We have been working on large scale QR decompositions which would fit this problem well -- TSQR [https://github.com/amplab/ml-matrix/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 (over-determined)
We plan to merge some of these into Spark as a part of https://issues.apache.org/jira/browse/SPARK-3434 as well Thanks Shivaram On Thu, Oct 23, 2014 at 1:54 AM, Sean Owen <so...@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 > over-determined. You can use a decomposition to find a least-squares > 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.enzin...@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 > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
