论文标题
乘数的惯性交替方向方法，用于解决两块可分离的凸最小化问题
An inertial alternating direction method of multipliers for solving a two-block separable convex minimization problem
论文作者
论文摘要
乘数的交替方向方法（ADMM）是一种广泛使用的方法，用于求解信号和图像处理中出现的许多凸最小化模型。在本文中，我们提出了一种惯性ADMM，用于通过线性平等约束解决两个可分离的凸最小化问题。该算法是通过利用惯性Douglas-rachford分裂算法与原始问题的相应双重双重算法获得的。我们研究了无限二维希尔伯特空间中提出的算法的收敛分析。此外，我们将提出的算法应用于可靠的主要组件追求问题，并将其与其他最先进的算法进行比较。数值结果证明了所提出的算法的优势。
The alternating direction method of multipliers (ADMM) is a widely used method for solving many convex minimization models arising in signal and image processing. In this paper, we propose an inertial ADMM for solving a two-block separable convex minimization problem with linear equality constraints. This algorithm is obtained by making use of the inertial Douglas-Rachford splitting algorithm to the corresponding dual of the primal problem. We study the convergence analysis of the proposed algorithm in infinite-dimensional Hilbert spaces. Furthermore, we apply the proposed algorithm on the robust principal component pursuit problem and also compare it with other state-of-the-art algorithms. Numerical results demonstrate the advantage of the proposed algorithm.
