> For no-noise data and perfect start guesses, both implementations fit > perfectly (you should hope so). When changing the guess just slightly > the lmsder fitter fails, whereas the simplex routine is robust for > much larger differences between the initial guess and the solution. > What can I do to improve the performance of the lmsder - is there any > tuning I can do to my function or the solver? (Improving the guess to > what appears to be required from lmsder will not be possible). Hi Soren,
as far as I know the lmsder algorithm inherently needs "good" initial guess of the parameters. If you needs a more robust method you can perform a preliminary search with the simplex minimizer and, when you have a reasonable starting point, you can feed it to the lmsder algorithm. The advantage of the lmsder is that it will converge much more rapidly toward the optimal solutions. I hope that helps. Best regards, Francesco _______________________________________________ Help-gsl mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-gsl
