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

Fingerprint

Metric
Competitive Ratio
Exponential time
Sampling
Path
Graph in graph theory
Class

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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

A combinatorial metrical task system problem under the uniform metric. / Nakazono, Takumi; Moridomi, Ken Ichiro; hatano, kohei; Takimoto, Eiji.

Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings. Vol. 9925 LNAI Springer Verlag, 2016. p. 276-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9925 LNAI).

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

Nakazono, T, Moridomi, KI, 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9925 LNAI, Springer Verlag, pp. 276-287, 27th International Conference on Algorithmic Learning Theory, ALT 2016, Bari, Italy, 10/19/16. https://doi.org/10.1007/978-3-319-46379-7_19
Nakazono T, Moridomi KI, hatano K, Takimoto E. A combinatorial metrical task system problem under the uniform metric. In Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings. Vol. 9925 LNAI. Springer Verlag. 2016. p. 276-287. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46379-7_19
Nakazono, Takumi ; Moridomi, Ken Ichiro ; hatano, kohei ; Takimoto, Eiji. / A combinatorial metrical task system problem under the uniform metric. Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings. Vol. 9925 LNAI Springer Verlag, 2016. pp. 276-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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