Nesterov-based parallel algorithm for large-scale nonnegative tensor factorizationNesterov-based parallel algorithm for large-scale nonnegative tensor factorization Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-05-082017enWe consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization (AO) framework and solve each matrix nonnegative least-squares problem via a Nesterov-type algorithm for strongly convex problems. We describe a parallel implementation of the algorithm and measure the speedup attained by itsMessage Passing Interface implementation on a parallel computing environment. It turns out that the attained speedup is significant, rendering our algorithm a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.http://creativecommons.org/licenses/by/4.0/5895-5899IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings2017 IEEE International Conference on Acoustics, Speech, and Signal Processing Liavas Athanasios Λιαβας Αθανασιος Kostoulas Georgios Κωστουλας Γεωργιος Lourakis Georgios Λουρακης Γεωργιος Huang Kejun Sidiropoulos, N. D Institute of Electrical and Electronics Engineers CANDECOMP Constrained optimization Nonnegative factorization PARAFAC Parallel algorithms Tensors