Multi-agent Gossip Consensus Algorithm with Quantized Data and Distributed Optimizing

As the traditional quantized asynchronous randomized gossip consensus algorithm is based on uniform selection probability time mode, the impact of network topology on local information transfer is not been fully considered. Thus, an improved quantized asynchronous randomized gossip consensus algorithm with non-uniform selection probability is proposed in this paper. Firstly, the asynchronous time model with non-uniform selection probability is proposed. Then the convergence of the algorithm is analyzed with randomized quantized information. The impact of the quantization resolution and the second largest eigenvalue of the probabilistic weighted matrix on convergence rate is also discussed. Furthermore, this paper proposes an optimization algorithm for selection probabilities with projection subgradient method in a distributed manner. The numerical example indicates that, the proposed algorithm improves the convergence rate by optimizing selection probabilities of agents.