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From Joseph Bradley <jos...@databricks.com>
Subject Re: Solve least square problem of the form min norm(A x - b)^2^ + lambda * n * norm(x)^2 ?
Date Tue, 03 Mar 2015 21:10:55 GMT
The minimization problem you're describing in the email title also looks
like it could be solved using the RidgeRegression solver in MLlib, once you
transform your DistributedMatrix into an RDD[LabeledPoint].

On Tue, Mar 3, 2015 at 11:02 AM, Shivaram Venkataraman <
shivaram@eecs.berkeley.edu> wrote:

> There are couple of solvers that I've written that is part of the AMPLab
> ml-matrix repo [1,2]. These aren't part of MLLib yet though and if you are
> interested in porting them I'd be happy to review it
>
> Thanks
> Shivaram
>
>
> [1]
> https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala
> [2]
> https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/NormalEquations.scala
>
> On Tue, Mar 3, 2015 at 9:01 AM, Jaonary Rabarisoa <jaonary@gmail.com>
> wrote:
>
>> Dear all,
>>
>> Is there a least square solver based on DistributedMatrix that we can use
>> out of the box in the current (or the master) version of spark ?
>> It seems that the only least square solver available in spark is private
>> to recommender package.
>>
>>
>> Cheers,
>>
>> Jao
>>
>
>

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