Hi Dimitri, Your problem has little to do with local versus global optimum. You can convince yourself that the solution you got is not even a local optimum by checking the gradient at the solution.
The main issue is that your objective function is not differentiable everywhere. So, you have 2 options: either you use a smooth objective function (e.g. squared residuals) or you use an optimization algorithm than can handle non-smooth objective function. Here I show that your problem is well solved by the `nmkb' function (a bound-constraints version of Nelder-Mead simplex method) from the "dfoptim" package. library(dfoptim) > myopt2 <- nmkb(fn=myfunc, par=c(0.1,max(IV)), lower=0) > myopt2 $par [1] 8.897590e-01 9.470163e+07 $value [1] 334.1901 $feval [1] 204 $restarts [1] 0 $convergence [1] 0 $message [1] "Successful convergence" Then, there is also the issue of properly scaling your function, because it is poorly scaled. Look how different the 2 parameters are - they are 7 orders of magnitude apart. You are really asking for trouble here. Hope this is helpful, Ravi. ------------------------------------------------------- Ravi Varadhan, Ph.D. Assistant Professor, Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University Ph. (410) 502-2619 email: rvarad...@jhmi.edu<mailto:rvarad...@jhmi.edu> [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
