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Nov 5, 2014 at 12:52 comment added tarhawk @Karsten7. Yes, that is the case. The actual models can only be solved numerically, contain many different coupled odes, and have different variations on the initial conditions. There is also a large number of free parameters for each of the models.
Nov 5, 2014 at 12:17 comment added Karsten7 In this example there is a symbolic solution for A (35 E^(-ka t)). Are the actual models more complicated because there is no symbolic fit function you can use and you only have an InterpolatingFunction to compare your data to, or what makes them too difficult to fit with the build in functions?
Nov 5, 2014 at 11:52 comment added tarhawk @Karsten7. I have updated the code to begin counting at one, and have also edited the question per your suggestions. This simple example will converge via built in Mathematica functions. However the actual data and models that I am running are very complicated, and have proven to difficult to fit with the built in functions. Or cannot be programmed in a manner to satisfy the modeling conditions with the built in functions.
Nov 5, 2014 at 11:46 history edited tarhawk CC BY-SA 3.0
I have edited the code and questions following @karsten7 advice. I have started counting at 1, however I do not notice any substantial speedup in execution time.
Nov 5, 2014 at 11:28 comment added Karsten7 It would be easier to help and prevent one form misinterpreting your code, if you would state your problem in a form like: this is my experimental data, this is the fit function with the following parameters and the variable .... Also, please explain why you can't use NonlinearModelFit.
Nov 5, 2014 at 11:15 comment added Karsten7 Are you aware that Mma starts counting with 1? With e.g. ka[[0]] = RandomReal[{0.0, 200.}]; you are replacing the head of ka (List) by a random number. This will undermine any code vectorization attempt.
Nov 4, 2014 at 23:22 history asked tarhawk CC BY-SA 3.0