A Linear Algorithm for Optimization Over Directed Graphs With Geometric Convergence

In this letter, we study distributed optimization, where a network of agents, abstracted as a directed graph, collaborates to minimize the average of locally known convex functions. Most of the existing approaches over directed graphs are based on push-sum (type) techniques, which use an independent...

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Bibliographic Details
Published inIEEE control systems letters Vol. 2; no. 3; pp. 315 - 320
Main Authors Ran Xin, Khan, Usman A.
Format Journal Article
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Convergence
Convex functions
directed graphs
Distributed optimization
Eigenvalues and eigenfunctions
Indexes
Linear programming
Optimization
Radio frequency
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Summary:In this letter, we study distributed optimization, where a network of agents, abstracted as a directed graph, collaborates to minimize the average of locally known convex functions. Most of the existing approaches over directed graphs are based on push-sum (type) techniques, which use an independent algorithm to asymptotically learn either the left or right eigenvector of the underlying weight matrices. This strategy causes additional computation, communication, and nonlinearity in the algorithm. In contrast, we propose a linear algorithm based on an inexact gradient method and a gradient estimation technique. Under the assumptions that each local function is strongly convex with Lipschitz-continuous gradients, we show that the proposed algorithm geometrically converges to the global minimizer with a sufficiently small step-size. We present simulations to illustrate the theoretical findings.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2018.2834316