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scipy.sparse.linalg.

# onenormest

scipy.sparse.linalg.onenormest(A, t=2, itmax=5, compute_v=False, compute_w=False)

  Compute a lower bound of the 1-norm of a sparse array.

  Parameters:
    Andarray or other linear operator

      A linear operator that can be transposed and that can produce matrix products.

    tint, optional

      A positive parameter controlling the tradeoff between accuracy versus time and memory usage. Larger values take longer and use more memory but give more accurate output.

    itmaxint, optional

      Use at most this many iterations.

    compute_vbool, optional

      Request a norm-maximizing linear operator input vector if True.

    compute_wbool, optional

      Request a norm-maximizing linear operator output vector if True.

  Returns:
    estfloat

      An underestimate of the 1-norm of the sparse array.

    vndarray, optional

      The vector such that ||Av||_1 == est*||v||_1. It can be thought of as an input to the linear operator that gives an output with particularly large norm.

    wndarray, optional

      The vector Av which has relatively large 1-norm. It can be thought of as an output of the linear operator that is relatively large in norm compared to the input.

  Notes

  This is algorithm 2.4 of [1].

  In [2] it is described as follows. “This algorithm typically requires the evaluation of about 4t matrix-vector products and almost invariably produces a norm estimate (which is, in fact, a lower bound on the norm) correct to within a factor 3.”

  Added in version 0.13.0.

  References

  [1]

  Nicholas J. Higham and Francoise Tisseur (2000), “A Block Algorithm for Matrix 1-Norm Estimation, with an Application to 1-Norm Pseudospectra.” SIAM J. Matrix Anal. Appl. Vol. 21, No. 4, pp. 1185-1201.

  [2]

  Awad H. Al-Mohy and Nicholas J. Higham (2009), “A new scaling and squaring algorithm for the matrix exponential.” SIAM J. Matrix Anal. Appl. Vol. 31, No. 3, pp. 970-989.

  Examples

      >>> import numpy as np
      >>> from scipy.sparse import csc_array
      >>> from scipy.sparse.linalg import onenormest
      >>> A = csc_array([[1., 0., 0.], [5., 8., 2.], [0., -1., 0.]], dtype=float)
      >>> A.toarray()
      array([[ 1.,  0.,  0.],
             [ 5.,  8.,  2.],
             [ 0., -1.,  0.]])
      >>> onenormest(A)
      9.0
      >>> np.linalg.norm(A.toarray(), ord=1)
      9.0