关键词: 机器学习, 交替方向乘子法, 坐标优化, 大规模, 非光滑损失

Abstract: Alternating Direction Method of Multipliers (ADMM) already has some practical applications in machine learning field. In order to adapt to the large-scale data processing and non-smooth loss convex optimization problem, the original batch ADMM algorithm was improved by using mirror descent method, and a new coordinate optimization algorithm was proposed for solving non-smooth loss convex optimization. This new algorithm has a simple operation and efficient computation. Through detailed theoretical analysis, the convergence of the new algorithm is verified and it also has the optimal convergence rate in general convex condition. Finally, the experimental results compared with the state-of-art algorithms demonstrate it gets better convergence rate under the sparsity of solution.

Key words: machine learning, Alternating Direction Method of Multipliers (ADMM), coordinate optimization, large-scale, non-smooth loss