A unified framework of FPT approximation algorithms for clustering problems

Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, Jianxin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a framework for designing FPT approximation algorithms for many k-clustering problems. Our results are based on a new technique for reducing search spaces. A reduced search space is a small subset of the input data that has the guarantee of containing k clients close to the facilities opened in an optimal solution for any clustering problem we consider. We show, somewhat surprisingly, that greedily sampling O(k) clients yields the desired reduced search space, based on which we obtain FPT(k)-time algorithms with improved approximation guarantees for problems such as capacitated clustering, lower-bounded clustering, clustering with service installation costs, fault tolerant clustering, and priority clustering.

Original languageEnglish (US)
Title of host publication31st International Symposium on Algorithms and Computation, ISAAC 2020
EditorsYixin Cao, Siu-Wing Cheng, Minming Li
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Pages51-517
Number of pages467
ISBN (Electronic)9783959771733
DOIs
StatePublished - Dec 2020
Event31st International Symposium on Algorithms and Computation, ISAAC 2020 - Virtual, Hong Kong, China
Duration: Dec 14 2020Dec 18 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume181
ISSN (Print)1868-8969

Conference

Conference31st International Symposium on Algorithms and Computation, ISAAC 2020
CountryChina
CityVirtual, Hong Kong
Period12/14/2012/18/20

All Science Journal Classification (ASJC) codes

  • Software

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