# `linalg.misc`¶

## Module Contents¶

### Functions¶

 `norm`(a,ord=None,axis=None,keepdims=False) Matrix or vector norm. `_datacopied`(arr,original) Strict check for arr not sharing any data with original,
`norm`(a, ord=None, axis=None, keepdims=False)

Matrix or vector norm.

This function is able to return one of seven different matrix norms, or one of an infinite number of vector norms (described below), depending on the value of the `ord` parameter.

a : (M,) or (M, N) array_like
Input array. If axis is None, a must be 1-D or 2-D.
ord : {non-zero int, inf, -inf, ‘fro’}, 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.
n : float or ndarray
Norm of the matrix or vector(s).

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
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]:

The `axis` and `keepdims` arguments are passed directly to `numpy.linalg.norm` and are only usable if they are supported by the version of numpy in use.

 [1] G. H. Golub and C. F. Van Loan, Matrix Computations, Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
```>>> 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
>>> norm(b, np.inf)
9
>>> norm(a, -np.inf)
0
>>> norm(b, -np.inf)
2
```
```>>> norm(a, 1)
20
>>> norm(b, 1)
7
>>> norm(a, -1)
-4.6566128774142013e-010
>>> norm(b, -1)
6
>>> norm(a, 2)
7.745966692414834
>>> norm(b, 2)
7.3484692283495345
```
```>>> norm(a, -2)
0
>>> norm(b, -2)
1.8570331885190563e-016
>>> norm(a, 3)
5.8480354764257312
>>> norm(a, -3)
0
```
`_datacopied`(arr, original)

Strict check for arr not sharing any data with original, under the assumption that arr = asarray(original)