Distributed Discrete-Time Convex Optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm Design. Academic Article

abstract

• The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Cybern

author list (cited authors)

• Luan, M., Wen, G., Liu, H., Huang, T., Chen, G., & Yu, W.

citation count

• 0

complete list of authors

• Luan, Meng||Wen, Guanghui||Liu, Hongzhe||Huang, Tingwen||Chen, Guanrong||Yu, Wenwu

publication date

• May 2023

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON CYBERNETICS  Journal

keywords

• Consensus
• Convergence
• Convex Functions
• Directed Interaction Topology
• Distributed Constrained Optimization
• Eigenvalues And Eigenfunctions
• Gradient Tracking
• Linear Convergence Rate
• Network Topology
• Numerical Simulation
• Optimization
• Topology

PubMed Central ID

• 37159318

Digital Object Identifier (DOI)

• 10.1109/TCYB.2023.3270185

start page

• 1

end page

• 13

volume

• PP

issue

• 99

URL

• http://dx.doi.org/10.1109/tcyb.2023.3270185