Re: [R] Optimisation does not optimise!
Thanks for this guidance. In light of your advice I have reduced the titanic to a dingy by reducing the size of my sample to just 100 instances, making this an optimisation of 100 parameters. I am, however, seeing similar output when I output the function value at each evaluation. This is the tail of the information in the optout results structure [1] 70104.64 [1] 70104.67 [1] 70104.64 [1] 70104.67 [1] 70104.64 [1] 70104.67 [1] 70104.64 [1] 70104.66 [1] 70104.64 [1] 70104.66 > optout $par [1] 1.003 1.003 1.003 1.003 1.003 1.003 1.003 1.003 1.003 1.003 1.003 1.003 [13] ... [97] 1.003 1.003 1.003 1.003 $value [1] 70104.4 $counts function gradient 501 NA $convergence [1] 1 $message NULL Can you or anyone suggest another optimisation routine I can use? I initially coded this into EXCEL and used the solver addin to do an optimisation of 200 parameters. R was my attempt to increase this number of parameters. I do not, unfortunately, have any derivative information. -- Stephen Clark, Second year PhD, School of Geography Tel : 0113 343 6707 Email : g...@leeds.ac.uk Web : http://www.geog.leeds.ac.uk/people/s.clark -Original Message- From: Prof J C Nash (U30A) [mailto:nas...@uottawa.ca] Sent: 13 July 2013 13:07 To: r-help@r-project.org; Stephen Clark Subject: [R] Optimisation does not optimise! Considering that I devised the code initially on a computer with only 8K bytes for program and data, and it appears that your problem has 1 parameters, I'm surprised you got any output. I suspect the printout is the BUILD phase where each weight is being adjusted in turn by the same shift. Don't try to move the Titanic on a pram. If you work out a gradient function, you can likely use Rcgmin (even though I wrote original CG in optim(), not recommended). spg from BB may also work OK. This problem is near linear, so there are other approaches. JN __ 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.
[R] Optimisation does not optimise!
Considering that I devised the code initially on a computer with only 8K bytes for program and data, and it appears that your problem has 1 parameters, I'm surprised you got any output. I suspect the printout is the BUILD phase where each weight is being adjusted in turn by the same shift. Don't try to move the Titanic on a pram. If you work out a gradient function, you can likely use Rcgmin (even though I wrote original CG in optim(), not recommended). spg from BB may also work OK. This problem is near linear, so there are other approaches. JN On 13-07-13 06:00 AM, r-help-requ...@r-project.org wrote: Date: Fri, 12 Jul 2013 21:22:00 +0100 From: Stephen Clark To:"r-help@R-project.org" Subject: [R] Optimisation does not optimise! Message-ID: <928c4f7877280844b906d12d63a3f15b01145e5b5...@hermes8.ds.leeds.ac.uk> Content-Type: text/plain; charset="us-ascii" Hello, I have the following code and data. I am basically trying to select individuals in a sample (by setting some weights) to match known counts for a zone. This is been done by matching gender and age bands. I have tested the function to be optimised and it does behave as I would expect when the weights are changed. However when I run the optimisation I get the following output >optout<-optim(weights0, func_opt, control=list(REPORT=1)) [1] 27164 [1] 27163.8 [1] 27163.8 [1] 27163.8 [1] 27163.8 [1] 27163.8 [1] 27163.8 [1] 27163.8 [1] 27163.8 etc which suggest an initial change but thereafter the optimisation does not appear to adapt the weights at all. Can anyone see what this is happening and how to make the problem optimise? __ 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.
[R] Optimisation does not optimise!
Hello,
I have the following code and data. I am basically trying to select individuals
in a sample (by setting some weights) to match known counts for a zone. This is
been done by matching gender and age bands. I have tested the function to be
optimised and it does behave as I would expect when the weights are changed.
