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Bart M. P. Jansen
Kernelization Lower Bounds
WorKer 2010, Leiden
Review of existing techniques and the introduction of cross-composition
Joint work with Hans L. Bodlaender and Stefan Kratsch
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Polynomial and Exponential Size Kernels
Some elusive FPT problems resisted all attempts to find polynomial kernels Connected Vertex Cover, k-Path, Treewidth, etc …
Existence of exponential-size kernels is implied by (uniform) fixed-parameter tractability
Tools to prove non-existence of polynomial kernels have been developed in recent years
Part I: Review of existing techniques for super-polynomial kernel lower bounds Emphasis on techniques Some applications as examples
Part II: Introducing cross-composition
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Outline
Part I Distillation algorithms OR-composition Poly-parameter
transformations
Part II
Cross composition
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PART I
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DISTILLATIONExisting techniques
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Weak distillation algorithms
Let A,B ⊆ S* be sets. A weak distillation of A into B is an algorithm which takes as input a sequence (x1, … , xt) of instances of A uses time polynomial in ∑i |xi| outputs x* with
x* ∈ B some xi ∈ A
|x*| is polynomial in maxi |xi|
If A = B then this is the notion of strong distillation (OR-distillation)
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poly(t*n) time
Weak distillation of A into B
x1 x2 x3 x4 x5 x6 x… xtA
instances
x*B
instance
n
poly(n)
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Consequences of weak distillation
Fortnow and Santhanam [STOC 2008] If set A is NP-hard under Karp reductions and there is a weak
distillation of A into any set B, then NP ⊆ coNP/poly Yap’s theorem [Theor. Comp. Sc. 1983]:
If NP ⊆ coNP/poly then the polynomial hierarchy collapses to the third level
Further collapses (Cai et al. [STACS 2003])
Intuitively: if 1 small instance of set B can express the logical OR of many
instances of the hard set A, then NP ⊆ coNP/poly small instance:
polynomial in size of largest input instance size independent of number of instances
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OR-COMPOSITIONExisting techniques
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Preliminaries
Given (x,k) ∈ S*×ℕ , its unparameterized version is the string: x#1111…1111 x#1k
If Q ⊆ S*×ℕ is a parameterized problem, then its unparameterized variant is Q := { x#1k | (x,k) ∈ Q }
1-to-1 correspondence between members of Q and Q
Parameter encoded in unary: polynomial-time transformation on an instance of Q yields polynomially-bounded blow-up in parameter size.
For a set A ⊆ S*, we define the set OR(A) as OR(A) := { (x1, x2, … , xt) | some xi ∈ A}
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OR-Composition
An OR-composition algorithm for a parameterized problem Q is an algorithm that takes as input a sequence (x1, k), (x2, k) , … , (xt, k) of
instances of Q with the same parameter value uses time polynomial in ∑i |xi| + k outputs (x*, k*) with
(x*, k*) ∈ Q some (xi, k) ∈ Q k* is polynomial in k
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poly(t*n + k) time
OR-composition of Q
Qinstance
Q instances x1 k x2 k x.. k xt k
n
x* k*
poly(k)
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Polynomial kernels for OR-compositional problems imply NP ⊆ coNP/poly
Bodlaender, Downey, Fellows, Hermelin: [ICALP 2008] If Q is a parameterized problem
which has a polynomial kernel which is OR-compositional whose unparameterized variant Q is NP-hard under Karp
reductions then there is a weak distillation from Q into OR(Q) and NP ⊆
coNP/poly*
Proof: we build a weak distillation algorithm from the given ingredients
