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Re: NonlinearRegress

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  • Subject: [mg49966] Re: [mg49915] NonlinearRegress
  • From: "Janos D. Pinter" <jdpinter at>
  • Date: Sun, 8 Aug 2004 05:37:52 -0400 (EDT)
  • References: <>
  • Sender: owner-wri-mathgroup at


in general, you will need to use a global search/optimization procedure to 
guarantee the satisfaction of parameter range constraints, as well as to 
find the best numerical fit (expressed in a given norm). Consult e.g. item 
140 in my list of publications at in that article 
my MathOptimizer package is used to solve model calibration problems 
globally, as opposed to built-in local model fitting functionality. You 
could also try to use the built-in function NMinimize to attain the same 

Frank Kampas and I have done some systematic numerical comparisons among 
global solvers for Mathematica - in solving some rather difficult global 
optimization problems - which we plan to make available in published form.

Janos Pinter

At 04:09 AM 8/6/2004, you wrote:
>I've been using the NonlinearRegress function to extract parameter values
>from experimental data, with mixed success. While normally the extraction
>works well, it seem to fail spectacularly when it does - extracting values
>several orders of magnitude outside the specified range.
>Is there any way to guarantee that the extracted parameters fall within the
>range given for them, even if those parameters do not represent the best
>global fit ?
>I have been using the following options -
>NonlinearRegress[<data>, <expected function>,  <variables>, {{<param1>,<
>startvalue>,< min>, <max>}. {<param2>, ....}}, MaxIterations->100000,
>Method->Automatic, RegressionReport-> BestFitParameters]
>to extract 3-4 parameters from ~350 data points.
>Thanks for your help,
>Christopher O'Brien
>University of Queensland

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