I'm experimenting with some novel item models for use with OpenMx's item factor analysis capabilities. I was wondering what optimization methods are currently available (e.g., for the M-step when doing Bock-Aitkin EM), and which one(s) can make use of analytical derivatives (which tend to be faster, at least in my experience). I've previously used my own hand-rolled Newton-Raphson code and discovered that N-R has trouble with some of these item models and usually requires reducing the step size or some backtracking, and sometimes ridging the hessian when early in the E-M cycles. I've been looking at mxComputeNewtonRaphson, but don't see in the documentation whether it just tries to take the full step each time or does anything else.
I also see mxComputeGradientDescent, which optionally uses the analytical gradient?
Did I miss any other optimization options?
Many thanks,
Carl