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        <identifier>oai:drops-oai.dagstuhl.de:16945</identifier>
        <datestamp>2024-03-06T10:58:47Z</datestamp>
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          <dc:title>Techniques for Generalized Colorful k-Center Problems</dc:title>
          <dc:creator>Anegg, Georg</dc:creator>
          <dc:creator>Vargas Koch, Laura</dc:creator>
          <dc:creator>Zenklusen, Rico</dc:creator>
          <dc:subject>Approximation Algorithms</dc:subject>
          <dc:subject>Fair Clustering</dc:subject>
          <dc:subject>Colorful k-Center</dc:subject>
          <dc:description>Fair clustering enjoyed a surge of interest recently. One appealing way of integrating fairness aspects into classical clustering problems is by introducing multiple covering constraints. This is a natural generalization of the robust (or outlier) setting, which has been studied extensively and is amenable to a variety of classic algorithmic techniques. In contrast, for the case of multiple covering constraints (the so-called colorful setting), specialized techniques have only been developed recently for k-Center clustering variants, which is also the focus of this paper. &#13;
While prior techniques assume covering constraints on the clients, they do not address additional constraints on the facilities, which has been extensively studied in non-colorful settings. In this paper, we present a quite versatile framework to deal with various constraints on the facilities in the colorful setting, by combining ideas from the iterative greedy procedure for Colorful k-Center by Inamdar and Varadarajan with new ingredients. To exemplify our framework, we show how it leads, for a constant number γ of colors, to the first constant-factor approximations for both Colorful Matroid Supplier with respect to a linear matroid and Colorful Knapsack Supplier. In both cases, we readily get an O(2^γ)-approximation.&#13;
Moreover, for Colorful Knapsack Supplier, we show that it is possible to obtain constant approximation guarantees that are independent of the number of colors γ, as long as γ = O(1), which is needed to obtain a polynomial running time. More precisely, we obtain a 7-approximation by extending a technique recently introduced by Jia, Sheth, and Svensson for Colorful k-Center.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Georg Anegg and Laura Vargas Koch and Rico Zenklusen</dc:contributor>
          <dc:date>2022</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ESA.2022.7</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-169458</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.7</dc:identifier>
          <dc:language>eng</dc:language>
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