Logarithmically Quantized Distributed Optimization over Dynamic
Multi-Agent Networks
Logarithmically Quantized Distributed Optimization over Dynamic
Multi-Agent Networks
Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data …