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        <identifier>oai:drops-oai.dagstuhl.de:25381</identifier>
        <datestamp>2026-03-19T13:04:17Z</datestamp>
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          <dc:title>Smoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion</dc:title>
          <dc:creator>Li, Yingxi</dc:creator>
          <dc:creator>Vitercik, Ellen</dc:creator>
          <dc:creator>Yang, Mingwei</dc:creator>
          <dc:subject>Online algorithm</dc:subject>
          <dc:subject>Metric matching</dc:subject>
          <dc:subject>Competitive analysis</dc:subject>
          <dc:subject>Smoothed analysis</dc:subject>
          <dc:description>In the online metric matching problem, n servers and n requests lie in a metric space. Servers are available upfront, and requests arrive sequentially. An arriving request must be matched immediately and irrevocably to an available server, incurring a cost equal to their distance. The goal is to minimize the total matching cost.&#13;
We study this problem in [0, 1]^d with the Euclidean metric, when servers are adversarial and requests are independently drawn from distinct distributions that satisfy a mild smoothness condition. Our main result is an O(1)-competitive algorithm for d ≠ 2 that requires no distributional knowledge, relying only on a single sample from each request distribution. To our knowledge, this is the first algorithm to achieve an o(log n) competitive ratio for non-trivial metrics beyond the i.i.d. setting. Our approach bypasses the Ω(log n) barrier introduced by probabilistic metric embeddings: instead of analyzing the embedding distortion and the algorithm separately, we directly bound the cost of the algorithm on the target metric space of a simple deterministic embedding. We then combine this analysis with lower bounds on the offline optimum for Euclidean metrics, derived via majorization arguments, to obtain our guarantees.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Yingxi Li and Ellen Vitercik and Mingwei Yang</dc:contributor>
          <dc:date>2026</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)</dc:relation>
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          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ITCS.2026.94</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-253815</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.94</dc:identifier>
          <dc:language>eng</dc:language>
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