Distributed Time-Varying Convex Optimization With Dynamic Quantization

Distributed Time-Varying Convex Optimization With Dynamic Quantization
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具有动态量化的分布式时变凸优化

DOI:
10.1109/tcyb.2021.3099905
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发表时间:
2021-08
影响因子:
11.8
通讯作者:
Yiguang Hong
Yiguang Hong
中科院分区:
计算机科学1区
文献类型:
--
作者:
Ziqin Chen;Peng Yi;Li Li;Yiguang Hong

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在这项工作中,我们设计了一个分布式算法的时变凸优化网络与量化通信。每个智能体都有自己的局部时变目标函数,而智能体需要合作跟踪全局时变函数的最优解轨迹。该分布式算法基于乘子交替方向法,但各智能体只能通过无向图共享量化信息。为了减少由于量化中的信息丢失而导致的跟踪误差,我们采用了具有衰减缩放函数的动态量化方案。跟踪误差的特点是明确相对于量化的衰减缩放函数的限制。此外,我们能够表明,该算法可以渐近跟踪时变函数收敛时的最优解,即使量化信息的损失。最后,通过数值仿真验证了理论分析的正确性.
In this work, we design a distributed algorithm for time-varying convex optimization over networks with quantized communications. Each agent has its local time-varying objective function, while the agents need to cooperatively track the optimal solution trajectories of global time-varying functions. The distributed algorithm is motivated by the alternating direction method of multipliers, but the agents can only share quantization information through an undirected graph. To reduce the tracking error due to information loss in quantization, we apply the dynamic quantization scheme with a decaying scaling function. The tracking error is explicitly characterized with respect to the limit of the decaying scaling function in quantization. Furthermore, we are able to show that the algorithm could asymptotically track the optimal solution when time-varying functions converge, even with quantization information loss. Finally, the theoretical results are validated via numerical simulation.
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期刊: Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004
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