hardness of approximation
DESCRIPTION
Breve introduzione alla teoria dell'inaporssimabilitàTRANSCRIPT
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Carlo Lombardi, June 2008 Theoretical Computer Science
Hardness of Approximation
A brief survey of Inapproximability theory
for NP optimization problems
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Carlo Lombardi, June 2008 Theoretical Computer Science
Overview
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Optimization problem:
Definition
NP Optimization
•Approximability and Inapproximability
Approximation-Preserving Reduction
Gap Problems, Karp reduction, PCP Theorem
Probabilistic Verification
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Carlo Lombardi, June 2008 Theoretical Computer Science
Optimization problem
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),(min)(
),(max)(
)(
)(
yxFxOPT
yxFxOPT
xDy
xDy
“find the best solution from all feasible solution”
•x= instance of input•y= “witness” solution•D(x)= set of all feasible solution•F(x,y)=real-valued function wich assigns a “score” to y
If both y belongs to D(X)
F(x,y)are polynomial time computable then OPT(x) belongs to NPO
class
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Carlo Lombardi, June 2008 Theoretical Computer Science
Approximation ratio
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Consider a map:
)()(:..)( xDxAxtsxAx This map is said to approximate OPT(x) within a factor r(x)>=1 if:
• The best such r(x) is said approximation ratio• If A is p-time computable we say that OPT(x) is approximable within a factor of r(x)•If there are no A p-time computable under some complexity theoretic hipothesis then r(x) is said inapproximability ratio
OPT(x) <= r(x) F(x,A(x)) if OPT(x) is a MAX-P
F(x,A(x)) <= r(x) OPT(x) if OPT(x) is a MIN-P
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Carlo Lombardi, June 2008 Theoretical Computer Science
Example: Set Cover Problem
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Let be:
• x : a polynomial size set system S
•y : subsystem S1 S
• iff )(xDy SS 1
Find s.t. is minimized
)(xDy 1S
Feige has shown that that the set cover cannot be approximate within a factor of
)(0ln)1( loglog nnDTIMENPunlessPinforS
Sln
is the approximation boundary for Set Cover Problem
but unfortunately…this is only an inapproximability result for a specific problem…not a more general theory…
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Carlo Lombardi, June 2008 Theoretical Computer Science
Question
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In which way we can obtain inapproximability results
for optimization problems?
Roughly speaking, how we can find a lower bound for approximation ratio
of optimization problems?
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Carlo Lombardi, June 2008 Theoretical Computer Science
Timeline for a more general inapproximability theory
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• 1972 – Graham: exact bounds on the performance of various bin packing heuristics•1974 – Jhonson: Subest Sum, Set Cover, MAX k-SAT bounds
•1976 – Shani & Gonzalez: TSP problem bound Garey & Jhonson: Gap amplification technique for Chromatic Number of a graph bound
•1991 – Feige: MIP for MIN-Clique bound Papadimitriou & Yannakakis:Using L reduction (app-reserving)
•1992 – Arora et al.: Using PCPs for MAX-3SAT Problem bound
“EUREKA!!!”
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Carlo Lombardi, June 2008 Theoretical Computer Science
Inapproximability results:the main ingredients
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Carlo Lombardi, June 2008 Theoretical Computer Science
Approximation-Preserving Reduction (1/2)
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If A Cook reduces to B we can state that
“the hardness of B follows from the hardness of A”
but we CANNOT state that
“if A is hard to approximate then B is hard to approximate”
To ensure reducing hardness of approximation we need a new definition of reduction…
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Carlo Lombardi, June 2008 Theoretical Computer Science
Approximation-Preserving Reduction (2/2)
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Let be:
• F1(x,y) and F2(x ’,y ’) to be optimized for y and y ‘ • OPT1(x) and OPT2(x ‘) the corresponding optimum
a KARP-LEVIN reduction involves two polynomial- time maps f and g s.t.:
12
21
)','(
)('
OPTyOPTyx
OPTxfxOPTxg
f
(Instance transformation)
(Witness transformation)
Let be:• opt1 = OPT1(x)• opt2 = OPT2(f(x))• appr1 = F1(x,g(f(x),y ’))• appr1 = F2(f(x),y ‘)
An approximation-preserving reduction scheme is a relation between this four entities that express the following:
“If appr2 well approximate opt2 then appr1 well approximate opt1 “
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Carlo Lombardi, June 2008 Theoretical Computer Science
Gap Problem
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Let be :
•OPT : a maximizationproblem•Tl(x) : a lower bound for OPT
•Tu(x) : an upper bound for OPT
•Both Tl(x) and Tu(x) are p-time computable in x
If we can efficiently approximate OPT(x) within a factor better than r(x)= Tu(x)/ Tl(x) then we can solve with only additive polynomial time also the so called GAP PROBLEM:
1 if OPT(x) >= Tu(x)
Gap(OPT, Tl, Tu ) 0 if OPT(x) <= Tl(x)
ANY if Tl(x)< OPT(x) < Tu(x)
If OPT is a minimization problem the roles of 0 and 1 in the above definition get exchanged
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Carlo Lombardi, June 2008 Theoretical Computer Science
From languages to gap problem
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• We can use Karp reduction to map a language into a Gap Problem
•A such reduction is a polynomial time computable map f from to input instances of OPT (max-problem) s.t.
