Online bounded analysis

Joan Boyar; Leah Epstein; Lene M. Favrholdt; Kim S. Larsen; Asaf Levin

doi:10.1007/978-3-319-34171-2_10

Online bounded analysis

Joan Boyar, Leah Epstein, Lene M. Favrholdt, Kim S. Larsen, Asaf Levin

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: the comparison of the performance of an online algorithm is made to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary’s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.

Original language	English
Title of host publication	Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings
Editors	Gerhard J. Woeginger, Alexander S. Kulikov
Publisher	Springer Verlag
Pages	131-145
Number of pages	15
ISBN (Print)	9783319341705
DOIs	https://doi.org/10.1007/978-3-319-34171-2_10
State	Published - 2016
Event	11th International Computer Science Symposium in Russia, CSR 2016 - St. Petersburg, Russian Federation Duration: 9 Jun 2016 → 13 Jun 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9691
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Computer Science Symposium in Russia, CSR 2016
Country/Territory	Russian Federation
City	St. Petersburg
Period	9/06/16 → 13/06/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-34171-2_10

Cite this

Boyar, J., Epstein, L., Favrholdt, L. M., Larsen, K. S., & Levin, A. (2016). Online bounded analysis. In G. J. Woeginger, & A. S. Kulikov (Eds.), Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings (pp. 131-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9691). Springer Verlag. https://doi.org/10.1007/978-3-319-34171-2_10

Boyar, Joan ; Epstein, Leah ; Favrholdt, Lene M. et al. / Online bounded analysis. Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings. editor / Gerhard J. Woeginger ; Alexander S. Kulikov. Springer Verlag, 2016. pp. 131-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Online bounded analysis",

abstract = "Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: the comparison of the performance of an online algorithm is made to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary{\textquoteright}s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.",

author = "Joan Boyar and Leah Epstein and Favrholdt, {Lene M.} and Larsen, {Kim S.} and Asaf Levin",

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doi = "10.1007/978-3-319-34171-2_10",

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Boyar, J, Epstein, L, Favrholdt, LM, Larsen, KS & Levin, A 2016, Online bounded analysis. in GJ Woeginger & AS Kulikov (eds), Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9691, Springer Verlag, pp. 131-145, 11th International Computer Science Symposium in Russia, CSR 2016, St. Petersburg, Russian Federation, 9/06/16. https://doi.org/10.1007/978-3-319-34171-2_10

Online bounded analysis. / Boyar, Joan; Epstein, Leah; Favrholdt, Lene M. et al.
Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings. ed. / Gerhard J. Woeginger; Alexander S. Kulikov. Springer Verlag, 2016. p. 131-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9691).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: the comparison of the performance of an online algorithm is made to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary’s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.

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Boyar J, Epstein L, Favrholdt LM, Larsen KS, Levin A. Online bounded analysis. In Woeginger GJ, Kulikov AS, editors, Computer Science - Theory and Applications - 11th International Computer Science Symposium in Russia, CSR 2016, Proceedings. Springer Verlag. 2016. p. 131-145. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-34171-2_10

Online bounded analysis

Abstract

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Bibliographical note

ASJC Scopus subject areas

Access to Document

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