I think problem is actually due to the inverse using a non-numerically
stable echelon form algorithm for inexact fields.  For example, if you
using matrices over RDF:

M=matrix(RDF,[[7,3,10,13],[1,1,2,2],[1,2,3,4],[1,3,5,7]]);det(M);invM=M^(-1);invM*M;det(invM)

you don't get this problem.  My guess is that if we implemented
something like partial pivoting for inexact fields.

--Mike
--Mike


On Mon, Jul 1, 2013 at 10:06 AM, Harald Schilly
<harald.schi...@gmail.com> wrote:
>
>
> On Monday, July 1, 2013 10:45:36 AM UTC+2, David Ingerman wrote:
>>
>> The following matrix operation produces wrong answer in online Sage:
>>
>> M=matrix(RR,[[7,3,10,13],[1,1,2,2],[1,2,3,4],[1,3,5,7]]);det(M);invM=M^(-1);invM*M;det(invM)
>
>
> RR stands for the "real numbers" with the usual 53bits of precision, e.g.
> 5.123957322….
> QQ are rational numbers where two large integers build up each number,
> e.g. 41000041/333000333000333000333000333000333000333
> Therefore, QQ has a much higher precision … but is much slower and uses more
> memory.
>
> In your case, the lack of precision in RR causes you troubles and you have
> to find a way to pose the problem you want to solve differently. You cannot
> rely on QQ, because in bad cases, the expressions blow up and eat all your
> memory. More generally, this is not a Sage related problem, but related to
> all calculations your are doing "natively" with your CPU.
>
> To see in advance when this happens, you have to calculate the conditional
> number of the matrix. I think that's only in numpy (or I haven't found it).
>
> sage: M=matrix(RR,[[7,3,10,13],[1,1,2,2],[1,2,3,4],[1,3,5,7]])
> sage: import numpy as np
> sage: np.linalg.cond(M)
> 104.85355762315329
>
> http://en.wikipedia.org/wiki/Condition_number
>
> Here are some decomposition methods that might help:
>
> http://en.wikipedia.org/wiki/Matrix_decomposition
>
> H
>
>
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