title: Higher precision eigenvalues with numpy
body: <p>I'm currently computing several eigenvalues of different matrices and trying to find their closed-form solutions. The matrix is hermitian/self-adjoint, and tri-diagonal. Additionally, every diagonal element is positive and every off-diagonal is negative.</p>
  
  <p>Due to what I suspect is an issue with trying to algebraically solve the quintic, sympy cannot solve the eigenvalues of my 14x14 matrix.</p>
  
  <p>Numpy has given me great results that I'm sometimes able to use via wolfram-alpha, but other times the precision is lacking to be able to determine which of several candidates the closed form solution could take. As a result, I'm wishing to increase the precision with which numpy.linalg.eigenvaluesh outputs eigenvalues. Any help would be greatly appreciated!</p>