Advice on Non-Linear Optimization Methods

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SUMMARY

The discussion focuses on non-linear optimization methods for mathematical inversion, specifically using the relation between measurements N(zi) at various altitudes and temperature T(zi). The user aims to minimize the chi-square (χ²) value to derive temperature from the measurements, considering the Marquardt Method from Numerical Recipes and the Levenberg-Marquardt algorithm available in Gnuplot as potential solutions. The conversation emphasizes the importance of selecting appropriate optimization techniques for accurate results in this context.

PREREQUISITES
  • Understanding of mathematical inversion techniques
  • Familiarity with non-linear optimization methods
  • Knowledge of chi-square (χ²) minimization
  • Experience with Gnuplot and its optimization algorithms
NEXT STEPS
  • Research the Marquardt Method in Numerical Recipes for non-linear optimization
  • Explore the Levenberg-Marquardt algorithm implementation in Gnuplot
  • Study the principles of chi-square minimization in data fitting
  • Investigate alternative non-linear optimization algorithms such as the Nelder-Mead method
USEFUL FOR

Researchers and practitioners in fields such as atmospheric science, data analysis, and mathematical modeling who are involved in temperature estimation from altitude measurements using non-linear optimization techniques.

NeedPhysHelp8
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Hi all,

Hopefully this is the right section for my post, if not I apologize.

I'm hoping I can just get some advice to help me get started in the right direction. I am trying to do a mathematical inversion for the following:

\frac{1}{N(zi)} \frac{dN}{dz}|_{z=zi} = -\frac{2}{zi} - \frac{1}{T(zi)} \frac{dT}{dz}|_{z=zi} - \frac{C}{T(zi)}

N(zi) are measurements made at a series of altitudes.

There is the above relation between measurements and temperature T(zi)
So temperature is what I am looking to find from the N(zi) measurements. C is just a system constant.

What I am trying to do is guess an initial temperature vector, then minimize the \chi^{2} between the measurements and the forward model above. So that once chi square is minimized as much as possible, we can determine the temperatures. I am very new to this, but have done some research into optimization and grid search methods. I was looking in the Marquardt Method listed in Numerical Recipes, but I figured I would post here to see if anyone else has any opinions on what would work the best.

If you guys need any more info just let me know. Thanks!
 
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