However when I run the optimisation I get the following output
> optout<-optim(weights0, func_opt, control=list(REPORT=1))
[1] 27164
[1] 27163.8
[1] 27163.8
[1] 27163.8
[1] 27163.8
[1] 27163.8
[1] 27163.8
[1] 27163.8
[1] 27163.8
etc
which suggest an initial change but thereafter the optimisation does not appear
to adapt the weights at all. Can anyone see what this is happening and how to
make the problem optimise?
sample<-read.csv(file="C:\\sample.csv")
cons1<-read.csv(file="C:\\Gender.csv")
cons2<-read.csv(file="C:\\Age9.csv")
weights0 <- array(dim = c(nrow(sample)))
for (zone in 1:2){
weights0 <- rep(1, nrow(sample))
optout<-optim(weights0, func_opt, control=list(REPORT=1))
optout.value
}
func_opt<-function(weights){
TAE <- 0.0
sumMale <- sum(weights[sample[1:nrow(sample),2]=="Male"])
sumFemale <- sum(weights[sample[1:nrow(sample),2]=="Female"])
sumAged50to54 <-sum(weights[sample[1:nrow(sample),3]=="Aged 50 to 54"])
sumAged55to59 <-sum(weights[sample[1:nrow(sample),3]=="Aged 55 to 59"])
sumAged60to64 <-sum(weights[sample[1:nrow(sample),3]=="Aged 60 to 64"])
sumAged65to69 <-sum(weights[sample[1:nrow(sample),3]=="Aged 65 to 69"])
sumAged70to74 <-sum(weights[sample[1:nrow(sample),3]=="Aged 70 to 74"])
sumAged75to79 <-sum(weights[sample[1:nrow(sample),3]=="Aged 75 to 79"])
sumAged80to84 <-sum(weights[sample[1:nrow(sample),3]=="Aged 80 to 84"])
sumAged85to89 <-sum(weights[sample[1:nrow(sample),3]=="Aged 85 to 89"])
sumAged90andolder <-sum(weights[sample[1:nrow(sample),3]=="Aged90 and older"])
TAE <- abs(cons1[zone, 2] - sumMale)
TAE <- TAE + abs(cons1[zone, 3] - sumFemale)
TAE <- TAE + abs(cons2[zone, 2] - sumAged50to54)
TAE <- TAE + abs(cons2[zone, 3] - sumAged55to59)
TAE <- TAE + abs(cons2[zone, 4] - sumAged60to64)
TAE <- TAE + abs(cons2[zone, 5] - sumAged65to69)
TAE <- TAE + abs(cons2[zone, 6] - sumAged70to74)
TAE <- TAE + abs(cons2[zone, 7] - sumAged75to79)
TAE <- TAE + abs(cons2[zone, 8] - sumAged80to84)
TAE <- TAE + abs(cons2[zone, 9] - sumAged85to89)
TAE <- TAE + abs(cons2[zone, 10] - sumAged90andolder)
print(TAE)
return(TAE)
}
sample.csv
id sex Age10
103712 Female Aged 50 to 54
103713 MaleAged 65 to 69
103715 Female Aged 60 to 64
103716 MaleAged 65 to 69
103717 MaleAged 70 to 74
103718 Female Aged 80 to 84
103721 Female Aged 65 to 69
103722 MaleAged 70 to 74
103723 MaleAged 65 to 69
103724 Female Aged 60 to 64
103728 MaleAged 65 to 69
103729 Female Aged 50 to 54
103730 MaleAged 75 to 79
103731 Female Aged 50 to 54
103733 Female Aged 55 to 59
(this goes on for 1 individuals)
Gender.csv
ZoneMaleFemale
Z1 10547 13234
Z2 16393 18759
Z3 57136462
Z4 19651 21834
Z5 26918 33992
Z6 17596 19665
Age9.csv
LA Aged50to54 Aged55to59 Aged60to64 Aged65to69
Aged70to74 Aged75to79 Aged80to84 Aged85to89 Aged90andolder
Z1 42743852330730963123272818961056449
Z2 74166015540248524304340522701047441
Z3 242520931864175715201218766 376 156
Z4 92367713601352574696407227021293503
Z5 965588418199825283757559551131981320
Z6 7797721057544851421636642376994 399
__
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.
