Dear mailing list, I would like to do a Levenberg-Marquardt least mean square fit (i. e. min( |f(x)|^2 ) for f: R^d -> R^n, d in [2,8], n in [~50, ~10000]) using org.apache.commons.math3.fitting.leastsquares.LevenbergMarquardtOptimizer and I am somewhat confused by the relevant API.
tl/dr: Is there a way to do this in a simple way, providing only a function f, its derivative function df and an initial guess for x? Long form: it seems that I have to create a LeastSquaresProblem which requires a lot of input * MultivariateVectorFunction model: done * MultivariateMatrixFunction jacobian: done * double[] observed: [0,...,0] in my case, since i want least mean squares, right? * double[] start: done * RealMatrix weight: should this be a "new DiagonalMatrix([1,...,1])" of the dimension of "observed"? * ConvergenceChecker<LeastSquaresProblem.Evaluation> checker: no clue * int maxEvaluations: no clue * int maxIterations: no clue Is there a way to get "reasonable defaults" for the three or four last arguments? Or put differently, what would be "typical" inputs for the last four arguments? I would be very glad, if you could help me with this problem. I have been trying to figure out, what would be sensible inputs be reading the source code, but I guess it would take ages until I really understood what is going on there. The results of a trial-and-error approach have also been rather disappointing so far. Best regards, Benjamin Eltzner --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@commons.apache.org For additional commands, e-mail: user-h...@commons.apache.org
