Approximation Algorithms for Clustering with Minimum Sum of Radii, Diameters, and Squared Radii

• Author / Creator

  Jamshidian, Mahya

• In this study, we present an improved approximation algorithm for three related problems. In the Minimum Sum of Radii clustering problem (MSR), we aim to select
  k balls in a metric space to cover all points while minimizing the sum of the radii. In the Minimum Sum of Diameters clustering problem (MSD), we are to pick k clusters to cover all the points such that sum of diameters of all the clusters is minimized.
  At last, in the Minimum Sum of Squared Radii problem (MSSR), the goal is to choose k balls, similar to MSR. However in MSSR, the goal is to minimize the sum of squares of radii of the balls. We present a 3.389-approximation for MSR and a
  6.546-approximation for MSD, improving over respective 3.504 and 7.008 developed by Charkar and Panigrahy (2001). In particular, our guarantee for MSD is better than
  twice our guarantee for MSR. In the case of MSSR, the best known approximation guarantee is 4 ·(540)^2 based on the work of Bhowmick, Inamdar, and Varadarajan in
  their general analysis of the t-Metric Multicover Problem. With our analysis, we get
  a 11.078-approximation algorithm for Minimum Sum of Squared Radii.

• Subjects / Keywords

  • Approximation Algorithms
  • Minimum Sum of Radii
  • Minimum Sum of Diameters

• Graduation date

  Fall 2022

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-ba50-d461

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.