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Subexponential Algorithms for Unique Games and Related Problems
Approximation Algorithms, June 2011
David SteurerMSR New England
Sanjeev Arora Princeton University & CCI
Boaz BarakMSR New England
U
Subexponential Algorithms for Unique Games and Related Problems
David SteurerMSR New England
Sanjeev Arora Princeton University & CCI
Boaz BarakMSR New England
Rounding Semidefinite ProgrammingHierarchies via Global Correlation
David SteurerMSR New England
Prasad RaghavendraGeorgia Tech
Boaz BarakMSR New England
UNIQUE GAMESInput: list of constraints of form
Goal: satisfy as many constraints as possible
[๐ ][๐ ]
๐ฅ ๐๐ฅ๐
UNIQUE GAMESInput: list of constraints of form
Goal: satisfy as many constraints as possible
Input: UNIQUE GAMES instance with (say)
Goal: Distinguish two cases
YES: more than of constraints satisfiableNO: less than of constraints
satisfiable
Unique Games Conjecture (UGC) [Khotโ02]
For every , the following is NP-hard:
UG (๐)
Implications of UGCFor many basic optimization problems, it is NP-hard to beat current algorithms (based on simple LP or SDP relaxations)
Examples:
VERTEX COVER [Khot-Regevโ03], MAX CUT [Khot-Kindler-Mossel-OโDonnellโ04,
Mossel-OโDonnell-Oleszkiewiczโ05],every MAX CSP [Raghavendraโ08], โฆ
Implications of UGCFor many basic optimization problems, it is NP-hard to beat current algorithms (based on simple LP or SDP relaxations)
Unique Games Barrier
Example: -approximation for MAX CUTat least as hard as
UNIQUE GAMES is common barrier for improving current algorithms of
many basic problems
GoemansโWilliamson
bound for MAX CUT
Subexponential Algorithm for Unique Games
Input: UNIQUE GAMES instance with alphabet size ksuch that of constraints are satisfiable,
Output:assignment satisfying of constraints Time:
in time
Time vs Approximation Trade-off
Analog of UGC with subconstant (say ) is false (*)(contrast: subconstant hardness for LABEL COVER [Moshkovitz-Razโ08])
NP-hardness reduction for must have blow-up (*)
rules out certain classes of reductions for proving UGC
(*) assuming 3-SAT does not have subexponential algorithms,
UGC-based hardness does not rule out subexponential algorithms, Possibility: -time algorithm for MAX CUT() ?
Subexponential Algorithm for Unique Games
in time
Consequences
poly (๐) exp (๐)
2-SAT
MAX 3-SAT()MAX CUT()
3-SAT (*)
FACTORING
exp (๐1 /2)exp (๐1 /3)exp (๐๐ 1/3 )
UG (๐ )MAX 3-SAT()
LABEL COVER()
[Moshkovitz-Razโ08+ Hรฅstadโ97]MAX CUT()?
(*) assuming Exponential Time Hypothesis [Impagliazzo-Paturi-Zaneโ01]( 3-SAT has no algorithm )
Subexponential Algorithm for Unique Games
in time
GRAPH ISOMORPHISM
Subexponential Algorithm for Unique Games
in time
here: via semidefinite programming hierarchies
What we want:
jointly distributed random variables over
UNIQUE GAMESInput: list of constraints of form
Goal: satisfy as many constraints as possible
Pr (๐ ๐โ ๐ ๐โก๐ )โฅ1โ๐ for typical constraint
= indicator of
p.s.d. for all with -
same marginal for in distributions and
-local
Goal: produce global random variables
for most constraints
Here: iterative procedure for [Arora-Barak-S.โ10+ Barak-Raghavendra-S.โ11]
jointly distributed random variables over
Pr (๐ ๐โ ๐ ๐โก๐ )โฅ1โ๐ for typical constraint
distributions over for all with
time
consistency
positive semidefiniteness
๐๐
๐โฉ๐
-level SDP hierarchy:What we want:
Components of iterative procedure
Rounding
Conditioning
sample variables independently according to their marginals
pick a vertex and sample
condition on sample for
Partitioning
find vertex subset
break dependence between and
enough if constraint graph has few significant eigenvalues
[BRSโ11]
general frameworkfor rounding
SDP hierarchies
Important fact:
can approximate by Gram matrix of unit vectors (tensoring trick)
Corr (๐ ๐ , ๐ ๐)(Pairwise) Correlation
max๐
โ๐
|Cov ( ๐ ๐๐ , ๐ ๐ (๐+๐ ) )|
โ๐
(๐๐๐ (๐ ๐๐ )โ๐๐๐ (๐ ๐๐|๐ ๐ยฟ )decrease in variance when conditioning on
statistical distance between and
Rounding Conditioning Partitioning
similar to mutual information
Rounding Conditioning Partitioning
sample variables independently according to their marginals
If then independent samplingsatisfies constraint with probability
Rounding fails ๐๐โผ ๐Corr (๐ ๐ ,๐ ๐ )>1โ๐ (๐)
Local Correlation(over edges of constraint graph)
Rounding Conditioning Partitioning
statistical distance between independent and correlated
sampling
pick a vertex and sample
condition on sample for
computationally expensive(level level )
condition on vertex only if
can condition times on such vertices
Conditioning fails
Global Correlation(over random vertex pairs)
Rounding Conditioning Partitioning
Issue:
Idea:
measures decrease in variance when conditioning
on
find vertex subset
break dependence between and
destroy correlation for constraints between and
๐ โ๐
๐
Rounding Conditioning Partitioning
Issue:
Wanted:set with small expansion & small cardinality
For endpoints of random path of length
๐๐โผ๐ก ๐Corr (๐ ๐ ,๐ ๐ )>(1โ๐ (๐) )๐กโซ๐โ๐ฝ
random walk stuck for steps on fraction of vertices vertex set with and expansion
A vertex is cut in at most partitioning steps break only edges
Rounding Conditioning Partitioning
๐ โ๐
๐
fails only if local correlation high
fails only if global correlation low
Correlation Propagation
is Grammatrix of unit
vectors
find vertex subset
break dependence between and
For general 2-CSP:
PTAS if constraint graph is random (degree alphabet)
QPTAS if constraint graph is hypercontractive very good expander
for small sets
[Barak-Raghavendra-S.โ11]
Subsequent work: [Arora-Geโ11]
better 3-COLORING approximation on some graph families
Independent work:
approximation schemes for quadratic integer programming with p.s.d. objective & few relevant eigenvalues
More SDP-hierarchy algorithms
[Guruswami-Sinopโ11]
Open Questions
Example: -approximation for SPARSEST CUT in time ?
How many large eigenvalues can a small-set expander have?
Is Boolean noise graph the worst case? (large eigenvalues)
Thank you!Questions?
What else can be done in subexponential time?
Towards settling the Unique Games Conjecture
Better approximations for MAX CUT or VERTEX COVER on general instances?
No: small set expander with large eigenvalues
[Barak-Gopalan-Hรฅstad-Meka-Raghavendra-S.โ11]