EIBEN, Eduard, Robert GANIAN, Iyad KANJ and 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. Advances in Neural Information Processing Systems 32 (NIPS 2019). USA: Neural Information Processing Systems Foundation, Inc., 2019, p. 7666-7676. ISBN 978-1-7281-1044-8.
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Basic information
Original name The Parameterized Complexity of Cascading Portfolio Scheduling
Authors EIBEN, Eduard (703 Slovakia), Robert GANIAN (203 Czech Republic, guarantor, belonging to the institution), Iyad KANJ (840 United States of America) and Stefan SZEIDER (40 Austria).
Edition USA, Advances in Neural Information Processing Systems 32 (NIPS 2019), p. 7666-7676, 11 pp. 2019.
Publisher Neural Information Processing Systems Foundation, Inc.
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14330/19:00113727
Organization unit Faculty of Informatics
ISBN 978-1-7281-1044-8
UT WoS 000534424307066
Keywords in English Parameterized Complexity
Tags core_A, firank_1
Changed by Changed by: Mgr. Michal Petr, učo 65024. Changed: 16/5/2022 15:14.
Abstract
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.
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