Subexponential Algorithms for Unique Games and Related Problems

著者: Sanjeev Arora, Boaz Barak, David Steurer - IEEE Symposium on Foundations of Computer Science, FOCS 2010 被引用: 180

Subexponential time approximation algorithms are presented for the U nique G ames and S mall -S et E xpansion problems. Specifically, for some absolute constant c , the following two algorithms are presented. (1) An exp( kn ϵ )-time algorithm that, given as input a k -alphabet unique game on n variables that has an assignment satisfying 1-ϵ c fraction of its constraints, outputs an assignment satisfying 1-ϵ fraction of the constraints. (2) An exp( n ϵ /δ)-time algorithm that, given as input an n -vertex regular graph that has a set S of δ n vertices with edge expansion at most ϵ c , outputs a set S' of at most δ n vertices with edge expansion at most ϵ. subexponential algorithm is also presented with improved approximation to M ax C ut , S parsest C ut , and V ertex C over on some interesting subclasses of instances. These instances are graphs with low threshold rank , an interesting new graph parameter highlighted by this work. Khot's Unique Games Conjecture (UGC) states that it is NP -hard to achieve approximation guarantees such as ours for U nique G ames . While the results here stop short of refuting the UGC, they do suggest that U nique G ames are significantly easier than NP -hard problems such as M ax 3-S at , M ax 3- Lin , L abel C over , and more, which are believed not to have a subexponential algorithm achieving a nontrivial approximation ratio. Of special interest in these algorithms is a new notion of graph decomposition that may have other applications. Namely, it is shown for every ϵ >0 and every regular n -vertex graph G , by changing at most δ fraction of G 's edges, one can break G into disjoint parts so that the stochastic adjacency matrix of the induced graph on each part has at most n ϵ eigenvalues larger than 1-η, where η depends polynomially on ϵ. The subexponential algorithm combines this decomposition with previous algorithms for U nique G ames on graphs with few large eigenvalues [Kolla and Tulsiani 2007; Kolla 2010].