Document Type
Article
Publication Date
10-1-2018
Abstract
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.
Publication Source (Journal or Book title)
Optimization Letters
First Page
1589
Last Page
1608
Recommended Citation
Bai, J., Zhang, H., & Li, J. (2018). A parameterized proximal point algorithm for separable convex optimization. Optimization Letters, 12 (7), 1589-1608. https://doi.org/10.1007/s11590-017-1195-9