*L
))(())((
))(())((
xfTxfOPTthenLxif
xfTxfOPTthenLxif
l
u
*
YN
Tu(f(x))
Tl(f(x))
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Carlo Lombardi, June 2008 Theoretical Computer Science
PCP Theorem
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Given an and a language there exists a polynomial-time computable function such that:
• if then f(x) is a formula in which all disjunctions are simultaneously satisfable
•if then f(x) is a formula in which one can satisfy at most 1-ε fraction of all clauses
0 NPL}3{: * formulasCNFf
Lx
Lx
•Considering we are discussing about optimization problem we can restate the Theorem:
Any language in NP is Karp reducible to Gap(Max-3SAT, 1-ε, 1)
where
Max-3SAT(φ)= maxy F(φ,y) being φ a 3CNF
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Carlo Lombardi, June 2008 Theoretical Computer Science
Karp reduction & gap problem
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• Karp reduction are approximation-preserving reduction
•We can reduce a gap problem G to a gap problem G’ preserving approximation results
Consider •two maximization problem OPT1 and OPT2 with bounds respectively T l , Tu , T’l , T’u and •A function f p-time computable from input instances of OPT1 to input instances of OPT2 s.t.:
1. if OPT1 (x)<= Tl (x) then OPT(f(x))<= Tl (f(x))2. if OPT1 (x)>= Tu(x) then OPT(f(x))>= Tu (f(x))
f is a Karp reduction from Gap(OPT1, T l, T l) to Gap(OPT2, T’ l, T’ l)
Gap(OPT1, T l, T u) is NP-Hard Gap(OPT2, T’ u, T’ u) is NP-Hard
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Carlo Lombardi, June 2008 Theoretical Computer Science
The main philosophy of PCP Theory
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Carlo Lombardi, June 2008 Theoretical Computer Science
Question
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How it’s possible to compute efficiently gap problems?
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Carlo Lombardi, June 2008 Theoretical Computer Science 17
Probabilistic Proof System
• A proof system consists of a verifier V and a prover P• Given a stastement x, such as “φ is satisfable”
•P produces a candidate proof y for the statement φ•V read the pair (φ,y) and either accepts or reject the proof y for φ
• Any proof system have two property:
•COMPLETENESS: if x is true exists y s.t. V(x,y) accepts
•SOUNDNESS: if x is false for every y V(x,y) rejects
A language L is in NP if there is a deterministic polynomial time verifier V and a polynomial p s.t.:
rejectsyxVxpytsyLx
acceptsyxVANDxpytsyLx
),())((..(
),()(..:
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Carlo Lombardi, June 2008 Theoretical Computer Science 18
Probabilistic Oracle Machine (1/2)
•What’s happen if we allow V to be randomized?
•Let be:
My a probabilistic RAM with oracle y and random string r
M is said to accept a language L with completness α and soudness β (1>= α >β>0) iff
•if then ther is a y s.t. Probr(My(x,r)=1)>= α
•if then for every y it holds that Probr(My(x,r)=1)<=β
Lx
Lx
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Carlo Lombardi, June 2008 Theoretical Computer Science 19
Parameters:
•Randomness: |r|•Query size: q•Completeness: α•Soundness: β
Probabilistic Oracle Machine (2/2)
The witness y written on a POM machine’s oracle tape is called Probabilistically Checkable Proof
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Carlo Lombardi, June 2008 Theoretical Computer Science 20
Connection between POM and OPT problem
)(
)(
xOTPthenLxif
xOTPthenLxif
M
M
L Karp reduces to Gap(OPTM, β, α)
If L is NP-Hard, approximating OPTM within a factor better than α/β is also NP-Hard