Jorge Cortés


Distributed augmentation-regularization for robust online convex optimization
M. Vaquero, J. Cortés
IFAC Workshop on Distributed Estimation and Control in Networked Systems, Groningen, The Netherlands, 2018, pp. 230-235


This paper studies the use of distributed, primal-dual dynamics to solve continuous, time-dependent optimization problems on the fly. When using primal-dual dynamics, the availability of a strongly convex-strongly concave Lagrangian is desirable, but this is a strong assumption not satisfied in many applications. To deal with this, we develop a new Lagrangian regularization technique that seeks to minimize the perturbation to the original solutions and is compatible with the distributed nature of the optimization problem. We provide analytic bounds of the tracking error of the optimal solution using standard Lyapunov stability analysis techniques. As an application, we consider a receding horizon formulation of a dynamic traffic assignment problem and illustrate the performance of our approach in simulation.


Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

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