论文标题
凸编程的乘数交替方向方法：提升和渗透方案
Alternating direction method of multipliers for convex programming: a lift-and-permute scheme
论文作者
论文摘要
为线性约束凸编程提出了交替方向方法（ADMM）的交替方向方法的提升和渗透方案。它不仅包含新开发的平衡的增强拉格朗日方法及其双重变化，还包含近端ADMM和Douglas-Rachford分裂算法。它有助于提出使用最差的$ o（1/k^2）$收敛速率加速算法，如果要最小化的目标函数是强烈凸起的。
A lift-and-permute scheme of alternating direction method of multipliers (ADMM) is proposed for linearly constrained convex programming. It contains not only the newly developed balanced augmented Lagrangian method and its dual-primal variation, but also the proximal ADMM and Douglas-Rachford splitting algorithm. It helps to propose accelerated algorithms with worst-case $O(1/k^2)$ convergence rates in the case that the objective function to be minimized is strongly convex.
