Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/F62D3787-9DAA-4ECD-9E45-0DF3A61CB2F6 | ||
| Year | 2017 | ||
| Type of Item | Master Thesis | ||
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| Bibliographic Citation | Georgios Lourakis, "Large scale optimization methods and applications in tensor optimization", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017 https://doi.org/10.26233/heallink.tuc.70431 | ||
| Appears in Collections |
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.
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