A consensus algorithm based on collective neurodynamic system for distributed optimization with linear and bound constraints. (February 2020)

• Record Type: Journal Article
• Authors: Zhao, Yan; Liu, Qingshan
• Is Part Of: Neural networks. Volume 122(2020)
• Page Start: 144
• Page End: 151
• Publication Date: 2020-02
• Subjects: Distributed optimization -- Collective neurodynamic system -- Consensus algorithm -- Lyapunov function; 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.2019.10.008
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, an algorithm based on collective neurodynamic system is investigated for distributed constrained convex optimization, whose objective function is a sum of smooth convex functions and non-smooth L 1 -norm functions. Inspired by recent advances in distributed convex optimization, the continuous-time and discrete-time distributed optimization algorithms described by collective neurodynamic systems are proposed. In the systems, each of the smooth objective functions is allocated to each node as well as each of the L 1 -norm functions. However, the L 1 -norm functions are realized by projection operators. Meanwhile, each node satisfies the local linear and bound constraints. Then a connected network is constituted from all the nodes with consensus to find the optimal solutions.