From: Paul Smith <phhs80_at_gmail.com>

Date: Thu, 10 May 2007 21:56:49 +0100

R-help_at_stat.math.ethz.ch 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. Received on Thu 10 May 2007 - 21:06:52 GMT

Date: Thu, 10 May 2007 21:56:49 +0100

Thanks a lot, Jasjeet. That is it.

Paul

On 5/10/07, Jasjeet Singh Sekhon <sekhon_at_berkeley.edu> wrote:

*>
*

> Hi Paul,

*>
**> I see. You want to increase the population size (pop.size)
**> option---of lesser importance are the max.generations,
**> wait.generations and P9 options. For more details, see
**> http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf.
**>
**> For example, if I run
**>
**> a <- genoud(myfunc, nvars=2,
**> Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.0000001,
**> pop.size=6000, P9=50)
**>
**> options("digits"=12)
**>
**> I obtain:
**>
**> #approx analytical solution
**> sum(c(0.707106781186548,0.707106781186548))
**> [1] 1.41421356237
**>
**> #genoud solution
**> #a$value
**> [1] 1.41421344205
**>
**> #difference
**> a$value-sum(c(0.707106781186548,0.707106781186548))
**>
**> [1] -2.91195978441e-09
**>
**> If that's not enough precision, increase the options (and the
**> run-time). This would be faster with analytical derivatives.
**>
**> Cheers,
**> Jas.
**>
**> =======================================
**> Jasjeet S. Sekhon
**>
**> Associate Professor
**> Travers Department of Political Science
**> Survey Research Center
**> UC Berkeley
**>
**> http://sekhon.berkeley.edu/
**> V: 510-642-9974 F: 617-507-5524
**> =======================================
**>
**>
**>
**> Paul Smith writes:
**> >Thanks, Jasjeet, for your reply, but maybe I was not enough clear.
**> >
**> >The analytical solution for the optimization problem is the pair
**> >
**> >(sqrt(2)/2,sqrt(2)/2),
**> >
**> >which, approximately, is
**> >
**> >(0.707106781186548,0.707106781186548).
**> >
**> >The solution provided by rgenoud, with
**> >
**> >solution.tolerance=0.000000001
**> >
**> >was
**> >
**> >$par
**> >[1] 0.7090278 0.7051806
**> >
**> >which is not very precise comparing with the values of the
**> >(analytical) solution. Is it possible to increase the degree of
**> >closeness of the rgenoud solutions with the analytical ones?
**> >
**> >Paul
**> >
**> >Paul Smith writes:
**> > > Dear All
**> > >
**> > > I am using rgenoud to solve the following maximization problem:
**> > >
**> > > myfunc <- function(x) {
**> > > x1 <- x[1]
**> > > x2 <- x[2]
**> > > if (x1^2+x2^2 > 1)
**> > > return(-9999999)
**> > > else x1+x2
**> > > }
**> > >
**> > > genoud(myfunc, nvars=2,
**> > > Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001)
**> > >
**> > > How can one increase the precision of the solution
**> > >
**> > > $par
**> > > [1] 0.7072442 0.7069694
**> > >
**> > > ?
**> > >
**> > > I have tried solution.tolerance but without a significant improvement.
**> > >
**> > > Any ideas?
**> > >
**> > > Thanks in advance,
**> > >
**> > > Paul
**> > >
**> > >
**> >
**>
*

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