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Date: Tue, 25 Sep 2012 03:11:08 +1100 (NCT)
From: "Frank Hess (JIRA)" <jira@apache.org>
To: issues@commons.apache.org
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Subject: [jira] [Comment Edited] (MATH-867) CMAESOptimizer with bounds fits
 finely near lower bound and coarsely near upper bound.
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    [ https://issues.apache.org/jira/browse/MATH-867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13461878#comment-13461878 ] 

Frank Hess edited comment on MATH-867 at 9/25/12 3:09 AM:
----------------------------------------------------------

To elaborate on my previous point, the CMAESOptimizer also doesn't allow mixing of bounded and unbounded parameters.  So, if I only want to apply a bound to one parameter of a multi-parameter fit, then the best I can do is set the bounds of the "unbounded" parameters to be [-VeryLargeValue, +VeryLargeValue].  This causes the fit precision around zero for the "unbounded" parameters to be much worse than when no bounds are specified at all.
                
      was (Author: fhess):
    To elaborate on my previous point, the CMAESOptimizer also doesn't allow mixing of bounded and unbounded parameters.  So, if I only want to apply a bound to one parameter of a multi-parameter fit, then the best I can do is set the bounds of the "unbounded" parameters to be [-VeryLargeValue, +VeryLargeValue].  This causes the fit precision around zero for the "unbounded" to be much worse than when no bounds are specified at all.
                  
> CMAESOptimizer with bounds fits finely near lower bound and coarsely near upper bound. 
> ---------------------------------------------------------------------------------------
>
>                 Key: MATH-867
>                 URL: https://issues.apache.org/jira/browse/MATH-867
>             Project: Commons Math
>          Issue Type: Bug
>            Reporter: Frank Hess
>         Attachments: Math867Test.java
>
>
> When fitting with bounds, the CMAESOptimizer fits finely near the lower bound and coarsely near the upper bound.  This is because it internally maps the fitted parameter range into the interval [0,1].  The unit of least precision (ulp) between floating point numbers is much smaller near zero than near one.  Thus, fits have much better resolution near the lower bound (which is mapped to zero) than the upper bound (which is mapped to one).  I will attach a example program to demonstrate.

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