Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers

Consider a set of networked agents endowed with private cost functions and seeking to find a consensus on the minimizer of the aggregate cost. A new class of random asynchronous distributed optimization methods is introduced. The methods generalize the standard Alternating Direction Method of Multipliers (ADMM) to an asynchronous setting where isolated components of the network are activated in an uncoordinated fashion. The algorithms rely on the introduction of randomized Gauss-Seidel iterations of Douglas-Rachford splitting leading to an asynchronous algorithm based on the ADMM. Convergence to the sought minimizers is provided under mild connectivity conditions.