On 12/28/12 7:08 AM, Dimitri Pourbaix wrote:
> Luc,
>
>> However, it is also possible to set a non-diagonal weight matrix,
>> and
>> one class (AbstractLeastSquaresOptimizer) performs an eigen
>> dcomposition
>> on the matrix to extract its square root. I don't know any use
>> case for
>> this, but it has been set up this way, so I guess someone has a
>> use for
>> non-diagonal weights.
>
> Such a situation occurs when observations are correlated.  That is
> actually the most general expression for a least square problem.
>
>> I wonder if I should simply add this as is or if we should rather
>> remove
>> the non-diagonal weights feature and support only vector weights.
>
> Even if a vector of weights is convenient, it would only cover a
> subset
> of situations.  However, even a vector of weights is not needed if
> both
> the models and the observations are pre-multiplied by the square root
> of their weight.  By the way, I remind you that those weights already
> caused some bugs in the 2.0 release.
>
> Personnally, I could live with a vector form.
>
> As a more general comment, I find it amazing that all the +1 for the
> release were only concerned by the compliance with (commons) rules,
> configuration files, ... Just 4 days after the release, you suddenly
> figure out that a user is in trouble and you want a quick fix.  Maybe
> such a test would have been need BEFORE the release!

You are certainly welcome to help verify future release candidates,
as well as the commits that go into them.

Phil
>
> Regards,
>  Dim.
> ----------------------------------------------------------------------------
>
> Dimitri Pourbaix                         *      Don't worry, be happy
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