📄 中文摘要
该研究提出了一种新的组合网络优化方法，旨在解决具有未知变量的复杂问题。通过引入多臂赌博机模型，研究者探索了如何在面对不确定性时进行有效的决策。所提出的方法利用线性奖励机制，优化了资源分配和网络结构设计，显著提高了系统的整体性能。实验结果表明，该方法在多个应用场景中均表现出色，能够有效应对动态变化的环境和未知变量的影响，为网络优化提供了新的思路和工具。
具有未知变量的组合网络优化：线性奖励的多臂赌博机
📄 English Summary
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Banditswith Linear Rewards
This research introduces a novel combinatorial network optimization approach aimed at addressing complex problems with unknown variables. By incorporating a multi-armed bandit model, the study explores effective decision-making strategies in the face of uncertainty. The proposed method leverages a linear reward mechanism to optimize resource allocation and network structure design, significantly enhancing overall system performance. Experimental results demonstrate that this approach excels across various application scenarios, effectively coping with dynamic environments and the influence of unknown variables, thereby providing new insights and tools for network optimization.