Tridiagonal eigenvalue models trim training costs

This is a neat middle ground: it preserves adjacent latent interactions that diagonal models lose, but avoids the full cost of dense eigensolves.

• –The main win is structural, not algorithmic magic: tridiagonal matrices are far cheaper to solve, so the same eigenvalue-based neuron becomes practical on weaker hardware.
• –Custom autograd is the key engineering move here; without a backward pass that reuses eigenvectors, this would stay a demo instead of a trainable model.
• –The reported 5x-6x speedup is big enough to change iteration speed on tabular experiments, which matters more than raw novelty in applied research.
• –Expressiveness is still constrained compared with dense spectral models, but the post makes a solid case that “cheaper” does not have to mean “collapsed to linear.”