Apache Spark commented on SPARK-17748:

User 'sethah' has created a pull request for this issue:

> One-pass algorithm for linear regression with L1 and elastic-net penalties
> --------------------------------------------------------------------------
>                 Key: SPARK-17748
>                 URL: https://issues.apache.org/jira/browse/SPARK-17748
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>            Reporter: Seth Hendrickson
>            Assignee: Seth Hendrickson
> Currently linear regression uses weighted least squares to solve the normal 
> equations locally on the driver when the dimensionality is small (<4096). 
> Weighted least squares uses a Cholesky decomposition to solve the problem 
> with L2 regularization (which has a closed-form solution). We can support 
> L1/elasticnet penalties by solving the equations locally using OWL-QN solver.
> Also note that Cholesky does not handle singular covariance matrices, but 
> L-BFGS and OWL-QN are capable of providing reasonable solutions. This patch 
> can also add support for solving singular covariance matrices by also adding 

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