LAUSR

This paper investigates resource allocation problems, where the cost functions of agents are nonsmooth and the decisions of agents are constrained by heterogeneous local constraints and network resource constraints. We design a distributed subgradient-based algorithm to achieve the optimal resource allocation. Moreover, we analyze the convergence of the algorithm to the optimal solution. The algorithm can solve resource allocation problems with strongly convex cost functions and weight-balanced digraphs, as well as resource allocation problems with strictly convex cost functions and connected undirected graphs. With the algorithm, the decisions of all agents asymptotically converge to the optimal allocation. Simulation examples verify the effectiveness of the algorithm.