Embeddings of negative-type metrics and an improved approximation to generalized sparsest cut

  • Authors:
  • Shuchi Chawla;Anupam Gupta;Harald Räcke

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh PA;Carnegie Mellon University, Pittsburgh PA;Carnegie Mellon University, Pittsburgh PA

  • Venue:
  • SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
  • Year:
  • 2005

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Abstract

In this paper, we study the metrics of negative type, which are metrics (V, d) such that √d is an Euclidean metric; these metrics are thus also known as "l2-squared" metrics.We show how to embed n-point negative-type metrics into Euclidean space l2 with distortion D = O(log3/4 n). This embedding result, in turn, implies an O(log3/4 k)-approximation algorithm for the Sparsest Cut problem with non-uniform demands. Another corollary we obtain is that n-point subsets of l1 embed into l2 with distortion O(log3/4 n).