Why almost all k - c o l o r a b l e graphs are easy ?
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Why almost all k-colorable graphs are easy?
A. Coja-Oghlan, M. Krivelevich, D. Vilenchik
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Talk Outline
Random graphs: phase transitions and clustering
How do typical k-colorable graphs look?
Efficient algorithm for coloring k-colorable graphs
Message passing and clustering (SAT)
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The k-Coloring Problem
Given a graph G=(V,E):
Find f :V ! [k] s.t. 8(u,v)2E(G): f(u)f(v)
Find f with minimal possible k
Such k is called the chromatic number of G, (G)
E.g. (G)=3
3 4
1 2
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The k-Coloring Problem Finding a k-coloring is NP Hard
No polynomial time algorithm approximates (G) within factor better than n1- (unless NPµZPP) [FK98]
How to proceed? random models and average case analysis
Gn,p - every possible edge is included w.p. p=p(n)
(Gn,p )=np/2ln(np) for np2[c0,n/log7n] [Bol88,Luc91]
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Phase transitions and clustering
Consider the variant Gn,m of Gn,p:
Choose uniformly at random m=m(n) edges
When m=p¢choose(2,n) - Gn,m and Gn,p are “close”
There exists a constant d=d(k) such that
2m/n>d: almost all graphs in Gn,m are not k-colorable
2m/n<d: almost all graphs are k-colorable [Fri99]
Such phenomena is called a phase transition
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Phase transitions and clustering
Gn,m with 2m/n just below the threshold is “hard” experimentally
Possible explanation (partially non-rigorous) comes from statistical physics [MPWZ02]
The “geometrical” structure of the space of proper k-colorings - the clustering phenomena
Need to define notion of distance
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Phase transitions and clustering
Two k-colorings are the same if they differ only by a permutation of the color classes
Two k-colorings , are at distance t if
they disagree on the color of at least t vertices in every permutation of the color classes.
There exists one permutation obtaining equality
Similar to Hamming distance
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Phase transitions and clustering
Gn,m with 2m/n just below the threshold:
• All colorings within a cluster are “close”• A linear number of vertices are “frozen”
• Every two clusters are “far” from each other• Exponentially many clusters
based on analysis that uses partially-rigorous tools
• Proved rigorously for k-SAT, k¸8 [AR06,MMZ05,MMZ05]• For k-SAT: not believed to be true for small k, say k=3 [MMW05]
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Phase transitions and clustering
Why does this structure make life hard?
Heuristics get “distracted” by this structure
Every cluster “pulls” in its direction
Heuristics try to find a compromise between clusters
This is impossible due to the structure
Survey Propagation does well in practice [BMWZ05]
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Random k-colorable graphs
Gn,m with 2m/n above the threshold – not suitable to study k-colorable graphs
Instead, consider Gn,m | { k-colorability }
The uniform distribution over k-colorable graphs with exactly m edges
Another possibility, the planted model Gn,m,k
Partition the vertex set into k color classes of size n/k
Include m random edges that respect the coloringV1
V3
V2
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Our Results
Characterization of Gn,m | { k –colorability }
2m/n=Ck, Ck a sufficiently large constant
Using rigorous analysis we show that typically:• Single cluster of proper k-colorings• Size of the cluster is exponential in n
• (1-exp{-(Ck)})n vertices are “frozen”
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Our Results
There exists a deterministic polynomial time algorithm that k-colors almost all k-colorable graphs with m>Ckn edges. Ck a sufficiently large constant.
Rigorously complement results for sparse case:When clustering is simple – the problem is easy
When clustering is “complicated” – the problem is harder (?)
Almost all k-colorable graphs are easy!
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Our Results
Show that Gn,m,k and Gn,m | { k –colorability } share many structural properties (“close”)
Justifying the somewhat unnatural usage of planted-solution models
Alon-Kahale’s coloring algorithm [AK97] works for Gn,m | { k –colorability } as well
Gn,m,k also has the same clustering structure
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Our Results
Our results also apply to the k-SAT setting
Similar threshold and clustering phenomena are known/believed for k-SAT
The planted and uniform SAT distributions are “close”
Flaxman’s algorithm for planted 3CNF formulas works for the uniform setting
Improving the exponential time algorithm for uniform satisfiable 3CNFs (only one known so far)
Answering open research questions in [BBG02]
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What was known so far?Dist.
Density
PlantedUniform
<dk Clustering phenomena
Survey Propagation
Ck Alon and Kahale’s
coloring algorithm [AK97]
Clustering Planted and Uniform “close” Alon and Kahale’s coloring
algorithm [AK97]
Ck log n Planted and Uniform coincide
[AK97]
Alon-Kahale’s coloring algorithm [AK97]
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What was known for SAT?Dist.
Density
PlantedUniform
>dk Clustering phenomena
Survey Propagation [BMZ05]
Ck Flaxman’s algorithm
[Fla03] Version of k-opt
[FV04]
Exponential time algorithm [Chen03]
Planted and Uniform “close”
Flaxman, k-opt
Clustering
Ck log n Planted and Uniform coincide
Majority vote
Majority vote works whp [BBG02]
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Clustering: Proof Techniques
Recall, Gn,m | { k-colorability }
The uniform distribution over k-colorable graphs with exactly m edges
Why more difficult than the planted distribution?
