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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 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]

  was:
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]


> 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 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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