Update: A Mathematica wrapper for (https://github.com/zitmen/cuLM) should allow for us to directly implement the Levenberg-Marquardt algorithm in CUDA for nonlinear least squares fitting.
In a previous question of mine (here) I asked how one could best use Mathematica's model fitting capabilities to fit a 2D Gaussian to a set of data.
Two very nice answers were provided by Rahul Narain (who directly computed a mean and covariance matrix for my example data and then used
MultinormalDistribution) and Sjoerd C. de Vries (who used
- Could these methods be adapted to work on a GPU core in Mathematica 9 (perhaps via
- Is there some way of using Mathematica to do GPU-based nonlinear least squares (or some other fitting strategy) to accomplish this?