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arXiv · 2109.09534

Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation

Ioanna Siaminou·Ioannis Marios Papagiannakos·Christos Kolomvakis·Athanasios P. Liavas
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Abstract

We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a stochastic variation of the accelerated gradient algorithm. We experimentally test the effectiveness and the efficiency of our algorithm using both real-world and synthetic data. We develop a shared-memory implementation of our algorithm using the multi-threaded API OpenMP, which attains significant speedup. We believe that our approach is a very competitive candidate for the solution of very large nonnegative tensor completion problems.

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Ioanna Siaminou, Ioannis Marios Papagiannakos, Christos Kolomvakis, Athanasios P. Liavas. 2021-09-20. Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation. https://arxiv.org/abs/2109.09534

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