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Posted to issues@commons.apache.org by "Dimitri Pourbaix (JIRA)" <ji...@apache.org> on 2010/02/21 22:51:27 UTC

[jira] Resolved: (MATH-342) SVD crashes when applied to a strongly rectangular matrix (typical case of least-squares problem)

     [ https://issues.apache.org/jira/browse/MATH-342?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dimitri Pourbaix resolved MATH-342.
-----------------------------------

       Resolution: Fixed
    Fix Version/s: Nightly Builds

The two identified troublesome behaviors of EigenDecomposition are corrected.  Besides the regular unit tests, the two classes SingularValueDecompositionimpl and EigenDecompositionImpl have now been successfully tested over 300k+ systems coming from some astronomical application.  No crash reported!

> SVD crashes when applied to a strongly rectangular matrix (typical case of least-squares problem)
> -------------------------------------------------------------------------------------------------
>
>                 Key: MATH-342
>                 URL: https://issues.apache.org/jira/browse/MATH-342
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: Nightly Builds
>            Reporter: Dimitri Pourbaix
>            Assignee: Dimitri Pourbaix
>             Fix For: Nightly Builds
>
>
> When SVD is applied to a strongly rectangular matrix (number of rows way larger than number of columns, typical case of least-squares problem), finite precision arithmetics shows up:
>  - in EigenDecompositionImpl.isSymmetric: a by-definition symmetric matrix returns false;
>  - in EigenDecompositionImpl.findEigenVectors: too many iterations exception 

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