What is the maximum size of a sparse matrix that can be diagonalized using Numpy Linalg package?
I have been working with exact diagonalisation methods such as Lanczos and Arnoldi in the context of diagonalizing my Hamiltonian matrices.
Recently I have started to use Numpy's linalg to do these numerical calculations.
I have been wondering about, what would be the maximum size of a very sparse matrix that can be diagonalised using Numpy. Are there any hacks to get beyond a certain limit and is it possible to get the code to work faster are there any best practices on this kind of coding with numpy?