[Numpy-discussion] matrix norm
Charles R Harris
charlesr.harris at gmail.com
Mon Oct 22 12:08:46 EDT 2012
On Mon, Oct 22, 2012 at 10:00 AM, Jason Grout
<jason-sage at creativetrax.com>wrote:
> On 10/22/12 10:56 AM, Charles R Harris wrote:
> > On Mon, Oct 22, 2012 at 9:44 AM, Jason Grout
> > <jason-sage at creativetrax.com <mailto:jason-sage at creativetrax.com>>
> > I'm curious why scipy/numpy defaults to calculating the Frobenius
> > for matrices , when Matlab, Octave, and Mathematica all default to
> > calculating the induced 2-norm . Is it solely because the
> > norm is easier to calculate, or is there some other good mathematical
> > reason for doing things differently?
> > Looks to me like Matlab, Octave, and Mathematica all default to the
> > Frobenius norm .
> Am I not reading their docs correctly?
> * Matlab (http://www.mathworks.com/help/matlab/ref/norm.html).
> "n = norm(X) is the same as n = norm(X,2)." (and "n = norm(X,2) returns
> the 2-norm of X.")
> * Octave (http://www.network-theory.co.uk/docs/octave3/octave_198.html).
The 2-norm and the Frobenius norm are the same thing.
> "Compute the p-norm of the matrix a. If the second argument is missing,
> p = 2 is assumed."
> * Mathematica (http://reference.wolfram.com/mathematica/ref/Norm.html)
> "For matrices, Norm[m] gives the maximum singular value of m."
OK, looks like Mathematica does return the induced (operator) norm. I
didn't see that bit.
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