A parameterized proximal point algorithm for separable convex optimization
Optimization and Control
2018-12-11 v1
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, wheret 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.
Cite
@article{arxiv.1812.03759,
title = {A parameterized proximal point algorithm for separable convex optimization},
author = {Jianchao Bai and Hongchao Zhang and Jicheng Li},
journal= {arXiv preprint arXiv:1812.03759},
year = {2018}
}