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Posted to issues@commons.apache.org by "Dimitri Pourbaix (JIRA)" <ji...@apache.org> on 2010/02/21 22:43:27 UTC
[jira] Created: (MATH-342) SVD crashes when applied to a strongly
rectangular matrix (typical case of least-squares problem)
SVD crashes when applied to a strongly rectangular matrix (typical case of least-squares problem)
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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
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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[jira] Closed: (MATH-342) SVD crashes when applied to a strongly
rectangular matrix (typical case of least-squares problem)
Posted by "Phil Steitz (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MATH-342?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Phil Steitz closed MATH-342.
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> 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: 2.0
> Reporter: Dimitri Pourbaix
> Assignee: Dimitri Pourbaix
> Fix For: 2.1
>
>
> 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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[jira] Updated: (MATH-342) SVD crashes when applied to a strongly
rectangular matrix (typical case of least-squares problem)
Posted by "Phil Steitz (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MATH-342?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Phil Steitz updated MATH-342:
-----------------------------
Affects Version/s: (was: Nightly Builds)
2.0
Fix Version/s: (was: Nightly Builds)
2.1
> 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: 2.0
> Reporter: Dimitri Pourbaix
> Assignee: Dimitri Pourbaix
> Fix For: 2.1
>
>
> 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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[jira] Resolved: (MATH-342) SVD crashes when applied to a strongly
rectangular matrix (typical case of least-squares problem)
Posted by "Dimitri Pourbaix (JIRA)" <ji...@apache.org>.
[ 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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You can reply to this email to add a comment to the issue online.