曲阜师范大学学报（自然科学版）2025，Vol.51Issue(3)：12-22,11.DOI:10.3969/j.issn.1001-5337.2025.3.012
基于随机量化的多智能体分布式凸优化算法
Multi-agent distributed convex optimization algorithm with random quantization
摘要
Abstract
In this paper,the state-constrained multi-agent distributed convex optimization problem over a time-varying balanced network is investigated.In response to the limited network communication capabil-ity,a random quantizer is introduced in the agents'information interaction process to reduce data transmis-sion effectively.Based on this,a distributed mirror descent algorithm with random quantization is pro-posed,and its convergence is analyzed under conventional assumptions,while the specific convergence rate is provided simultaneously.Finally,the feasibility of the developed algorithm is verified by using the dis-tributed linear regression problem as a simulation example.关键词
分布式凸优化/镜面下降算法/随机量化Key words
distributed convex optimization/mirror descent method/random quantization分类
数学引用本文复制引用
熊梦辉,张保勇,袁德明..基于随机量化的多智能体分布式凸优化算法[J].曲阜师范大学学报（自然科学版）,2025,51(3):12-22,11.基金项目
国家自然科学基金(62022042,62273181,62373190). （62022042,62273181,62373190）
