Ganian, R., Kanj, I.A., Ordyniak, S. orcid.org/0000-0003-1935-651X et al. (1 more author)
(2018)
Parameterized Algorithms for the Matrix Completion Problem.
In: Dy, J.G. and Krause, A., (eds.)
Proceedings of Machine Learning Research.
International Conference on Machine Learning, 10-15 Jul 2018, Stockholmsmässan, Stockholm Sweden.
Proceedings of Machine Learning Research
, pp. 1642-1651.
Abstract
We consider two matrix completion problems, in which we are given a matrix with missing entries and the task is to complete the matrix in a way that (1) minimizes the rank, or (2) minimizes the number of distinct rows. We study the parameterized complexity of the two aforementioned problems with respect to several parameters of interest, including the minimum number of matrix rows, columns, and rows plus columns needed to cover all missing entries. We obtain new algorithmic results showing that, for the bounded domain case, both problems are fixed-parameter tractable with respect to all aforementioned parameters. We complement these results with a lower-bound result for the unbounded domain case that rules out fixed-parameter tractability w.r.t. some of the parameters under consideration.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | Copyright 2018 The author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Aug 2018 09:37 |
Last Modified: | 19 Dec 2022 13:50 |
Published Version: | http://proceedings.mlr.press/v80/ |
Status: | Published |
Publisher: | Proceedings of Machine Learning Research |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133932 |