  We consider a resource allocation problem over an undirected network of
agents, where edges of the network define communication links. The goal is to
minimize the sum of agent-specific convex objective functions, while the
agents' decisions are coupled via a convex conic constraint. We derive two
methods by applying the alternating direction method of multipliers (ADMM) for
decentralized consensus optimization to the dual of our resource allocation
problem. Both methods are fully parallelizable and decentralized in the sense
that each agent exchanges information only with its neighbors in the network
and requires only its own data for updating its decision. We prove convergence
of the proposed methods and demonstrate their effectiveness with a numerical
example.
