| FreeMat
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Section: Array Generation and Manipulations
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))  Here are the various norms calculated for a sample matrix
--> A = float(rand(3,4))
A = 
    0.8462    0.9465    0.6874    0.8668 
    0.1218    0.9206    0.5877    0.5837 
    0.7081    0.6608    0.2035    0.5083 
--> norm(A,1)
ans = 
    2.5280 
--> norm(A,2)
ans = 
    2.2997 
--> norm(A,inf)
ans = 
    3.3470 
--> norm(A,'fro')
ans = 
    2.3712 
Next, we calculate some vector norms.
--> A = float(rand(4,1))
A = 
    0.3458 
    0.1427 
    0.3998 
    0.7194 
--> norm(A,1)
ans = 
    1.6078 
--> norm(A,2)
ans = 
    0.9041 
--> norm(A,7)
ans = 
    0.7217 
--> norm(A,inf)
ans = 
    0.7194 
--> norm(A,-inf)
ans = 
    0.1427