`scipy.linalg._flapack:ssyevd`

Signature

def ssyevd(a, compute_v = 1, lower = 0, lwork = Ellipsis, liwork = Ellipsis, overwrite_a = 0)

Compute the eigenvalues and optionally the eigenvectors of a real symmetric matrix by divide-and-conquer (LAPACK ssyevd).

a : ndarray: real symmetric matrix of shape (n, n).
compute_v : int, optional: If nonzero, eigenvectors are computed as well as eigenvalues. Default is 1.
lower : int, optional: If nonzero, the lower triangle of a is referenced; otherwise the upper. Default is 0.
lwork : int, optional: Default is 1 + 6 * n + 2 * n ** 2 with eigenvectors, 2 * n + 1 without.
liwork : int, optional: Default is 3 + 5 * n with eigenvectors, 1 without.
overwrite_a : int, optional: If nonzero, a may be overwritten in place. Default is 0.

w : ndarray: Eigenvalues in ascending order, length n. Real for every flavor.
v : ndarray: Orthonormal eigenvectors as columns when compute_v is nonzero.
info : int: 0 on success; if negative, the -info-th argument had an illegal value; if positive, the algorithm failed to converge.