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        <datestamp>2026-09-05T19:44:18Z</datestamp>
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          <dc:title>On Tight FPT Time Approximation Algorithms for k-Clustering Problems</dc:title>
          <dc:creator>Dai, Han</dc:creator>
          <dc:creator>Li, Shi</dc:creator>
          <dc:creator>Peng, Sijin</dc:creator>
          <dc:subject>Approximation algorithms</dc:subject>
          <dc:subject>Monotone symmetric norms</dc:subject>
          <dc:subject>Clustering</dc:subject>
          <dc:subject>Fixed parameter tractability</dc:subject>
          <dc:description>Following recent advances in combining approximation algorithms with fixed-parameter tractability (FPT), we study FPT-time approximation algorithms for minimum-norm k-clustering problems, parameterized by the number k of open facilities.&#13;
For the capacitated setting, we give a tight (3+ε)-approximation for the general-norm capacitated k-clustering problem in FPT-time parameterized by k and ε. Prior to our work, such a result was only known for the capacitated k-median problem [Cohen-Addad and Li, 2019]. As a special case, our result yields an FPT-time 3-approximation for capacitated k-center. The problem has not been studied in the FPT-time setting, with the previous best known polynomial-time approximation ratio being 9 [An et al., 2015]. &#13;
In the uncapacitated setting, we consider the top-cn norm k-clustering problem, where the goal of the problem is to minimize the top-cn norm of the connection distance vector. Our main result is a tight (1 + 2/(ec) + ε)-approximation algorithm for the problem with c ∈ (1/e, 1]. (For the case c ≤ 1/e, there is a simple tight (3+ε)-approximation.) Our framework can be easily extended to give a tight (3, 1 + 2/e + ε)-bi-criteria approximation for the (k-center, k-median) problem in FPT time, improving the previous best polynomial-time (4, 8) guarantee [Soroush Alamdari and David B. Shmoys, 2017]. &#13;
All results are based on a unified framework: computing a (1+ε)-approximate solution using O((k log n)/ε) facilities S via LP rounding, sampling a few client representatives R based on the solution S, guessing a few pivots from S ∪ R and some radius information on the pivots, and solving the problem using the guesses. We believe this framework can lead to further results on k-clustering problems.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Han Dai and Shi Li and Sijin Peng</dc:contributor>
          <dc:date>2026</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 374, 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2026.72</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-264613</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2026.72</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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