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Generalized ADMM with Optimal Inde nite Proximal Term for Linearly Constrained Convex Optimization

Fan Jiang (15905154902***at***163.com)
Zhongming Wu (wuzm1991***at***126.com)
Xingju Cai (caixingju***at***njnu.edu.cn)

Abstract: We consider the generalized alternating direction method of multipliers (ADMM) for linearly constrained convex optimization. Many problems derived from practical applications have showed that usually one of the subproblems in the generalized ADMM is hard to solve, thus a special proximal term is added. In the literature, the proximal term can be inde nite which plays a vital role in accelerating numerical performance. In this paper, we are devoted to deriving the optimal lower bound of the proximal parameter and result in the generalized ADMM with optimal inde nite proximal term. The global convergence and the O(1/t) convergence rate measured by the iteration complexity of the proposed method are proved. Moreover, some preliminary numerical experiments on LASSO and total variation based denoising problems are presented to demonstrate the eciency of the proposed method and the advantage of the optimal lower bound.

Keywords: Alternating direction method of multipliers, generalized, convex programming, convergence rate, inde nite proximal term, optimal range.

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: Fan Jiang, Zhongming Wu and Xingju Cai. Generalized ADMM with Optimal Inde nite Proximal Term for Linearly Constrained Convex Optimization, 2017, 1-24.

Download: [PDF]

Entry Submitted: 10/12/2017
Entry Accepted: 10/12/2017
Entry Last Modified: 10/13/2017

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