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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-
Αναγνωριστικόhttps://doi.org/10.1109/SPAWC.2018.8445941-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/8445941-
Γλώσσαen-
Μέγεθος5 pagesen
ΤίτλοςNesterov-based alternating optimization for nonnegative tensor completion: algorithm and parallel implementationen
ΔημιουργόςLourakis Georgiosen
ΔημιουργόςΛουρακης Γεωργιοςel
ΔημιουργόςLiavas Athanasiosen
ΔημιουργόςΛιαβας Αθανασιοςel
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηWe 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2019-06-06-
Ημερομηνία Δημοσίευσης2018-
Θεματική ΚατηγορίαNonnegative tensor completionen
Θεματική ΚατηγορίαOptimal first-order optimization algorithmsen
Θεματική ΚατηγορίαParallel algorithmsen
Θεματική ΚατηγορίαTensorsen
Βιβλιογραφική ΑναφοράG. 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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