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Posted to issues@commons.apache.org by "greg sterijevski (Created) (JIRA)" <ji...@apache.org> on 2011/10/01 04:00:45 UTC

[jira] [Created] (MATH-678) NonLinear Optimizers seem to have a hard time hitting NIST standards

NonLinear Optimizers seem to have a hard time hitting NIST standards
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                 Key: MATH-678
                 URL: https://issues.apache.org/jira/browse/MATH-678
             Project: Commons Math
          Issue Type: Bug
    Affects Versions: 3.0
         Environment: Java 
            Reporter: greg sterijevski
            Assignee: greg sterijevski
            Priority: Minor


As per a discussion on the mailing list, I am opening this ticket. In applying the nonlinear optimizers in commons, I noticed what I believe to be instability in the techniques. Further investigation investigation (both of my tests) and the code in prod is warranted. 

I will be pushing a first set of tests which should illustrate what I am seeing. 

 

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[jira] [Updated] (MATH-678) NonLinear Optimizers seem to have a hard time hitting NIST standards

Posted by "Gilles (JIRA)" <ji...@apache.org>.
     [ https://issues.apache.org/jira/browse/MATH-678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Gilles updated MATH-678:
------------------------

    Assignee:     (was: greg sterijevski)

Greg Sterijevski does not seem to be around this forum anymore.

                
> NonLinear Optimizers seem to have a hard time hitting NIST standards
> --------------------------------------------------------------------
>
>                 Key: MATH-678
>                 URL: https://issues.apache.org/jira/browse/MATH-678
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 3.0
>         Environment: Java 
>            Reporter: greg sterijevski
>            Priority: Minor
>              Labels: NIST, NonLinear, Optimization
>
> As per a discussion on the mailing list, I am opening this ticket. In applying the nonlinear optimizers in commons, I noticed what I believe to be instability in the techniques. Further investigation investigation (both of my tests) and the code in prod is warranted. 
> I will be pushing a first set of tests which should illustrate what I am seeing. 
>  

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If you think it was sent incorrectly, please contact your JIRA administrators
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[jira] [Commented] (MATH-678) NonLinear Optimizers seem to have a hard time hitting NIST standards

Posted by "Gilles (JIRA)" <ji...@apache.org>.
    [ https://issues.apache.org/jira/browse/MATH-678?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13416262#comment-13416262 ] 

Gilles commented on MATH-678:
-----------------------------

IMHO, this issue is too vague to be useful.
The unit tests referred to have been removed from the CM test suite (cf. MATH-763) because they were all failing (although such big problems were not revealed by the other unit tests). Nobody seems eager to determine whether there could be a problem in the test methodology (e.g. too stringent tolerances) or in some way clean them up so that the potential problems can be sorted out.

I would thus suggest to resolve this issue as "Won't fix".

                
> NonLinear Optimizers seem to have a hard time hitting NIST standards
> --------------------------------------------------------------------
>
>                 Key: MATH-678
>                 URL: https://issues.apache.org/jira/browse/MATH-678
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 3.0
>         Environment: Java 
>            Reporter: greg sterijevski
>            Assignee: greg sterijevski
>            Priority: Minor
>              Labels: NIST, NonLinear, Optimization
>
> As per a discussion on the mailing list, I am opening this ticket. In applying the nonlinear optimizers in commons, I noticed what I believe to be instability in the techniques. Further investigation investigation (both of my tests) and the code in prod is warranted. 
> I will be pushing a first set of tests which should illustrate what I am seeing. 
>  

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