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Distributed Maximum Likelihood using Dynamic Average Consensus

Citation Author(s):
Submitted by:
Jemin George
Last updated:
19 April 2018 - 2:52pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Jemin George
Paper Code:
ICASSP18001
 

This paper presents the formulation and analysis of a novel distributed maximum likelihood algorithm that utilizes a first-order optimization scheme. The proposed approach utilizes a static average consensus algorithm to reach agreement on the initial condition to the iterative optimization scheme and a dynamic average consensus algorithm to reach agreement on the gradient direction. The current distributed algorithm is guaranteed to exponentially recover the performance of the centralized algorithm. Though the current formulation focuses on maximum likelihood algorithm built on first-order methods, it can be easily extended to higher order methods. Numerical simulations validate the theoretical contributions of the paper.

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