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  • 标题:Partitioning Vectors into Quadruples: Worst-Case Analysis of a Matching-Based Algorithm
  • 本地全文:下载
  • 作者:Annette M. C. Ficker ; Thomas Erlebach ; Mat{\'u}s Mihal{\'a}k
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2018
  • 卷号:123
  • 页码:1-12
  • DOI:10.4230/LIPIcs.ISAAC.2018.45
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm and prove that this algorithm is a 3/2-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem and prove that, in one particular case, the algorithm is a 5/4-approximation algorithm. Our analysis is tight in all cases except one.
  • 关键词:approximation algorithm; matching; clustering problem
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