NumPy: difference between linalg. eig () and linalg. eigh () Attention, eigh doesn't check if your matrix is indeed symmetric, it by default just takes the lower triangular part of the matrix and assumes that the upper triangular part is defined by the symmetry of the matrix eig works for general matrices and therefore uses a slower algorithm, you can check that for example with IPythons magic command
python - how does numpy. linalg. eigh vs numpy. linalg. svd . . . - Stack . . . Indeed, numpy linalg svd and numpy linalg eigh do not call the same routine of Lapack On the one hand, numpy linalg eigh refers to LAPACK's dsyevd() while numpy linalg svd makes use LAPACK's dgesdd() The common point between these routines is the use of Cuppen's divide and conquer algorithm, first designed to solve tridiagonal eigenvalue
Solve Generalized Eigenvalue Problem in Numpy - Stack Overflow For real symmetric or complex Hermitian dense matrices, you can use scipy linalg eigh() to solve a generalized eigenvalue problem To avoid extracting all the eigenvalues you can specify only the desired ones by using subset_by_index: from scipy linalg import eigh eigvals, eigvecs = eigh(A, B, eigvals_only=False, subset_by_index=[0, 1, 2])
Why is scipys eigh returning unexpected negative eigenvalues? I think not, considering that eigh returns both the eigenvalues and eigenvectors, whereas eigvals does something similar, but without returning the eigenvectors The main difference I would think exists between them is that eigh is specialized for symmetric (or Hermitian) matrices -- in fact, I would probably use eigvalsh instead of eigvals for
Numpys eigh and eig yield inconsistent eigenvalues Currently I'm trying to solve the generalized eigenvalue problem in NumPy for two symmetric matrices and I've been running into massive trouble as I'm expecting all eigenvalues to be positive, but eigh returns several very large numbers that are not all positive, while eig returns the correct, expected values (but is, of course, very, very slow)
python - SciPy generalized eigenvalues: eig and eigh yield different . . . eigh is only for symmetric matrices and thus uses a faster (and different) algorithm This is why it produces different results This is why it produces different results There are an infinite number of eigenvectors for any given eigenvalue, so I don't think you need to be concerned