标题： 网络上的分布式线性模型聚类：基于树的融合lasso交替方向乘子法方法 显示英文标题

标题： Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach

摘要： 在这项工作中，我们考虑通过聚合邻居的信息来提高模型估计效率，并识别网络中每个节点的子组成员资格。 提出了一种基于树的$l_1$惩罚方法以节省计算和通信成本。 我们设计了一种去中心化的广义交替方向乘子法算法，用于并行求解目标函数。 推导出理论性质以保证模型一致性及算法收敛性。 还进行了全面的数值实验以支持我们的理论，实验结果表明我们的方法在估计准确性、计算速度和通信成本方面均优于其他方法。 显示英文摘要

摘要： In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network. A tree-based $l_1$ penalty is proposed to save the computation and communication cost. We design a decentralized generalized alternating direction method of multiplier algorithm for solving the objective function in parallel. The theoretical properties are derived to guarantee both the model consistency and the algorithm convergence. Thorough numerical experiments are also conducted to back up our theory, which also show that our approach outperforms in the aspects of the estimation accuracy, computation speed and communication cost.