Edges are not independent
For starters, consider the planted distribution Gn,p,k (k=3) V1
V3
V2
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Proof Techniques – The Core
Every vertex is expected to have d/3 neighbors in every other color class (d=np)
Claim 1: whp there is no subgraph H of G s.t. |V(H)|>n/100 and E(H)<d|H|/10
Claim 2: whp there are no two proper 3-colorings at distance greater than n/100
d¸d0, d0 a sufficiently large constant
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Proof Techniques – The Core
Claim 3: Suppose that every vertex has the expected degree, and Claims 1 and 2 hold. Then the graph G is uniquely 3-colorable.
Proof: - the planted coloring. If not unique, 9, dist(,)>n/100 (Claim 1).U - set of disagreeing vertices.v)(v) ) v has d/3 neighbors in U.|U|>n/100, |E(U)|<d|U|/6 – Contradicting Claim 2.
V1
V3
V2
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Proof Techniques – The CoreThis is whp the case when np < Ck log n
When np=O(1) – whp not the case
Definition of Core H : v2H if v has at least np/4 neighbors in G[H] in
every other color class
v has at most np/10 neighbors outside of H.
Claim 4: 9 Core H s.t. whp
|H | ¸ (1-exp{-(np)})n
H is uniquely 3-colorable
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Proof Techniques – The Core
Corollary:
(1-exp{-(np)})n vertices are frozen in every proper 3-coloring
Only one cluster of exponential size
V1
V3
V2V1 V2
V3
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Moving to the Uniform Case
A – a “bad” graph property (e.g. the graph has no big core)
– the expected number of proper k-colorings of random graph in the planted distribution
Claim 5: Pruniform[A] · ¢Prplanted[A]
Intuition: typically there are at most ways to generate G in the planted model. Now use a union bound.
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Moving to the Uniform Case
A – “the graph has no big core”
Claim 6: Prplanted[A] · e-exp{-C1}n
Claim 7: · eexp{-C2}n, C2 < C1
Corollary: Pruniform[A] = o(1)
There exists no proper 3-coloring w.r.t which there exists a big core
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Algorithmic Perspective
Show that Alon and Kahale’s algorithm [AK97] works in the uniform case
What is Alon and Kahale’s algorithm?
Approximate a proper 3-coloring (spectral techniques)
Refine the coloring – recoloring step
Uncolor “suspicious” vertices
G[U] – graph induced by uncolored vertices
Exhaustively color G[U] according to G[V\U]
Outcome differs from planted on n/1000
vertices
Outcome agrees on the core
• Core remains colored• Every colored vertex agrees with planted
Logarithmic size connected components
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Algorithmic Perspective - Analysis
Typically, uniform graphs have a big core
Two more facts needed for the analysis:
Claim 1 in the uniform case
Logarithmic size components in G[V \ H]
Both properties hold w.p. 1-1/poly(n) in the planted model - cannot use “union bound”
Solution: analyze directly the uniform distribution
Difficulty: edges are strongly dependent
Solution: careful, non-trivial, counting argument
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Algorithmic Perspective - SAT
Show that Flaxman’s algorithm [Fla03] works in the uniform case
What is Flaxman’s algorithm?
Approximate a satisfying assignment (majority vote)
Unassign “suspicious” variables
G[U] – graph induced by unassigned variables
Exhaustively satisfy G[U] according to G[V \ U]
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SAT and Message Passing
Warning Propagation:
Given a formula F – define Factor Graph G(F)
Bipartite graph: V1 = variables, V2 = clauses
(x,C)2E(G) iff x appears in C
Two types of messages: C=(xÇyÇz)
Cx = 1 if yC > 0 and zC > 0; 0 otherwise
xC = (x2 C’,C’C C’x) – (¬x2 C’’C’’x)
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SAT and Message Passing
WP(F)
Repeat until no message changes:
• Initialize all messages Cx to 1/0 w.p. 0.5
• Randomly order the edges of G(F)
• Evaluate all messages Cx
Assign every x according to (x2 C’ C’x) – (¬x2 C’’ C’’x)
Theorem [FMV06]: If F sampled according to Planted 3SAT p=d/n2, d sufficiently large constant, then whp:
• WP converges after O(log n) iterations• Assigned variables agree with some satisfying assignment• All but exp{-(d)}n variables are assigned• Clauses of unassigned variables are “easy” to satisfy
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SAT and Message Passing
Our work implies – [FMV06] applies for the uniform SAT setting as well
Reinforces the following thesis:
When clustering is complicated ) formulas are hard ) sophisticated algorithms needed: Survey Propagation
When clustering is simple ) formulas are easy ) naïve algorithms work: Warning Propagation
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Further Research
Loose
Rigorously analyze Survey Propagation on near-threshold formulas/graphs
First step – analyze Survey Propagation on Planted instances
Prove the near-threshold clustering phenomena
Rigorously analyze message passing algorithms
Analyze instances with an arbitrary constant (above the threshold) density
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