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        <identifier>oai:drops-oai.dagstuhl.de:15390</identifier>
        <datestamp>2024-03-06T10:55:38Z</datestamp>
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          <dc:title>Dynamic Kernels for Hitting Sets and Set Packing</dc:title>
          <dc:creator>Bannach, Max</dc:creator>
          <dc:creator>Heinrich, Zacharias</dc:creator>
          <dc:creator>Reischuk, Rüdiger</dc:creator>
          <dc:creator>Tantau, Till</dc:creator>
          <dc:subject>Kernelization</dc:subject>
          <dc:subject>Dynamic Algorithms</dc:subject>
          <dc:subject>Hitting Set</dc:subject>
          <dc:subject>Set Packings</dc:subject>
          <dc:description>Computing small kernels for the hitting set problem is a well-studied computational problem where we are given a hypergraph with n vertices and m hyperedges, each of size d for some small constant d, and a parameter k. The task is to compute a new hypergraph, called a kernel, whose size is polynomial with respect to the parameter k and which has a size-k hitting set if, and only if, the original hypergraph has one. State-of-the-art algorithms compute kernels of size k^d (which is a polynomial kernel size as d is a constant), and they do so in time m⋅ 2^d poly(d) for a small polynomial poly(d) (which is a linear runtime as d is again a constant).&#13;
We generalize this task to the dynamic setting where hyperedges may continuously be added or deleted and one constantly has to keep track of a size-k^d hitting set kernel in memory (including moments when no size-k hitting set exists). This paper presents a deterministic solution with worst-case time 3^d poly(d) for updating the kernel upon hyperedge inserts and time 5^d poly(d) for updates upon deletions. These bounds nearly match the time 2^d poly(d) needed by the best static algorithm per hyperedge. Let us stress that for constant d our algorithm maintains a dynamic hitting set kernel with constant, deterministic, worst-case update time that is independent of n, m, and the parameter k. As a consequence, we also get a deterministic dynamic algorithm for keeping track of size-k hitting sets in d-hypergraphs with update times O(1) and query times O(c^k) where c = d - 1 + O(1/d) equals the best base known for the static setting.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Max Bannach and Zacharias Heinrich and Rüdiger Reischuk and Till Tantau</dc:contributor>
          <dc:date>2021</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 214, 16th International Symposium on Parameterized and Exact Computation (IPEC 2021)</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.IPEC.2021.7</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-153900</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2021.7</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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