  The paper considers a distributed algorithm for global minimization of a
nonconvex function. The algorithm is a first-order consensus + innovations type
algorithm that incorporates decaying additive Gaussian noise for annealing,
converging 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.
