Yuan Zhou Carnegie Mellon University Joint works with
- Slides: 48
Yuan Zhou Carnegie Mellon University Joint works with Boaz Barak, Fernando G. S. L. Brandão, Aram W. Harrow, Jonathan Kelner, Ryan O'Donnell and David Steurer
Constraint Satisfaction Problems • Given: – a set of variables: V – a set of values: Ω – a set of "local constraints": E • Goal: find an assignment σ : V -> Ω to maximize #satisfied constraints in E • α-approximation algorithm: always outputs a solution of value at least α*OPT
Example 1: Max-Cut • Vertex set: V = {1, 2, 3, . . . , n} • Value set: Ω = {0, 1} • Typical local constraint: (i, j) э E wants σ(i) ≠ σ(j) • Alternative description: – Given G = (V, E), divide V into two parts, – to maximize #edges across the cut • Best approx. alg. : 0. 878 -approx. [GW'95] • Best NP-hardness: 0. 941 [Has'01, TSSW'00]
Example 2: Balanced Seperator • Vertex set: V = {1, 2, 3, . . . , n} • Value set: Ω = {0, 1} • Minimize #satisfied local constraints: (i, j) э E : σ(i) ≠ σ(j) • Global constraint: n/3 ≤ |{i : σ(i) = 0}| ≤ 2 n/3 • Alternative description: – given G = (V, E) – divide V into two "balanced" parts, – to minimize #edges across the cut
Example 2: Balanced Seperator (cont'd) • Vertex set: V = {1, 2, 3, . . . , n} • Value set: Ω = {0, 1} • Minimize #satisfied local constraints: (i, j) э E : σ(i) ≠ σ(j) • Global constraint: n/3 ≤ |{i : σ(i) = 0}| ≤ 2 n/3 • Best approx. alg. : sqrt{log n}-approx. [ARV'04] • Only (1+ε)-approx. alg. is ruled out even assuming 3 -SAT does not have subexp time alg. [AMS'07]
Example 3: Unique Games • Vertex set: V = {1, 2, 3, . . . , n} • Value set: Ω = {0, 1, 2, . . . , q - 1} • Maximize #satisfied local constraints: (i, j) э E : σ(i) - σ(j) = c (mod q) • Unique Games Conjecture (UGC) [Kho'02, KKMO'07] No poly-time algorithm, given an instance where optimal solution satisfies (1 -ε) constraints, finds a solution satisfying ε constraints • Stronger than (implies) "no constant approx. alg. "
Example 3: Unique Games (cont'd) • Vertex set: V = {1, 2, 3, . . . , n} • Value set: Ω = {0, 1, 2, . . . , q - 1} • Maximize #satisfied local constraints: (i, j) э E : σ(i) - σ(j) = c (mod q) • UG(ε): to tell whether an instance has a solution satisfying (1 -ε) constraints, or no solution satisfying ε constraints • Unique Games Conjecture (UGC). UG(ε) is hard for sufficiently large q
Example 3: Unique Games (cont'd) • Implications of UGC – For large class of problems, BASIC-SDP (semidefinite programming relaxation) achieves optimal approximation ratio Max-Cut: 0. 878 -approx. Vertex-Cover: 2 -approx. Max-CSP [KKMO '07, MOO '10, KV '03, Rag '08]
Open questions • Is UGC true? • Are the implications of UGC true? – Is Max-Cut hard to approximate better than 0. 878? – Is Balanced Seperator hard to approximate with in constant factor?
SDP Relaxation hierarchies • A systematic way to write tighter and tighter SDP relaxations BASIC-SDP rounds SDP relaxation in roughly time ? … UG(ε) ARV SDP for Balanced Seperator GW SDP for Maxcut (0. 878 -approx. ) • Examples – Sherali-Adams+SDP [SA'90] – Lasserre hierarchy [Par'00, Las'01]
How many rounds of tighening suffice? • Upperbounds – rounds of SA+SDP suffice for UG(ε) [ABS'10, BRS'11] • Lowerbounds [KV'05, DKSV'06, RS'09, BGHMRS '12] (also known as constructing integrality gap instances) – – for UG(ε) rounds of SA+SDP needed for better-than-0. 878 approx for Max-Cut – rounds for SA+SDP needed for constant approx. for Balanced Seperator
Our Results • We study the performance of Lasserre SDP hierarchy against known lowerbound instances for SA+SDP hierarchy, and show that • 8 -round Lasserre solves the Unique Games lowerbound instances [BBHKSZ'12] • 4 -round Lasserre solves the Balanced Seperator lowerbound instances [OZ'12] • Constant-round Lasserre gives better-than 0. 878 approximation for Max-Cut lowerbound instances [OZ'12]
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
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 ("Sum-of-squares proof system") – lift the soundness proof to the proof system
Remarks • Using a connection between SDP hierarchies and algebraic proof systems, we refute all known UG lowerbound instances and many instances for its related problems • We provide new insight in designing integrality gap instances -- should avoid soundness proofs that can be lifted to the powerful Sum-of. Squares proof system • We show that Lasserre is strictly stronger than other hierarchies on UG and its related problems (as it was believed to be)
Outline of the rest of the talk • Sum-of-Squares proof system and Lasserre hierarchy • Lift the soundness proofs to the So. S proof system
Sum-of-Squares proof system and Lasserre hierarchy
Polynomial optimization • Maximize/Minimize • Subject to all functions are low-degree n-variate polynomial functions • Max-Cut example: Maximize s. t.
