  This paper proposes decentralized resource-aware coordination schemes for
solving network optimization problems defined by objective functions which
combine locally evaluable costs with network-wide coupling components. These
methods are well suited for a group of supervised agents trying to solve an
optimization problem under mild coordination requirements. Each agent has
information on its local cost and coordinates with the network supervisor for
information about the coupling term of the cost. The proposed approach is
feedback-based and asynchronous by design, guarantees anytime feasibility, and
ensures the asymptotic convergence of the network state to the desired
optimizer. Numerical simulations on a power system example illustrate our
results.
