论文标题
基于惩罚方法的分散多代理优化
Decentralized Multi-Agent Optimization Based on a Penalty Method
论文作者
论文摘要
我们提出了一种分散的惩罚方法，用于一般凸的受约束多代理优化问题。每个辅助惩罚问题大约通过一种特殊的平行下降分裂方法解决。该方法可以在计算网络中实现，每个代理只会将信息发送给最近的邻居。该方法的融合是在相当弱的假设下建立的。我们还描述了针对可行性问题的建议方法的专业化。
We propose a decentralized penalty method for general convex constrained multi-agent optimization problems. Each auxiliary penalized problem is solved approximately with a special parallel descent splitting method. The method can be implemented in a computational network where each agent sends information only to the nearest neighbours. Convergence of the method is established under rather weak assumptions. We also describe a specialization of the proposed approach to the feasibility problem.
