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https://issues.apache.org/jira/browse/SPARK-17824?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yanbo Liang updated SPARK-17824:
--------------------------------
    Description: 
Cholesky decomposition is unstable (for near-singular and rank deficient 
matrices) and only works on positive definite matrices which can not 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]

  was:
Cholesky decomposition is unstable (for near-singular and rank deficient 
matrices), it was often used when matrix A is very large and sparse due to 
faster calculation. Cholesky decomposition only works on positive definite 
matrices which can not guaranteed in all cases.
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]


> 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
>
> Cholesky decomposition is unstable (for near-singular and rank deficient 
> matrices) and only works on positive definite matrices which can not 
> 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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