A New Regularized Consensus Perspective for Distributed Optimization

被引：1

作者：

Ye, Maojiao ^{[1
]}

Ding, Lei ^{[2
]}

Xu, Shengyuan ^{[1
]}

Shi, Jun ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210023, Peoples R China

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2024年 / 69卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Optimization; Heuristic algorithms; Linear programming; Vectors; Manganese; Vehicle dynamics; Dynamical systems; Communication-efficient scheme; distributed optimization; linear dynamic agents; regularized consensus; CONVEX-OPTIMIZATION; CONVERGENCE; ALGORITHMS;

D O I：

10.1109/TAC.2024.3378170

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, a new regularized consensus perspective is presented for solving distributed optimization problems among a network of heterogeneous linear dynamic agents. The distributed optimization problem can be viewed as a consensus problem, in which the consensus direction should be regularized to optimize the sum of the agents' objective functions. Based on this idea, a regularized consensus mapping is defined, by which a continuous-time distributed optimization algorithm is established and analyzed for heterogeneous linear dynamic agents. It is shown that the outputs of the linear dynamic agents can evolve to the distributed optimization solution. Moreover, a dynamic event-triggered scheme is proposed to achieve communication-efficient distributed optimization by reducing the communication costs among neighboring agents. Numerical simulation examples are provided to verify the effectiveness of the proposed methods.

引用

页码：6301 / 6308

页数：8

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