Boosting Double Coverage for k-Server via Imperfect Predictions

We study the online k -server problem in a learning-augmented setting. While in the traditional online model, an algorithm has no information about the request sequence, we assume that there is given some advice (for example, machine-learned predictions) on an algorithm’s decision. There is, however...

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Veröffentlicht in:	Algorithmica Jg. 87; H. 11; S. 1477 - 1517
Hauptverfasser:	Lindermayr, Alexander, Megow, Nicole, Simon, Bertrand
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2025 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithm Analysis and Problem Complexity Algorithms Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Lower bounds Machine learning Mathematics of Computing Theory of Computation Learning-augmented algorithms Online k-server problem Competitive analysis Consistency Robustness Advice Imperfect predictions
ISSN:	0178-4617, 1432-0541
Online-Zugang:	Volltext
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