Abstract:In this paper, we focus on a class of decentralized constraint-coupled optimization problem: $\min_{x_i \in \mathbb{R}^{d_i}, i \in \mathcal{I}; y \in \mathbb{R}^p}$ $\sum_{i=1}^n\left(f_i(x_i) + g_i(x_i)\right) + h(y) \ \text{s.t.} \ \sum_{i=1}^{n}A_ix_i = y$, over an undirected and connected network of $n$ agents. Here, $f_i$, $g_i$, and $A_i$ represent private information of agent $i \in \mathcal{I} = \{1, \cdots, n\}$, while $h$ is public for all agents. Building on a novel dual$^2$ approach, we develop two accelerated algorithms to solve this problem: the inexact Dual$^2$ Accelerated (iD2A) gradient method and the Multi-consensus inexact Dual$^2$ 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.
From: Jingwang Li [view email]
[v1]
Tue, 6 May 2025 17:46:49 UTC (783 KB)
[v2]
Mon, 12 May 2025 15:20:56 UTC (783 KB)
[v3]
Wed, 17 Dec 2025 11:18:46 UTC (443 KB)
[v4]
Sun, 12 Apr 2026 09:54:26 UTC (781 KB)
[v5]
Wed, 24 Jun 2026 16:08:39 UTC (781 KB)
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