Large scale optimization methods and applications in tensor optimization

 
This item is provided by the institution :

Repository :
Institutional Repository Technical University of Crete
see the original item page
in the repository's web site and access all digital files if the item*
share




2017 (EN)

Τεχνικές βελτιστοποίησης μεγάλης κλίμακας με εφαρμογές σε tensors (EL)
Large scale optimization methods and applications in tensor optimization (EN)

Λουρακης Γεωργιος (EL)
Lourakis Georgios (EN)

Πολυτεχνείο Κρήτης (EL)
Λιαβας Αθανασιος (EL)
Διγαλακης Βασιλης (EL)
Καρυστινος Γεωργιος (EL)
Technical University of Crete (EN)
Karystinos Georgios (EN)
Liavas Athanasios (EN)
Digalakis Vasilis (EN)

We 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. (EN)

masterThesis

Nonnegative tensor factorization (EN)
Tensors (EN)
Optimal first-order optimization algorithms (EN)
Parallel algorithms (EN)


English

2017


Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών (EL)
Technical University of Crete::School of Electrical and Computer Engineering (EN)




*Institutions are responsible for keeping their URLs functional (digital file, item page in repository site)