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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. (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.
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