On distributed optimization under inequality and equality constraints via penalty primal-dual methods

We consider a multi-agent convex optimization problem where the agents are to minimize a sum of local objective functions subject to a global inequality constraint, a global equality constraint and a global constraint set. We devise a distributed primal-dual subgradient algorithm which is based on t...

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Bibliographic Details
Published inProceedings of the 2010 American Control Conference pp. 2434 - 2439
Main Authors Minghui Zhu, Martínez, Sonia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Communication system control
Communications technology
Constraint optimization
Heuristic algorithms
Information processing
Multiagent systems
Network topology
Optimization methods
Process control
Utility programs
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ISBN9781424474264
1424474264
ISSN0743-1619
DOI10.1109/ACC.2010.5530577

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Summary:We consider a multi-agent convex optimization problem where the agents are to minimize a sum of local objective functions subject to a global inequality constraint, a global equality constraint and a global constraint set. We devise a distributed primal-dual subgradient algorithm which is based on the characterization of the primal-dual optimal solutions as the saddle points of the penalty function. This algorithm allows the agents exchange information over networks with time-varying topologies and asymptotically agree on an optimal solution and the optimal value.
ISBN:9781424474264
1424474264
ISSN:0743-1619
DOI:10.1109/ACC.2010.5530577