Question: How do I solve a nonsymmetric eigenproblem. In terms of scipy.sparse.linalg.eigsh the matrix needs to be "real symmetric square matrix or complex Hermitian matrix A" (https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html). and the same for scipy.sparse.linalg.eigs. "M must represent a real symmetric matrix if A is real, and must represent a complex Hermitian matrix if A is complex. For best results, the data type of M should be the same as that of A" (https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html#scipy.sparse.linalg.eigs). I need this for a vibroacoustic problem finite element problem. Which would have the matrix shape matrix description for vibroacoustic problem I am using a windows computer. ev, phi = scipy.sparse.linalg.eigs(A=stiff, k=nev, M=mass, which='SM') ev, phi = scipy.sparse.linalg.eigs(A=stiff, k=nev, M=mass, sigma=0) Before realizing the functions do not work for nonsymmetric problems. The matrices are sparse. Matlab Code: k = load('k_global.mat'); m = load('m_global.mat'); [V,D] = eigs(k.array, m.array, 20,0); D = diag(D) natural_frequency = sqrt(D)/(2*pi)  Returns: 0.00000000000000 + 0.000356591992067188i 0.000668911454165071 + 0.00000000000000i 0.00000000000000 + 0.000973128785222090i 0.00222975851379527 + 0.00000000000000i 0.00246434216130016 + 0.00000000000000i 0.00000000000000 + 0.00372951940564144i 8.06883871646537 + 0.00000000000000i 64.7482150103242 + 0.00000000000000i 234.453670549319 + 0.00000000000000i 268.154072409059 + 0.00000000000000i 312.537263749716 + 0.00000000000000i 356.103849178590 + 0.00000000000000i 389.038117338274 + 0.00000000000000i 412.048267727649 + 0.00000000000000i 473.729345964820 + 0.00000000000000i 2996.35112385098 + 0.00000000000000i 3240.96766107255 + 0.00000000000000i 4186.42444133727 + 0.00000000000000i 4585.99172192305 + 0.00000000000000i 4794.52737053778 + 0.00000000000000i  Python code: import pickle import scipy import numpy as np if __name__ == '__main__': with open('../k_full.pickle', 'rb') as f: print('loading matrix K') k_global = pickle.load(f) with open('../m_full.pickle', 'rb') as f: print('loading matrix M') m_global = pickle.load(f) eigvalues, eigvectors = scipy.sparse.linalg.eigs(k_global, M=m_global, k = 20, which='SM') natural_frequency_Hz = np.sqrt(np.abs(eigvalues))/(2*np.pi) for i, nat_freq in enumerate(natural_frequency_Hz): print(f'[{i + 1: 3.0f}] : Freq = {nat_freq: 8.2f}')  Returns: scipy.sparse.linalg._eigen.arpack.arpack.ArpackNoConvergence: ARPACK error -1: No convergence (321 iterations, 4/20 eigenvectors converged)  Stiffness: https://pastebin.com/Ri0rebyt Mass https://pastebin.com/mPKnTt8A

Answer: Despite your vibroacoustic link, you are (presumably) solving the undamped system  where M is the mass matrix and K is the stiffness matrix. With solutions proportional to eiωt this becomes  or  This is the system that you want to solve. However, M should be an invertible matrix, so you can rewrite this as  so looking for the eigensolutions of M–1K. If you look at the documentation ( https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html#scipy.sparse.linalg.eigs ), the routine scipy.sparse.linalg.eigs does NOT require the main matrix to be symmetric/hermitian and it doesn’t require the second matrix as an argument at all: that is optional. For completeness, the final frequency f (in Hz) comes from  BTW, your matlab snippet is picking up some negative eigenvalues for lambda / complex values for omega. They are rather small in magnitude, so may be an artefact of floating-point operations. However, you should check for, and be careful how you deal with, such values. EDIT - You have set k=20 (and which='SM'#allest). Thus, you aren't actually finding all the eigenvalues. If you increase k significantly you can find more, together with a warning about the scheme switching to the non-sparse version, scipy.linalg.eig. Given the relatively small size of your matrices, even if sparse, I recommend switching to that anyway. This then finds the particular eigenvalue that you want. Code: import numpy as np import scipy K = np.loadtxt( 'stiffness.txt' ) M = np.loadtxt( 'mass.txt' ) M1K = np.linalg.inv( M ) @ K #eigenvalues, eigenvectors = scipy.sparse.linalg.eigs(M1K, k=20, which='SM') # sparse version; 20 eigenvalues eigenvalues, eigenvectors = scipy.linalg.eig(M1K) # general version -> 32 eigenvalues natural_frequency_Hz = np.sqrt( np.abs( eigenvalues ) ) / ( 2 * np.pi ) for i, nat_freq in enumerate( natural_frequency_Hz ): print( f'[{i + 1: 3.0f}] : Freq = {nat_freq: 9.3f}' )  Output: [ 1] : Freq = 291049.779 [ 2] : Freq = 291052.098 [ 3] : Freq = 291056.220 [ 4] : Freq = 291053.901 [ 5] : Freq = 155654.905 [ 6] : Freq = 155654.199 [ 7] : Freq = 155632.590 [ 8] : Freq = 155567.798 [ 9] : Freq = 155596.123 [ 10] : Freq = 155588.053 [ 11] : Freq = 155630.579 [ 12] : Freq = 155578.601 [ 13] : Freq = 4794.527 [ 14] : Freq = 4585.992 [ 15] : Freq = 3240.968 [ 16] : Freq = 2996.351 [ 17] : Freq = 4186.424 [ 18] : Freq = 473.730 [ 19] : Freq = 411.719 [ 20] : Freq = 390.610 [ 21] : Freq = 356.104 [ 22] : Freq = 312.524 [ 23] : Freq = 268.154 [ 24] : Freq = 234.454 [ 25] : Freq = 64.748 [ 26] : Freq = 8.068 [ 27] : Freq = 0.022 [ 28] : Freq = 0.002 [ 29] : Freq = 0.002 [ 30] : Freq = 0.002 [ 31] : Freq = 0.002 [ 32] : Freq = 0.002