Optimal Parameter Design for DIGing on Minimizing Unweighted Sum of Squares

What the math gives to ML

The paper converts a distributed gradient-tracking iteration into independent two-dimensional dynamical systems indexed by Laplacian eigenvalues, then chooses gains by minimizing the largest pole modulus over a spectral interval. The transferable asset is an explicit pole-placement rule linking communication-graph spectral conditioning to stable optimizer parameters. This can be tested in decentralized neural-network training and, more speculatively, in graph-neural-network propagation blocks where disagreement modes should be controlled.