论文标题
当地的Stackelberg均衡在广义聚合游戏中寻求
Local Stackelberg equilibrium seeking in generalized aggregative games
论文作者
论文摘要
我们提出了一种两层半居中的算法，以计算具有耦合约束的聚合游戏中Stackelberg平衡问题的局部解决方案。具体而言，我们专注于单个领导者，多个游客问题，在等效地将Stackelberg游戏重新铸造为具有互补性约束（MPCC）的数学程序之后，我们迭代地将MPCC的正则化版本作为内部问题凸起，其解决方案为原始MPCC的可行下降方向生成了可行的下降方向的序列。因此，通过在每次外迭代中追求下降方向，我们建立了与局部stackelberg平衡的融合。最后，在数值案例研究中测试了所提出的算法，该案例研究涉及插件电动汽车（PEV）的层次结构实例。
We propose a two-layer, semi-decentralized algorithm to compute a local solution to the Stackelberg equilibrium problem in aggregative games with coupling constraints. Specifically, we focus on a single-leader, multiple-follower problem, and after equivalently recasting the Stackelberg game as a mathematical program with complementarity constraints (MPCC), we iteratively convexify a regularized version of the MPCC as inner problem, whose solution generates a sequence of feasible descent directions for the original MPCC. Thus, by pursuing a descent direction at every outer iteration, we establish convergence to a local Stackelberg equilibrium. Finally, the proposed algorithm is tested on a numerical case study involving a hierarchical instance of the charging coordination of Plug-in Electric Vehicles (PEVs).
