Matlab uses QR for rectangular matrices, and LU for (general) square
matrices.
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It seems like the rank() method does not implement a fuzzy zero, so it does
not make too much sense for floating-point numbers. But for any cutoff for
the eigenvalues there is going to be some matrix with m.rank() !=
m.transpose().rank(), though hopefully not a 3x3 one ;-) As Jason already
ment
I was just shown even better example of how bad this can get:
sage: m=matrix(RDF,[[1.24,0.2,2],[2.48,0.4,4],[3.72,0.6,1]])
sage: print m.rank()
3
sage: print m.transpose().rank()
2
it seems that a more robust way here would be to call the LU-decomposition,
which calls scipy (or/and numpy?),
so th
On Thu, Sep 8, 2011 at 7:09 PM, Dima Pasechnik wrote:
>
>
> On Thursday, 8 September 2011 23:55:00 UTC+8, William wrote:
>>
>> On Thu, Sep 8, 2011 at 8:28 AM, Rado wrote:
>> > sage: L = [[2,4,16],[1/2,1,2],[1,1,1]]
>> > sage: A = matrix(RDF, L)
>> > sage: xvalues = [sqrt(2), 2, 4]
>> > sage: L2 =
On Thursday, 8 September 2011 23:55:00 UTC+8, William wrote:
>
> On Thu, Sep 8, 2011 at 8:28 AM, Rado wrote:
> > sage: L = [[2,4,16],[1/2,1,2],[1,1,1]]
> > sage: A = matrix(RDF, L)
> > sage: xvalues = [sqrt(2), 2, 4]
> > sage: L2 = [[i^2 for i in xvalues], [log(i, 2) for i in xvalues], [1 for
>
On Thu, Sep 8, 2011 at 8:28 AM, Rado wrote:
> sage: L = [[2,4,16],[1/2,1,2],[1,1,1]]
> sage: A = matrix(RDF, L)
> sage: xvalues = [sqrt(2), 2, 4]
> sage: L2 = [[i^2 for i in xvalues], [log(i, 2) for i in xvalues], [1 for i
> in xvalues]]
> sage: A2 = matrix(RDF, L2)
> sage: print (A - A2).norm()
>
sage: L = [[2,4,16],[1/2,1,2],[1,1,1]]
sage: A = matrix(RDF, L)
sage: xvalues = [sqrt(2), 2, 4]
sage: L2 = [[i^2 for i in xvalues], [log(i, 2) for i in xvalues], [1 for i
in xvalues]]
sage: A2 = matrix(RDF, L2)
sage: print (A - A2).norm()
1.11022302463e-16
sage: b = vector(RDF, [5, 20, 106])
sag