论文标题
通过DC编程优化随机开关缓冲区网络
Optimization of stochastic switching buffer network via DC programming
论文作者
论文摘要
这封信涉及随机切换缓冲区网络的优化问题，在该网络中，开关定律受马尔可夫流程的约束。引入了动态缓冲网络，并且还提出了其在建模汽车共享网络中的应用。为了解决最接近全球优化问题的近乎全球的解决方案的问题，我们采用了一种简洁但有效的非convex优化方法，称为\ emph {dc {dc（convex函数差异）编程}。通过诉诸于一类称为posynomials的非线性函数的对数凸凸度，可以将优化问题降低为DC编程问题。最后，我们通过模拟实验来验证结果的有效性。
This letter deals with the optimization problems of stochastic switching buffer networks, where the switching law is governed by Markov process. The dynamical buffer network is introduced, and its application in modeling the car-sharing network is also presented. To address the nonconvexity for getting a solution as close-to-the-global-optimal as possible of the optimization problem, we adopt a succinct but effective nonconvex optimization method called \emph{ DC (difference of convex functions) programming}. By resorting to the log-log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to DC programming problems. Finally, we verify the effectiveness of our results by simulation experiments.
