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The unique games conjecture, integrality gap for cut problems and embeddability of negative type metrics into l1

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2 Author(s)
Khot, S.A. ; Coll. of Comput., Georgia Tech., Atlanta, GA, USA ; Vishnoi, N.K.

In this paper, we disprove the following conjecture due to Goemans (1997) and Linial (2002): "Every negative type metric embeds into with constant distortion." We show that for every δ > 0, and for large enough n, there is an n-point negative type metric which requires distortion at-least (log log n) 16-δ/ to embed into l1. Surprisingly, our construction is inspired by the Unique Games Conjecture (UGC) of Khot (2002), establishing a previously unsuspected connection between PCPs and the theory of metric embeddings. We first prove that the UGC implies super-constant hardness results for (non-uniform) sparsest cut and minimum uncut problems. It is already known that the UGC also implies an optimal hardness result for maximum cut (2004). Though these hardness results depend on the UGC, the integrality gap instances rely "only" on the PCP reductions for the respective problems. Towards this, we first construct an integrality gap instance for a natural SDP relaxation of unique games. Then, we "simulate" the PCP reduction and "translate"the integrality gap instance of unique games to integrality gap instances for the respective cut problems! This enables us to prove a (log log n) 16-δ/ integrality gap for (nonuniform) sparsest cut and minimum uncut, and an optimal integrality gap for maximum cut. All our SDP solutions satisfy the so-called "triangle inequality" constraints. This also shows, for the first time, that the triangle inequality constraints do not add any power to the Goemans-Williamson's SDP relaxation of maximum cut. The integrality gap for sparsest cut immediately implies a lower bound for embedding negative type metrics into li. It also disproves the non-uniform version of Arora, Rao and Vazirani's Conjecture (2004), asserting that the integrality gap of the sparsest cut SDP, with the triangle inequality constraints, is bounded from above by a constant.

Published in:

Foundations of Computer Science, 2005. FOCS 2005. 46th Annual IEEE Symposium on

Date of Conference:

23-25 Oct. 2005