A combinatorial metrical task system problem under the uniform metric

Takumi Nakazono, Ken Ichiro Moridomi, kohei hatano, Eiji Takimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider a variant of the metrical task system (MTS) problem under the uniform metric, where each decision corresponds to some combinatorial object in a fixed set (e.g., the set of all s-t paths of a fixed graph). Typical algorithms such as Marking algorithm are not known to solve this problem efficiently and straightforward implementations takes exponential time for many classes of combinatorial sets. We propose a modification of Marking algorithm, which we call Weighted Marking algorithm. We show that Weighted Marking algorithm still keeps O(log n) competitive ratio for the standard MTS problem with n states. On the other hand, combining with known sampling techniques for combinatorial sets, Weighted Marking algorithm works efficiently for various classes of combinatorial sets.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings
PublisherSpringer Verlag
Pages276-287
Number of pages12
Volume9925 LNAI
ISBN (Print)9783319463780
DOIs
Publication statusPublished - Jan 1 2016
Event27th International Conference on Algorithmic Learning Theory, ALT 2016 - Bari, Italy
Duration: Oct 19 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9925 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other27th International Conference on Algorithmic Learning Theory, ALT 2016
CountryItaly
CityBari
Period10/19/1610/21/16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nakazono, T., Moridomi, K. I., hatano, K., & Takimoto, E. (2016). A combinatorial metrical task system problem under the uniform metric. In Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings (Vol. 9925 LNAI, pp. 276-287). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9925 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-46379-7_19