Euclidean distortion and the sparsest cut

We prove that every n n -point metric space of negative type (and, in particular, every n n -point subset of L 1 L_1 ) embeds into a Euclidean space with distortion O ( log ⁡ n ⋅ log ⁡ log ⁡ n ) O(\sqrt {\log n} \cdot \log \log n) , a result which is tight up to the iterated logarithm 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 k , we achieve an approximation ratio of O ( log ⁡ k ⋅ log ⁡ log ⁡ k ) O(\sqrt {\log k}\cdot \log \log k) .

On lipschitz embeddingof finite metric spaces…On lipschitz embedding of finite metric spaces in Hilbert spaceSmall Distortion andVolume Preserving…Small Distortion and Volume Preserving Embeddings for Planar and Euclidean MetricsMeasured Descent: A NewEmbedding Method for…Measured Descent: A New Embedding Method for Finite MetricsExpander flows,geometric embeddings an…Expander flows, geometric embeddings and graph partitioningExtending Lipschitzfunctions via random…Extending Lipschitz functions via random metric partitionsMetric structures in L1:dimension, snowflakes…Metric structures in L1: dimension, snowflakes, and average distortionEmbeddings ofnegative-type metrics…Embeddings of negative-type metrics and an improved approximation to generalized sparsest cutThe Unique GamesConjecture, Integrality…The Unique Games Conjecture, Integrality Gap for Cut Problems and Embeddability of Negative Type Metrics into l1On distance scales,embeddings, and…On distance scales, embeddings, and efficient relaxations of the cut coneQuasisymmetricembeddings, the…Quasisymmetric embeddings, the observable diameter, and expansion properties of graphsImproved lower boundsfor embeddings into L1Improved lower bounds for embeddings into L1Fréchet Embeddings ofNegative Type MetricsFréchet Embeddings of Negative Type MetricsExpander flows,geometric embeddings an…Expander flows, geometric embeddings and graph partitioningImproved ApproximationAlgorithms for Minimum…Improved Approximation Algorithms for Minimum Weight Vertex SeparatorsFréchet Embeddings ofNegative Type MetricsFréchet Embeddings of Negative Type MetricsVertex cuts, randomwalks, and dimension…Vertex cuts, random walks, and dimension reduction in series-parallel graphsA ( log n)^ Omega (1)integrality gap for the…A ( log n)^ Omega (1) integrality gap for the Sparsest Cut SDPApproximating SparsestCut in Graphs of Bounde…Approximating Sparsest Cut in Graphs of Bounded TreewidthCompression bounds forLipschitz maps from the…Compression bounds for Lipschitz maps from the Heisenberg group to L1L1 Embeddings of theHeisenberg Group and…L1 Embeddings of the Heisenberg Group and Fast Estimation of Graph IsoperimetryNear-optimal distortionbounds for embedding…Near-optimal distortion bounds for embedding doubling spaces into L1Sparsest Cut on BoundedTreewidth Graphs…Sparsest Cut on Bounded Treewidth Graphs: Algorithms and Hardness ResultsThe integrality gap ofthe Goemans-Linial SDP…The integrality gap of the Goemans-Linial SDP relaxation for Sparsest Cut is at least a constant multiple of √log nVertical perimeterversus horizontal…Vertical perimeter versus horizontal perimeterEuclidean distortion andthe sparsest cutEuclidean distortion and the sparsest cutEarlier referencesFocus paperCiting papersOlderNewer

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