optim seems to be finding a local minimum

Some tips:

1) Excel did not, as far as I can determine, find a solution. No point seems to satisfy
the KKT conditions (there is a function kktc in optfntools on R-forge project optimizer.
It is called by optimx).

2) Scaling of the input vector is a good idea given the seeming wide range of values. That
is, assuming this can be done. If the function depends on the relative values in the input
vector rather than magnitude, this may explain the trouble with your function. That is, if
the function depends on the relative change in the input vector and not its scale, then
optimizers will have a lot of trouble if the scale factor for this vector is implicitly
one of the optimization parameters.

3) If you can get the gradient function you will almost certainly be able to do better,
especially in finding whether you have a minimum i.e., null gradient, positive definite
Hessian. When you have gradient function, kktc uses Jacobian(gradient) to get the Hessian,
avoiding one level of digit cancellation.

JN


On 11/11/2011 10:20 AM, Dimitri Liakhovitski wrote:

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 <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