2025/05/06 by Li, Jingwang, Lau, Vincent
#FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2505.03719
In this paper, we focus on a class of decentralized constraint-coupled optimization problem: minxi ∈ ℝdi, i ∈ I; y ∈ ℝp ∑i=1n(fi(xi) + gi(xi)) + h(y) s.t. ∑i=1nAixi = y, over an undirected and connected network of n agents. Here, fi, gi, and Ai represent private information of agent i ∈ I = \1, ⋯, n\, while h is public for all agents. Building on a novel dual2 approach, we develop two accelerated algorithms to solve this problem: the inexact Dual2 Accelerated (iD2A) gradient method and the Multi-consensus inexact Dual2 Accelerated (MiD2A) gradient method. We demonstrate that both iD2A and MiD2A can guarantee asymptotic convergence under a milder condition on h compared to existing algorithms. Furthermore, under additional assumptions, we establish linear convergence rates and derive significantly lower communication and computational complexity bounds than those of existing algorithms. Several numerical experiments validate our theoretical analysis and demonstrate the practical superiority of the proposed algorithms.