Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/134250
Type: | Conference paper |
Title: | Pareto optimization for subset selection with dynamic partition matroid constraints |
Author: | Do, V.A. Neumann, F. |
Citation: | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2021, vol.35, iss.14, pp.12284-12292 |
Publisher: | AAAI Press |
Publisher Place: | online |
Issue Date: | 2021 |
Series/Report no.: | AAAI Conference on Artificial Intelligence |
ISBN: | 9781577358664 |
ISSN: | 2159-5399 2374-3468 |
Conference Name: | AAAI Conference on Artificial Intelligence (AAAI) (2 Feb 2021 - 9 Feb 2021 : virtual online) |
Statement of Responsibility: | Anh Viet Do, Frank Neumann |
Abstract: | In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. We focus on POMC, a simple Pareto optimization approach that has been shown to be effective on such problems. Our analysis departs from singular constraint problems and extends to problems of multiple constraints. We show that previous results of POMC’s performance also hold for multiple constraints. Our experimental investigations on random undirected maxcut problems demonstrate POMC’s competitiveness against the classical GREEDY algorithm with restart strategy. |
Description: | AAAI-21 Technical Tracks 14 |
Rights: | © 2021, Association for the Advancement of Artificial Intelligence |
Grant ID: | http://purl.org/au-research/grants/arc/DP160102401 http://purl.org/au-research/grants/arc/DP190103894 |
Published version: | https://ojs.aaai.org/index.php/AAAI/article/view/17458 |
Appears in Collections: | Computer Science publications |
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