A parameterized proximal point algorithm for separable convex optimization

作者：Bai, Jianchao; Zhang, Hongchao; Li, Jicheng^*

来源：Optimization Letters, 2018, 12(7): 1589-1608.

DOI：10.1007/s11590-017-1195-9

• Separable convex programming
• Proximal point algorithm
• Global convergence
• Statistical learning

In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. The proposed algorithm is provable to be globally convergent with a worst-case O(1/t) convergence rate, where t denotes the iteration number. By properly choosing the algorithm parameters, numerical experiments on solving a sparse optimization problem arising from statistical learning show that our P-PPA could perform significantly better than other state-of-the-art methods, such as the alternating direction method of multipliers and the relaxed proximal point algorithm.

• 出版日期2018-10
• 单位西安交通大学