Large scale optimization methods and applications in tensor optimizationLarge scale optimization methods and applications in tensor optimizationΤεχνικές βελτιστοποίησης μεγάλης κλίμακας με εφαρμογές σε tensors
Μεταπτυχιακή Διατριβή
Master Thesis
2017-12-142017enWe consider the problems of nonnegative tensor factorization and completion. Our aim is to derive efficient algorithms that are also suitable for parallel implementation. We adopt the alternating optimization framework and solve each matrix nonnegative least-squares problem via a Nesterov-type algorithm for convex and strongly convex problems. We describe parallel implementations of the algorithms and measure the attained speedup in a multi-core computing environment. It turns out that the derived algorithms are competitive candidates for the solution of very large-scale nonnegative tensor factorization and completion.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνLourakis_Georgios_MSc_2017.pdfChania [Greece]Library of TUC2017-12-14application/pdf322.1 kBfree
Lourakis Georgios
Λουρακης Γεωργιος
Liavas Athanasios
Λιαβας Αθανασιος
Digalakis Vasilis
Διγαλακης Βασιλης
Karystinos Georgios
Καρυστινος Γεωργιος
Πολυτεχνείο Κρήτης
Technical University of Crete
Parallel algorithms
Optimal first-order optimization algorithms
Nonnegative tensor factorization
Tensors