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Section: Array Generation and Manipulations
Calculates the condition number of a matrix. To compute the 2-norm condition number of a matrix (ratio of largest to smallest singular values), use the syntax
y = cond(A)
where A is a matrix. If you want to compute the condition number in a different norm (e.g., the 1-norm), use the second syntax
y = cond(A,p)
 where p is the norm to use when computing the condition number. 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)  The condition number is defined as
![\[ \frac{\|A\|_p}{\|A^{-1}\|_p} \]](form_0.png) 
 This equation is precisely how the condition number is computed for the case p ~= 2. For the p=2 case, the condition number can be computed much more efficiently using the ratio of the largest and smallest singular values. 
The condition number of this matrix is large
--> A = [1,1;0,1e-15]
A = 
    1.0000    1.0000 
         0    0.0000 
--> cond(A)
ans = 
 2.0000e+15 
--> cond(A,1)
ans = 
 2000000000000002 
You can also (for the case p=1 use rcond to calculate an estimate of the condition number
--> 1/rcond(A) ans = 2.0000e+15