论文标题
部分可观测时空混沌系统的无模型预测
Distributed optimization on directed graphs based on inexact ADMM with partial participation
论文作者
论文摘要
我们考虑将与代理有关的成本函数的总和最小化，该问题与代理有关，该网络被有向图（即不对称通信）捕获的网络。我们通过共识约束将问题投入到ADMM设置中，这两个原始子问题都不恰当地解决。在特定的情况下，计算要求的局部最小化步骤被单个梯度步骤取代，而平均步骤则以分布式方式近似。此外，在实施算法时允许部分参与。根据对强凸度和Lipschitz连续梯度的标准假设，我们建立线性收敛并根据图的连通性和问题的条件来表征速率。我们的分析线提供了与推销相比的更清晰的收敛速度。数值实验证实了所提出的解决方案的优点，其速率以及计算和沟通节省优于基准。
We consider the problem of minimizing the sum of cost functions pertaining to agents over a network whose topology is captured by a directed graph (i.e., asymmetric communication). We cast the problem into the ADMM setting, via a consensus constraint, for which both primal subproblems are solved inexactly. In specific, the computationally demanding local minimization step is replaced by a single gradient step, while the averaging step is approximated in a distributed fashion. Furthermore, partial participation is allowed in the implementation of the algorithm. Under standard assumptions on strong convexity and Lipschitz continuous gradients, we establish linear convergence and characterize the rate in terms of the connectivity of the graph and the conditioning of the problem. Our line of analysis provides a sharper convergence rate compared to Push-DIGing. Numerical experiments corroborate the merits of the proposed solution in terms of superior rate as well as computation and communication savings over baselines.
