Emergent Mind
Distributed Global Optimization by Annealing
(1907.08802)
Published Jul 20, 2019
in
math.OC
and
cs.MA
Abstract
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.
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