QUT ePrints

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.

[img] Published Version (PDF 270kB)
Administrators only | Request a copy from author

View at publisher

Abstract

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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
0 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 47461
Item Type: Journal Article
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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page