The paper considers distributed global minimization of a nonconvex function. We study a first-order consensus + innovations type algorithm that incorporates decaying additive Gaussian noise for annealing to converge to the set of global minima under certain technical assumptions. The paper presents simple methods for verifying that the required technical assumptions hold and illustrates it with a distributed target-localization application.
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