`scipy.linalg._flapack:dsygv`

Signature

def dsygv(a, b, itype = 1, jobz = V, uplo = L, lwork = Ellipsis, overwrite_a = 0, overwrite_b = 0)

Solve the generalized symmetric-definite eigenproblem (LAPACK dsygv).

a : ndarray: Symmetric matrix of shape (n, n).
b : ndarray: Positive definite matrix of shape (n, n).
itype : int, optional: Which generalized problem to solve: 1 for a @ x = w * b @ x, 2 for a @ b @ x = w * x, 3 for b @ a @ x = w * x. Default is 1.
jobz : str, optional: 'V' to compute eigenvectors, 'N' for eigenvalues only. Default is 'V'. This family spells it as a letter where syev uses compute_v.
uplo : str, optional: 'U' or 'L' for the triangle of a to reference. Default is 'L' -- note that is the opposite of the lower=0 default elsewhere in this group, so the same matrix can give different answers between families.
lwork : int, optional: Size of the workspace. Default is max(3 * n - 1, 1). Use dsygv_lwork for the optimal value.
overwrite_a : int, optional: If nonzero, a may be overwritten in place. Default is 0.
overwrite_b : int, optional: If nonzero, b may be overwritten in place. Default is 0.

w : ndarray: Eigenvalues in ascending order, length n. Real for every flavor.
v : ndarray: Eigenvectors as columns when jobz is 'V', normalized per itype.
info : int: 0 on success; if negative, the -info-th argument had an illegal value; if positive, the algorithm failed to converge.