On Mon, Jul 1, 2013 at 5:27 AM, David Ingerman <daviddavif...@gmail.com> wrote:
> On Monday, July 1, 2013 2:06:54 AM UTC-7, Harald Schilly 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
>
>  But this is a very small matrix and conditional number is not large...

Using RDF is better:

sage: 
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)
-7.0
[               1.0                0.0                0.0   -8.881784197e-16]
[               0.0                1.0                0.0   -8.881784197e-16]
[  4.4408920985e-16                0.0                1.0    8.881784197e-16]
[-5.55111512313e-17 -2.77555756156e-16 -1.66533453694e-16                1.0]
-0.142857142857

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