scipy.sparse.linalg._eigen.arpack.arpack:eigsh

Other Parameters

M : An N x N matrix, array, sparse matrix, or linear operator representing

the operation M @ x for the generalized eigenvalue problem

A @ x = w * M @ x.

M must represent a real symmetric matrix if A is real, and must represent a complex Hermitian matrix if A is complex. For best results, the data type of M should be the same as that of A. Additionally:

If sigma is None, M is symmetric positive definite.

If sigma is specified, M is symmetric positive semi-definite.

In buckling mode, M is symmetric indefinite.

If sigma is None, eigsh requires an operator to compute the solution of the linear equation M @ x = b. This is done internally via a (sparse) LU decomposition for an explicit matrix M, or via an iterative solver for a general linear operator. Alternatively, the user can supply the matrix or operator Minv, which gives x = Minv @ b = M^-1 @ b.

sigma : real

Find eigenvalues near sigma using shift-invert mode. This requires an operator to compute the solution of the linear system [A - sigma * M] x = b, where M is the identity matrix if unspecified. This is computed internally via a (sparse) LU decomposition for explicit matrices A & M, or via an iterative solver if either A or M is a general linear operator. Alternatively, the user can supply the matrix or operator OPinv, which gives x = OPinv @ b = [A - sigma * M]^-1 @ b. Regardless of the selected mode (normal, cayley, or buckling), OPinv should always be supplied as OPinv = [A - sigma * M]^-1.

Note that when sigma is specified, the keyword 'which' refers to the shifted eigenvalues w'[i] where:

if mode == 'normal': w'[i] = 1 / (w[i] - sigma).

if mode == 'cayley': w'[i] = (w[i] + sigma) / (w[i] - sigma).

if mode == 'buckling': w'[i] = w[i] / (w[i] - sigma).

(see further discussion in 'mode' below)

v0 : ndarray, optional

Starting vector for iteration. Default: random

ncv : int, optional

The number of Lanczos vectors generated ncv must be greater than k and smaller than n; it is recommended that ncv > 2*k. Default: min(n, max(2*k + 1, 20))

which : str ['LM' | 'SM' | 'LA' | 'SA' | 'BE']

If A is a complex Hermitian matrix, 'BE' is invalid. Which k eigenvectors and eigenvalues to find:

'LM'Largest (in magnitude) eigenvalues.

'SM'Smallest (in magnitude) eigenvalues.

'LA'Largest (algebraic) eigenvalues.

'SA'Smallest (algebraic) eigenvalues.

'BE'Half (k/2) from each end of the spectrum.

When k is odd, return one more (k/2+1) from the high end. When sigma != None, 'which' refers to the shifted eigenvalues w'[i] (see discussion in 'sigma', above). ARPACK is generally better at finding large values than small values. If small eigenvalues are desired, consider using shift-invert mode for better performance.

maxiter : int, optional

Maximum number of Arnoldi update iterations allowed. Default: n*10

tol : float

Relative accuracy for eigenvalues (stopping criterion). The default value of 0 implies machine precision.

Minv : N x N matrix, array, sparse matrix, or LinearOperator

See notes in M, above.

OPinv : N x N matrix, array, sparse matrix, or LinearOperator

See notes in sigma, above.

return_eigenvectors : bool

Return eigenvectors (True) in addition to eigenvalues. This value determines the order in which eigenvalues are sorted. The sort order is also dependent on the which variable.

For which = 'LM' or 'SA':

If return_eigenvectors is True, eigenvalues are sorted by algebraic value.

If return_eigenvectors is False, eigenvalues are sorted by absolute value.

For which = 'BE' or 'LA':

eigenvalues are always sorted by algebraic value.

For which = 'SM':

If return_eigenvectors is True, eigenvalues are sorted by algebraic value.

If return_eigenvectors is False, eigenvalues are sorted by decreasing absolute value.

mode : string ['normal' | 'buckling' | 'cayley']

Specify strategy to use for shift-invert mode. This argument applies only for real-valued A and sigma != None. For shift-invert mode, ARPACK internally solves the eigenvalue problem OP @ x'[i] = w'[i] * B @ x'[i] and transforms the resulting Ritz vectors x'[i] and Ritz values w'[i] into the desired eigenvectors and eigenvalues of the problem A @ x[i] = w[i] * M @ x[i]. The modes are as follows:

'normal' :

OP = [A - sigma * M]^-1 @ M, B = M, w'[i] = 1 / (w[i] - sigma)

'buckling' :

OP = [A - sigma * M]^-1 @ A, B = A, w'[i] = w[i] / (w[i] - sigma)

'cayley' :

OP = [A - sigma * M]^-1 @ [A + sigma * M], B = M, w'[i] = (w[i] + sigma) / (w[i] - sigma)

The choice of mode will affect which eigenvalues are selected by the keyword 'which', and can also impact the stability of convergence (see [2] for a discussion).

rng : `numpy.random.Generator`, optional

Pseudorandom number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator.