- normP2
Computes the p=2 norm. If A is a matrix then the induced norm is computed.
- normF
Computes the Frobenius matrix norm:
normF = Sqrt{ ∑i=1:m ∑j=1:n { aij2} }
This is equivalent to
- conditionP2
The condition p = 2 number of a matrix is used to measure the sensitivity of
the linear system Ax=b
- normP1
Computes the p=1 norm. If A is a matrix then the induced norm is computed.
- normPInf
Computes the p=∞ norm. If A is a matrix then the induced norm is computed.
- elementP
Element wise p-norm:
norm = {∑i=1:m ∑j=1:n { |aij|p}}1/p
This is not the same as the induced p-n
- fastElementP
Same as #elementP but runs faster by not mitigating overflow/underflow related
problems.
- fastNormF
This implementation of the Frobenius norm is a straight forward implementation
and can be susceptib
- fastNormP2
Computes the p=2 norm. If A is a matrix then the induced norm is computed. This
implementation is fa
- inducedP1
Computes the induced p = 1 matrix norm.
||A||1= max(j=1 to n; sum(i=1 to m; |aij|))
- inducedP2
Computes the induced p = 2 matrix norm, which is the largest singular value.
- normP
Computes either the vector p-norm or the induced matrix p-norm depending on A
being a vector or a ma