A Regularization Approach to Metrical Task Systems

Abernethy, Jacob, Bartlett, Peter L., Buchbinder, Niv, & Stanton, Isabelle (2010) A Regularization Approach to Metrical Task Systems. LNCS- Algorithmic Learning Theory, 6331, pp. 270-284.

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We address the problem of constructing randomized online algorithms for the Metrical Task Systems (MTS) problem on a metric δ against an oblivious adversary. Restricting our attention to the class of “work-based” algorithms, we provide a framework for designing algorithms that uses the technique of regularization. For the case when δ is a uniform metric, we exhibit two algorithms that arise from this framework, and we prove a bound on the competitive ratio of each. We show that the second of these algorithms is ln n + O(loglogn) competitive, which is the current state-of-the art for the uniform MTS problem.

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5 citations in Scopus
3 citations in Web of Science®
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ID Code: 47461
Item Type: Journal Article
Refereed: Yes
DOI: 10.1007/978-3-642-16108-7_23
ISSN: 0302-9743
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2010 Springer
Deposited On: 02 Dec 2011 04:52
Last Modified: 11 Mar 2012 05:42

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