scipy.linalg.lapack.ssyevd

scipy.linalg.lapack.ssyevd(a, compute_v=1, lower=0, lwork=..., liwork=..., overwrite_a=0) = <flapack function ssyevd>

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

Parameters:

andarray

real symmetric matrix of shape (n, n).

compute_vint, optional

If nonzero, eigenvectors are computed as well as eigenvalues. Default is 1.

lowerint, optional

If nonzero, the lower triangle of a is referenced; otherwise the upper. Default is 0.

lworkint, optional

Default is 1 + 6 * n + 2 * n ** 2 with eigenvectors, 2 * n + 1 without.

liworkint, optional

Default is 3 + 5 * n with eigenvectors, 1 without.

overwrite_aint, optional

If nonzero, a may be overwritten in place. Default is 0.

Returns:

wndarray

Eigenvalues in ascending order, length n. Real for every flavor.

vndarray

Orthonormal eigenvectors as columns when compute_v is nonzero.

infoint

0 on success; if negative, the -info-th argument had an illegal value; if positive, the algorithm failed to converge.