Distributed convex optimization as a tool for solving ƒ-consensus problems

Chao Huang; Brian D.O. Anderson; Hao Zhang; Huaicheng Yan

doi:10.1016/j.automatica.2023.111087

Distributed convex optimization as a tool for solving ƒ-consensus problems

Chao Huang, Brian D.O. Anderson, Hao Zhang^*, Huaicheng Yan

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

For a group of networked agents, ƒ-consensus means reaching consensus upon the value of a desired function, ƒ, of the initial state of the individual agents. This paper shows how one can often convert a given ƒ-consensus problem into a suitable distributed convex optimization (DCO) problem, which can be readily solved with existing DCO algorithms in the literature. A computational advantage may then accrue. Particular classes of ƒ-consensus problems shown to be solvable with this approach include weighted power mean consensus, and kth smallest value or kth order statistic consensus (which includes max/min consensus and median consensus as special cases).

Original language	English
Article number	111087
Number of pages	9
Journal	Automatica
Volume	155
DOIs	https://doi.org/10.1016/j.automatica.2023.111087
Publication status	Published - Sept 2023

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title = "Distributed convex optimization as a tool for solving ƒ-consensus problems",

abstract = "For a group of networked agents, ƒ-consensus means reaching consensus upon the value of a desired function, ƒ, of the initial state of the individual agents. This paper shows how one can often convert a given ƒ-consensus problem into a suitable distributed convex optimization (DCO) problem, which can be readily solved with existing DCO algorithms in the literature. A computational advantage may then accrue. Particular classes of ƒ-consensus problems shown to be solvable with this approach include weighted power mean consensus, and kth smallest value or kth order statistic consensus (which includes max/min consensus and median consensus as special cases).",

keywords = "Distributed optimization, Multiagent system, f-consensus",

author = "Chao Huang and Anderson, {Brian D.O.} and Hao Zhang and Huaicheng Yan",

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AU - Huang, Chao

AU - Anderson, Brian D.O.

AU - Zhang, Hao

AU - Yan, Huaicheng

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Y1 - 2023/9

N2 - For a group of networked agents, ƒ-consensus means reaching consensus upon the value of a desired function, ƒ, of the initial state of the individual agents. This paper shows how one can often convert a given ƒ-consensus problem into a suitable distributed convex optimization (DCO) problem, which can be readily solved with existing DCO algorithms in the literature. A computational advantage may then accrue. Particular classes of ƒ-consensus problems shown to be solvable with this approach include weighted power mean consensus, and kth smallest value or kth order statistic consensus (which includes max/min consensus and median consensus as special cases).

AB - For a group of networked agents, ƒ-consensus means reaching consensus upon the value of a desired function, ƒ, of the initial state of the individual agents. This paper shows how one can often convert a given ƒ-consensus problem into a suitable distributed convex optimization (DCO) problem, which can be readily solved with existing DCO algorithms in the literature. A computational advantage may then accrue. Particular classes of ƒ-consensus problems shown to be solvable with this approach include weighted power mean consensus, and kth smallest value or kth order statistic consensus (which includes max/min consensus and median consensus as special cases).

KW - Distributed optimization

KW - Multiagent system

KW - f-consensus

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M3 - Article

SN - 0005-1098

VL - 155

JO - Automatica

JF - Automatica

M1 - 111087

ER -

Distributed convex optimization as a tool for solving ƒ-consensus problems

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