Hi Sebastien, Try to formulate your fitting function so that you minimize the correlation between the parameters . After the fit you can always transform into the shape you desire . Strong correlated parameters will just lead to near-singular matrices in the solution engine. I only know of the usage of Lagrangian Multipliers to enforce parameter equality constaints . If you know how to use them in correlated parameter searches , please enlighten us :) Eddy --- Sébastien Gadrat <gadrat@clermont.in2p3.fr> wrote: > Hi Rene, hi rooters, > > I am trying to fit several spectra obtained with different set of > cuts. > Most of them can be fit easily with one or both minimizers (I usually > > use both as cross check), i.e. TMinuit and TFumili. But sometimes, > the > fit failed for both. This seem to be due to some little bit > correleted > parameters of my user defined functionnal. I could try to rewrite it > but > I put it in an easy way for later use. So I would like to keep it as > it > is and I was thinking about new minimizers... Especially, I heard > some > minimizers can overcome correlation within parameters (Lagrange > multiplicators). So I wondered whether we have such minimizers in > ROOT. > Thanks in advance, > Best regards, > > Sebastien > > Rene Brun wrote: > > Hi Sebastien, > > > > Currently, we have only two classes of minimizers (The Minuit > family > > and TFumili). > > We are currently working on a linear fitter that could be used > > to fit distributions with a combination of linear functions. > > Instead of a conventional minimisation, this implies only a matrix > > inversion. > > A RobustFitter algorithm is also in the pipeline. > > > > Could you indicate the rationale behind your question? > > > > Rene Brun > >
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