## NORM Norm Calculation

### 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
```