# scipy.linalg.eigh_tridiagonal#

scipy.linalg.eigh_tridiagonal(d, e, eigvals_only=False, select='a', select_range=None, check_finite=True, tol=0.0, lapack_driver='auto')[source]#

Solve eigenvalue problem for a real symmetric tridiagonal matrix.

Find eigenvalues w and optionally right eigenvectors v of `a`:

```a v[:,i] = w[i] v[:,i]
v.H v    = identity
```

For a real symmetric matrix `a` with diagonal elements d and off-diagonal elements e.

Parameters:
dndarray, shape (ndim,)

The diagonal elements of the array.

endarray, shape (ndim-1,)

The off-diagonal elements of the array.

eigvals_onlybool, optional

Compute only the eigenvalues and no eigenvectors. (Default: calculate also eigenvectors)

select{‘a’, ‘v’, ‘i’}, optional

Which eigenvalues to calculate

select

calculated

‘a’

All eigenvalues

‘v’

Eigenvalues in the interval (min, max]

‘i’

Eigenvalues with indices min <= i <= max

select_range(min, max), optional

Range of selected eigenvalues

check_finitebool, optional

Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

tolfloat

The absolute tolerance to which each eigenvalue is required (only used when ‘stebz’ is the lapack_driver). An eigenvalue (or cluster) is considered to have converged if it lies in an interval of this width. If <= 0. (default), the value `eps*|a|` is used where eps is the machine precision, and `|a|` is the 1-norm of the matrix `a`.

lapack_driverstr

LAPACK function to use, can be ‘auto’, ‘stemr’, ‘stebz’, ‘sterf’, or ‘stev’. When ‘auto’ (default), it will use ‘stemr’ if `select='a'` and ‘stebz’ otherwise. When ‘stebz’ is used to find the eigenvalues and `eigvals_only=False`, then a second LAPACK call (to `?STEIN`) is used to find the corresponding eigenvectors. ‘sterf’ can only be used when `eigvals_only=True` and `select='a'`. ‘stev’ can only be used when `select='a'`.

Returns:
w(M,) ndarray

The eigenvalues, in ascending order, each repeated according to its multiplicity.

v(M, M) ndarray

The normalized eigenvector corresponding to the eigenvalue `w[i]` is the column `v[:,i]`. Only returned if `eigvals_only=False`.

Raises:
LinAlgError

If eigenvalue computation does not converge.

`eigvalsh_tridiagonal`

eigenvalues of symmetric/Hermitian tridiagonal matrices

`eig`

eigenvalues and right eigenvectors for non-symmetric arrays

`eigh`

eigenvalues and right eigenvectors for symmetric/Hermitian arrays

`eig_banded`

eigenvalues and right eigenvectors for symmetric/Hermitian band matrices

Notes

This function makes use of LAPACK `S/DSTEMR` routines.

Examples

```>>> import numpy as np
>>> from scipy.linalg import eigh_tridiagonal
>>> d = 3*np.ones(4)
>>> e = -1*np.ones(3)
>>> w, v = eigh_tridiagonal(d, e)
>>> A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1)
>>> np.allclose(A @ v - v @ np.diag(w), np.zeros((4, 4)))
True
```