On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems

In this paper we propose a fully distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of convergence. In particular, we prove that under the assumption of strong convexity and Lipschitz continuity of the gradient of the primal objecti...

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Veröffentlicht in:Automatica (Oxford) Jg. 55; S. 209 - 216
Hauptverfasser: Necoara, Ion, Nedelcu, Valentin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2015
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ISSN:0005-1098, 1873-2836
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Abstract In this paper we propose a fully distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of convergence. In particular, we prove that under the assumption of strong convexity and Lipschitz continuity of the gradient of the primal objective function we have a global error bound type property for the dual problem. Using this error bound property we devise a fully distributed dual gradient scheme, i.e. a gradient scheme based on a weighted step size, for which we derive global linear rate of convergence for both dual and primal suboptimality and for primal feasibility violation. Numerical simulations are also provided to confirm our theory.
AbstractList In this paper we propose a fully distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of convergence. In particular, we prove that under the assumption of strong convexity and Lipschitz continuity of the gradient of the primal objective function we have a global error bound type property for the dual problem. Using this error bound property we devise a fully distributed dual gradient scheme, i.e. a gradient scheme based on a weighted step size, for which we derive global linear rate of convergence for both dual and primal suboptimality and for primal feasibility violation. Numerical simulations are also provided to confirm our theory.
Author Necoara, Ion
Nedelcu, Valentin
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  fullname: Nedelcu, Valentin
  email: valentin.nedelcu@acse.pub.ro
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Cites_doi 10.1137/070708111
10.1007/s10589-013-9609-9
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Keywords Dual decomposition
Separable convex problems
Error bound
Linear convergence
Distributed gradient algorithm
Language English
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Snippet In this paper we propose a fully distributed dual gradient algorithm for minimizing linearly constrained separable convex problems and analyze its rate of...
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SubjectTerms Distributed gradient algorithm
Dual decomposition
Error bound
Linear convergence
Separable convex problems
Title On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems
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