  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.
