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 form)%2C where %24f_i%24 and %24g_i%24 are potentially nonsmooth. The agents communicate via a directed graph containing %24n%24 nodes. We create smooth approximations of the functions %24f_i%24 and %24g_i%24 and develop a distributed algorithm utilizing the gradients of the smooth surrogates and a finite-time approximate consensus protocol. We term this algorithm as DDC-Consensus. The developed DDC-Consensus algorithm allows for non-symmetric directed graph topologies and can be synthesized distributively. We establish that the DDC-Consensus algorithm converges to a stationary point of the nonconvex distributed optimization problem. The performance of the DDC-Consensus algorithm is evaluated via a simulation study to solve a nonconvex DC-regularized distributed least squares problem. The numerical results corroborate the efficacy of the proposed algorithm.?quality=80&w=800)
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