关键词: 机器学习, 随机经典动量算法, 收敛阶, 动量系数, 学习率

Abstract: In this paper, we have conducted in-depth research on the convergence rate of the Stochastic Classical Momentum Algorithm （CM）. By modifying the iterative formula of traditional stochastic gradient descent algorithm with momentum, we obtain the convergence rate of algorithm with non-strongly convex and non-smooth. When the momentum coefficient p_t  is a constant, the algorithm achieves a convergence rate of O.When the momentum coefficient p_t is not a constant, we obtain the convergence rate by setting different learning rates. Finally, the rationality is demonstrated through numerical experiments.

Key words: machine learning, Stochastic Classical Momentum Algorithm, convergence rate, momentum coefficient, learning rates