Polynomial optimization (cont'd) • Maximize/Minimize • Subject to all functions are low-degree n-variate polynomial functions • Balanced Seperator example: Minimize s. t.
Certifying no good solution • Maximize • Subject to • To certify that there is no solution better than , simply say that the following equations & inequalities are infeasible
The Sum-of-Squares proof system • To show the following equations & inequalities are infeasible, • Show that • where is a sum of squared polynomials, including 's • A degree-d "Sum-of-Squares" refutation, where
Example 1 • To refute • We simply write • A degree-2 So. S refutation
Example 2: Max-Cut on triangle graph • To refute • We "simply" write. . .
Example 2: Max-Cut on triangle graph (cont'd) • A degree-4 So. S refutation
Relation between So. S proof system and Lasserre SDP hierarchy
Finding So. S refutation by SDP • A degree-d So. S refutation corresponds to solution of an SDP with variables • The SDP is the same as the dual of -round Lasserre relaxation Bounding SDP value by So. S refutation • An So. S refutation => upperbound on the dual of optimum of Lasserre => upperbound on the value of Lasserre – e. g. 4 -round Lasserre says that Max-Cut of the triangle graph is at most 2 (BASIC-SDP gives 9/4)
Remarks • Positivestellensatz. [Krivine'64, Stengle'73] If the given equalities & inequalities are infeasible, there is always an So. S refutation (degree not bounded). • The degree-d So. S proof system was first proposed by Grigoriev and Vorobjov in 1999 • Grigoriev showed degree is needed to refute unsatisfiable sparse -linear equations – later rediscovered by Schoenbeck in Lasserre world
So. S proofs (in contrast to refutations) • Given assumptions to prove that • A degree-d So. S proof writes where are sums of squared polynomials • Remark. Degree-d So. S proof => degree-d So. S refutation for
Technical Part: Lift the proofs to So. S proof system
Components of the soundness proof (of known UG instances) • • • Cauchy-Schwarz/Hölder's inequality Hypercontractivity inequality Smallsets expand in the noisy hypercube Invariance Principle Influence decoding
Hypercontractivity Inequality • 2 ->4 hypercontractivity inequality: for low degree polynomial we have • Goal of an So. S proof: write Note that 's are indeterminates
Traditional proof of hypercontractivity • 2 ->4 hypercontractivity inequality: for low degree polynomial we have • (Traditional) proof. Apply induction on d and n. – Let – g and h are (n-1)-variate polynomials,
Traditional proof of hypercontractivity (cont'd) (Cauchy-Schwartz) (induction) All equalities are polynomial identities about indeterminates
So. S proof of hypercontractivity? • The square-root in the Cauchy-Schwartz step looks difficult for polynomials • Solution: Prove a stronger statement -- twofunction hypercontractivity inequality • Theorem. Suppose • then
So. S proof of two-fcn hypercontractivity • Write using (induction) unroll the induction to get the So. S proof
Components of the soundness proof (of known UG instances) • • • Cauchy-Schwarz/Hölder's inequality Hypercontractivity inequality Smallsets expand in the noisy hypercube Invariance Principle Influence decoding
Smallset expansion of noisy hypercube • For , let • Theorem. If • then • Traditional proof. Let be the projection operator onto the eigenspace of with eigenvalue. I. e. the space spanned by
Traditional proof of SSE of noisy hypercube (cont'd) (poly. identity) (So. S friendly) (Holder's) (So. S friendly) (hypercontractivity) (So. S friendly)
Traditional proof of SSE of noisy hypercube (cont'd) (So. S friendly) (take ) Key problem: fractional power involved in the Holder's step Solution: Cauchy-Schwartz/Holders with no fractional power
So. S-izable Cauchy-Schwartz • Theorem. For any constant a > 0 where So. S is a sum of squared polynomials of degree at most 2 • Remark. and the equality holds when • Proof. Skipped. • Corollary. (Holder's) For any constant a > 0 • Proof. Apply C-S twice
So. S proof of SSE (So. S friendly) (Holder's) (hypercontractivity) (take )
So. S proof of SSE (cont'd) (take )
Components of the soundness proof (of known UG instances) • • • Cauchy-Schwarz/Hölder's inequality Hypercontractivity inequality Smallsets expand in the noisy hypercube Invariance Principle Influence decoding
A few words on Invariance Principle • trickier • "bump function" is used in the original proof --- not a polynomial! • but. . . a polynomial substitution is enough for UG
Max-Cut and Balanced Seperator • An So. S proof for "Majority Is Stablest" theorem is needed for Max-Cut instances – We don't know how to get around the bump function issue in the invariance step – Instead, we proved a weaker theorem: "2/pi theorem" -- suffices to give better-than 0. 878 algorithms for known Max-Cut instances • Balanced Seperator. Key is to So. S-ize the proof for KKL theorem – Hypercontractivity and SSE is also useful there – Some more issues to be handled
Summary • So. S/Lasserre hierarchy refutes all known UG instances and Balanced Seperator instances, gives better-than-0. 878 approximation for known Max. Cut instances, – certain types of soundness proof does not work for showing a gap of So. S/Lasserre hierarchy
Open problems • Show that So. S/Lasserre hierarchy fully refutes Max-Cut instances? – So. S-ize Majority Is Stablest theorem. . . • More lowerbound instances for So. S/Lasserre hierarchy?
Thank you!
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