Re: Fast vs. Slow NonlinearModelFit models
- To: mathgroup at smc.vnet.net
- Subject: [mg123838] Re: Fast vs. Slow NonlinearModelFit models
- From: DrMajorBob <btreat1 at austin.rr.com>
- Date: Thu, 22 Dec 2011 04:26:50 -0500 (EST)
- Delivered-to: l-mathgroup@mail-archive0.wolfram.com
- References: <201112211152.GAA02211@smc.vnet.net>
- Reply-to: drmajorbob at yahoo.com
You might fit using a short Series expansion in place of Erfc, then use
the fitted parameters as initial guess in a second fit for the exact model.
Bobby
On Wed, 21 Dec 2011 05:52:34 -0600, Dan O'Brien <danobrie at gmail.com> wrote:
> Hello,
>
> I'm wondering if anyone has a suggestion for speeding up the
> NonlinearModelFit computation for model2 below. The model used to fit
> my experimental data can be a simple complex function who's magnitude
> squared is a lorentzian (model1), or, some may argue a more complete
> model is the same function convolved with a gaussian which gives a
> function that involves the complementary error function (model2), the
> Voigt profile. As indicated below with AbsoluteTiming using these two
> different models show VERY different computational times. I imagine it
> has something to do with mathematica's internal implementation of Erfc
>
> In[1]:= $Version
> (*Lorentzian Lineshape*)
> \[Chi]R[\[Omega]_, {A_, \[Phi]A_, \[CapitalGamma]_, \[Omega]v_}] := (
> A E^(I \[Phi]A))/(-(\[Omega] - \[Omega]v) - I \[CapitalGamma]);
>
> (*Voigt Lineshape*)
> z[\[Sigma]_, \[CapitalGamma]_, \[Omega]v_, \[Omega]_] := (\[Omega] - \
> \[Omega]v + I \[CapitalGamma])/(Sqrt[2] \[Sigma])
> FadeevaF[z_] := Exp[-z^2] Erfc[-I z]
> IH\[Chi]R[\[Omega]_, {\[Sigma]_,
> A_, \[Phi]A_, \[CapitalGamma]_, \[Omega]v_}] :=
> I Sqrt[\[Pi]/2] (
> A E^(I \[Phi]A))/\[Sigma] FadeevaF[
> z[\[Sigma], \[CapitalGamma], \[Omega]v, \[Omega]]]
> (*Redefine functions so A is peak amplitude of the function magnitude \
> squared*)
> A\[Chi]R[\[Omega]_, {\[Sigma]_,
> A_, \[Phi]A_, \[CapitalGamma]_, \[Omega]v_}] := \[Chi]R[ \[Omega], \
> {Sqrt[Abs[A]] Abs[\[CapitalGamma]], \[Phi]A,
> Abs[\[CapitalGamma]], \[Omega]v}]
> AIH\[Chi]R[\[Omega]_, {\[Sigma]_,
> A_, \[Phi]A_, \[CapitalGamma]_, \[Omega]v_}] :=
> IH\[Chi]R[\[Omega], {\[Sigma], (
> Sqrt[Abs[A]] E^(-(1/2) \[CapitalGamma]^2/\[Sigma]^2) Sqrt[2/\[Pi]]
> Abs[\[Sigma]])/
> Erfc[Abs[\[CapitalGamma]]/(
> Sqrt[2] Abs[\[Sigma]])], \[Phi]A, \[CapitalGamma], \[Omega]v}]
> (*Generate data with noise*)
> dataplot =
> ListPlot[data =
> Table[{\[Omega],
> Abs[AIH\[Chi]R[\[Omega], {4, 1.1, 0, 2.1, 0}] +
> AIH\[Chi]R[\[Omega], {4, .6, \[Pi], 2.1, 18}]]^2 +
> RandomReal[{-0.05, 0.05}]}, {\[Omega], -30, 50, .5}],
> Joined -> True]
> (*Models*)
> model1 = Abs[
> A\[Chi]R[#, {4, a1, 0, \[CapitalGamma]1, \[Omega]vo1}] +
> A\[Chi]R[#, {4, a2, \[Pi], \[CapitalGamma]2, \[Omega]vo2}]]^2 &;
> model2 =
> Abs[AIH\[Chi]R[#, {4, a1, 0, \[CapitalGamma]1, \[Omega]vo1}] +
> AIH\[Chi]R[#, {4, a2, \[Pi], \[CapitalGamma]2, \[Omega]vo2}]]^2 &;
>
> (*Fit*)
> AbsoluteTiming[
> mod1 = NonlinearModelFit[data,
> model1[\[Omega]], {{a1, 1}, {\[CapitalGamma]1, 2}, {\[Omega]vo1,
> 1}, {a2, .8}, {\[CapitalGamma]2, 2}, {\[Omega]vo2,
> 15}}, \[Omega]]][[1]]
> AbsoluteTiming[
> mod2 = NonlinearModelFit[data,
> model2[\[Omega]], {{a1, 1}, {\[CapitalGamma]1, 2}, {\[Omega]vo1,
> 1}, {a2, .8}, {\[CapitalGamma]2, 2}, {\[Omega]vo2,
> 15}}, \[Omega]]][[1]]
>
> Show[{dataplot,
> Plot[{Normal[mod1[\[Omega]]],
> Normal[mod2[\[Omega]]]}, {\[Omega], -30, 50}]}]
>
> Out[1]= "8.0 for Microsoft Windows (32-bit) (October 6, 2011)"
>
> Out[8]=****Plot Snipped
>
> Out[11]= 0.0390625 *Here is AbsoluteTiming for model1*
>
> Out[12]= 75.0078125 *Here is AbsoluteTiming for model2*
>
> Out[13]=****Plot Snipped
--
DrMajorBob at yahoo.com
- References:
- Fast vs. Slow NonlinearModelFit models
- From: "Dan O'Brien" <danobrie@gmail.com>
- Fast vs. Slow NonlinearModelFit models