Hi,
Notice cross-post, I hope you bear over with me for doing that (and I
imagine that some of you also like python in the matlab-group like
myself)...
------------------------------------------
Python vs. Matlab:
------------------------------------------
Python:
========
from numpy import matrix
from numpy import linalg
A = matrix( [[1,2,3],[11,12,13],[21,22,23]] )
print "A="
print A
print "A.I (inverse of A)="
print A.I
A.I (inverse of A)=
[[ 2.81466387e+14 -5.62932774e+14 2.81466387e+14]
[ -5.62932774e+14 1.12586555e+15 -5.62932774e+14]
[ 2.81466387e+14 -5.62932774e+14 2.81466387e+14]]
Matlab:
========
>> A=[1 2 3; 11 12 13; 21 22 23]
A =
1 2 3
11 12 13
21 22 23
>> inv(A)
Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.067522e-17.
ans =
1.0e+15 *
0.3002 -0.6005 0.3002
-0.6005 1.2010 -0.6005
0.3002 -0.6005 0.3002
------------------------------------------
Python vs. Matlab:
------------------------------------------
So Matlab at least warns about "Matrix is close to singular or badly
scaled", which python (and I guess most other languages) does not...
Which is the most accurate/best, even for such a bad matrix? Is it
possible to say something about that? Looks like python has a lot more
digits but maybe that's just a random result... I mean.... Element 1,1 =
2.81e14 in Python, but something like 3e14 in Matlab and so forth -
there's a small difference in the results...
With python, I would also kindly ask about how to avoid this problem in
the future, I mean, this maybe means that I have to check the condition
number at all times before doing anything at all ? How to do that?
I hope you matlabticians like this topic, at least I myself find it
interesting and many of you probably also program in some other language
and then maybe you'll find this worthwhile to read about.
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