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        <identifier>oai:drops-oai.dagstuhl.de:21173</identifier>
        <datestamp>2024-09-23T09:13:18Z</datestamp>
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          <dc:title>SubModST: A Fast Generic Solver for Submodular Maximization with Size Constraints</dc:title>
          <dc:creator>Woydt, Henning Martin</dc:creator>
          <dc:creator>Komusiewicz, Christian</dc:creator>
          <dc:creator>Sommer, Frank</dc:creator>
          <dc:subject>Branch-and-Cut</dc:subject>
          <dc:subject>Lazy Evaluations</dc:subject>
          <dc:subject>Facility Location</dc:subject>
          <dc:subject>Group Closeness Centrality</dc:subject>
          <dc:subject>Partial Dominating Set</dc:subject>
          <dc:description>In the Cardinality-Constrained Maximization (Minimization) problem the input is a universe 𝒰, a function f: 2^{{𝒰}} → ℝ, and an integer k, and the task is to find a set S ⊆ 𝒰 with |S| ≤ k that maximizes (minimizes) f(S). Many well-studied problems such as Facility Location, Partial Dominating Set, Group Closeness Centrality and Euclidean k-Medoid Clustering are special cases of Cardinality-Constrained Maximization (Minimization). All the above-mentioned problems have the diminishing return property, that is, the improvement of adding an element e ∈ 𝒰 to a set S is at least as large as adding e to any superset of S. This property is called submodularity for maximization problems and supermodularity for minimization problems.&#13;
In this work we develop a new exact branch-and-cut algorithm SubModST for the generic Submodular Cardinality-Constrained Maximization and Supermodular Cardinality-Constrained Minimization. We develop several speed-ups for SubModST and we show their effectiveness on six example problems. We show that SubModST outperforms the state-of-the-art solvers developed by Csókás and Vinkó [J. Glob. Optim. '24] and Uematsu et al. [J. Oper. Res. Soc. Japan '20] for Submodular Cardinality-Constrained Maximization by orders of magnitudes.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Henning Martin Woydt and Christian Komusiewicz and Frank Sommer</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 308, 32nd Annual European Symposium on Algorithms (ESA 2024)</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.ESA.2024.102</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-211730</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2024.102</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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