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10 May 2010 Screenshot MATLAB Sensor Contest data set + run on test data Submission data of the Matlab Spring Contest 2010 and results for running against the test data. Author: Jan sensor, contest, compressed sensing 17 0
23 Jul 2008 Screenshot Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan optimization, nonlinear fit, nd, ndimensional data, mle, maximum likelihood 33 7
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Comments and Ratings by Jan
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11 Jul 2014 Fast and Robust Curve Intersections Computes intersection points of two curves. Author: Douglas Schwarz

It also erroneously finds contact points (no real intersections).
Example: [x0, y0] = intersections([-1,0,-1], [-1,0,1], [1, 0, 1], [-1,0,1], 1);
returns point (0, 0) as intersection point although it isn't.

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28 May 2012 Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan GUO, ZM

28 Dec 2009 Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan Krishna

Jan, Your submission was very helpful for me. I am quite new to matlab and had trouble figuring out how to use lsqcurvefit for 2-d fitting. Going through your program helped me figure out the mistakes I was making.

Thanks,

18 Aug 2008 Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan Bitter, Greg

17 Jul 2008 Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan Keller, Jan

I have just submitted a new version. There I have changed the aim of the whole package towards demonstrating the possibilities of the functions lsqcurvefit/fmincon/fminsearch. The syntax is now much more clear and is similar to the syntax of the underlying matlab functions. Much more comments, explanation, h1 lines and error checks have been included. (Indeed much more than usual for me. :)) However also some additions have been taking place that might be of interest. In particular fit_nl_ex() now maximizes the likelihood and is therefore more universal than only least squares. Treatment of Poissonian noise which often occurs e.g. in imaging is incorporated. And I have included the possibility to add penalties depending on the parameter on the likelihood. Matlab itself has only all-or-nothing penalties via the extended options of fmincon. But when you know something about the distribution of parameters instead of hard borders an additive penalty is the choice.

However, instead of the assuring results of the test routine fit_test there might still be errors, both conceptual and coming from programming. Please let me know about them. I will correct them. However, I do not plan to add any functionality further than this.

07 Jul 2008 Nonlinear fitting n-dimensional data with arbitrary functions Demonstrates the abilities of Matlab functions lsqcurvefit, fmincon and fminsearch to fit complex mo Author: Jan Keller, Jan

The second version only has one or two more comments. The definitions of the built-in functions are explained. I am still working on an improved version.

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