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        <datestamp>2024-03-06T10:36:16Z</datestamp>
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          <dc:title>Terminal Embeddings</dc:title>
          <dc:creator>Elkin, Michael</dc:creator>
          <dc:creator>Filtser, Arnold</dc:creator>
          <dc:creator>Neiman, Ofer</dc:creator>
          <dc:subject>embedding</dc:subject>
          <dc:subject>distortion</dc:subject>
          <dc:subject>terminals</dc:subject>
          <dc:description>In this paper we study terminal embeddings, in which one is given a finite metric (X,d_X) (or a graph G=(V,E)) and a subset K of X of its points are designated as terminals. The objective is to embed the metric into a normed space, while approximately preserving all distances among pairs that contain a terminal. We devise such embeddings in various settings, and conclude that even though we have to preserve approx |K| * |X| pairs, the distortion depends only on |K|, rather than on |X|.&#13;
&#13;
We also strengthen this notion, and consider embeddings that approximately preserve the distances between all pairs, but provide improved distortion  for pairs containing a terminal. Surprisingly, we show that such embeddings exist  in many settings, and have optimal distortion bounds both with respect to X \times  X and with respect to K * X.&#13;
&#13;
Moreover, our embeddings have implications to the areas of Approximation and Online Algorithms. In particular, Arora et. al. devised an ~O(sqrt(log(r))-approximation algorithm for sparsest-cut instances with r demands. Building on their framework, we provide an ~O(sqrt(log |K|)-approximation for sparsest-cut instances in which each demand is incident on one of the vertices of K (aka, terminals). Since |K| &lt;= r, our bound generalizes that of Arora et al.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Michael Elkin and Arnold Filtser and Ofer Neiman</dc:contributor>
          <dc:date>2015</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 40, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.APPROX-RANDOM.2015.242</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-53064</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2015.242</dc:identifier>
          <dc:language>eng</dc:language>
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