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From "Dimitri Pourbaix (JIRA)" <j...@apache.org>
Subject [jira] Resolved: (MATH-342) SVD crashes when applied to a strongly rectangular matrix (typical case of least-squares problem)
Date Sun, 21 Feb 2010 21:51:27 GMT

     [ 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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