On 01.03.2010 18:34, Jeffrey Racine wrote:
Thanks Uwe.
I appreciate your feedback.. in the paragraph in my email beginning "The
problem..."
Whoops, apologies, I was too quickly reading your message, apparently.
CCing R-help to add:
There is the Optimization Task View on CRAN:
http://cran.r-project.org/web/views/Optimization.html
See particularly the hints related to Mixed integer programming and its
variants
Best wishes,
uwe
> I point out that yes, I indeed do what you suggest for small
problems, but encounter problems where this is not feasible (e.g., 10
variables with integer ranging from 0-20 for each variable for instance).
Thanks again!
-- Jeff
On 2010-03-01, at 12:28 PM, Uwe Ligges wrote:
On 01.03.2010 16:34, Jeffrey Racine wrote:
Dear R users,
I have a problem for which my objective function depends on both discrete and
continuous arguments.
The problem is that the number of combinations for the (multivariate) discrete
arguments can become overwhelming (when it is univariate this is not an issue)
hence search over the continuous arguments for each possible combination of the
discrete arguments may not be feasible. Guided search over the discrete and
continuous arguments would be infinitely preferable. That is, exhaustive search
over all possible combinations works perfectly, but for large problems
exhaustive search simply is not in the feasible set.
Both the discrete and continuous arguments are bounded (the discrete lie in
[0,K] and the continuous in [0,1]) and I am using L-BFGS-B with lower and upper
vectors defining these bounds.
The issue is that when I feed optim my objective function and par (whose first
`k' elements must necessarily be rounded by my objective function while the
remaining `l' arguments are continuous), the default settings naturally do not
perform well at all. Typically if the initial values for the discrete variables
are, say, par[1:3]= c(2,3,4) while those for the continuous are, say, par[4:6]
= c(.17, .35, .85), then optim finds the minimum for only the continuous
variables and dumps back the initial values for the discrete variables. I
presume that the numerical approximation to the gradients/hessian is thrown off
by the `flat spots' or step-like-nature of the objective function with respect
to the discrete variables.
I have played with ndeps, parscale etc. but nothing really works. I realize
this is a mixed combinatorial optimization/continuous optimization problem and
ideally would love pointers to any related literature or ideally an R package
that implements such a beast.
However, if anyone has attempted to use optimization routines native to R with
any success in similar settings, I would love to get your feedback.
If your 3 first discrete variables have a rather limited number of possible
values (sounds like that is the case), you may want to apply optim() on the
other variables on a complete grid of the first 3 variables and select the best
result. Even with this complete evaluation of the whole grid with say 20
possible values in each dimension the results should be there within minutes
without a need for more specialized optimization procedures. ...
Best wishes,
Uwe Ligges
Many thanks in advance for your advice.
-- Jeff
Professor J. S. Racine Phone: (905) 525 9140 x 23825
Department of Economics FAX: (905) 521-8232
McMaster University e-mail: raci...@mcmaster.ca
1280 Main St. W.,Hamilton, URL: http://www.economics.mcmaster.ca/racine/
Ontario, Canada. L8S 4M4
`The generation of random numbers is too important to be left to chance'
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R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Professor J. S. Racine Phone: (905) 525 9140 x 23825
Department of Economics FAX: (905) 521-8232
McMaster University e-mail: raci...@mcmaster.ca
1280 Main St. W.,Hamilton, URL: http://www.economics.mcmaster.ca/racine/
Ontario, Canada. L8S 4M4
`The generation of random numbers is too important to be left to chance'
______________________________________________
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.
