svds(solver=’propack’)#
- scipy.sparse.linalg.svds(A, k=6, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack', rng=None, options=None)
- Partial singular value decomposition of a sparse matrix using PROPACK. - Compute the largest or smallest k singular values and corresponding singular vectors of a sparse matrix A. The order in which the singular values are returned is not guaranteed. - In the descriptions below, let - M, N = A.shape.- Parameters:
- Asparse matrix or LinearOperator
- Matrix to decompose. If A is a - LinearOperatorobject, it must define both- matvecand- rmatvecmethods.
- kint, default: 6
- Number of singular values and singular vectors to compute. Must satisfy - 1 <= k <= min(M, N).
- ncvint, optional
- Ignored. 
- tolfloat, optional
- The desired relative accuracy for computed singular values. Zero (default) means machine precision. 
- which{‘LM’, ‘SM’}
- Which k singular values to find: either the largest magnitude (‘LM’) or smallest magnitude (‘SM’) singular values. Note that choosing - which='SM'will force the- irloption to be set- True.
- v0ndarray, optional
- Starting vector for iterations: must be of length - A.shape[0]. If not specified, PROPACK will generate a starting vector.
- maxiterint, optional
- Maximum number of iterations / maximal dimension of the Krylov subspace. Default is - 10 * k.
- return_singular_vectors{True, False, “u”, “vh”}
- Singular values are always computed and returned; this parameter controls the computation and return of singular vectors. - True: return singular vectors.
- False: do not return singular vectors.
- "u": compute only the left singular vectors; return- Nonefor the right singular vectors.
- "vh": compute only the right singular vectors; return- Nonefor the left singular vectors.
 
- solver{‘arpack’, ‘propack’, ‘lobpcg’}, optional
- This is the solver-specific documentation for - solver='propack'. ‘arpack’ and ‘lobpcg’ are also supported.
- rngnumpy.random.Generator, optional
- Pseudorandom number generator state. When rng is None, a new - numpy.random.Generatoris created using entropy from the operating system. Types other than- numpy.random.Generatorare passed to- numpy.random.default_rngto instantiate a- Generator.
- optionsdict, optional
- A dictionary of solver-specific options. No solver-specific options are currently supported; this parameter is reserved for future use. 
 
- Returns:
- undarray, shape=(M, k)
- Unitary matrix having left singular vectors as columns. 
- sndarray, shape=(k,)
- The singular values. 
- vhndarray, shape=(k, N)
- Unitary matrix having right singular vectors as rows. 
 
 - Notes - This is an interface to the Fortran library PROPACK [1]. The current default is to run with IRL mode disabled unless seeking the smallest singular values/vectors ( - which='SM').- References [1]- Larsen, Rasmus Munk. “PROPACK-Software for large and sparse SVD calculations.” Available online. URL http://sun.stanford.edu/~rmunk/PROPACK (2004): 2008-2009. - Examples - Construct a matrix - Afrom singular values and vectors.- >>> import numpy as np >>> from scipy.stats import ortho_group >>> from scipy.sparse import csc_array, diags_array >>> from scipy.sparse.linalg import svds >>> rng = np.random.default_rng() >>> orthogonal = csc_array(ortho_group.rvs(10, random_state=rng)) >>> s = [0.0001, 0.001, 3, 4, 5] # singular values >>> u = orthogonal[:, :5] # left singular vectors >>> vT = orthogonal[:, 5:].T # right singular vectors >>> A = u @ diags_array(s) @ vT - With only three singular values/vectors, the SVD approximates the original matrix. - >>> u2, s2, vT2 = svds(A, k=3, solver='propack') >>> A2 = u2 @ np.diag(s2) @ vT2 >>> np.allclose(A2, A.todense(), atol=1e-3) True - With all five singular values/vectors, we can reproduce the original matrix. - >>> u3, s3, vT3 = svds(A, k=5, solver='propack') >>> A3 = u3 @ np.diag(s3) @ vT3 >>> np.allclose(A3, A.todense()) True - The singular values match the expected singular values, and the singular vectors are as expected up to a difference in sign. - >>> (np.allclose(s3, s) and ... np.allclose(np.abs(u3), np.abs(u.toarray())) and ... np.allclose(np.abs(vT3), np.abs(vT.toarray()))) True - The singular vectors are also orthogonal. - >>> (np.allclose(u3.T @ u3, np.eye(5)) and ... np.allclose(vT3 @ vT3.T, np.eye(5))) True