Nonnegative tensor completion: step-sizes for an accelerated variation of the stochastic gradient descentNonnegative tensor completion: step-sizes for an accelerated variation of the stochastic gradient descent Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2024-10-112022enWe consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix least-squares problem via an accelerated variation of the stochastic gradient descent. The step-sizes used by the algorithm determine, to a high extent, its behavior. We propose two new strategies for the computation of step-sizes and we experimentally test their effectiveness using both synthetic and real-world data.All authors were partially supported by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code : T1EΔK − 03360).http://creativecommons.org/licenses/by/4.0/1976-198030th European Signal Processing ConferenceProceedings of the 30th European Signal Processing Conference Liavas Athanasios Λιαβας Αθανασιος Papagiannakos Ioannis-Marios Παπαγιαννακος Ιωαννης-Μαριος Kolomvakis Christos Institute of Electrical and Electronics Engineers Tensors Nonnegative tensor completion Stochastic gradient descent Accelerated gradient Step-size selection Armijo line-search Parallel algorithms OpenMP