eigvalsh_tridiagonal — SciPy v1.15.2 Manual (original) (raw)
scipy.linalg.
scipy.linalg.eigvalsh_tridiagonal(d, e, 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 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.
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 lapack_driver='stebz'
). 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. ‘sterf’ and ‘stev’ can only be used whenselect='a'
.
Returns:
w(M,) ndarray
The eigenvalues, in ascending order, each repeated according to its multiplicity.
Raises:
LinAlgError
If eigenvalue computation does not converge.
See also
eigenvalues and right eiegenvectors for symmetric/Hermitian tridiagonal matrices
Examples
import numpy as np from scipy.linalg import eigvalsh_tridiagonal, eigvalsh d = 3np.ones(4) e = -1np.ones(3) w = eigvalsh_tridiagonal(d, e) A = np.diag(d) + np.diag(e, k=1) + np.diag(e, k=-1) w2 = eigvalsh(A) # Verify with other eigenvalue routines np.allclose(w - w2, np.zeros(4)) True