Sum-of-Squares Lower Bounds for Sherrington-Kirkpatrick via Planted Affine Planes
Mrinalkanti Ghosh, Fernando Granha Jeronimo, Chris Jones, Aaron, Potechin, Goutham Rajendran

TL;DR
This paper establishes high-degree Sum-of-Squares lower bounds for the Planted Affine Planes problem, which implies similar bounds for certifying energy bounds in the Sherrington-Kirkpatrick model, highlighting computational hardness.
Contribution
It introduces the Planted Affine Planes problem and proves degree-$n^{ ext{Omega}( ext{epsilon})}$ SoS lower bounds, connecting it to the SK model's certification hardness.
Findings
High-degree SoS fails to refute the PAP problem for $m \,\leq\, n^{3/2-\epsilon}$
Lower bounds imply hardness for certifying SK Hamiltonian energy bounds
Connects average-case hardness of PAP to SK model via SoS lower bounds
Abstract
The Sum-of-Squares (SoS) hierarchy is a semi-definite programming meta-algorithm that captures state-of-the-art polynomial time guarantees for many optimization problems such as Max--CSPs and Tensor PCA. On the flip side, a SoS lower bound provides evidence of hardness, which is particularly relevant to average-case problems for which NP-hardness may not be available. In this paper, we consider the following average case problem, which we call the \emph{Planted Affine Planes} (PAP) problem: Given random vectors in , can we prove that there is no vector such that for all , ? In other words, can we prove that random vectors are not all contained in two parallel hyperplanes at equal distance from the origin? We prove that for , with high probability,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
