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        <identifier>oai:drops-oai.dagstuhl.de:19958</identifier>
        <datestamp>2024-06-06T06:21:13Z</datestamp>
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          <dc:title>Topological k-Metrics</dc:title>
          <dc:creator>Barkan, Willow</dc:creator>
          <dc:creator>Bennett, Huck</dc:creator>
          <dc:creator>Nayyeri, Amir</dc:creator>
          <dc:subject>k-metrics</dc:subject>
          <dc:subject>metric embeddings</dc:subject>
          <dc:subject>computational topology</dc:subject>
          <dc:subject>simplicial complexes</dc:subject>
          <dc:description>Metric spaces (X, d) are ubiquitous objects in mathematics and computer science that allow for capturing pairwise distance relationships d(x, y) between points x, y ∈ X. Because of this, it is natural to ask what useful generalizations there are of metric spaces for capturing "k-wise distance relationships" d(x_1, …, x_k) among points x_1, …, x_k ∈ X for k &gt; 2. To that end, Gähler (Math. Nachr., 1963) (and perhaps others even earlier) defined k-metric spaces, which generalize metric spaces, and most notably generalize the triangle inequality d(x₁, x₂) ≤ d(x₁, y) + d(y, x₂) to the "simplex inequality" d(x_1, …, x_k) ≤ ∑_{i=1}^k d(x_1, …, x_{i-1}, y, x_{i+1}, …, x_k). (The definition holds for any fixed k ≥ 2, and a 2-metric space is just a (standard) metric space.)&#13;
In this work, we introduce strong k-metric spaces, k-metric spaces that satisfy a topological condition stronger than the simplex inequality, which makes them "behave nicely." We also introduce coboundary k-metrics, which generalize 𝓁_p metrics (and in fact all finite metric spaces induced by norms) and minimum bounding chain k-metrics, which generalize shortest path metrics (and capture all strong k-metrics). Using these definitions, we prove analogs of a number of fundamental results about embedding finite metric spaces including Fréchet embedding (isometric embedding into 𝓁_∞) and isometric embedding of all tree metrics into 𝓁₁. We also study relationships between families of (strong) k-metrics, and show that natural quantities, like simplex volume, are strong k-metrics.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Willow Barkan and Huck Bennett and Amir Nayyeri</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 293, 40th International Symposium on Computational Geometry (SoCG 2024)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.SoCG.2024.13</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-199585</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2024.13</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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