[jira] [Resolved] (SPARK-17824) QR solver for WeightedLeastSquares

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17824?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-17824.
----------------------------------
    Resolution: Incomplete

> QR solver for WeightedLeastSquares
> ----------------------------------
>
>                 Key: SPARK-17824
>                 URL: https://issues.apache.org/jira/browse/SPARK-17824
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>            Reporter: Yanbo Liang
>            Assignee: Yanbo Liang
>            Priority: Major
>              Labels: bulk-closed
>
> Cholesky decomposition is unstable (for near-singular and rank deficient matrices) and only works on positive definite matrices which can not be guaranteed in all cases, it was often used when matrix A is very large and sparse due to faster calculation. QR decomposition has better numerical properties than Cholesky and can works on matrices which are not positive definite. Spark MLlib {{WeightedLeastSquares}} use Cholesky decomposition to solve normal equation currently, we should also support or move to QR solver for better stability. I'm preparing to send a PR.
> cc [~dbtsai] [~sethah]



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