Distributed aggregative optimization with affine coupling constraints

This paper investigates a distributed aggregative optimization problem subject to coupling affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents’ variables. The formulated problem encompasses num...

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Vydáno v:Neural networks Ročník 184; s. 107085
Hlavní autoři: Du, Kaixin, Meng, Min
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.04.2025
Témata:
Algorithms
Computer Simulation
Coupling affine inequality constraints
Distributed aggregative optimization
Humans
Linear convergence rate
Neural Networks, Computer
Primal–dual algorithm
Linear convergence rate
Distributed aggregative optimization
Coupling affine inequality constraints
Primal–dual algorithm
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:This paper investigates a distributed aggregative optimization problem subject to coupling affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents’ variables. The formulated problem encompasses numerous practical applications, such as commodity distribution, electric vehicle charging, and energy consumption control in power grids. Hence, there is a compelling need to explore a new neurodynamic approach to address this. To this end, a novel distributed aggregative primal–dual algorithm is proposed based on the dual diffusion strategy and distributed tracking technique, which typically makes a slight yet important modification to the traditional primal–dual methods. Leveraging an elaborately constructed weighted error norm sum, it is rigorously proved that the devised algorithm converges to the optimal solution at a linear rate. Finally, numerical simulations are conducted to demonstrate the theoretical results and show the advantages of the proposed algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2024.107085