I am trying to solve a generalized eigenvalue problem using Arpack, right now the code is using LAPACK but that's too slow, we only need a few eigenvalues and the matrices are sparse so using Arpack should be the way to go.
Before I start working with the original code I decided to test a simple case using scipy wrapper for Arpack (eigs) but the results that I am getting are wrong and change every time the code runs.
Minimum working example:
import numpy as np from scipy.linalg import eig from scipy.sparse.linalg import eigs n = 8 A = np.diag(np.arange(1,n+1,1.0)) B = np.eye(n) # We want symmetric but a non-diagonal B. eigs gives correct answer for B=np.eye(n) B[0][n-1] = 2 B[n-1][0] = 2 evals,_ = eigs(A,k=3,M=B,which='LM') print("The eigenvalues obtained by eigs (uses Arpack)") print(evals) print("Correct eigenvalues using eig (uses Lapack):") evals_l,_ = eig(A,b=B) print(evals_l) ``` 
