Distributed Adaptive Optimization with Weight-Balancing

Dongdong Yue; S. Baldi; Jinde Cao; Bart De Schutter

doi:10.1109/TAC.2021.3071651

Distributed Adaptive Optimization with Weight-Balancing

Dongdong Yue, S. Baldi, Jinde Cao, Bart De Schutter

Research output: Contribution to journal › Article › Scientific › peer-review

10 Citations (Scopus)

29 Downloads (Pure)

Abstract

This article addresses the continuous-time distributed optimization of a strictly convex summation-separable cost function with possibly nonconvex local functions over strongly connected digraphs. Distributed optimization methods in the literature require convexity of local functions, or balanced weights, or vanishing step sizes, or algebraic information (eigenvalues or eigenvectors) of the Laplacian matrix. The solution proposed here covers both weight-balanced and unbalanced digraphs in a unified way, without any of the aforementioned requirements.

Original language	English
Pages (from-to)	2068-2075
Journal	IEEE Transactions on Automatic Control
Volume	67
Issue number	4
DOIs	https://doi.org/10.1109/TAC.2021.3071651
Publication status	Published - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Cost function
Couplings
directed graphs
Distributed optimization
Eigenvalues and eigenfunctions
Laplace equations
multi-agent systems
Optimization
Radio frequency
Standards
weight balancing

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10.1109/TAC.2021.3071651

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Cite this

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title = "Distributed Adaptive Optimization with Weight-Balancing",

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KW - Distributed optimization

KW - Eigenvalues and eigenfunctions

KW - Laplace equations

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Distributed Adaptive Optimization with Weight-Balancing

Abstract

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Keywords

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