A parameterized proximal point algorithm for separable convex optimization |
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Authors: | Jianchao Bai Hongchao Zhang Jicheng Li |
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Affiliation: | 1.School of Mathematics and Statistics,Xi’an Jiaotong University,Xi’an,People’s Republic of China;2.Department of Mathematics,Louisiana State University,Baton Rouge,USA |
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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. |
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