Euclidean distortion and the sparsest cut

Authors:
Sanjeev Arora;James R. Lee;Assaf Naor
Affiliations:
Princeton University, Princeton, NJ;U.C. Berkeley;Microsoft Research
Venue:
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Year:
2005

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Abstract

We prove that every n-point metric space of negative type (in particular, every n-point subset of L1) embeds into a Euclidean space with distortion O(√log n log log n), a result which is tight up to the O(log log n) factor. As a consequence, we obtain the best known polynomial-time approximation algorithm for the Sparsest Cut problem with general demands. If the demand is supported on a subset of size k, we achieve an approximation ratio of O(√log k log log k).