Accelerated stochastic gradient for nonnegative tensor completion and parallel implementationAccelerated stochastic gradient for nonnegative tensor completion and parallel implementation Δημοσίευση σε Συνέδριο Conference Publication 2023-05-262021enAll 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).We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a stochastic variation of the accelerated gradient algorithm. We experimentally test the effectiveness and the efficiency of our algorithm using both real-world and synthetic data. We develop a shared-memory implementation of our algorithm using the multithreaded API OpenMP, which attains significant speedup. We believe that our approach is a very competitive candidate for the solution of very large nonnegative tensor completion problems.http://creativecommons.org/licenses/by/4.0/1790-17942021 29th European Signal Processing ConferenceSiaminou_et_al_EUSIPCO_2021 .pdfChania [Greece]Library of TUC2023-05-26application/pdf5.2 MBfree Siaminou Ioanna Σιαμινου Ιωαννα Papagiannakos Ioannis-Marios Παπαγιαννακος Ιωαννης-Μαριος Kolomvakis Christos Κολομβακης Χρηστος Liavas Athanasios Λιαβας Αθανασιος Institute of Electrical and Electronics Engineers Tensors Stochastic gradient Nonnegative tensor completion Optimal first-order optimization algorithms Parallel algorithms OpenMP