We have been working on large scale QR decompositions which would fit
this problem well  TSQR
[https://github.com/amplab/mlmatrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala]
for instance can be used to solve the least squares system if you have
more equations than variables (overdetermined)
We plan to merge some of these into Spark as a part of
https://issues.apache.org/jira/browse/SPARK3434 as well
Thanks
Shivaram
On Thu, Oct 23, 2014 at 1:54 AM, Sean Owen <sowen@cloudera.com> wrote:
> The 0 vector is a trivial solution. Is the data big, such that it
> can't be computed on one machine? if so I assume this system is
> overdetermined. You can use a decomposition to find a leastsquares
> solution, but the SVD is overkill and in any event distributed
> decompositions don't exist in the project. You can solve it a linear
> regression as Mr Das says.
>
> If it's small enough to fit locally you should just use a matrix
> library to solve Ax = b with the QR decomposition or something, with
> Breeze or Commons Math or octave or R. Lots of options if it's
> smallish.
>
> On Thu, Oct 23, 2014 at 12:15 AM, Martin Enzinger
> <martin.enzinger@gmail.com> wrote:
>> Hi,
>>
>> I'm wondering how to use Mllib for solving equation systems following this
>> pattern
>>
>> 2*x1 + x2 + 3*x3 + .... + xn = 0
>> x1 + 0*x2 + 3*x3 + .... + xn = 0
>> ..........
>> ..........
>> 0*x1 + x2 + 0*x3 + .... + xn = 0
>>
>> I definitely still have some reading to do to really understand the direct
>> solving techniques, but at the current state of "knowledge" SVD could help
>> me with this right?
>>
>> Can you point me to an example or a tutorial?
>>
>> best regards
>
> 
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