TY - GEN

T1 - A combinatorial metrical task system problem under the uniform metric

AU - Nakazono, Takumi

AU - Moridomi, Ken Ichiro

AU - Hatano, Kohei

AU - Takimoto, Eiji

N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84994116122&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994116122&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46379-7_19

DO - 10.1007/978-3-319-46379-7_19

M3 - Conference contribution

AN - SCOPUS:84994116122

SN - 9783319463780

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 276

EP - 287

BT - Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings

A2 - Simon, Hans Ulrich

A2 - Zilles, Sandra

A2 - Ortner, Ronald

PB - Springer Verlag

T2 - 27th International Conference on Algorithmic Learning Theory, ALT 2016

Y2 - 19 October 2016 through 21 October 2016

ER -