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scipy.linalg.

# norm

scipy.linalg.norm(a, ord=None, axis=None, keepdims=False, check_finite=True)

  Matrix or vector norm.

  This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the `ord` parameter. For tensors with rank different from 1 or 2, only ord=None is supported.

  Parameters:
    a array_like

      Input array. If axis is None, a must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of `a.ravel` will be returned.

    ord{int, inf, -inf, ‘fro’, ‘nuc’, None}, optional

      Order of the norm (see table under `Notes`). inf means NumPy’s inf object.

    axis{int, 2-tuple of ints, None}, optional

      If axis is an integer, it specifies the axis of a along which to compute the vector norms. If axis is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed. If axis is None then either a vector norm (when a is 1-D) or a matrix norm (when a is 2-D) is returned.

    keepdims bool, optional

      If this is set to True, the axes which are normed over are left in the result as dimensions with size one. With this option the result will broadcast correctly against the original a.

    check_finite bool, optional

      Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

  Returns:
    n float or ndarray

      Norm of the matrix or vector(s).

  Notes

  For values of `ord <= 0`, the result is, strictly speaking, not a mathematical ‘norm’, but it may still be useful for various numerical purposes.

  The following norms can be calculated:

  ord | norm for matrices | norm for vectors
  --- | --- | ---
  None | Frobenius norm | 2-norm
  ‘fro’ | Frobenius norm | –
  ‘nuc’ | nuclear norm | –
  inf | max(sum(abs(a), axis=1)) | max(abs(a))
  -inf | min(sum(abs(a), axis=1)) | min(abs(a))
  0 | – | sum(a != 0)
  1 | max(sum(abs(a), axis=0)) | as below
  -1 | min(sum(abs(a), axis=0)) | as below
  2 | 2-norm (largest sing. value) | as below
  -2 | smallest singular value | as below
  other | – | sum(abs(a)**ord)**(1./ord)

  The Frobenius norm is given by [1]:

  > \(||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}\)

  The nuclear norm is the sum of the singular values.

  Both the Frobenius and nuclear norm orders are only defined for matrices.

  References

  [1]

  G. H. Golub and C. F. Van Loan, Matrix Computations, Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15

  Examples

      >>> import numpy as np
      >>> from scipy.linalg import norm
      >>> a = np.arange(9) - 4.0
      >>> a
      array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
      >>> b = a.reshape((3, 3))
      >>> b
      array([[-4., -3., -2.],
             [-1.,  0.,  1.],
             [ 2.,  3.,  4.]])

      >>> norm(a)
      7.745966692414834
      >>> norm(b)
      7.745966692414834
      >>> norm(b, 'fro')
      7.745966692414834
      >>> norm(a, np.inf)
      4.0
      >>> norm(b, np.inf)
      9.0
      >>> norm(a, -np.inf)
      0.0
      >>> norm(b, -np.inf)
      2.0

      >>> norm(a, 1)
      20.0
      >>> norm(b, 1)
      7.0
      >>> norm(a, -1)
      -4.6566128774142013e-010
      >>> norm(b, -1)
      6.0
      >>> norm(a, 2)
      7.745966692414834
      >>> norm(b, 2)
      7.3484692283495345

      >>> norm(a, -2)
      0.0
      >>> norm(b, -2)
      1.8570331885190563e-016
      >>> norm(a, 3)
      5.8480354764257312
      >>> norm(a, -3)
      0.0