Lecture 20: Inapproximability of Minimum Vertex Cover


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1 CS 880: Advanced Complexity Theory 3//008 Lecture 0: Inapproximability of Minimum Vertex Cover Instructor: Dieter van Melkebeek Scribe: Mark Liu Last time we examined a generic approach for inapproximability results based on the Unique Games Conjecture. Before, we had already shown that approximating MAX3LIN to within a constant factor larger than is NPhard. To do this we used a tweaked version of our dictatorship test that we came up with earlier in the semester. Last time we (re)proved that approximating MAX3LIN to within a constant larger than is UGhard. The latter is a weaker statement than the earlier NPhardness result, but the argument used the dictatorship test as a blackbox. In this lecture we show that when we replace the dictatorship test by a noise sensitivity test, then we obtain that MAXCUT is UGhard to approximate to within any factor larger than ρ GW, where ρ GW refers to the approximation ratio achieved by the GoemansWilliamson algorithm. The numerical value of ρ GW is approximately.878. We end the lecture wiht a proof sketch that approximating MINVC to within any constant factor smaller than is UGhard. The Generic Approach of Last Lecture We briefly go over the framework we saw last lecture. We start by restating the necessary properties of the family of dictatorship tests T α, α A. Dictatorship test T g α (α A): Completeness Condition: If g is a dictator then Pr[T g α accepts] c α. Soundness Condition: For every α and δ > 0 there exists τ > 0 and d > 0 such that the following holds for every distribution g = E u [f u ] over Boolean functions f u in l variables: If Pr[Tα g accepts] s α + δ then there exists i [l] with I d j (g) τ. Given such a test T g α, we developed the following test S f α, where f represents the purported long encoding of the labels of the lefthand side of a constraint graph game G = (L,R,E,[l],[r],C) of permutation type with underlying permutations e for e E. Test S f α: Pick v R at random. Run T gv α, where on g v (y) = E [f u(y e )] and accept iff T gv α accepts. Note that S α uses T α as a blackbox. We established the following key properties in the case where G is rightregular. Completeness: There exists an f such that Pr[S f α accepts] c α ν (G). Soundness: If there exists an f such that Pr[S f α accepts] s α + δ, then ν(g) δτ 4d.
2 We can view Pr[Sα f accepts] as the fraction of test conditions that are satisfied by f. The completeness result for Sα f implies that if ν (G) γ then MAXSAT(S α ) c α ( γ), were MAXSAT(S α ) is the maximum probability of acceptance of the test S α over all possible functions f. By the soundness result, we have that if ν(g) γ < δτ 4d then MAXSAT(S α) < s α + δ. From this we can conclude that it is UGhard to approximate any problem that contains all problems of the form MAXSAT(S α ) for α A, to within a constant factor ρ > inf α A ( sα c α ). Applications We first recall teh application to MAX3LIN from last class and then develop the new one for MAXCUT.. Application to MAX3LIN When we use the 3query dictatorship test T α from the beginning of the course for α (,), then MAXSAT(S α ) becomes an instance of MAX3LIN. We argued that c α = ǫ for ǫ = α, and s α =. We conclude that approximating MAX3LIN to within any constant factor rho > inf α (,) ( sα c α ) = frac is UGhard.. Application to MAXCUT To achieve our MAXCUT inapproximability result, we will replace our 3query dictatorship test with a query noise sensitivity test defined as follows. Test T g α: Pick x {,} l at random Pick y α x Accept iff g(x) g(y) Note that the conditions induced by the resulting test Sα f are all of the form f u (x) f v (y) for u,v L and x,y {,} l. Thus, when we consider a graph H with a vertex for each combo f u (x) and an edge (f u (x),f v (y)) for each condition f u (x) f v (y), then MAXSAT(S α ) is equivalent to finding a twocoloring of the vertices of H that maximizes the number of mixed edges. In other words, MAXSAT(S α ) is equivalent to finding a maximum cut in H. The above test is an effectivization of the notion of noise sensitivity. As a result, we have that Pr[Tα g accepts] = NS α (g). This allows us to establish the parameters c α and s α for which this test meets the conditions listed at the end of Section. As for the completeness condition, we can set c α = α since NS ǫ (dictator) = ǫ. As for the soundness condition, recall the Reverse Majority Stablest Theorem: For any g : {,} l [,] and all α such that < α <, if for all i [l],i log(/τ) i (g) τ, then NS α (g) log log arccos( α) + O( τ α ). The contrapositive log τ essentially gives us the soundness condition we need to hold. Note that for any α (,) we can make the error term δ = O( log log τ α ) arbitrarily small by choosing τ sufficiently small. Setting log τ
