# numpy.linalg.eigvalsh¶

numpy.linalg.eigvalsh(a, UPLO='L')

Compute the eigenvalues of a Hermitian or real symmetric matrix.

Main difference from eigh: the eigenvectors are not computed.

Parameters : a : array_like, shape (M, M) A complex- or real-valued matrix whose eigenvalues are to be computed. UPLO : {‘L’, ‘U’}, optional Specifies whether the calculation is done with the lower triangular part of a (‘L’, default) or the upper triangular part (‘U’). w : ndarray, shape (M,) The eigenvalues, not necessarily ordered, each repeated according to its multiplicity. LinAlgError : If the eigenvalue computation does not converge.

eigh
eigenvalues and eigenvectors of symmetric/Hermitian arrays.
eigvals
eigenvalues of general real or complex arrays.
eig
eigenvalues and right eigenvectors of general real or complex arrays.

Notes

This is a simple interface to the LAPACK routines dsyevd and zheevd that sets those routines’ flags to return only the eigenvalues of real symmetric and complex Hermitian arrays, respectively.

Examples

```>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> LA.eigvalsh(a)
array([ 0.17157288+0.j,  5.82842712+0.j])
```

#### Previous topic

numpy.linalg.eigvals

#### Next topic

numpy.linalg.norm