Finite-Time-Consensus-Based Methods for Distributed optimization
Zhihai Qu, Xuyang Wu, Jie Lu · 2019
In this paper, we provide a class of distributed optimization methods based on a finite-time consensus (FTC) scheme. We first provide a general algorithmic form for such methods, and then specialize it to a FTC-based distributed Newton method, which is proved to be locally convergent to the optimum at a quadratic rate. To reduce the computational cost of calculating the consensus coefficients in the FTC scheme, we introduce a clustering strategy for the network to implement the algorithm. Finally, numerical results show the effectiveness of our proposed algorithms.