On 10/22/12 10:56 AM, Charles R Harris wrote: > > > On Mon, Oct 22, 2012 at 9:44 AM, Jason Grout > <jason-s...@creativetrax.com <mailto:jason-s...@creativetrax.com>> wrote: > > I'm curious why scipy/numpy defaults to calculating the Frobenius norm > for matrices [1], when Matlab, Octave, and Mathematica all default to > calculating the induced 2-norm [2]. Is it solely because the Frobenius > 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). "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." Thanks, Jason _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
