Hi Francesco, Thanks a lot for your suggestion which seems like a good idea. Fortunately I found I mistake in one of the derivatives and that got the performance beyond that of the simplexfitter.
Cheers Soren On Tue, Dec 21, 2010 at 2:17 AM, Francesco Abbate <[email protected]>wrote: > > 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
