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.

翻译：我们考虑的是非负偏差完成率问题。我们采用交替优化框架,通过加速梯度算法的随机变异解决每个非负矩阵完成率问题。我们用真实世界和合成数据实验我们的算法的有效性和效率。我们利用多读的API OpenMP开发了我们算法的共同模拟实施方法,该方法已经大大加快了速度。我们认为,我们的方法对于解决非常大的非负数的Exmor 完成问题来说是非常有竞争力的候选方法。