This paper studies sequences of graphs satisfying the finite-time consensus property (ie,
iterating through such a finite sequence is equivalent to performing global or exact …

Decentralized stochastic optimization has become a crucial tool for addressing large-scale
machine learning and control problems. In decentralized algorithms, all computing nodes …

This paper proposes two distributed random reshuffling methods, namely Gradient Tracking
with Random Reshuffling (GT-RR) and Exact Diffusion with Random Reshuffling (ED-RR), to …

This paper studies decentralized stochastic empirical risk minimization over a network of
nodes, where each node has access to a finite collection of risk functions. While this …

In this paper, a fractional proportional-integral-derivative (PID) distributed optimization
algorithm is proposed to solve the network resource allocation problem. The algorithm …