发表机构
Linköping University（林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于时间共享和ActiveSet算法的中继辅助拓扑优化方法，通过概率激活多个中继配置并联合优化混合矩阵与激活概率，加速弱连接网络上的分布式一致性收敛，实现更优的性能-成本权衡。
AI 中文摘要
本文聚焦于中继辅助的拓扑优化，以加速弱连接网络上分布式一致性算法的收敛。不同于永久性地添加一组固定的中继链路，我们引入了一种时间共享框架，其中多个中继配置以概率方式被激活。一种基于ActiveSet的算法逐步构建候选中继边集，并联合优化混合矩阵及相应的中继集激活概率，从而在优化过程中实现自适应的中继选择。仿真结果表明，与固定基数中继选择策略相比，所提方法在性能-成本权衡方面取得了显著改善。
英文摘要
This paper focuses on relay-assisted topology optimization to accelerate the convergence of distributed consensus algorithms over weakly connected networks. Instead of permanently adding a fixed set of relay links, we introduce a time-sharing framework where multiple relay configurations are activated in a probabilistic manner. An ActiveSet-based algorithm incrementally constructs candidate relay edge sets and jointly optimizes the mixing matrices and the associated relay set activation probabilities, allowing adaptive relay selection in the optimization process. Simulation results demonstrate a substantially improved performance-cost trade-off compared with fixed-cardinality relay selection strategies.
Comments5 pages, 4 figures. Accepted by IEEE SPAWC 2026
