Τεχνικές βελτιστοποίησης μεγάλης κλίμακας με εφαρμογές σε tensors

Summarization: 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