AI 中文总结
本文针对分布式优化问题，提出将IC - ADMM与有限时间去中心化量化通信算法结合的新型去中心化优化算法，该算法在有向图运行，仅需量化局部信息，不依赖精确求解局部子问题，实验表明其在收敛速度和通信效率上优于现有方法。
AI 中文摘要
分布式优化在解决大规模问题时，相较于集中式方法在可扩展性和鲁棒性方面具有显著优势。本文提出一种新型去中心化优化算法，它将不精确共识交替方向乘子法（IC - ADMM）与有限时间去中心化量化通信算法相结合。该方法有三个主要优点：在有向通信图上运行；仅需量化局部信息而非精确值；不依赖精确求解局部子问题。在各节点局部目标为强凸且L - 光滑的假设下，算法能保证全局线性收敛到最优解邻域。大量数值实验证明其在收敛速度和通信效率上优于现有方法。
英文摘要
Distributed optimization offers significant advantages over centralized methods in terms of scalability and robustness when solving large-scale problems. In this paper, we propose a novel decentralized optimization algorithm that integrates Inexact Consensus ADMM (IC-ADMM) with a finite-time decentralized quantized communication algorithm. The proposed method enjoys three main benefits: (i) it operates on directed communication graphs, (ii) it requires only quantized local information instead of exact values, and (iii) it does not rely on solving the local subproblems exactly. Under the assumption that each node's local objective is strongly convex and L-smooth, the algorithm is guaranteed to achieve global linear convergence to a neighborhood of the optimal solution. Extensive numerical experiments demonstrate its advantages over existing methods in terms of convergence speed and communication efficiency.
