A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems. (November 2021)

• Record Type: Journal Article
• Authors: Wen, Xingnan; Luan, Linhua; Qin, Sitian
• Is Part Of: Neural networks. Volume 143(2021)
• Page Start: 52
• Page End: 65
• Publication Date: 2021-11
• Subjects: Distributed optimization problem -- Neurodynamic approach -- Penalty method -- Multi-agent systems -- Convergence; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.05.020
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Distributed optimization problem (DOP) over multi-agent systems, which can be described by minimizing the sum of agents' local objective functions, has recently attracted widespread attention owing to its applications in diverse domains. In this paper, inspired by penalty method and subgradient descent method, a continuous-time neurodynamic approach is proposed for solving a DOP with inequality and set constraints. The state of continuous-time neurodynamic approach exists globally and converges to an optimal solution of the considered DOP. Comparisons reveal that the proposed neurodynamic approach can not only resolve more general convex DOPs, but also has lower dimension of solution space. Additionally, the discretization of the neurodynamic approach is also introduced for the convenience of implementation in practice. The iteration sequence of discrete-time method is also convergent to an optimal solution of DOP from any initial point. The effectiveness of the neurodynamic approach is verified by simulation examples and an application in L 1 -norm minimization problem in the end.