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https://issues.apache.org/jira/browse/MATH-867?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13461957#comment-13461957
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Frank Hess commented on MATH-867:
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bq. It seems important to point out that a parameter transformation or scaling
and variable boundaries are two different things. That they are mixed in the
code/interface I would indeed consider as a bug. Generally, the boundary
handling can be done without any variable transformation and therefore does not
need to effect precision.
That's good news, I think things would work a lot closer to my expectations if
the boundary handling was done without a transformation.
bq. If we want positive variable values only, the code should IMHO support
only applying a lower bound in some way.
AFAICT the variable transformation is the main reason CMAESOptimizer doesn't
let you use infinite values as bounds. So if the transformation is dropped, we
can have infinity as a bound value, which gives support for only specifying a
bound on one side, plus mixing bounded and unbounded parameters.
> 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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