Quantized Subgradient Algorithm and Data-Rate Analysis for Distributed Optimization
Quantized Subgradient Algorithm and Data-Rate Analysis for Distributed Optimization
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DOI：
10.1109/tcns.2014.2357513
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发表时间：
2014-09
影响因子：
4.2
通讯作者：
Peng Yi;Yiguang Hong
中科院分区：
文献类型：
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作者：
Peng Yi;Yiguang Hong
在本文中，我们考虑了有限的通信容量和时变通信拓扑结构的量化分布式优化问题。提出了一种分布式量化次梯度算法，实现了智能体之间的量化信息交换。基于所提出的编码器-解码器方案和放大技术，可以在没有任何量化误差的情况下获得最优解。此外，我们探讨了如何最小化量化的分布式优化问题的量化级别数。实际上，在开关拓扑的情况下，可以用五电平量化器来解决优化问题，而在固定拓扑的情况下，可以用三电平量化器来解决优化问题。
In this paper, we consider quantized distributed optimization problems with limited communication capacity and time-varying communication topology. A distributed quantized subgradient algorithm is presented with quantized information exchange between agents. Based on a proposed encoder-decoder scheme and a zooming-in technique, the optimal solution can be obtained without any quantization errors. Moreover, we explore how to minimize the quantization level number for quantized distributed optimization problems. In fact, the optimization problem can be solved with five-level quantizers in the switching topology case, while it can be solved with three-level quantizers in the fixed topology case.