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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 matrix.

  Parameters:

  A : ndarray or other linear operator

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

  t : int, 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.

  itmax : int, optional

  > Use at most this many iterations.

  compute_v : bool, optional

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

  compute_w : bool, optional

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

  Returns:

  est : float

  > An underestimate of the 1-norm of the sparse matrix.

  v : ndarray, 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.

  w : ndarray, 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.”

  New in version 0.13.0.

  References

  [R284] | 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.

  [R285] | 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.