I am working with a non-linear model that uses seven independent variables to estimate a Bernoulli probability.
To estimate the parameters of the model, I am optimizing a likelihood function using the
optim function in R. I have tried several of the optimizing algorithms available in the
The optimizing algorithm is frequently running into saddle points. Mathematically, I understand how increasing the dimensions increases the number of saddle points the optimizing algorithm can run into. I also understand how my non-linear model makes this a non-Convex optimization problem.
What I am trying to obtain is an intuitive understanding of what is it about the nature of the data that could make this model run into a saddle point? Obviously you don't have my data at hand and can't answer specifically, but I want to understand what is it in general that can make a model run into a saddle point for one data set but not another.