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          <dc:title>Approximation Algorithms for Clustering Problems with Lower Bounds and Outliers</dc:title>
          <dc:creator>Ahmadian, Sara</dc:creator>
          <dc:creator>Swamy, Chaitanya</dc:creator>
          <dc:subject>Approximation algorithms</dc:subject>
          <dc:subject>facililty-location problems</dc:subject>
          <dc:subject>primal-dual method</dc:subject>
          <dc:subject>Lagrangian relaxation</dc:subject>
          <dc:subject>k-center problems</dc:subject>
          <dc:subject>minimizing sum of radii</dc:subject>
          <dc:description>We consider clustering problems with non-uniform lower bounds and outliers, and obtain the first approximation guarantees for these problems. We have a set F of facilities with lower bounds {L_i}_{i in F} and a set D of clients located in a common metric space {c(i,j)}_{i,j in F union D}, and bounds k, m. A feasible solution is a pair (S subseteq F, sigma: D -&gt; S union {out}), where sigma specifies the client assignments, such that |S| &lt;=k, |sigma^{-1}(i)| &gt;= L_i for all i in S, and |sigma^{-1}(out)| &lt;= m. In the lower-bounded min-sum-of-radii with outliers P (LBkSRO) problem, the objective is to minimize sum_{i in S} max_{j in sigma^{-1})i)}, and in the lower-bounded k-supplier with outliers (LBkSupO) problem, the objective is to minimize max_{i in S} max_{j in sigma^{-1})i)} c(i,j).&#13;
&#13;
We obtain an approximation factor of 12.365 for LBkSRO, which improves to 3.83 for the non-outlier version (i.e., m = 0). These also constitute the first approximation bounds for the min-sum-of-radii objective when we consider lower bounds and outliers separately. We apply the primal-dual method to the relaxation where we Lagrangify the |S| &lt;= k constraint. The chief technical contribution and novelty of our algorithm is that, departing from the standard paradigm used for such constrained problems, we obtain an O(1)-approximation despite the fact that we do not obtain a Lagrangian-multiplier-preserving algorithm for the Lagrangian relaxation. We believe that our ideas have broader applicability to other clustering problems with outliers as well.&#13;
&#13;
We obtain approximation factors of 5 and 3 respectively for LBkSupO and its non-outlier version. These are the first approximation results for k-supplier with non-uniform lower bounds.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Sara Ahmadian and Chaitanya Swamy</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 55, 43rd International Colloquium on Automata, Languages, and Programming (ICALP 2016)</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.ICALP.2016.69</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-62153</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2016.69</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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