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mnori1-Visitor
1-Visitor
November 27, 2015
Question

Minerr: Quasi-Newton, Levenberg-Marquardt, Conjugate Gradient?

  • November 27, 2015
  • 7 replies
  • 3909 views


How to choose one of the  Minerr Options? How initial values and Objective functions are affected by the selected Method? Are there Mathcad alternative solutions for non-linear regression of parameters?

7 replies

V
ValeryOchkov24-Ruby IV
24-Ruby IV
November 27, 2015

Only in Mathcad 15

See please

Решение алгебраических уравнений и систем

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    RichardJ19-Tanzanite
    19-Tanzanite
    November 27, 2015

    For non-linear regression (i.e. fitting a curve to data) you should use the L-M algorithm. The objective function should generate a vector of residuals (do NOT sum and square them!).

    Mathcad really offers nothing else for this. The genfit function also uses the L-M algorithm, and is not as flexible as minerr. Simulated annealing was available at one point, with an extension pack, but that's unfortunately long gone. Is there a reason you want something other than the L-M algorithm? It's old, but it's very good.

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      mnori1-VisitorAuthor
      1-Visitor
      November 30, 2015

      I am using Minerr for the non-linear regression of the parameters of a Volumetric Equation of State. I read that Quasi-Newton could be a good option for time consuming solution.

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      RichardJ19-Tanzanite
      19-Tanzanite
      November 30, 2015

      I have a paper (although a very old one) that shows that the LM algorithm significantly out performs other downhill algorithms for fitting of data. The algorithm doesn't know what it's minimizing though, so the only difference between fitting data and any other minimization problem would be the number of residuals (when fitting data there are many: one for each data point). One reason LM is fast is that it adapts the step size as it iterates to the solution. It takes big steps to begin, and avoids overshoot by reducing them as it approaches the minimum.

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