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# scipy.linalg.norm

`scipy.linalg.``norm`(x, ord=None)

  Matrix or vector norm.

  > Parameters: | x : array, shape (M,) or (M, N) ord : number, or {None, 1, -1, 2, -2, inf, -inf, ‘fro’} Order of the norm:
  > ord | norm for matrices | norm for vectors
  > --- | --- | ---
  > None | Frobenius norm | 2-norm
  > ‘fro’ | Frobenius norm | –
  > inf | max(sum(abs(x), axis=1)) | max(abs(x))
  > -inf | min(sum(abs(x), axis=1)) | min(abs(x))
  > 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)
  Returns: | n : float Norm of the matrix or vector

  Notes

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

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