Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation

• URL: http://arxiv.org/abs/2109.09534v1
• Date: Mon, 20 Sep 2021 13:32:12 GMT
• Title: Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation
• Authors: Ioanna Siaminou, Ioannis Marios Papagiannakos, Christos Kolomvakis, Athanasios P. Liavas
• Abstract summary: We adopt the alternating optimization framework and solve each nonnegative matrix completion problem via a variation of the gradient accelerated algorithm. We develop a shared-memory implementation of our algorithm using the multi-threaded API OpenMP, which attains significant speedup.
• Score: 0.3670422696827525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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