## NORM Norm Calculation

Section: Array Generation and Manipulations

### Usage

Calculates the norm of a matrix. There are two ways to use the `norm` function. The general syntax is

       y = norm(A,p)

where `A` is the matrix to analyze, and `p` is the type norm to compute. The following choices of `p` are supported

- `p = 1` returns the 1-norm, or the max column sum of A
- `p = 2` returns the 2-norm (largest singular value of A)
- `p = inf` returns the infinity norm, or the max row sum of A
- `p = 'fro'` returns the Frobenius-norm (vector Euclidean norm, or RMS value)

For a vector, the regular norm calculations are performed:

- `1 <= p < inf` returns `sum(abs(A).^p)^(1/p)`
- `p` unspecified returns `norm(A,2)`
- `p = inf` returns max(abs(A))
- `p = -inf` returns min(abs(A))

### Examples

Here are the various norms calculated for a sample matrix

    --> A = float(rand(3,4))

    A =
        0.0063    0.2224    0.7574    0.9848
        0.7319    0.1965    0.7191    0.7010
        0.8319    0.6392    0.8905    0.9280

    --> norm(A,1)

    ans =
        2.6138

    --> norm(A,2)

    ans =
        2.3403

    --> norm(A,inf)

    ans =
        3.2896

    --> norm(A,'fro')

    ans =
        2.4353

Next, we calculate some vector norms.

    --> A = float(rand(4,1))

    A =
        0.5011
        0.3269
        0.8192
        0.7321

    --> norm(A,1)

    ans =
        2.3792

    --> norm(A,2)

    ans =
        1.2510

    --> norm(A,7)

    ans =
        0.8671

    --> norm(A,inf)

    ans =
        0.8192

    --> norm(A,-inf)

    ans =
        0.3269