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https://issues.apache.org/jira/browse/MATH-664?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13100318#comment-13100318
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Phil Steitz commented on MATH-664:
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At the expense of returning nonsense if the problem is singular. The only
positive rationale for this change is that QR is more stable numerically for
near-singular positive definite matrices. It would be great to add some
references supporting that statement. While the covariance matrices are
generally going to be small, performance should also be considered. Cholesky
should also be considered as a possible replacement for LU.
I will open a separate issue on QR's lack of a meaningful singularity test.
> Replace "LUDecompostionImpl" with "QRDecompositionImpl" in
> "AbstractLeastSquaresOptimizer"
> ------------------------------------------------------------------------------------------
>
> Key: MATH-664
> URL: https://issues.apache.org/jira/browse/MATH-664
> Project: Commons Math
> Issue Type: Improvement
> Reporter: Gilles
> Assignee: Gilles
> Priority: Minor
> Fix For: 3.0
>
>
> In some cases, the "getCovariances()" method throws a
> "SingularMatrixException". This can be avoided by using "QR" instead of "LU"
> decomposition.
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