numpy.linalg.eigvals¶

numpy.linalg.
eigvals
(a)[source]¶ Compute the eigenvalues of a general matrix.
Main difference between
eigvals
andeig
: the eigenvectors aren’t returned.Parameters:  a : (…, M, M) array_like
A complex or realvalued matrix whose eigenvalues will be computed.
Returns:  w : (…, M,) ndarray
The eigenvalues, each repeated according to its multiplicity. They are not necessarily ordered, nor are they necessarily real for real matrices.
Raises:  LinAlgError
If the eigenvalue computation does not converge.
See also
Notes
New in version 1.8.0.
Broadcasting rules apply, see the
numpy.linalg
documentation for details.This is implemented using the
_geev
LAPACK routines which compute the eigenvalues and eigenvectors of general square arrays.Examples
Illustration, using the fact that the eigenvalues of a diagonal matrix are its diagonal elements, that multiplying a matrix on the left by an orthogonal matrix, Q, and on the right by Q.T (the transpose of Q), preserves the eigenvalues of the “middle” matrix. In other words, if Q is orthogonal, then
Q * A * Q.T
has the same eigenvalues asA
:>>> from numpy import linalg as LA >>> x = np.random.random() >>> Q = np.array([[np.cos(x), np.sin(x)], [np.sin(x), np.cos(x)]]) >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :]) (1.0, 1.0, 0.0)
Now multiply a diagonal matrix by
Q
on one side and byQ.T
on the other:>>> D = np.diag((1,1)) >>> LA.eigvals(D) array([1., 1.]) >>> A = np.dot(Q, D) >>> A = np.dot(A, Q.T) >>> LA.eigvals(A) array([ 1., 1.]) # random