A Distributed Optimization Problem Subject to Partial-Impact Cost Functions

This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates to two decision variable vectors. To this end, two algorithms are presented with the aim of solving the considered optimization problem in a structure fashion and in a gradient fashion, r...

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Bibliographic Details
Published inIEEE transactions on cybernetics Vol. 52; no. 11; pp. 12612 - 12617
Main Authors Xia, Zicong, Liu, Yang, Lu, Jianquan, Qiu, Jianlong, Cao, Jinde
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Convergence
Cost function
Cybernetics
Distributed optimization
Laplace equations
Linear programming
Mathematical analysis
Minimization methods
nonsmooth analysis
Optimization
partial-impact cost function
subdifferentiation
unified dimension
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Summary:This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates to two decision variable vectors. To this end, two algorithms are presented with the aim of solving the considered optimization problem in a structure fashion and in a gradient fashion, respectively. Furthermore, a connection between the equilibrium of the induced algorithm and the involved optimization problem is established, with the aid of the tools from nonsmooth analysis and change of coordinate theorem. Two numerical examples with practical significance are given to demonstrate the efficiency of the designed algorithm.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3086183