Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation
This work addresses nonnegative tensor completion for large-scale data applications, representing an incremental improvement with a focus on computational efficiency.
The authors tackled the problem of nonnegative tensor completion by developing a stochastic accelerated gradient algorithm within an alternating optimization framework, achieving significant speedup through a parallel OpenMP implementation.
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.