Is there already a way to get eigenvalues and eigenvectors with BigInt/BigFloat accuracy? I know about the GenericLinearAlgebra.jl implementation for eigvals, but I need the high accuracy in the eigenvectors. Anybody knows about an implementation already out there? If not, what could be the easiest way to get a working code (I don’t care too much about speed as long as its faster than symbolic math tools like SymPy or Mathematica) ?
Do you need all of the eigenvalues and eigenvectors, or just particular ones? Is your matrix arbitrary, or special in some way (real-symmetric, tridiagonal, etcetera)?
I have arbitrary real matrices. Actually I need to solve a generalized problem, but in my case the matrix is invertable to make it a standard problem. I’d be happy about both a targeted and a dense solver, no real preference.
Take a look at GitHub - RalphAS/GenericSchur.jl: Julia package for Schur decomposition of matrices with generic element types. To get the eigenvectors, you’d have to convert the input matrix to complex before computing the Schur factorization.
Thanks. That’s basically what i needed.