Lu Sha, Wei Zengxin
1School of Science, East China University of Science and Technology, Shanghai, China.
2School of Mathematics and Statistics, Guangxi Teachers Education University, Nanning, China.
J Inequal Appl. 2018;2018(1):129. doi: 10.1186/s13660-018-1721-z. Epub 2018 Jun 8.
The alternating direction method of multipliers (ADMM) is one of the most powerful and successful methods for solving convex composite minimization problem. The generalized ADMM relaxes both the variables and the multipliers with a common relaxation factor in , which has the potential of enhancing the performance of the classic ADMM. Very recently, two different variants of semi-proximal generalized ADMM have been proposed. They allow the weighting matrix in the proximal terms to be positive semidefinite, which makes the subproblems relatively easy to evaluate. One of the variants of semi-proximal generalized ADMMs has been analyzed theoretically, but the convergence result of the other is not known so far. This paper aims to remedy this deficiency and establish its convergence result under some mild conditions in the sense that the relaxation factor is also restricted into .
乘子交替方向法(ADMM)是求解凸复合最小化问题最强大且成功的方法之一。广义ADMM在 中用一个公共松弛因子对变量和乘子进行松弛,这有可能提升经典ADMM的性能。最近,提出了半近端广义ADMM的两种不同变体。它们允许近端项中的加权矩阵为半正定,这使得子问题相对易于求解。半近端广义ADMM的其中一种变体已得到理论分析,但另一种的收敛结果目前尚不清楚。本文旨在弥补这一不足,并在一些温和条件下建立其收敛结果,即松弛因子也被限制在 内。