Minimizers
The GModelFit.jl main purpose is to act as an high-level interface between the user and the underlying minimizer. Currently, two non-linear least squares minimizers are supported:
More minimizers may be added in the future.
To choose a specific minimizer use the minimizer= keyword when invoking the fit() function, e.g. minimizer=GModelFit.lsqfit() or minimizer=GModelFit.cmpfit(). If the keyword is not provided the lsqfit() minimizer is used.
There is also a dummy minimizer, GModelFit.dry(), whose purpose is to compare the model and the data, and to generate a FitStats object without modifying the model parameters. The dry minimizer is used by the compare() function.
Examples
using GModelFit
model = Model(:main => @fd (x, T=3.14) -> sin.(x ./ T) ./ (x ./ T))
data = GModelFit.mock(Measures, model, Domain(1:0.1:50), seed=1)
bestfit, stats = fit(model, data, minimizer=GModelFit.lsqfit())or
bestfit, stats = fit(model, data, minimizer=GModelFit.cmpfit())The above minimizers typically provide the same results, although in some complex case the CMPFit may be more robust and less sensitive to initial guess parameters.
The cmpfit() minimizer
The cmpfit() minimizer allows to specify several options to fine-tune the minimizer behaviour. Specifically:
the
CMPFit.Configstructure allows to specify the convergence criteria, the maximum number of iterations, etc. (see the "CONFIGURING MPFIT()" section here;the
ftol_after_maxiterallows to specify a threshold on the relative difference in fit statistics before and after thempfit()execution. If the latter terminates because the maximum number of iterations has been reached, and the relative difference in fit statistics is still greater thanftol_after_maxiterthe minimization process will continue. E.g.:using GModelFit dom = Domain(1:0.1:50) model = Model(:main => @fd (x, T=3.14) -> sin.(x ./ T) ./ (x ./ T)) data = GModelFit.mock(Measures, model, dom, seed=1) # Set minimizer options mzer = GModelFit.cmpfit() mzer.config.maxiter = 1 mzer.ftol_after_maxiter = 1e-8 # Run the fit model[:main].T.val = 10 # guess value, purposely far from true one bestfit, stats = fit(model, data, minimizer=mzer)Reached max. number of iteration but relative Δfitstat = 0.4467037509897797 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.3398651527027074 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.34271480181977343 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.3708010965597173 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.6716288838922451 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.601924729770853 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 0.018222103308912982 > 1.0e-8, continue minimization... Reached max. number of iteration but relative Δfitstat = 5.668182181168992e-6 > 1.0e-8, continue minimization...