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dc.contributor.authorLi, J.
dc.contributor.authorWu, Changzhi
dc.contributor.authorWu, Z.
dc.contributor.authorLong, Q.
dc.contributor.authorWang, Xiangyu
dc.identifier.citationLi, J. and Wu, C. and Wu, Z. and Long, Q. and Wang, X. 2014. Distributed proximal-gradient methods for convex optimization with inequality constraints. The ANZIAM Journal. 56: pp. 160-178.

We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penalty function method. Our transformed problem has a smaller number of variables and a simpler structure than the existing distributed primal–dual subgradient methods for constrained distributed optimization problems. Using the special structure of this problem, we then propose a distributed proximal-gradient algorithm over a time-changing connectivity network, and establish a convergence rate depending on the number of iterations, the network topology and the number of agents. Although the transformed problem is nonsmooth by nature, our method can still achieve a convergence rate, O(1/k) , after k iterations, which is faster than the rate, O(1/ sqrt k), of existing distributed subgradient-based methods. Simulation experiments on a distributed state estimation problem illustrate the excellent performance of our proposed method.

dc.publisherAustralian Mathematical Society
dc.subjectproximal-gradient method
dc.subjectconvex optimization
dc.subjectexact penalty function method
dc.subjectdistributed algorithm
dc.titleDistributed proximal-gradient methods for convex optimization with inequality constraints
dc.typeJournal Article
dcterms.source.titleThe ANZIAM Journal
curtin.departmentDepartment of Construction Management
curtin.accessStatusFulltext not available

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