论文标题
通过量化通信分布式聚合优化
Distributed aggregative optimization with quantization communication
论文作者
论文摘要
在本文中，我们关注沟通瓶颈下的总体优化问题。总体优化是最大程度地减少本地成本函数的总和。每个成本函数不仅取决于局部状态变量，还取决于全局状态变量的功能之和。目的是通过在没有中央协调器的代理网络上通过分布式计算和局部有效通信来解决聚合优化问题。使用变量跟踪方法寻求全局状态变量和量化方案来减少优化过程中所花费的通信成本，我们开发了一种新颖的分布式量化量化算法，称为D-QAGT，以跟踪具有有限位通信的最佳变量。尽管量化可能会丢失传输信息，但我们的算法仍然可以通过线性收敛速率实现精确的最佳解决方案。对最佳放置问题进行了模拟实验，以验证理论结果的正确性。
In this paper, we focus on an aggregative optimization problem under the communication bottleneck. The aggregative optimization is to minimize the sum of local cost functions. Each cost function depends on not only local state variables but also the sum of functions of global state variables. The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator. Using the variable tracking method to seek the global state variables and the quantization scheme to reduce the communication cost spent in the optimization process, we develop a novel distributed quantized algorithm, called D-QAGT, to track the optimal variables with finite bits communication. Although quantization may lose transmitting information, our algorithm can still achieve the exact optimal solution with a linear convergence rate. Simulation experiments on an optimal placement problem is carried out to verify the correctness of the theoretical results.
