1. Boaz Barak (MSR New England) Fernando G.S.L. Brandão (Universidade Federal de Minas Gerais) Aram W. Harrow (University of Washington) Jonathan Kelner (MIT) David Steurer (MSR New England) Yuan Zhou (CMU)

2. Motivation Unique Games Conjecture (UGC) [Khot'02] –For every constant ε, the following problem is NP-Hard –UG(ε) : given a set of linear modulo equations in the form –Distinguish YES case: at least (1-ε) of the equations are satisfiable NO case: at most ε of the equations are satisfiable

3. Motivation (cont'd) Unique Games Conjecture [Khot'02] Implications of UGC –For large class of problems, BASIC-SDP (semidefinite programming relaxation) achieves optimal approximation Examples: Max-Cut, Vertex-Cover, any Max-CSP [KKMO '07, MOO '10, KV '03, Rag '08] Open question: Is UGC true?

4. Previous results Evidences in favor of UGC –(Certain) strong SDP relaxation hierarchies cannot solve UG in polynomial time [RS'09, KPS'10, BGHMRS'11] Evidences opposing UGC –Subexponential time algorithms solve UG [ABS '10]

5. Our results Evidences in favor of UGC –(Certain) strong SDP relaxation hierarchies cannot solve UG in polynomial time [RS'09, KPS'10, BGHMRS'11] Evidences opposing UGC –Subexponential time algorithms solve UG [ABS '10] 1.New evidence: a natural SDP hierarchy solves all previous "hard instances" for UG 2.New (indirect) evidence: natural generalization of UG still has ABS-type subexp-time algorithm cannot be solved in poly-time assuming ETH

6. More about our first result 1.New evidence: a natural SDP hierarchy solves all previous "hard instances" for UG

7. Semidefinite programming (SDP) relaxation hierarchies … ? rounds SDP relaxation in roughly time e.g. [SA'90,LS'91] UG(ε) For certain SDP relaxation hierarchies –Upper bound : rounds suffice [ABS'10,BRS'11,GS'11] –Lower bound : rounds needed [RS'09,BGHMRS'11]

8. Semidefinite programming (SDP) relaxation hierarchies … ? UG(ε) Theorem. –Sum-of-Squares (SoS) hierarchy (a.k.a. Lasserre hierarchy [Par'00, Las'01]) solves all known UG instances within 8 rounds (cont'd) rounds SDP relaxation in roughly time e.g. [SA'90,LS'91]

9. Proof overview Integrality gap instance –SDP completeness: a good vector solution –Integral soundness: no good integral solution A common method to construct gaps (e.g. [RS'09]) –Use the instance derived from a hardness reduction –Lift the completeness proof to vector world –Use the soundness proof directly

10. Proof overview (cont'd) Our goal: to prove there is no good vector solution –Rounding algorithms? Instead, –we bound the value of the dual of the SDP –interpret the dual of the SDP as a proof system –lift the soundness proof to the proof system

11. Sum-of-Squares proof system AxiomsGoal derive Rules – – – – all intermediate polynomials have bounded degree d ("level-d" SoS proof system) "Positivstellensatz" [Stengle'74]

12. Example SoS proof: AxiomsGoal derive Axiom squared term Formulate UG as a polynomial optimization problem, and re-write the existing soundness proof in a SoS fashion (as above) !

13. Components of the soundness proof "Easy" parts –Cauchy-Schwarz/Hölder's inequality –Influence decoding "Hard" parts –Hypercontractivity inequality –Invariance Principle (of known UG instances)

14. SoS proof of hypercontractivity The 2->4 hypercontractivity inequality: for low degree polynomial we have The goal of an SoS proof is to show is sum of squares Prove by induction (very similar to the well- known inductive proof of the inequality itself)...

15. Components of the soundness proof "Easy" parts –Cauchy-Schwarz/Hölder's inequality –Influence decoding –Independent rounding "Hard" parts –Hypercontractivity inequality –Invariance Principle trickier "bump function" is used in the original proof --- not a polynomial! but... a polynomial substitution is enough for UG (of known UG instances)

16. (A little) more about our second result 2.New (indirect) evidence: natural generalization of UG still has ABS-type subexp-time algorithm cannot be solved in poly-time assuming ETH

17. Hypercontractive Norm Problem For any matrix A, q > 2 (even number), define Problem: to approximate A natural generalization of the Small Set Expansion (SSE) problem, which is closely related to UG [RS '09] Theorem. Constant approximation for 2->q norm is as hard as SSE

18. Hypercontractive Norm Problem (cont'd) Theorem. 2->q norm can be "well approximated" in time exp(n^(2/q)). Moreover, the algorithm can be used to recover the subexp time algorithm for SSE. –Proof idea: subspace enumeration Theorem. Assuming the Exponential Time Hypothesis (i.e. 3-SAT does not have subexp time algorithm), there is no poly-time constant approximation algorithm for 2->q norm. –Proof idea: through quantum information theory [BCY '10]

19. Summary SoS hierarchy refutes all known UG instances –certain types of soundness proof does not work for showing a gap of SoS hierarchy New connection between hypercontractivity, small set expansion and... quantum separability

20. Open problems Show SoS hierarchy refutes Max-Cut instances? More study on 2->q norms... –New UG hard instances from hardness of 2->q norms? –Stronger 2->q hardness results (NP- Hardness)? –dequantumize the current 2->q hardness proof?

21. Thank you!