* Refined statement and proof due to Holger Dell
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OR-composition + polynomial kernel Weak distillation of Q into OR(Q)
x1 x2 x3 x4 x5 x6 x… xtQ
instances
(x1,k1)Q instances
(x1,k2) (x1,k3) (x1,k4) (x1,k5) (x1,k6) (x…,k…) (xt,kt)
OR-Composed Q instances (y1,ki1) (y2,ki2) (y3,ki3) (yr,kir)
1 2 3 r
KernelizedQ
instances
(y’1,k’i1) (y’2,k’i2) (y’3,k’i3) (y’r,k’ir)
Q instances
x’1 x’2 x’3 x’r
Single OR(Q) instance (x’1, x’2 , x’3, x’r )
Parameterize
Group
Compose
Kernelize
Unparameterize
Tuple
Input
Output
n
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Application: OR-Composition for k-Path
Input: t instances of k-Path
Take disjoint union, output as (G’, k)
G’ has a k-path some Gi has a k-path Output parameter trivially bounded in poly(k)
,k ,k ,k ,k ,k
,k
k-Path does not admit a polynomial kernel unless
NP⊆coNP/poly
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POLYNOMIAL-PARAMETER TRANSFORMATIONS
Existing techniques
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Polynomial-parameter transformations
Let P and Q be parameterized problems A polynomial-parameter transformation from P to Q is an
algorithm which takes an instance (x,k) of P as input uses time polynomial in |x| + k outputs an instance (x’, k’) of Q with
(x,k) ∈ P (x’, k’) ∈ Q k’ is polynomial in k
Intuition: polynomial-time answer-preserving transformation of P to Q with bounded parameter increase
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Consequences of polynomial-parameter transformations
Bodlaender, Thomasse, Yeo: [ESA 2009] If there is a polynomial-parameter transformation from P to
Q and P and Q are NP-complete Q has a polynomial kernel
then P has a polynomial kernel
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Application of Polynomial-Parameter Transformations: Disjoint Cycles
Disjoint Cycles Input: Undirected simple graph G, integer k Parameter: k Question: Does G contain k vertex-disjoint simple cycles?
Goal: prove that Disjoint Cycles does not admit a polynomial kernel
Use polynomial-parameter transformations
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Proving a lower bound for Disjoint Cycles
MethodA. Introduce the NP-complete problem “Disjoint Factors”, prove it
does not have a polynomial kernel unless NP ⊆ coNP/polyB. Give a polynomial-parameter transformation from Disjoint
Factors to Disjoint Cycles
Reasoning Disjoint Cycles poly kernel Disjoint Factors poly kernel
(Theorem) No poly kernel for Disjoint Factors unless NP ⊆ coNP/poly Hence no poly kernel for Disjoint Cycles unless NP ⊆ coNP/poly
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A) Introducing Disjoint Factors
Disjoint Factors Input: Integer k, string S on alphabet {1, 2, … , k} Parameter: k Question: Can we find disjoint substrings S1, S2, … , Sk in S such
that Si starts and ends with i?
1432414132414231241214324141324142312412143241413241423124121432414132414231241214324141324142312412
Disjoint Factors does not admit a polynomial kernel
unless NP⊆coNP/poly
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B) Polynomial-parameter transformation
14324141324142312412
2 3 41
Input: Instance (S,k) of Disjoint Factors Output: Instance (G,k) of Disjoint Cycles String S has disjoint factorsG has k vertex-disjoint cycles
Disjoint Cycles does not admit a polynomial kernel
unless NP⊆coNP/poly
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Results through polynomial-parameter transformations
Incompressibility through colors and IDs Dom, Lokshtanov, Saurabh [ICALP 2009]
These problems do not have polynomial kernels unless NP ⊆ coNP/poly: Small Universe Set Cover
Parameter: |U| + k Small Universe Hitting Set
Parameter: |U| + k
Dominating Set parameterized by size of a vertex cover, Connected Vertex Cover, Steiner Tree, Small Subset Sum, etc.
