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Nesterov-based alternating optimization for nonnegative tensor completion: algorithm and parallel implementation

Lourakis Georgios, Liavas Athanasios

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URIhttp://purl.tuc.gr/dl/dias/4E29216F-7959-40E5-A83D-F3730C1C47D6-
Identifierhttps://doi.org/10.1109/SPAWC.2018.8445941-
Identifierhttps://ieeexplore.ieee.org/document/8445941-
Languageen-
Extent5 pagesen
TitleNesterov-based alternating optimization for nonnegative tensor completion: algorithm and parallel implementationen
CreatorLourakis Georgiosen
CreatorΛουρακης Γεωργιοςel
CreatorLiavas Athanasiosen
CreatorΛιαβας Αθανασιοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe consider the problem of nonnegative tensor completion. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a Nesterov-type algorithm for smooth convex problems. We describe a parallel implementation of the algorithm and measure the attained speedup in a multi-core computing environment. It turns out that the derived algorithm is an efficient candidate for the solution of very large-scale sparse nonnegative tensor completion problems. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2019-06-06-
Date of Publication2018-
SubjectNonnegative tensor completionen
SubjectOptimal first-order optimization algorithmsen
SubjectParallel algorithmsen
SubjectTensorsen
Bibliographic CitationG. Lourakis and A. P. Liavas, "Nesterov-based alternating optimization for nonnegative tensor completion: algorithm and parallel implementation," in IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, 2018. doi: 10.1109/SPAWC.2018.8445941en

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