分散网络上的鲁棒分组LASSO
Robust Group LASSO Over Decentralized Networks
摘要：
本文研究了多智能体网络中测量值存在稀疏误差时的群稀疏信号恢复问题。我们首先研究了鲁棒的组LASSO模型及其基于交替方向乘法（ADMM）的集中式算法，该算法需要一个中央融合中心来计算全局行支持检测器。为了在去中心化网络环境中实现它，我们采用动态平均共识策略，可以动态跟踪全局行支持检测器。数值实验证明了算法的有效性。
英文摘要：
This paper considers the recovery of group sparse signals over a multi-agent network, where the measurements are subject to sparse errors. We first investigate the robust group LASSO model and its centralized algorithm based on the alternating direction method of multipliers (ADMM), which requires a central fusion center to compute a global row-support detector. To implement it in a decentralized network environment, we then adopt dynamic average consensus strategies that enable dynamic tracking of the global row-support detector. Numerical experiments demonstrate the effectiveness of the proposed algorithms.
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