3 0. /acos()*acos(*x).875*x Figure : d = log τ then gives the soundness property with s α = arccos( α). We conclude that it is UGhard to approximate MAXCUT to within any constant ρ > inf ( <α< arccos( α) ). () α To see the infimum visually, we plot h(x) = arccos( α) against α in Figure. The infimum is reached where a line drawn through the origin is tangent to the curve. We claim that the righthand side of () is exactly the approximation factor ρ GW guaranteed by the GoemansWilliamson algorithm. To see why, we quickly review the algorithm and its analysis. For a given graph H = (V,E), we have MAXCUT(H) max x v =,v V x u,x v, () where the x v s range over all vectors in R d for unrestricted d. The problem on the righthand side of () is a semidefinite program, which can be solved in polynomial time. It is a relaxation of the MAXCUT problem, which is equivalent to the problem on the righthand side with teh additional restriction that d =. Once we have an optimal solution x v for v V to the semidefinite program, we need to extract a cut in H whose value is not too much worse than the righthand side of (). We do so by randomized rounding: We pick a random hyperplane through the origin of R d and use it to partition V in those vertices v for which x v is on one side of the hyperplane, and the others. The resulting cut in H has the property that for any fixed edge e = (u,v) E, Pr[e = (u,v) is cut] = θ = arccos( x u,x v ). (3) This follows from an analysis in the plane spanned by the vectors x u and x v (see Figure ). The intersection of the random hyperplane with this plane is a random line through the origin. The probability that this line separates x u and x v equals θ, where θ is the angle between the two vectors, i.e., cos(θ) = x u,x v. 3
4 x v cut θ x u Figure : Probability of a Random Cut By linearity of expectation and comparing the righthand side of (3) with the corresponding term on the righthand side of (), we have E[# edges cut] = min min z arccos( x u,x v ) ) ( arccos( xu,x v ) x u,x v ( arccos(z) ) z x u,x v x u,x v, where the last line follows by substituting z = x u,x v and further relaxation. Since x v is the optimal solution for the righthand side of (), we conclude that where E[# edges cut] ρ GW MAXCUT(H), ( arccos(z) ) ρ GW = min z z. (4) By substituting z = α and observing that the minimum is reached for z 0, the righthand side of (4) transforms into the righthand side of (), which is what we wanted to argue. Note that the GoemansWilliamson algorithm can be derandomized to yield a deterministic approximation ρ GW approximation algorithm for MAXCUT. 3 Minimum Vertex Cover We now switch to another standard optimization problem minimum vertex cover or MINVC for short. The following theorem shows that approximating MINVC to within any constant factor less than is UGhard. 4
5 u e e v v L R Figure 3: Unique Constraint Graph Games Theorem. For all δ > 0, there exists a γ > 0 such that there is a reduction from unique constraint graph games over [l] to graphs H such that the following hold: ν (G) γ MINVC(H) + δ ν(g) γ MINVC(H) δ. We will not prove Theorem in full. We only describe the underlying construction and argue the completeness property, but not the soundness property. Given a unique constraint graph game G = (L, R, E,[l],[r], C) we construct H as follows. The vertices of H represent the bits of the purported long codes g v of the labels of vertices v R. More precisely, for each v R and y {,} r, we include a vertex representing g v (y) in H. In order to define the edge relationship, we view strings in {,} r as the characteristic vector of subsets of [r]. For v,v R with v v and y,y {,} r, we stipulate the following. (v,y) (v,y ) y y =. (v,y) (v,y ) there exists a u L such that e = (u,v) E, e = (u,v ) E and y e y e =. Note that the first condition can be viewed as a special case of the second one for v = v. For v v, the second condition involves some kind of consistency check. See Figure 4 for a visual aid. To argue the completeness property in Theorem, we will think in terms of independent sets rather than vertex covers. Recall that a set B is a vertex cover iff A = B is an independent set. Thus, to establish the completeness property we need to exhibit an independent set of relative size at least δ whenever ν (G) γ. Fix a labeling and a set R R realizing ν (G). The set R denotes the vertices in R all of whose incident edge constraints are met by the underlying labeling. Also, let A = {(v,y) v R and g v (y) = }, where g v denotes the long code of the label given to v, i.e., g v (y) = y j where j = value(v). Claim. A is an independent set of relative size at least γ. The claim about the relative size follows because the relative size of R is at least γ and for each v R exactly half of the y {,} r map to  under the dictator g v. 5
6 For the claim of independence, consider any v R with label j. We have that g v (y) = iff y j = iff j y. Thus, if (v,y) A and (v,y ) A, then j y y, which means that there is no edge between (v,y) and (v,y ). In addition, consider any v R with value j and such that v v. Let u be any common neighbor of v and v. Since the edge constraints on e = (u,v) and e = (u,v ) are satisfied by the underlying labeling, we have that e (j) = e (j ). Now, if (v,y) A then j y so e (j) y e. So, if both (v,y) A and (v,y ) A then e (j) = e (j ) (y e ) (y e ). Since this holds for all common neighbors u of v and v, there is no edge between (v,y) and (v,y ). This finishes the proof of the completeness property in Theorem. The proof of the soundness property is significantly more involved and we will not cover it due to lack of time. 6
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