[P] Tridiagonal eigenvalue models in PyTorch: cheaper training/inference than dense spectral models

This post is part of a series I'm working on with a broader goal: understand what one nonlinear "neuron" can do when the nonlinearity is a matrix eigenvalue, and whether that gives a useful middle ground between linear models that are easy to explain and larger neural networks that are expressive but much less transparent. Something unusual, in this "attention is all you need" world In this installment, I look at a cheaper variant of the model family by cons