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

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2 Author(s)
Minghui Zhu ; Dept. of Mech. & Aerosp. Eng., Univ. of California, San Diego, La Jolla, CA, USA ; Martínez, S.

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.

Published in:

American Control Conference (ACC), 2010

Date of Conference:

June 30 2010-July 2 2010