I'm successfully using Eigen's Levenberg-Marquart class following an example from: <http://techblog.rga.com/determining-indoor-position-using-ibeacon/>

I'm trying to figure out how to translate the damping parameter, lambda, into the parameters available in Eigen:

<https://en.wikipedia.org/wiki/Levenberg-Marquardt_algorithm#Choice_of_damping_parameter>

<http://eigen.tuxfamily.org/dox/unsupported/classEigen_1_1LevenbergMarquardt.html#a20efa2f5a684efaf2499ba5bdd51e792>

It's not clear to me what the "step bound for the diagonal shift" does via setFactor() - is this related to the damping parameter?

```
distance_functor functor(matrix, count);
Eigen::NumericalDiff<distance_functor> numDiff(functor);     

Eigen::LevenbergMarquardt<Eigen::NumericalDiff<distance_functor>,double> lm(numDiff);

lm.parameters.factor = 100; //step bound for the diagonal shift, is this related to damping parameter, lambda?
lm.parameters.maxfev = 2000;//max number of function evaluations
lm.parameters.xtol = 1.49012e-08; //tolerance for the norm of the solution vector
lm.parameters.ftol = 1.49012e-08; //tolerance for the norm of the vector function
lm.parameters.gtol = 0; // tolerance for the norm of the gradient of the error vector
lm.parameters.epsfcn = 0; //error precision
Eigen::LevenbergMarquardtSpace::Status ret = lm.minimize(x);

```