Nonnegative Tensor Completion: step-sizes for an accelerated variation of the stochastic gradient descent

We consider the problem of nonnegative tensor completion. We adopt the alternating optimization framework and solve each nonnegative matrix least-squares problem via an accelerated variation of the stochastic gradient descent. The step-sizes used by the algorithm determine, to a high extent, its behavior. We propose two new strategies for the computation of step-sizes and we experimentally test their effectiveness using both synthetic and real-world data.