Thank you very much to everyone who replied! As I mentioned - I am not a mathematician, so sorry for stupid comments/questions. I intuitively understand what you mean by scaling. While the solution space for the first parameter (.alpha) is relatively compact (probably between 0 and 2), the second one (.beta) is "all over the place" - because it is a function of IV (input vector). And that's, probably, my main challenge - that I am trying to write a routine for different possible IVs that I might be facing (they may be in hundreds, in thousands, in millions). Should I be rescaling the IV somehow (e.g., by dividing it by its max) - or should I do something with the parameter .beta inside my function?
So far, I've written a loop over many different starting points for both parameters. Then, I take the betas around the best solution so far, split it into smaller steps for beta (as starting points) and optimize again for those starting points. What disappoints me is that even when I found a decent solution (the minimized value of 336) it was still worse than the Solver solution! And I am trying to prove to everyone here that we should do R, not Excel :-) Thanks again for your help, guys! Dimitri On Fri, Nov 11, 2011 at 9:10 AM, John C Nash <nas...@uottawa.ca> wrote: > I won't requote all the other msgs, but the latest (and possibly a bit > glitchy) version of > optimx on R-forge > > 1) finds that some methods wander into domains where the user function fails > try() (new > optimx runs try() around all function calls). This includes L-BFGS-B > > 2) reports that the scaling is such that you really might not expect to get a > good solution > > then > > 3) Actually gets a better result than the > >> xlf<-myfunc(c(0.888452533990788,94812732.0897449)) >> xlf > [1] 334.607 >> > > with Kelley's variant of Nelder Mead (from dfoptim package), with > >> myoptx > method par fvalues fns grs itns conv KKT1 > 4 LBFGSB NA, NA 8.988466e+307 NA NULL NULL 9999 NA > 2 Rvmmin 0.1, 200186870.6 25593.83 20 1 NULL 0 FALSE > 3 bobyqa 6.987875e-01, 2.001869e+08 1933.229 44 NA NULL 0 FALSE > 1 nmkb 8.897590e-01, 9.470163e+07 334.1901 204 NA NULL 0 FALSE > KKT2 xtimes meths > 4 NA 0.01 LBFGSB > 2 FALSE 0.11 Rvmmin > 3 FALSE 0.24 bobyqa > 1 FALSE 1.08 nmkb > > But do note the terrible scaling. Hardly surprising that this function does > not work. I'll > have to delve deeper to see what the scaling setup should be because of the > nature of the > function setup involving some of the data. (optimx includes parscale on all > methods). > > However, original poster DID include code, so it was easy to do a quick > check. Good for him. > > JN > >> ## Comparing this solution to Excel Solver solution: >> myfunc(c(0.888452533990788,94812732.0897449)) >> >> -- Dimitri Liakhovitski marketfusionanalytics.com > > ______________________________________________ > 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. > -- Dimitri Liakhovitski marketfusionanalytics.com ______________________________________________ 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.