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This paper considers distributed multi-agents optimization problems where agents collaborate to minimize the sum of locally known convex functions. We focus on the case when the communication between agents is described by a directed graph. The proposed algorithm achieves the best known rate of convergence for this class of problems, O(μk) for 0 < μ < 1, given that the objective functions are...
Distributed Gradient Descent (DGD) is a well established algorithm to solve the minimization of a sum of multi-agents' objective functions in the network, with the assumption that the network is undirected, i.e., requiring the weight matrices to be doubly-stochastic. In this paper, we present a distributed algorithm, called Directed-Distributed Gradient Descent (D-DGD), to solve the same problem over...
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