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        <datestamp>2024-03-06T10:41:37Z</datestamp>
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          <dc:title>Near-Optimal Approximate Shortest Paths and Transshipment in Distributed and Streaming Models</dc:title>
          <dc:creator>Becker, Ruben</dc:creator>
          <dc:creator>Karrenbauer, Andreas</dc:creator>
          <dc:creator>Krinninger, Sebastian</dc:creator>
          <dc:creator>Lenzen, Christoph</dc:creator>
          <dc:subject>Shortest Paths</dc:subject>
          <dc:subject>Shortest Transshipment</dc:subject>
          <dc:subject>Undirected Min-cost Flow</dc:subject>
          <dc:subject>Gradient Descent</dc:subject>
          <dc:subject>Spanner</dc:subject>
          <dc:description>We present a method for solving the shortest transshipment problem - also known as uncapacitated minimum cost flow - up to a multiplicative error of (1 + epsilon) in undirected graphs with non-negative integer edge weights using a tailored gradient descent algorithm. Our gradient descent algorithm takes epsilon^(-3) polylog(n) iterations, and in each iteration it needs to solve an instance of the transshipment problem up to a multiplicative error of polylog(n), where n is the number of nodes. In particular, this allows us to perform a single iteration by computing a solution on a sparse spanner of logarithmic stretch. Using a careful white-box analysis, we can further extend the method to finding approximate solutions for the single-source shortest paths (SSSP) problem. As a consequence, we improve prior work by obtaining the following results:&#13;
&#13;
(1) Broadcast CONGEST model: (1 + epsilon)-approximate SSSP using ~O((sqrt(n) + D) epsilon^(-O(1))) rounds, where D is the (hop) diameter of the network.&#13;
&#13;
(2) Broadcast congested clique model: (1 + epsilon)-approximate shortest transshipment and SSSP using ~O(epsilon^(-O(1))) rounds.&#13;
&#13;
(3) Multipass streaming model: (1 + epsilon)-approximate shortest transshipment and SSSP using ~O(n) space and ~O(epsilon^(-O(1))) passes.&#13;
&#13;
The previously fastest SSSP algorithms for these models leverage sparse hop sets. We bypass the hop set construction; computing a spanner is sufficient with our method. The above bounds assume non-negative integer edge weights that are polynomially bounded in n; for general non-negative weights, running times scale with the logarithm of the maximum ratio between non-zero weights. In case of asymmetric costs for traversing an edge in opposite directions, running times scale with the maximum ratio between the costs of both directions over all edges.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Ruben Becker and Andreas Karrenbauer and Sebastian Krinninger and Christoph Lenzen</dc:contributor>
          <dc:date>2017</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 91, 31st International Symposium on Distributed Computing (DISC 2017)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.DISC.2017.7</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-80031</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2017.7</dc:identifier>
          <dc:language>eng</dc:language>
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