Yuan Zhou, Ryan O’Donnell Carnegie Mellon University
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Transcript of Yuan Zhou, Ryan O’Donnell Carnegie Mellon University
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Yuan Zhou, Ryan O’DonnellCarnegie Mellon University
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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
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Example 1: Max-Cut• Vertex set: V = {1, 2, 3, ..., n}• Value set: Ω = {0, 1}• Typical local constraint: (i, j) in 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]
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Example 2: Balanced Separator• Vertex set: V = {1, 2, 3, ..., n}• Value set: Ω = {0, 1}• Minimize #satisfied local constraints: (i, j) in E : σ(i) ≠ σ(j)• Global constraint: n/3 ≤ |{i : σ(i) = 0}| ≤
2n/3
• Alternative description:– given G = (V, E)– divide V into two "balanced" parts,– to minimize #edges across the cut
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Example 2: Balanced Saperator (cont'd)
• Vertex set: V = {1, 2, 3, ..., n}• Value set: Ω = {0, 1}• Minimize #satisfied local constraints: (i, j) in E : σ(i) ≠ σ(j)• Global constraint: n/3 ≤ |{i : σ(i) = 0}| ≤
2n/3
• Best approx. alg.: sqrt{log n}-approx. [ARV'04]
• Only (1+ε)-approx. alg. is ruled out assuming 3-SAT does not have subexp time alg. [AMS'07]
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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), c} in 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."
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Open questionsIs UGC true?
Is Max-Cut hard to approximate better than 0.878?
Is Balanced Separator hard to approximate with in constant factor?
Easier questionsDo the known powerful optimization algorithms
solve UG/Max-Cut/Balanced Separator?
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SDP Relaxation hierarchies• A systematic way to write tighter and tighter
SDP relaxations
• Examples– Sherali-Adams+SDP [SA'90]– Lasserre hierarchy [Par'00, Las'01]
…?
UG(ε)
r -round SDP relaxation in roughly time)(rOn
BASIC-SDP
GW SDP for Maxcut (0.878-approx.)ARV SDP for Balanced Separator
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How many rounds of tighening suffice?• Upperbounds
– rounds of SA+SDP suffice for UG [ABS'10,
BRS'11]• Lowerbounds [KV'05, DKSV'06, RS'09, BGHMRS
'12] (also known as constructing integrality gap
instances)– rounds of SA+SDP needed
for UG– rounds of SA+SDP needed
for better-than-0.878 approx for Max-Cut– rounds for SA+SDP needed for
constant approx. for Balanced Separator
)1(n
))logexp((log )1(n
)1()log(log n
))logexp((log )1(n
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From SA+SDP to Lasserre SDP• Are the integrality gap instances for SA+SDP
also hard for Lasserre SDP?
• Previous result [BBHKSZ'12]– No for UG– 8-round Lasserre solves the Unique Games
lowerbound instances
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From SA+SDP to Lasserre SDP (cont’d)
• Are the integrality gap instances for SA+SDP also hard for Lasserre SDP?
• This paper– No for Max-Cut and Balanced Separator– Constant-round Lasserre gives better-than-
0.878 approximation for Max-Cut lowerbound instances
– 4-round Lasserre gives constant approximation for the the Balanced Separator lowerbound instances
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Proof overview • Integrality gap instance
– SDP completeness: good vector solution– Integral soundness: no good integral
solution
• Show the instance is not integrality gap instance for Lasserre SDP – no good vector solution– we bound the value of the dual of the SDP– interpret the dual as a proof system (”SOSd/sum-of-squares proof system")– lift the soundness proof to the proof
system
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What is the SOSd proof system?
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Polynomial optimization• Maximize/Minimize• Subject to
all functions are low-degree n-variate polynomials
• Max-Cut example: Maximize s.t.
)(xp
0)(,0)(,0)( 21 xqxqxq m0)(,0)(,0)( '21 xrxrxr m
2)(E jiE(i,j)xx
ixx ii ,0)1(
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Polynomial optimization (cont'd)• Maximize/Minimize• Subject to
all functions are low-degree n-variate polynomials
• Balanced Separator example: Minimize s.t.
)(xp
0)(,0)(,0)( 21 xqxqxq m0)(,0)(,0)( '21 xrxrxr m
32
31 ][E,][E
,0)1(
iiii
ii
xxixx
2)(E jiE(i,j)xx
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Certifying no good solution• Maximize• Subject to
• To certify that there is no solution better than , simply say that the following equalities & inequalities are infeasible
)(xp
0)(,0)(,0)( 21 xqxqxq m0)(,0)(,0)( '21 xrxrxr m
)(xp0)(,0)(,0)( 21 xqxqxq m0)(,0)(,0)( '21 xrxrxr m
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The Sum-of-Squares proof system• To show the following equalities &
inequalities are infeasible,
• Show that
• where is a sum of squared polynomials, including 's
• A degree-d "Sum-of-Squares" refutation, where
0)(,0)(,0)( 21 xqxqxq m0)(,0)(,0)( '21 xrxrxr m
)}deg(),deg(){deg(max hqfd iii
)()()(1...1
xhxqxfmi
ii
)(xh)(xri
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PositivstellensatzSubject to some mild technical conditions,
every infeasible system has such a refutation
Caveat: fi’s and h might need to have high degree.
Lasserre SDP and SOSd proof systemA degree-d SOS refutation
O(d)-round Lasserre SDP is infeasible
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Summary• Defined the degree-d SOS proof system
• Remaining task Integral soundness proof low-degree refutation in the SOS proof system
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Example 1• To refute
• We simply write
• A degree-2 SOS refutation
2)1()2()1(1 xxxx
0)1(2
xx
x
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One-slide How-to
Thm: Min-Balanced-Separator in this graph is ≥ blahProof: … hypercontractivity…“Check out these polynomials.”
Thm: Max-Cut of this graph is ≤ blahProof: … Invariance Principle … … Majority-Is-Stablest… “Check out these polynomials.”
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Example 2: Max-Cut on triangle graph• To refute
• We "simply" write ... ...
0)1(,0)1(,0)1( 332211 xxxxxx
2)()()( 213
232
221 xxxxxx
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)12)(1()3222)(1(
)12)(1(
)1()1()(
2)()()(
212133
313123122
3223
2211
232
221
22313221
213
232
221
xxxxxxxxxxxxxx
xxxxxx
xxxxxxxxxxx
xxxxxx
Example 2: Max-Cut on triangle graph (cont'd)
• A degree-4 SoS refutation
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Latest results• Our theorem on Max-Cut is improved by
[DMN’12]– Constant-round Lasserre SDP almost exactly
solves the known instances
• Constant-round Lasserre SDP solves the hard instances for Vertex-Cover [KOTZ’12]Open question
• Does constant-round Lasserre SDP solve the known instances for all the CSPs?– I.e. SOS-ize Raghavendra’s theorem.
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Thank you!