Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI: | http://purl.tuc.gr/dl/dias/9DFBC286-9CF2-4781-8681-A678A6998E37 | ||
| Year | 2022 | ||
| Type of Item | Master Thesis | ||
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| Bibliographic Citation | Ioannis-Marios Papagiannakos, "Efficient optimization algorithms for large tensor processing and applications", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.91443 | ||
| Appears in Collections |
We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix least-squares with missing elements problem via a stochastic variation of the accelerated gradient algorithm, where we propose and experimentally test the efficiency of various step-sizes. We develop a parallel shared-memory implementation of our algorithm using the multi-threaded API OpenMP, which attains significant speedup. We test the effectiveness and the performance of our algorithm using both real-world and synthetic data. We focus on real-world applications that can be interpreted as nonnegative tensor completion problems. We believe that our approach is a very competitive candidate for the solution of very large nonnegative tensor completion problems.
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