Distributed Resource Allocation Over Dynamic Networks With Uncertainty

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate fixed quantity resources over nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented parallel and based only on local data resulting need for distributed algorithms. In this article, Lagrangian method such problems. By utilizing so-called subgradient methods solve dual problem, our approach eliminates central coordination updating variables, which often required classic methods. Our focus understand performance algorithm when number unknown may time-varying. particular, obtain an upper bound convergence rate optimal value, expectation, as function topology. The effectiveness proposed demonstrated its application economic dispatch power systems, simulations completed benchmark IEEE-14 IEEE-118 bus test systems.