基于Chebyshev周期逐次超松弛的协同最小均方算法加速
Acceleration of Cooperative Least Mean Square via Chebyshev Periodical Successive Over-Relaxation
摘要：
分布式最小均方（LMS）算法可用于分布式信号估计和多元回归模型的分布式训练。算法的收敛速度是一个关键因素，因为更快的算法需要更少的通信开销，并导致更窄的网络带宽。本文的目的是提出使用Chebyshev周期连续超松弛（PSOR）可以加速分布式LMS算法在一个自然的方式。Chbyshev PSOR的基本思想是引入指数相关的PSOR因子来控制控制矩阵的谱半径，该矩阵控制修改的不动点迭代的收敛行为。收敛速度的加速在大范围的网络中被经验地证实，诸如已知的小图（例如，Karate图）和随机图，例如Erdos-Renyi（ER）随机图和Barabasi-Albert随机图。
英文摘要：
A distributed algorithm for least mean square (LMS) can be used in distributed signal estimation and in distributed training for multivariate regression models. The convergence speed of an algorithm is a critical factor because a faster algorithm requires less communications overhead and it results in a narrower network bandwidth. The goal of this paper is to present that use of Chebyshev periodical successive over-relaxation (PSOR) can accelerate distributed LMS algorithms in a naturally manner. The basic idea of Chbyshev PSOR is to introduce index-dependent PSOR factors that control the spectral radius of a matrix governing the convergence behavior of the modified fixed-point iteration. Accelerations of convergence speed are empirically confirmed in a wide range of networks, such as known small graphs (e.g., Karate graph), and random graphs, such as Erdos-Renyi (ER) random graphs and Barabasi-Albert random graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