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PART II
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THE MAIN IDEACross-composition
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Polynomial equivalence relationship
Let L be a set of strings R is a polynomial equivalence relationship on L if
R is an equivalence relationship R partitions any set of strings on at most n characters each
into poly(n) groups equivalency under R can be tested in polynomial time
Informally: an efficient way of grouping instances of size ≤n each into poly(n) groups
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Definition of cross-composition
Let L be a set of strings and Q a parameterized problem Set L cross-composes into Q if there is a polynomial
equivalence relationship R and an algorithm which takes as input t instances x1, … , xt of L which are equivalent
under R uses time polynomial in ∑i |xi| outputs an instance (x*, k*) of Q such that
(x*,k*) ∈ Q some xi ∈ L
k* is polynomial in maxi |xi| + log t
If set L cross-composes into parameterized problem Q: Then Q can express the OR of instances of L for a small
parameter value
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Comparison
OR-Composition An OR-composition for a
parameterized problem Q is an algorithm which
takes as input a sequence (x1, k), (x2, k) , … , (xt, k) of Q-instances
which share the same parameter
uses time polynomial in ∑i |xi| + k outputs (x*, k*) with
(x*, k*) ∈ Q some (xi, k) ∈ Q k* is polynomial in k
Cross-Composition A cross-composition of the set L
into parameterized problem Q is an algorithm which
takes as input a sequence x1, … , xt of L-instances
which are equivalent under some polynomial equivalence relationship
uses time polynomial in ∑i |xi| outputs (x*, k*) with
(x*,k*) ∈ Q some xi ∈ L,
k* is polynomial in maxi|xi|+log t
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Polynomial kernels for cross-compositional problems imply NP ⊆ coNP/poly
If there is a set A and parameterized problem Q such that set A is NP-hard under Karp reductions set A cross-composes into Q Q has a polynomial kernel
then there is a weak distillation from A into OR(Q) and NP⊆coNP/poly
Proof: We build a weak distillation
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Cross-composition + Polynomial kernel Weak distillation of A into OR(Q)
• In: t instances (x1, …, xt) of NP-hard set A• Define n := maxi |xi|
A) Input
• At most (|S|+1)n distinct inputs• Pairwise comparison to eliminate duplicates• Afterwards log t O(n)
B) Eliminate duplicates
• Partition inputs into groups X1, X2, … , Xr of inputs which are R-equivalent
• We get r poly(n) groups
C) Group by equivalence
• Cross-compose all inputs in group Xi into instance (xi*, ki*) of parameterized problem Q
• ki* is poly(n + log t), which is poly(n) since log t O(n)
D) Apply cross-composition
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Cross-composition + Polynomial kernel Weak distillation of A into OR(Q)
• Cross-compose all inputs in group Xi into instance (xi*, ki*) of parameterized problem Q
• ki* is poly(n + log t), which is poly(n) since log t O(n)
D) Apply cross-composition
• Kernelize each (xi*, ki*) to (xi’, ki’)• Afterwards |xi’|, ki’ ≤ poly(n)
E) Apply polynomial kernel for Q
• Transform (xi’, ki’) to unparameterized instance yi of Q• Size poly(n) per instance
F) Unparameterize
• Make tuple y* := (y1, y2, … , yr) which is an instance of OR(Q)• |y*| is r * poly(n)• |y*| is poly(n)
G) Build tuple: instance of OR(Q)
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AN APPLICATIONCross-composition
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Chromatic Number parameterized by Vertex Cover
Chromatic Number parameterized by Vertex Cover Input: Graph G, vertex cover Z of G, integer l. Parameter: k := |Z|. Question: Can the vertices of G be properly l -colored?
ZYES for l = 4
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Chromatic Number parameterized by Vertex Cover
Problem is FPT Simple
exponential-size kernel
No polynomial kernel unless NP ⊆ coNP/poly
Z
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Overview of the proof
Ingredients of the proofA. NP-completeness of 3-coloring on triangle split graphsB. Polynomial equivalence relationshipC. 3-coloring triangle split graphs cross-composes into Chromatic
Number parameterized by Vertex Cover
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A) Triangle split graphs
A triangle split graph is a graph G with vertex subset X: G[V – X] consists of vertex-disjoint triangles X is an independent set in G
V –X is a vertex cover
3-coloring is NP-complete on triangle split graphs
X
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B) Polynomial equivalence relationship
Two instances (G1, X1) and (G2, X2) of 3-coloring on triangle split graphs are equivalent under R if |V(G1)| = |V(G2)|, and |X1| = |X2|
Any set of instances on at most n vertices each is partitioned into n2 groups
R is a polynomial equivalence relationship
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?
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χ(G…)≤3?χ(G1)≤3? χ(Gt)≤3?
χ(G*)≤log t + 4?Klog t+4
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Conclusion of proof
For any fixed q, the q-Coloring problem parameterized by Vertex Cover does admit a polynomial kernel [BJK??]
