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https://issues.apache.org/jira/browse/MATH-1100?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13907168#comment-13907168
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Luc Maisonobe commented on MATH-1100:
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In your code, you don't set the singularity threshold as you call new
QRDecomposition(M2) with only the matrix as an argument.
This ends up with using an exact 0 as the threshold.
If you use new QRDecomposition(M2, 2.2e-14), the matrix is corrctly identified
as singular. There seem to be two very small values after decomposition on the
diagonal of the triangula matrix. One is about 2.05e-14 the other is about
2.2e-14, so depending on the threshold, you should get a rank of 379, 380, or
381.
Do you agree with this analysis?
> QR factorization fails in revealing rank-deficient matrix
> ---------------------------------------------------------
>
> Key: MATH-1100
> URL: https://issues.apache.org/jira/browse/MATH-1100
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 3.2
> Environment: Java HotSpot(TM) Client VM (build 19.1-b02, mixed mode,
> sharing) an windows 7 professional
> Reporter: alberto trivellato
> Attachments: QRtest.zip
>
>
> given a matrix that has not full rank, the method getSolver().isNonSingular()
> of the class QRDecomposition returns true.
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