Hi Anton,
If we start to tinker/rewrite Minuit, I would like to see the following
additions/improvements:
1) This refers to linear fits:
When fitting N data points and get M additional ones, I would like
to ADD the information of the new ones to my Hessian matrix and
gradient. I do NOT want to analyze all M+N data points again !
This is important for on-line applications and non-cheating
data analyss with time series. It is straightforward to implement
for a least-squares objective function.
2) Be able to easily specify Bayesian priors for my parameters
3) Have possibilty of applying a robust algorithm instead
of least squares like least median squares
What do others wish/think ??
Best regards Eddy
(out of town for the next 2weeks)
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> From: "Anton Fokin" <anton.fokin@smartquant.com>
> To: "roottalk" <roottalk@pcroot.cern.ch>
> Subject: [ROOT] Fitting with stochastic optimizers
> Date: Fri, 2 Mar 2001 11:42:49 +0100
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> Hi rooters,
>
> I've just seen several postings about fitting. I am curious if someone ever
> went into troubles with root minuit and would like to have stochastic
> optimization as an alternative? I can provide simulated annealing/genetics
> if enough people want it.
>
> Currently I have minimization functionality for TF1/2 user defined functions
> on www.smartquant.com/neural.html I can extend the package so that it could
> be used with TVirtualFitter.
>
> Regards,
> Anton
>
> http://www.smartquant.com
>
>
>
Eddy A.J.M. Offermann
Renaissance Technologies Corp.
Route 25A, East Setauket NY 11733
e-mail: eddy@rentec.com
http://www.rentec.com
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