Compare: 3-coloring parameterized by treewidth does not have a polynomial kernel (unless …) [BDFH ’08]
Chromatic Number par. by Vertex Cover does not admit a polynomial kernel unless
NP⊆coNP/poly
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CLIQUE PARAMETERIZED BY VERTEX COVER
Cross-composition
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Clique parameterized by Vertex Cover
Clique parameterized by Vertex Cover Input: Graph G, vertex cover Z of G, integer l. Parameter: k := |Z|. Question: Does G have a clique of size l?
ZYES for l = 5
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Clique parameterized by Vertex Cover
Problem is trivially FPT
Simple exponential-size kernel
Turing kernel: O(n) instances of |Z| + 1 vertices each
No polynomial kernel unless NP ⊆ coNP/poly
Z
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Cross-composing Clique into Clique parameterized by Vertex Cover
Input t instances (Gi, l) of unparameterized Clique, each looking for
an l-clique in a graph on n vertices
Output One instance (G’, l’, Z’) of Clique parameterized by Vertex
Cover, such that G’ has an l’-clique some Gi has an l-clique k’ = |Z’| is polynomial in n
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Vertex sets of G’
n
l
tInstance selectors
Vertex selectors
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Vertex sets of G’
n
l
2
n
tInstance selectors
Vertex selectors
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Vertex sets of G’
n
l
2
n
tInstance selectors
Vertex selectors
Edge checkers
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The vertex cover
n
l
2
n
tInstance selectors
Vertex selectors
Edge checkers
Vertex cover
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Conclusion of proof
Strengthens result of [BDFH ‘08] that Clique parameterized by Treewidth does not have a polynomial kernel
Clique par. by Vertex Cover does not admit a polynomial kernel unless NP⊆coNP/poly
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THE BIGGER PICTURECross-composition
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Advantages of cross-composition
• No need for problem-specific padding arguments
Polynomial equivalence relationship
• No need for single-exponential FPT algorithm
Output parameter may depend on log t
• Facilitates the encoding of input instances at bounded parameter cost
Output parameter may depend on maxi |xi|
• Starting from a restricted version of the problem makes the input instances well-behaved
Start from any NP-hard problem
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Cross-composition unifies existing techniques
OR-composition OR-composition of Q Unparameterized variant Q
cross-composes into Q
Poly-param. transforms OR-composition of P and
polynomial-parameter transformation P Q
Unparameterized variant P cross-composes into Q
Both existing techniques for kernel lower bounds actually prove that there is a cross-composition
Intuition: parameterized problem Q does not admit a polynomial kernel if it can express the OR of some NP-hard problem at small parameter cost
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Conclusions and discussion
Techniques for proving conditional kernel lower-bounds: show that polynomial kernel weak distillation
AND-composition Prove kernel lower bounds based on a conjecture; no interesting
consequences known if this conjecture fails Treewidth, Cliquewidth, (…)-width do not have polynomial kernels
unless this conjecture fails Cross-composition relaxes the requirements and hence
simplifies the proofs of lower bounds Clique and Chromatic Number parameterized by the size of a
Vertex Cover do not admit polynomial kernels unless NP ⊆ coNP/poly
Future work: prove kernel lower bounds for more problems! Edge Clique Cover H-Minor-free Deletion
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List of FPT problems without polynomial kernels unless NP ⊆ coNP/poly
[HN06+FS08] k-Variable CNF-SAT [BDFH08] Longest Path, Longest Cycle [BTY09] Vertex Disjoint Paths, Cycles [DLS09] Bounded Universe Hitting Set, Bounded Universe Set Cover,
Connected Vertex Cover, Steiner Tree, Capacitated Vertex Cover [KW09] Windmill-free Edge-Deletion [KW09’] Cases of MinOnesSat [FJLRS10] Dogson Score [CPPW10] Connectivity problems in d-degenerate graphs: Connected
Feedback Vertex Set, Connected Dominating Set, Connected Odd Cycle Transversal
[KMW10] MaxOnesSat and ExactOnesSat [BJ??] Weighted Vertex Cover parameterized by P2-deletion distance [BJK??] Clique parameterized by Vertex Cover, Chromatic Number
parameterized by Vertex Cover, non-standard parameterizations of Feedback Vertex Set
[FFPS11] Total Vertex (Edge) Cover
Thank you!
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Part I Distillation OR-composition Poly-parameter transformations
Part II Cross-composition Chromatic Number Clique