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dc.contributor.authorYuan, K.
Ling, Q.
Yin, W.
Ribeiro, A.
dc.date.accessioned 2018-06-19T17:48:49Z
dc.date.available 2018-06-19T17:48:49Z
dc.date.issued 2013-04
dc.identifier.citation Yuan, K., Ling, Q., Yin, W., et al.. "A Linearized Bregman Algorithm for Decentralized Basis Pursuit." (2013) https://hdl.handle.net/1911/102218.
dc.identifier.urihttps://hdl.handle.net/1911/102218
dc.description.abstract We 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 l1 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 i, the error obeys |x_i(k) - x*|<=e(k) and e(k)<=rho e(k-1)+gamma, where rho<=1 and gamma>=0 are independent of k or i. Numerical experiments demonstrate this convergence.
dc.format.extent 5 pp
dc.title A Linearized Bregman Algorithm for Decentralized Basis Pursuit
dc.type Technical report
dc.date.note April 2013
dc.identifier.digital TR13-06
dc.type.dcmi Text


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