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# 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)

  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.

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