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# numpy.linalg.norm

linalg.norm(x, ord=None, axis=None, keepdims=False)

  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.

  Parameters:
    x array_like

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

    ord{int, float, inf, -inf, ‘fro’, ‘nuc’}, optional

      Order of the norm (see table under `Notes` for what values are supported for matrices and vectors respectively). inf means numpy’s `inf` object. The default is None.

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

      If axis is an integer, it specifies the axis of x 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 x is 1-D) or a matrix norm (when x is 2-D) is returned. The default is None.

    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 x.

  Returns:
    n float or ndarray

      Norm of the matrix or vector(s).

  See also

  `scipy.linalg.norm`

    Similar function in SciPy.

  Notes

  For values of `ord < 1`, 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(x), axis=1)) | max(abs(x))
  -inf | min(sum(abs(x), axis=1)) | min(abs(x))
  0 | – | sum(x != 0)
  1 | max(sum(abs(x), axis=0)) | as below
  -1 | min(sum(abs(x), axis=0)) | as below
  2 | 2-norm (largest sing. value) | as below
  -2 | smallest singular value | as below
  other | – | sum(abs(x)**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 and raise a ValueError when `x.ndim != 2`.

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

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

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

      >>> LA.norm(a, -2)
      0.0
      >>> LA.norm(b, -2)
      1.8570331885190563e-016 # may vary
      >>> LA.norm(a, 3)
      5.8480354764257312 # may vary
      >>> LA.norm(a, -3)
      0.0

  Using the axis argument to compute vector norms:

      >>> c = np.array([[ 1, 2, 3],
      ...               [-1, 1, 4]])
      >>> LA.norm(c, axis=0)
      array([ 1.41421356,  2.23606798,  5.        ])
      >>> LA.norm(c, axis=1)
      array([ 3.74165739,  4.24264069])
      >>> LA.norm(c, ord=1, axis=1)
      array([ 6.,  6.])

  Using the axis argument to compute matrix norms:

      >>> m = np.arange(8).reshape(2,2,2)
      >>> LA.norm(m, axis=(1,2))
      array([  3.74165739,  11.22497216])
      >>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
      (3.7416573867739413, 11.224972160321824)