分散的在线优化与压缩通信在指向图的指向图
IEEE transactions on neural networks and learning systems
|October 28, 2025
概括
本研究介绍了一种压缩通信算法,用于在多代理系统中进行去中心化的在线优化. 这种新的方法实现了与强烈凸起的函数相结合的线下遗憾,提高了大型网络的效率.
科学领域:
- 控制理论 控制理论
- 分布式优化 分布式优化
- 网络化系统 网络化系统
背景情况:
- 分散的在线优化问题对于多代理系统至关重要.
- 通信瓶阻碍了大规模,高维度网络的性能.
研究的目的:
- 设计一个去中心化的在线算法,解决通信瓶.
- 在压缩通信的多代理系统中实现高效的全球损失最小化.
主要方法:
- 开发了一种新的去中心化在线梯度推送和与压缩通信 (CC-DOGPS) 算法.
- 该算法在多代理系统中运行在强度连接的定向图上.
- 理论分析侧重于强烈凸起的函数和时间变化的局部损失.
主要成果:
- 对于CC-DOGPS算法来说,得到了O (((ln T) ^2) 的线下遗憾,其中T是时间视界.
- 该算法有效地管理时间变化的局部损失函数,只有单个代理才知道.
- 数字模拟验证了理论发现,并证明了算法效率.
结论:
- 拟议的CC-DOGPS算法为在通信限制下进行去中心化在线优化提供了有效的解决方案.
- 线下遗憾边界突出显示了算法的可扩展性和对大型网络的有效性.
- 这项工作有助于在复杂的多代理系统中推进分布式优化技术.
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