Couple of changes. If number of eigenvalues is maximum, Hamiltonian is converted from sparse to dense (very expensive) since iterative algorithm cannot calculate all eigenpairs. Other change has to do with a multiT-like functionality for scaling terms of the Hamiltonian. This was useful for Hopfield networks when we needed to scale the bias term over problem instances. There was a work-around, but this allows the same problem to always be varied, instead of taking a completely new set of random problem instances.