scipy.sparse.linalg.svds¶
- scipy.sparse.linalg.svds(A, k=6, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True)[source]¶
Compute the largest k singular values/vectors for a sparse matrix.
Parameters : A : sparse matrix
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.
ncv : integer, optional
The number of Lanczos vectors generated ncv must be greater than k+1 and smaller than n; it is recommended that ncv > 2*k
tol : float, optional
Tolerance for singular values. Zero (default) means machine precision.
which : str, [‘LM’ | ‘SM’], optional
Which k singular values to find:
- ‘LM’ : largest singular values
- ‘SM’ : smallest singular values
New in version 0.12.0.
v0 : ndarray, optional
Starting vector for iteration, of length min(A.shape). Should be an (approximate) right singular vector if N > M and a right singular vector otherwise.
New in version 0.12.0.
maxiter: integer, optional
Maximum number of iterations.
New in version 0.12.0.
return_singular_vectors : bool, optional
Return singular vectors (True) in addition to singular values
New in version 0.12.0.
Returns
——-
u : ndarray, shape=(M, k)
Unitary matrix having left singular vectors as columns.
s : ndarray, shape=(k,)
The singular values.
vt : ndarray, shape=(k, N)
Unitary matrix having right singular vectors as rows.
Notes
This is a naive implementation using ARPACK as an eigensolver on A.H * A or A * A.H, depending on which one is more efficient.