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@inproceedings{1648298,
author = {Eiben, Eduard and Ganian, Robert and Kanj, Iyad and Szeider, Stefan},
address = {USA},
booktitle = {Advances in Neural Information Processing Systems 32 (NIPS 2019)},
editor = {Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alche-Buc and Emily B. Fox and Roman Garnett},
keywords = {Parameterized Complexity},
howpublished = {elektronická verze "online"},
language = {eng},
location = {USA},
isbn = {978-1-7281-1044-8},
pages = {7666-7676},
publisher = {Neural Information Processing Systems Foundation, Inc.},
title = {The Parameterized Complexity of Cascading Portfolio Scheduling},
url = {http://papers.nips.cc/paper/8983-the-parameterized-complexity-of-cascading-portfolio-scheduling},
year = {2019}
}
TY - JOUR
ID - 1648298
AU - Eiben, Eduard - Ganian, Robert - Kanj, Iyad - Szeider, Stefan
PY - 2019
TI - The Parameterized Complexity of Cascading Portfolio Scheduling
PB - Neural Information Processing Systems Foundation, Inc.
CY - USA
SN - 9781728110448
KW - Parameterized Complexity
UR - http://papers.nips.cc/paper/8983-the-parameterized-complexity-of-cascading-portfolio-scheduling
L2 - http://papers.nips.cc/paper/8983-the-parameterized-complexity-of-cascading-portfolio-scheduling
N2 - Cascading portfolio scheduling is a static algorithm selection strategy which uses a sample of test instances to compute an optimal ordering (a cascading schedule) of a portfolio of available algorithms. The algorithms are then applied to each future instance according to this cascading schedule, until some algorithm in the schedule succeeds. Cascading algorithm scheduling has proven to be effective in several applications, including QBF solving and the generation of ImageNet classification models. It is known that the computation of an optimal cascading schedule in the offline phase is NP-hard. In this paper we study the parameterized complexity of this problem and establish its fixed-parameter tractability by utilizing structural properties of the success relation between algorithms and test instances. Our findings are significant as they reveal that in spite of the intractability of the problem in its general form, one can indeed exploit sparseness or density of the success relation to obtain non-trivial runtime guarantees for finding an optimal cascading schedule.
ER -
EIBEN, Eduard, Robert GANIAN, Iyad KANJ a Stefan SZEIDER. The Parameterized Complexity of Cascading Portfolio Scheduling. Online. In Hanna M. Wallach and Hugo Larochelle and Alina Beygelzimer and Florence d'Alche-Buc and Emily B. Fox and Roman Garnett. \textit{Advances in Neural Information Processing Systems 32 (NIPS 2019)}. USA: Neural Information Processing Systems Foundation, Inc., 2019. s.~7666-7676. ISBN~978-1-7281-1044-8. [citováno 2024-04-23]
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