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Distributed Optimization Methods for Estimation and Control over Networks

In this presentation we present several distributed optimization methods for solving estimation and control problems arising in large-scale networks. We show that centralized control problems for networks consisting of subsystems with interacting dynamics can be recast as a separable convex optimization problem but with coupling constraints. Furthermore, the state estimation problem for a system, using a network of sensors can also be posed as a separable convex optimization problem with specific structure on the coupling constraints. We present several distributed optimization algorithms for solving this type of optimization problems and we provide results on convergence and efficiency estimates for these algorithms. The new distributed optimization methods are suitable for application to control and estimation in networks since they are highly parallelizable, each subsystem/sensor uses only local information and the coordination between the local controllers/sensors is performed via the Lagrange multipliers corresponding to the coupled dynamics/the consensus weights.

Type of Seminar:
IfA Seminar
Prof. Ion Necoara
University of Bucharest
Oct 12, 2010   10:15

Contact Person:

Colin Jones
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