Publication | Closed Access
Euclidean distortion and the sparsest cut
112
Citations
33
References
2005
Year
Unknown Venue
Mathematical ProgrammingEngineeringGeometryEducationComputational ComplexityAtomic DecompositionDiscrete MathematicsCombinatorial OptimizationComputational GeometryApproximation TheoryLow-rank ApproximationN-point Metric SpaceSublinear AlgorithmEuclidean DistortionNegative TypeComputer ScienceEuclidean SpaceSparse RepresentationCompressive SensingApproximation Method
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).
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