Sparse#

This page provides pylibraft class references for the publicly-exposed elements of the pylibraft.sparse.linalg.eigsh package.

pylibraft.sparse.linalg.eigsh( A, k=6, which='LM', v0=None, ncv=None, maxiter=None, tol=0, seed=None, handle=None, )[source]#

Find k eigenvalues and eigenvectors of the real symmetric square matrix or complex Hermitian matrix A.

Solves Ax = wx, the standard eigenvalue problem for w eigenvalues with corresponding eigenvectors x.

Args:

a (spmatrix): A symmetric square sparse CSR matrix with: dimension (n, n). a must be of type cupyx.scipy.sparse._csr.csr_matrix
k (int): The number of eigenvalues and eigenvectors to compute. Must be: 1 <= k < n.
which (str): ‘LM’ or ‘LA’ or ‘SA’.: ‘LM’: finds k largest (in magnitude) eigenvalues. ‘LA’: finds k largest (algebraic) eigenvalues. ‘SA’: finds k smallest (algebraic) eigenvalues. ‘SM’: finds k smallest (in magnitude) eigenvalues.
v0 (ndarray): Starting vector for iteration. If None, a random: unit vector is used.
ncv (int): The number of Lanczos vectors generated. Must be: k + 1 < ncv < n. If None, default value is used.
maxiter (int): Maximum number of Lanczos update iterations.: If None, default value is used.
tol (float): Tolerance for residuals ||Ax - wx||. If 0, machine: precision is used.

Returns:

tuple:: It returns w and x where w is eigenvalues and x is eigenvectors.