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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:
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Description:
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. QR decomposition has better numerical properties than
Cholesky. Spark MLlib {{WeightedLeastSquares}} use Cholesky decomposition to
solve normal equation currently, we should also support or move to QR solver
for better stability.
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. QR decomposition has better numerical properties than
Cholesky. Spark MLlib {{WeightedLeastSquares}} use Cholesky decomposition to
solve normal equation currently, we should also support or move to QR solver
for better stability.
> 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), it was often used when matrix A is very large and sparse due to
> faster calculation. QR decomposition has better numerical properties than
> Cholesky. Spark MLlib {{WeightedLeastSquares}} use Cholesky decomposition to
> solve normal equation currently, we should also support or move to QR solver
> for better stability.
> cc [~dbtsai] [~sethah]
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