A cluster-based continuous-time augmented Lagrangian algorithm is shown to converge asymptotically or exponentially for constrained convex optimization, with an epsilon-exact penalty rule for inequality constraints.
Simultaneous routing and resource allocation via dual decomposition,
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Cluster-based Distributed Augmented Lagrangian Algorithm for a Class of Constrained Convex Optimization Problems
A cluster-based continuous-time augmented Lagrangian algorithm is shown to converge asymptotically or exponentially for constrained convex optimization, with an epsilon-exact penalty rule for inequality constraints.