Liao Shengbin, Sun Jianyong
National Engineering Center for E-Learning, Huazhong Normal University, Wuhan 430079, China.
The National Engineering Laboratory for Educational Big Data Technology, Huazhong Normal University, Wuhan 430079, China.
Entropy (Basel). 2019 Jul 19;21(7):708. doi: 10.3390/e21070708.
Classical network utility maximization (NUM) models fail to capture network dynamics, which are of increasing importance for modeling network behaviors. In this paper, we consider the NUM with delivery contracts, which are constraints to the classical model to describe network dynamics. This paper investigates a method to distributively solve the given problem. We first transform the problem into an equivalent model of linear equations by dual decomposition theory, and then use Gaussian belief propagation algorithm to solve the equivalent issue distributively. The proposed algorithm has faster convergence speed than the existing first-order methods and distributed Newton method. Experimental results have demonstrated the effectiveness of our proposed approach.
经典的网络效用最大化(NUM)模型无法捕捉网络动态,而网络动态对于建模网络行为的重要性日益增加。在本文中,我们考虑带有交付合同的NUM,这些合同是对经典模型的约束,用于描述网络动态。本文研究了一种分布式求解给定问题的方法。我们首先通过对偶分解理论将问题转化为线性方程组的等价模型,然后使用高斯信念传播算法分布式地求解该等价问题。所提出的算法比现有的一阶方法和分布式牛顿法具有更快的收敛速度。实验结果证明了我们所提方法的有效性。