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From "Nikolaus Hansen (JIRA)" <>
Subject [jira] [Commented] (MATH-867) CMAESOptimizer with bounds fits finely near lower bound and coarsely near upper bound.
Date Sat, 29 Sep 2012 20:50:07 GMT


Nikolaus Hansen commented on MATH-867:

Revision 1391840 contains modified "encode" and "decode" functions. Both unit tests now pass
(for "testConstrainedRosen" I had to move the initial guess closer to the solution).
No change was required for "inputSigma"; I still do not understand why it works as is (cf.
lines 588, 589). 
to me it makes perfectly sense: luckily enough line 589 performs the same transformation on
inputSigma as the encode function on getStartPoint() (maybe this should be mentioned in a
comment?). As the transformation is linear, the situations before and after the transformations
are mathematically equivalent (the bug came from loosing digits due to a subtraction, which
is now omitted). As said before it would simplify the code if both transformations were omitted
(but we could leave this to another issue). 

And I have no idea how to improve the documentation...
I'll think about it. 

> CMAESOptimizer with bounds fits finely near lower bound and coarsely near upper bound.

> ---------------------------------------------------------------------------------------
>                 Key: MATH-867
>                 URL:
>             Project: Commons Math
>          Issue Type: Bug
>            Reporter: Frank Hess
>         Attachments: MATH867_patch,
> 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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