I do not understand completely what you are trying to do, but may be this works
for you!?
f=function(x) {
# x = c(0.16,80)
Vcmax = x[2]
gi = x[1]
# First dataset
f.1=function(x){
(-(((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))^2-4*(-1/gi)*(0.89189*(164.6573+272.38*(1+21*10/165.82))-Vcmax*(164.6573-(5*21/2.605459)))))/(-2/gi)
}
# Second data set
f.2=function(x){
(-(((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))^2-4*(-1/gi)*(0.89189*(164.3077+272.38*(1+2*10/165.82))-Vcmax*(164.3077-(5*2/2.605459)))))/(-2/gi)
}
# Values here are the measured values. f.1 and f.2 should be equal or close to
the value on their left.
y.1 = abs(7.478327 - f.1(x))
y.2 = abs(12.73134 - f.2(x))
# This should be close to 0.
y = (y.1 - y.2)^2
return(y*y)
}
dom = matrix(c(0,0,200,1.5), 2, 2)
res <- optim(par=c(1,1), fn=f, method="BFGS") # no need for "genoud" here
# First dataset
f.1=function(x){
Vcmax = res$par[1]
gi = res$par[2]
(-(((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))^2-4*(-1/gi)*(0.89189*(164.6573+272.38*(1+21*10/165.82))-Vcmax*(164.6573-(5*21/2.605459)))))/(-2/gi)
}
# Second data set
f.2=function(x){
Vcmax = res$par[1]
gi = res$par[2]
(-(((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))^2-4*(-1/gi)*(0.89189*(164.3077+272.38*(1+2*10/165.82))-Vcmax*(164.3077-(5*2/2.605459)))))/(-2/gi)
}
f.1(res$par)
f.2(res$par)
Hope this is helpful,
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: Guillaume Théroux Rancourt <guillaume.theroux-ranco...@fsaa.ulaval.ca>
Date: Thursday, February 4, 2010 3:02 pm
Subject: [R] Minimizing two non-linear functions with genoud - Trying to
minimize or converge near zero
To: "r-help@r-project.org" <r-help@r-project.org>
> Hello R users,
>
> I am trying to minimize two functions with genoud. It is actually one
> function with two sets of data, each of them having two unknown
> variables (called Vcmax and gi) which have the same value in each of
> the function. They are called f.1 and f.2 in the code below.
>
> My objective to minimize the functions in order to get the two
> variables equal in each of the functions. Furthermore, I have a
> measured comparison value for each of the function expression, and the
> results of f.1 and f.2 should be very close or equal to their measured
> value, so that measured.1 - f.1 = 0.
>
> I have been able to run genoud with the code below. However, I
> haven't been able to restrain the values of the difference between the
> measured and estimated value to 0. I am fairly new at writing R
> functions and I think there might be something I haven't written that
> makes the output parameters of genoud not replicable.
>
> I have made several runs of this function and when comparing with the
> measured value, I got answers between 1 and 12, where it should have
> been very close to 7.47.
>
> This example has already been solved with the solver Excel add-in and
> theh result are:
> Vcmax = 104.32, gi = 0.11
>
> The values were also estimated using another approach and they are:
> Vcmax = 64.48, gi = 0.28
>
>
> Here is my code.
>
>
> ######
>
> f=function(x) {
>
> x = c(0.16,80)
> Vcmax = x[2]
> gi = x[1]
>
> # First dataset
> f.1=function(x){
>
> (-(((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))^2-4*(-1/gi)*(0.89189*(164.6573+272.38*(1+21*10/165.82))-Vcmax*(164.6573-(5*21/2.605459)))))/(-2/gi)
> }
>
> # Second data set
> f.2=function(x){
>
> (-(((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.3077+272.38*(1+2*10/165.82))^2-4*(-1/gi)*(0.89189*(164.3077+272.38*(1+2*10/165.82))-Vcmax*(164.3077-(5*2/2.605459)))))/(-2/gi)
> }
>
> # Values here are the measured values. f.1 and f.2 should be equal or
> close to the value on their left.
> y.1 = (7.478327 - f.1(x))
> y.2 = (12.73134 - f.2(x))
>
> # This should be close to 0.
> y = y.1 - y.2
>
> return(y)
> }
>
> dom = matrix(c(0,0,200,1.5), 2, 2)
>
> res = genoud(f, nvars=2, max=FALSE,Domains=dom,pop.size=5000,print.level=0)
>
>
> # In order to test the results to see I the estimated variables make
> the "test" function = 7.478327 or near.
> # This is the same as f.1
>
> test=function(Vcmax, gi){
>
> (-(((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))+sqrt((((Vcmax-0.89189)/gi)+164.6573+272.38*(1+21*10/165.82))^2-4*(-1/gi)*(0.89189*(164.6573+272.38*(1+21*10/165.82))-Vcmax*(164.6573-(5*21/2.605459)))))/(-2/gi)
> }
>
> test(res$par[1],res$par[2])
>
> ## End
>
>
> Thank you for your help!
>
>
> Guillaume Théroux Rancourt
> Ph.D. candidate --- Plant Biology
> Université Laval, Québec, QC, Canada
> guillaume.theroux-rancour...@ulaval.ca
>
> ______________________________________________
> R-help@r-project.org mailing list
>
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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