论文标题
分布式ADMM具有导向网络的线性更新
Distributed ADMM with linear updates over directed networks
论文作者
论文摘要
我们提出了乘数的交替方向方法（ADMM）的分布式版本，并具有针对有向网络的线性更新。我们表明，如果最小化问题的目标函数是平稳且强烈凸出的，那么我们的分布式ADMM算法就可以达到收敛的几何速率，从而达到最佳点。我们的算法通过不执行准确的共识来利用ADMM固有的鲁棒性，从而显着提高了收敛率。我们通过数值示例来说明这一点，其中我们将算法的性能与有向图的最新ADMM方法进行了比较。
We propose a distributed version of the Alternating Direction Method of Multipliers (ADMM) with linear updates for directed networks. We show that if the objective function of the minimization problem is smooth and strongly convex, our distributed ADMM algorithm achieves a geometric rate of convergence to the optimal point. Our algorithm exploits the robustness inherent to ADMM by not enforcing accurate consensus, thereby significantly improving the convergence rate. We illustrate this by numerical examples, where we compare the performance of our algorithm with that of state-of-the-art ADMM methods over directed graphs.
