对随机优化算法的收敛速度问题进行深入研究，考虑目标函数由L1正则项组成的最小二乘回归问题，提出一个有效的加速随机逼近算法。基于一个非强凸性的条件和利用一个光滑函数近似L1 正则项，讨论了学习算法的收敛速度，并得到算法的收敛速度。该结论优于前人的收敛结果。

In this paper, we have in-depth and systematic research on the convergence rate of stochastic optimization problems; the least-square regression problem that the objective function consists of the L1 regular term is concerned and an effective accelerating stochastic approximation algorithm is proposed. Based on a non-strong convexity condition and using a smooth function to approximate the L1-regular term, the convergence speed of the learning algorithm is considered, and we obtain the convergence speed of the algorithm. This conclusion is superior to the previous convergence results.
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