Lifting Sum-of-Squares Lower Bounds: Degree-$2$ to Degree-$4$

TL;DR

This paper establishes a method to lift lower bounds from degree-2 to degree-4 Sum-of-Squares SDPs, providing new insights into the limitations of degree-4 relaxations for problems like MaxCut and tensor PCA.

Contribution

It introduces an explicit mapping from degree-2 to degree-4 SoS solutions, enabling the transfer of lower bounds and advancing understanding of degree-4 SDP limitations.

Findings

Lower bounds for degree-2 SoS extend to degree-4 SoS.

New spectral norm bounds for graphical matrices are developed.

Applications include MaxCut, SK model, and PSD Grothendieck problem.

Abstract

The degree-$4$ Sum-of-Squares (SoS) SDP relaxation is a powerful algorithm that captures the best known polynomial time algorithms for a broad range of problems including MaxCut, Sparsest Cut, all MaxCSPs and tensor PCA. Despite being an explicit algorithm with relatively low computational complexity, the limits of degree-$4$ SoS SDP are not well understood. For example, existing integrality gaps do not rule out a $(2-\varepsilon)$-algorithm for Vertex Cover or a $(0.878+\varepsilon)$-algorithm for MaxCut via degree-$4$ SoS SDPs, each of which would refute the notorious Unique Games Conjecture. We exhibit an explicit mapping from solutions for degree-$2$ Sum-of-Squares SDP (Goemans-Williamson SDP) to solutions for the degree-$4$ Sum-of-Squares SDP relaxation on boolean variables. By virtue of this mapping, one can lift lower bounds for degree-$2$ SoS SDP relaxation to corresponding lower bounds for degree-$4$ SoS SDPs. We use this approach to obtain degree-$4$ SoS SDP lower bounds for MaxCut on random $d$-regular graphs, Sherington-Kirkpatrick model from statistical physics and PSD Grothendieck problem. Our constructions use the idea of pseudocalibration towards candidate SDP vectors, while it was previously only used to produce the candidate matrix which one would show is PSD using much technical work. In addition, we develop a different technique to bound the spectral norms of _graphical matrices_ that arise in the context of SoS SDPs. The technique is much simpler and yields better bounds in many cases than the _trace method_ -- which was the sole technique for this purpose.