I have non-symmetric real-valued matrices with real-valued eigensystems. How to compute eigenvectors efficiently?
scipy.linalg.eig (which calls
dgeev) is 3-4 times slower than
dsyevd) and 2x slower than
gesdd), but neither SVD nor hermitian eigenvalue decomposition are appropriate here. I'm guessing the need to support complex valued storage is responsible for extra overhead. Is there a faster routine to use for matrices which have real-valued eigenvalue decomposition?