Eigen-Levenberg-Marquardt-Optimization | Code for tutorial on how to use Eigen 's LM optimization | Learning library

I'm using Eigen's Levenberg-Marquardt implementation and wondering how to set some boundaries on the parameters which should be optimized.

As I'm migrating some GNU octave programs to Eigen I expected that there might be some boundaries which can be easily provided as parameters to the module.

The layout of my implemenation is nearly the same as in this example. I'm not providing the df() implemenatation but rather use Eigen::NumericalDiff in order to approximate it.

So how do I enforce some boundaries on the parameters which are supplied to minimize()? I thought about setting the errors(fvec) in the operator() to some high values when leaving my expected ranges, but in some small tests this resulted in strange results.