Parameterized Approximation Algorithms and Lower Bounds for k-Center Clustering and Variants

k -center is one of the most popular clustering models. While it admits a simple 2-approximation in polynomial time in general metrics, the Euclidean version is NP-hard to approximate within a factor of 1.82, even in the plane, if one insists the dependence on k in the running time be polynomial. Wi...

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Veröffentlicht in:	Algorithmica Jg. 86; H. 8; S. 2557 - 2574
Hauptverfasser:	Bandyapadhyay, Sayan, Friggstad, Zachary, Mousavi, Ramin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2024 Springer Nature B.V
Schlagworte:	Algorithm Analysis and Problem Complexity Algorithms Approximation Clustering Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Design factors Euclidean geometry Euclidean space Lower bounds Mathematics of Computing Polynomials Theory of Computation Time dependence Euclidean Parameterized algorithms Hardness Non-uniform center
ISSN:	0178-4617, 1432-0541
Online-Zugang:	Volltext
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