Spectral-like gradient method for distributed optimization

We consider a standard distributed multi-agent optimization setting where n nodes (agents) in a network minimize the aggregate sum of their local convex cost functions. We present a distributed spectral-like gradient method, wherein step sizes are node- and iteration-varying, and they are inspired by classical spectral methods from centralized optimization. Simulation examples illustrate the performance of the presented method.