# [R] optim seems to be finding a local minimum

Dimitri Liakhovitski dimitri.liakhovitski at gmail.com
Fri Nov 11 16:20:21 CET 2011

```Thank you very much to everyone who replied!
As I mentioned - I am not a mathematician, so sorry for stupid
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 <nashjc at 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
>
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