Distributed optimization over weighted directed graphs using row stochastic matrix
This paper deals with an optimization problem over a network of agents, in which the cost function is the sum of the individual objectives of the agents. The underlying communication graph is assumed to be directed and the weight matrix to be only row stochastic. A distributed projected subgradient algorithm is presented that allows the agents to solve the problem under the conditions that the network is fixed and the cost functions are convex and Lipschitz continuous.
2016 American Control Conference (ACC)