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        <identifier>oai:drops-oai.dagstuhl.de:20162</identifier>
        <datestamp>2024-07-02T07:52:49Z</datestamp>
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          <dc:title>Sublinear Algorithms for TSP via Path Covers</dc:title>
          <dc:creator>Behnezhad, Soheil</dc:creator>
          <dc:creator>Roghani, Mohammad</dc:creator>
          <dc:creator>Rubinstein, Aviad</dc:creator>
          <dc:creator>Saberi, Amin</dc:creator>
          <dc:subject>Sublinear Algorithms</dc:subject>
          <dc:subject>Traveling Salesman Problem</dc:subject>
          <dc:subject>Approximation Algorithm</dc:subject>
          <dc:subject>(1</dc:subject>
          <dc:subject>2)-TSP</dc:subject>
          <dc:subject>Graphic TSP</dc:subject>
          <dc:description>We study sublinear time algorithms for the traveling salesman problem (TSP). First, we focus on the closely related maximum path cover problem, which asks for a collection of vertex disjoint paths that include the maximum number of edges. We show that for any fixed ε &gt; 0, there is an algorithm that (1/2 - ε)-approximates the maximum path cover size of an n-vertex graph in Õ(n) time. This improves upon a (3/8-ε)-approximate Õ(n √n)-time algorithm of Chen, Kannan, and Khanna [ICALP'20].&#13;
Equipped with our path cover algorithm, we give an Õ(n) time algorithm that estimates the cost of (1,2)-TSP within a factor of (1.5+ε) which is an improvement over a folklore (1.75 + ε)-approximate Õ(n)-time algorithm, as well as a (1.625+ε)-approximate Õ(n√n)-time algorithm of [CHK ICALP'20]. For graphic TSP, we present an Õ(n) algorithm that estimates the cost of graphic TSP within a factor of 1.83 which is an improvement over a 1.92-approximate Õ(n) time algorithm due to [CHK ICALP'20, Behnezhad FOCS'21]. We show that the approximation can be further improved to 1.66 using n^{2-Ω(1)} time.&#13;
All of our Õ(n) time algorithms are information-theoretically time-optimal up to polylog n factors. Additionally, we show that our approximation guarantees for path cover and (1,2)-TSP hit a natural barrier: We show better approximations require better sublinear time algorithms for the well-studied maximum matching problem.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Soheil Behnezhad and Mohammad Roghani and Aviad Rubinstein and Amin Saberi</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 297, 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2024.19</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-201623</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2024.19</dc:identifier>
          <dc:language>eng</dc:language>
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