Re: [R] Increasing precision of rgenoud solutions
Hi Paul,
Solution.tolerance is the right way to increase precision. In your
example, extra precision *is* being obtained, but it is just not
displayed because the number of digits which get printed is controlled
by the options(digits) variable. But the requested solution
precision is in the object returned by genoud().
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=0.01)
I get
a$par
[1] 0.7062930 0.7079196
But if I set options(digits=12), and without rerunning anything I check
a$par again, I observe that:
a$par
[1] 0.706293049455 0.707919577559
Cheers,
Jas.
===
Jasjeet S. Sekhon
Associate Professor
Survey Research Center
UC Berkeley
http://sekhon.berkeley.edu/
V: 510-642-9974 F: 617-507-5524
===
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(-999)
else x1+x2
}
genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.01)
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
__
R-help@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.
Re: [R] Increasing precision of rgenoud solutions
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=0.1
was
$par
[1] 0.7090278 0.7051806
which is not very precise comparing with the values of the
(analytical) solution. Is it possible to increase the degree of
closeness of the rgenoud solutions with the analytical ones?
Paul
On 5/10/07, Jasjeet Singh Sekhon [EMAIL PROTECTED] wrote:
Hi Paul,
Solution.tolerance is the right way to increase precision. In your
example, extra precision *is* being obtained, but it is just not
displayed because the number of digits which get printed is controlled
by the options(digits) variable. But the requested solution
precision is in the object returned by genoud().
For example, if I run
a - genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.01)
I get
a$par
[1] 0.7062930 0.7079196
But if I set options(digits=12), and without rerunning anything I check
a$par again, I observe that:
a$par
[1] 0.706293049455 0.707919577559
Cheers,
Jas.
===
Jasjeet S. Sekhon
Associate Professor
Survey Research Center
UC Berkeley
http://sekhon.berkeley.edu/
V: 510-642-9974 F: 617-507-5524
===
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(-999)
else x1+x2
}
genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.01)
How can one increase the precision of the solution
$par
[1] 0.7072442 0.7069694
?
I have tried solution.tolerance but without a significant improvement.
Any ideas?
Thanks in advance,
Paul
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Increasing precision of rgenoud solutions
Hi Paul,
I see. You want to increase the population size (pop.size)
option---of lesser importance are the max.generations,
wait.generations and P9 options. For more details, see
http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf.
For example, if I run
a - genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.001,
pop.size=6000, P9=50)
options(digits=12)
I obtain:
#approx analytical solution
sum(c(0.707106781186548,0.707106781186548))
[1] 1.41421356237
#genoud solution
#a$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=0.1
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(-999)
else x1+x2
}
genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.01)
How can one increase the precision of the solution
$par
[1] 0.7072442 0.7069694
?
I have tried solution.tolerance but without a significant improvement.
Any ideas?
Thanks in advance,
Paul
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Increasing precision of rgenoud solutions
Thanks a lot, Jasjeet. That is it.
Paul
On 5/10/07, Jasjeet Singh Sekhon [EMAIL PROTECTED] wrote:
Hi Paul,
I see. You want to increase the population size (pop.size)
option---of lesser importance are the max.generations,
wait.generations and P9 options. For more details, see
http://sekhon.berkeley.edu/papers/rgenoudJSS.pdf.
For example, if I run
a - genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.001,
pop.size=6000, P9=50)
options(digits=12)
I obtain:
#approx analytical solution
sum(c(0.707106781186548,0.707106781186548))
[1] 1.41421356237
#genoud solution
#a$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=0.1
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(-999)
else x1+x2
}
genoud(myfunc, nvars=2,
Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.01)
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
__
R-help@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.
