One more thing: trying to defend R's honor, I've run optimx instead of
optim (after dividing the IV by its max - same as for optim). I did
not use L-BFGS-B with lower bounds anymore. Instead, I've used
Nelder-Mead (no bounds).
First, it was faster: for a loop across 10 different IVs BFGS took
6.14
Actually, Interval Analysis can be used to find _all_ optima (including the
global optimum) within a starting box. It's not particularly well-known in
statistical circles.
See this (for example):
http://bib.tiera.ru/ShiZ/math/other/Global%20Optimization%20Using%20Interval%20Analysis%20-%20E.%20H
Just to provide some closure:
I ended up dividing the IV by its max so that the input vector (IV) is
now between zero and one. I still used optim:
myopt <- optim(fn=myfunc, par=c(1,1), method="L-BFGS-B", lower=c(0,0))
I was able to get great fit, in 3 cases out of 10 I've beaten Excel
Solver, but
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
val
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 o
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
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 i
Hans W Borchers googlemail.com> writes:
>
> Ben Bolker gmail.com> writes:
>
> >
> > Simulated annealing and other stochastic global optimization
> > methods are also possible solutions, although they may or may not
> > work better than the many-starting-points solution -- it depends
> > on
Ben Bolker gmail.com> writes:
>
> Simulated annealing and other stochastic global optimization
> methods are also possible solutions, although they may or may not
> work better than the many-starting-points solution -- it depends
> on the problem, and pretty much everything has to be tuned.
Rolf Turner xtra.co.nz> writes:
>
> On 11/11/11 08:55, Dimitri Liakhovitski wrote:
> > Bert,
> > that's exactly where I started. I found optim in the first paragraph
> > under "General Purpose Continuous Solvers" and used bounded BFGS for a
> > constrained optimization for a situation with more
On 11/11/11 08:55, Dimitri Liakhovitski wrote:
Bert,
that's exactly where I started. I found optim in the first paragraph
under "General Purpose Continuous Solvers" and used bounded BFGS for a
constrained optimization for a situation with more than 1 parameters.
Again, not being an engineer / mat
Bert,
that's exactly where I started. I found optim in the first paragraph
under "General Purpose Continuous Solvers" and used bounded BFGS for a
constrained optimization for a situation with more than 1 parameters.
Again, not being an engineer / mathematician - would greatly
appreciate any pointer
Refer to the CRAN "Optimization" task view, please. That is a much more
appropriate place to begin than posting a query here.
All numerical optimizers only produce local optima.
-- Bert
On Thu, Nov 10, 2011 at 11:24 AM, Dimitri Liakhovitski <
dimitri.liakhovit...@gmail.com> wrote:
> Just to add
Just to add:
I also experimented with the starting parameters (par) under optim,
especially with the second one. I tried 1, 10, 100, 1000, etc.
When I tried 100,000,000 then I got a somewhat better solution (but
still not as good as in Excel). However, under message it said:
"ERROR: ABNORMAL_TERM
Hello!
I am trying to create an R optimization routine for a task that's
currently being done using Excel (lots of tables, formulas, and
Solver).
However, otpim seems to be finding a local minimum.
Example data, functions, and comparison with the solution found in
Excel are below.
I am not experie
15 matches
Mail list logo
