## A linearized Bregman algorithm for decentralized basis pursuitK. Yuan, Q. Ling, W. Yin, A. Ribeiro Published in ## OverviewWe solve a decentralized basis pursuit problem in a multiagent system, where each agent holds part of the linear observations on a common sparse vector, and all the agents collaborate to recover the sparse vector through limited neighbor-to-neighbor communication. The proposed decentralized linearized Bregman algorithm solves the Lagrange dual of an augmented model that is equivalent to basis pursuit. The fact that this dual problem is unconstrained and differentiable enables a lightweight yet efﬁcient decentralized gradient algorithm. We prove nearly linear convergence of the algorithm in the sense that uniformly for every agent , the error obeys and , where and are independent of or . Numerical experiments demonstrate this convergence. ## Citation
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