Li Bo, Saad David, Yeung Chi Ho
Non-linearity and Complexity Research Group, Aston University, Birmingham B4 7ET, United Kingdom.
School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Phys Rev E. 2022 Oct;106(4):L042301. doi: 10.1103/PhysRevE.106.L042301.
Optimizing embedded systems, where the optimization of one depends on the state of another, is a formidable computational and algorithmic challenge, that is ubiquitous in real world systems. We study flow networks, where bilevel optimization is relevant to traffic planning, network control, and design, and where flows are governed by an optimization requirement subject to the network parameters. We employ message passing algorithms in flow networks with sparsely coupled structures to adapt network parameters that govern the network flows, in order to optimize a global objective. We demonstrate the effectiveness and efficiency of the approach on randomly generated graphs.
优化嵌入式系统是一项艰巨的计算和算法挑战，在现实世界系统中普遍存在，其中一个系统的优化取决于另一个系统的状态。我们研究流网络，其中双层优化与交通规划、网络控制和设计相关，并且流受网络参数的优化要求支配。我们在具有稀疏耦合结构的流网络中采用消息传递算法来调整控制网络流的网络参数，以优化全局目标。我们在随机生成的图上证明了该方法的有效性和效率。
