Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Programming

Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Programming
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凸复合规划乘子交替方向法的线性速率收敛

DOI:
10.1287/moor.2017.0875
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发表时间:
2017-12
影响因子:
1.7
通讯作者:
Liwei Zhang
Liwei Zhang
中科院分区:
数学2区
文献类型:
--
作者:
Deren Han;Defeng Sun;Liwei Zhang

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在本文中,我们旨在证明交替方向乘子法(ADMM)在线性约束凸复合优化问题求解中的线性速率收敛性。在一个温和的平静性条件下(该条件对于……自动成立)
In this paper, we aim to prove the linear rate convergence of the alternating direction method of multipliers (ADMM) for solving linearly constrained convex composite optimization problems. Under a mild calmness condition, which holds automatically for convex composite piecewise linear-quadratic programming, we establish the global Q-linear rate of convergence for a general semi-proximal ADMM with the dual step-length being taken in (0, (1+51/2)/2). This semi-proximal ADMM, which covers the classic one, has the advantage to resolve the potentially nonsolvability issue of the subproblems in the classic ADMM and possesses the abilities of handling the multi-block cases efficiently. We demonstrate the usefulness of the obtained results when applied to two- and multi-block convex quadratic (semidefinite) programming.
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