norm(B) computes the L2 norm of (the greatest singular value) while
norm(B,'fro') and sqrt(B'*B) compute the frobennius norm of B
__
B=rand(100,100);
tic();norm(B);t2=toc();
B=rand(100,100);
tic();norm(B,'fro');tf=toc();
that explain why /sqrt(trace(A_transp * A)) can be more efficient than
norm(A)
/
/Serge Steer
/
Le 26/09/2012 19:42, Paul Carrico a écrit :
Dear All,
A funny result for calculating the norm of a tensor ... for ounce a
"traditional" method (that probably uses vectorization in back stage)
is faster than the norm function ...
Paul
mode(0);
A= [ 1 2 3 ; 4 5 6; 7 8 9]
n= 1000
fori = 1 : n
for j = 1 : n
B(i,j) = i*j;
end
end
/// Scilab function/
_tic_();
_norm_(B)
t1= _toc_()
/// "traditional" method/
/// norm = sqrt(trace(A_transp * A))/
_tic_
norm_= (_trace_(B'*B))**0.5
t2= _toc_()
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