Sum of squares lower bounds for refuting any CSP

Pravesh K. Kothari, Ryuhei Mori, Ryan O'Donnell, David Witmer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

Let P: {0, 1}k → {0,1} be a nontrivial k-ary predicate. Consider a random instance of the constraint satisfaction problem CSP(P) on n variables with An constraints, each being P applied to k randomly chosen literals. Provided the constraint density satisfes Δ ≥≥ 1, such an instance is unsatisfable with high probability. The refutation problem is to efficiently find a proof of unsatisfability. We show that whenever the predicate P supports a t-wise uniform probability distribution on its satisfying assignments, the sum of squares (SOS) algorithm of degree d = Θ(n/Δ2/(t-1)longΔ) (which runs in time nO(d)) cannot refute a random instance of CSP(P). In particular, the polynomial-time SOS algorithm requires Ω(n(t+1)/2) constraints to refute random instances of CSP(P) when P supports a t-wise uniform distribution on its satisfying assignments. Together with recent work of Lee, Raghavendra, Steurer (2015), our result also implies that any polynomial-size semidefinite programming relaxation for refutation requires at least Ω(n(t+1)/2) constraints. More generally, we consider the δ-refutation problem, in which the goal is to certify that at most a (1 - δ)-fraction of constraints can be simultaneously satisfed. We show that if P is δ-close to supporting a t-wise uniform distribution on satisfying assignments, then the degree-Ω(n/Δ2/(t-1)logΔ) SOS algorithm cannot (δ + o(1))- refute a random instance of CSP(P). This is the first result to show a distinction between the degree SOS needs to solve the refutation problem and the degree it needs to solve the harder δ-refutation problem. Our results (which also extend with no change to CSPs over larger alphabets) subsume all previously known lower bounds for semialgebraic refutation of random CSPs. For every constraint predicate P, they give a three-way hardness tradeof between the density of constraints, the SOS degree (hence running time), and the strength of the refutation. By recent algorithmic results of Allen, O'Donnell, Witmer (2015) and Raghavendra, Rao, Schramm (2016), this full three-way tradeof is tight, up to lower-order factors.

Original languageEnglish (US)
Title of host publicationSTOC 2017 - Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing
EditorsPierre McKenzie, Valerie King, Hamed Hatami
PublisherAssociation for Computing Machinery
Pages132-145
Number of pages14
ISBN (Electronic)9781450345286
DOIs
StatePublished - Jun 19 2017
Event49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017 - Montreal, Canada
Duration: Jun 19 2017Jun 23 2017

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
VolumePart F128415
ISSN (Print)0737-8017

Other

Other49th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2017
Country/TerritoryCanada
CityMontreal
Period6/19/176/23/17

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Constraint satisfaction
  • Lower bounds
  • Sum-of-Squares semidefinite programming hierarchy

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