (2026)

## Abstract (arXiv)

We consider the sum-of-squares hierarchy of approximations for the problem of minimizing a polynomial f over the boolean hypercube mathbbBn=0,1n. This hierarchy provides for each integer r ∈ mathbbN a lower bound f(r) on the minimum fmin of f, given by the largest scalar λ for which the polynomial f - λ is a sum-of-squares on mathbbBn with degree at most 2r. We analyze the quality of these bounds by estimating the worst-case error fmin - f(r) in terms of the least roots of the Krawtchouk polynomials. As a consequence, for fixed t ∈ [0, 1/2], we can show that this worst-case error in the regime r ≈ t · n is of the order 1/2 - √t(1-t) as n tends to ∞. Our proof combines classical Fourier analysis on mathbbBn 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 (mathbbZ/qmathbbZ)n.