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Title: Online k-Median with Consistent Clusters
We consider the problem in which n points arrive online over time, and upon arrival must be irrevocably assigned to one of k clusters where the objective is the standard k-median objective. Lower-bound instances show that for this problem no online algorithm can achieve a competitive ratio bounded by any function of n. Thus we turn to a beyond worst-case analysis approach, namely we assume that the online algorithm is a priori provided with a predicted budget B that is an upper bound to the optimal objective value (e.g., obtained from past instances). Our main result is an online algorithm whose competitive ratio (measured against B) is solely a function of k. We also give a lower bound showing that the competitive ratio of every algorithm must depend on k.  more » « less
Award ID(s):
2121744 1845146
PAR ID:
10563544
Author(s) / Creator(s):
; ;
Editor(s):
Kumar, Amit; Ron-Zewi, Noga
Publisher / Repository:
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Date Published:
Volume:
317
ISSN:
1868-8969
ISBN:
978-3-95977-348-5
Page Range / eLocation ID:
317-317
Subject(s) / Keyword(s):
k-median online algorithms learning-augmented algorithms beyond worst-case analysis Theory of computation → Online algorithms
Format(s):
Medium: X Size: 22 pages; 1055498 bytes Other: application/pdf
Size(s):
22 pages 1055498 bytes
Right(s):
Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
Sponsoring Org:
National Science Foundation
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