Abstract
This paper develops a novel distributed jointly sparse optimization algorithm to recover the sparse signal, in which the joint sparse structure is used to improve the quality of recovery. In distributed networked multi-agent system, each agent collects measurement vectors and aims to recover its own sparse signal collaboratively in a distributed manner. We propose to use the factored gradient approach to calculate the solution iteratively, and introduce a energy vector of the current estimates as the consensus constrain which is updated by inexact consensus optimization for its distributed implementation. This algorithm does not require excessive data transmissions from distributed agents to fusion center, which reduces communication overhead and the computational complexity of the agents. Simulation results demonstrate that the proposed distributed algorithm has strong recovery performance and can reach global convergence as well as fast convergence rate.
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