对于多代理分布式优化的神经动力学方法
Luyao Guo1, Iakov Korovin2, Sergey Gorbachev3
1School of Mathematics, Southeast University, Nanjing 210096, China.
概括
这项研究引入了新的连续时间神经动力学方法,用于多代理分布式凸优化. 这些方法有效地处理约束,并实现顺和不顺的成本函数的融合.
科学领域:
- 分布式优化 分布式优化
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 多代理系统需要高效的分布式优化策略.
- 在这些系统中处理共同的约束是一个重大挑战.
- 现有的方法可能涉及复杂的变量转换或辅助信息交换.
研究的目的:
- 开发新的连续时间神经动力学方法,用于多代理分布式凸优化.
- 在不引入辅助变量的情况下解决共同约束的问题.
- 为非光滑和光滑成本函数提供算法.
主要方法:
- 使用l1和l2惩罚方法将线性共识约束转化为目标函数.
- 开发差异性包含与投影操作员用于非光滑的成本函数.
- 使用平均共识估计器设计有限和固定的时间收算法,以实现平滑的成本函数.
主要成果:
- 提出的方法避免了辅助变量,仅依赖于主变量信息交换.
- 对差异性包含进行分析,即便没有凸度,也会分析异面性行为和收性质.
- 对于平稳的成本函数,有限和固定时间的收得到了证明.
- 数字模拟验证了神经动力学方法在多剂环境中的有效性.
结论:
- 提出的连续时间神经动力学方法为具有共同约束的多代理分布凸优化提供了有效的解决方案.
- 这些技术具有多功能性,可以处理既不光滑的,又光滑的成本函数,具有经过证明的收性质.
- 这些方法为复杂的多代理协调问题提供了强大而高效的框架.
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