similar to: Maximization

Dear R user fellows, I would like to ask you about the package 'rgenoud' which is a genetic optimization tool. I ran the function 'genoud' with two variables to be minimized by the following command. result<-genoud(fn,nvars=2,starting.values=c(0.5,0), pop.size=1000, max.generations=10, wait.generations=3) Then, I had the following error message. Error in

Hi, can I understand why this message was rejected ? Thanks, Eliano Sent from my iPhone On 6 Mar 2013, at 19:18, Eliano <eliano.m.marques at gmail.com> wrote: > Hi everyone, > > I am writing some code to generate a function. I am passing that code to a > dataset which i'm importing in R, e.g. > Test=read.table('C:/test.txt', header=F, sep='\t',

Dear all, I would like to solve the following problem, which can be done with optimal control theory or dynamic programming: max(x,y) a*u1+b*u2+c*f1(u2) s.t. 0<u1<x, 0<u2<f2(x,u2), x'=f3(u1,u2,x) which can be rewritten if optimal control theory should be applied as H=a*u1+b*u2+c*f1(u2)+lambda*(x') s.t. 0<u1<x, 0<u2<f2(x,u2) The maximum principle

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

People, I'm researching some Bayesian statistic topics and in the midle of my study i found a very simple problem and i'm trying to find a simple package to solve this type of equations: Lets say that i need to compute beta values for the beta distribution and i now for example: E(teta)=a/(a+b) = 0,5 Var(teta)=ab/((a+b)^2(a+b+1))=0.05 So if i want to solve this to non-linear system to

Hi; I have a R script that includes a call to genoud(); genoud process lasts about 4 seconds, what would be OK if I hadn't have to call it about 2000 times. This yields about 2 hours of processing. And I would like to use this script operationally; so that it should be run twice a day. It seems to me that the parallel processing option included in genoud() divides the task inside the function

Hello - and sorry for a possibly stupid question, I'm just starting to learn rgenoud. I am defining a function with 5 parameters (p1, p2, p3, p4a, and p4b) and then want to optimize it using genoud. But I am doing something wrong. Before genoud is even able to run it says: "Error in p2 + 1.2 : 'p2' is missing". I assume I did not specify it right. My code is below. The task

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

Hi R users, "genoud" function of "rgenoud" package will optimize my function. If opt = genoud(fn,2,max=TRUE,starting.value=c(1,10),........) opt$value will give the optimized value of the function, "fn". My problem is from the same opt, can I get the value of the function at the initial parameter values? I need the initial value of the function for

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

Happy 2013! Can someone with more knowledge of edge case scoping/eval rules explain what is happening below? Happens in all the versions of R I have on hand. Behavior itself is confusing, but ?as.environment also provides no clue. The term used in that doc is 'search list', which is ambiguous, but the see also section mentions search(), so I would *think* that is what is intended.

I am trying to use the optimizing function genoud() with the snow package on a couple of i686 machines running Redhat Linux WS4 . I don't know anything about PVM or MPI, so I just followed the directions in snow and rgenoud for the simplest method and started a socket cluster. My function fn for genoud involves updating an input file for a separate numerical model with the latest parameter

On Aug 22, 2008, at 4:40 PM, Devang Patel wrote: > The LLVM passes are responsible to take appropriate actions based on > Function > Notes associated with function definition. For example, > > define void @fn1() notes("opt-size=1") { ... } > > The function fn1() is being optimized for size without losing > significant > performance. The inliner will

Dear all, as per my understanding, outer() function is to facilitate 2-dimensional function evaluation, like to evaluate f(x,y) for different values of x and y. However I have slightly modified version of that, where x is a matrix with, say, 5 rows and 2 columns and y is a vector. Let take following example: fn1 <- function(x1, x2, y) return (x1+x2+y) x <- matrix(1:10, nc = 2) y

Hi Joerg, I would love to have this behavior(solaris/hp-ux linkers used to do this). cat > main.c << \! int main() { fn(); fn1(); } ! cat > fn.c << \! int fn() { return 0; } ! cat > fn1.c << \! int fn1() { return 0; } ! gcc -c -fPIC main.c fn.c fn1.c ld -shared fn1.o -o libfn1.so ld -shared fn.o -o libfn.so -L. -lfn1 ld main.o libfn.so ld: warning:

Full_Name: James Signorovitch Version: 2.2.1 OS: WinXP Submission from: (NULL) (134.174.182.203) In the code below, fn1() and fn2() fail with the messages given in the comments. Strangely, fn2() fails for all data sets I've tried except for those with 100 rows. The same errors occur if glm() is used in place of lm(), or if R 2.1.1 is used on a unix system. Thanks for looking into this.

Dear List, I've been looking tirelessly for a solution to this dilemma but without success. Perhaps someone has an idea that will guide me in the right direction. Suppose I have the following data.frame: DF = data.frame(X = c(114.5508, 114.6468, 114.6596, 114.6957, 114.6828, 114.8903, 114.9519, 114.8842, 114.8579, 114.8489), Y = c(47.14094, 46.98874, 46.91235, 46.88265, 46.80584, 46.67022,

Hello list, I found that system.time works correctly when I used "<-" to assign a value to a variable but when I happened to use "=" instead of "<-", R gave an error message: "Error in system.time(your argument here...)". It happened with a few functions I tried. Is this a bug or is there any circumstances that "=" cannot be used for

Dear all, I would really appreciate if somebody can help me to understand what does the phrase "Vectorize your function" mean? And what is the job of Vectorize() function in doing that? I have read many threads where experts suggest to Vectorize the function, which will speed up entire calculation (and more elegant ofcourse.) I used to think that vectorizing function means, to create a

On Thu, Dec 15, 2016 at 1:30 PM Teresa Johnson <tejohnson at google.com> wrote: On Thu, Dec 15, 2016 at 11:38 AM, David Blaikie <dblaikie at gmail.com> wrote: On Thu, Dec 15, 2016 at 11:26 AM Teresa Johnson <tejohnson at google.com> wrote: Trying to wrap my brain around this, so a few questions below. =) Sure thing - sorry, did assume a bit too much arcane context here.