Deng Zhenhua, Nian Xiaohong, Hu Chen
IEEE Trans Cybern. 2020 Jul;50(7):3208-3217. doi: 10.1109/TCYB.2019.2901256. Epub 2019 Mar 14.
This paper investigates resource allocation problems, where the cost functions of agents are nonsmooth and the decisions of agents are constrained by heterogeneous local constraints and network resource constraints. We design a distributed subgradient-based algorithm to achieve the optimal resource allocation. Moreover, we analyze the convergence of the algorithm to the optimal solution. The algorithm can solve resource allocation problems with strongly convex cost functions and weight-balanced digraphs, as well as resource allocation problems with strictly convex cost functions and connected undirected graphs. With the algorithm, the decisions of all agents asymptotically converge to the optimal allocation. Simulation examples verify the effectiveness of the algorithm.
本文研究资源分配问题，其中代理的成本函数是非光滑的，且代理的决策受到异构局部约束和网络资源约束的限制。我们设计了一种基于分布式次梯度的算法来实现最优资源分配。此外，我们分析了该算法收敛到最优解的情况。该算法可以解决具有强凸成本函数和权重平衡有向图的资源分配问题，以及具有严格凸成本函数和连通无向图的资源分配问题。通过该算法，所有代理的决策渐近收敛到最优分配。仿真示例验证了该算法的有效性。
