For a project im in need of a nonlinear least squares solver for the purpose of curve fitting with custom functions. Thus I have a set of observed points, a set of target points and a (user specific) function whose parameters I seek to optimise in order to minimize the square error.
As far as I can tell algorithms to solve this sort of problem are nonlinear conjugate gradient (NLCG) or the Levenberg-Marquardt algorithm (LMA). Unfortunately I could not find any OpenCL (or even CUDA) library solving this particularly problem.
Does someone know about an existing implementation?