similar to: DEoptim example illustrating use of fnMap parameter for enforcement of cardinality constraints

Dear R users, During the the running of DEoptim function which belongs to "DEoptim" package it automatically gives the output like the following: Iteration: 1 bestvalit: 181.379847 bestmemit: 0.226499 1.395852 Iteration: 2 bestvalit: 14.062649 bestmemit: 2.290357 5.597838 Iteration: 3 bestvalit: 14.062649 bestmemit: 2.290357 5.597838 Iteration: 4 bestvalit: 14.062649

Hello, How do I implement a cardinality constraint with constrOptim? I want to minimize (least square) a%*%x = 4 subject to x1<2 x2<1 x3<4 count(x1, x2, x3)= 2 (cardinality constraint) Is there a way to specify binary integer variables with constrOptim? Here's my code so far: a <-matrix(1:3,1,3) fr <- function(x) { (a%*%x-4)^2 }

Dear All, We are happy to announce the release of the new version of DEoptim (version 2.0-0) which is now available from CRAN. The DEoptim package [3] performs Differential Evolution (DE) minimization, a genetic algorithm-based optimization technique [2,3]. This allows robust minimization over a continuous (bounded or not) domain. The new DEoptim function calls a C implementation of the DE

Dear All, We are happy to announce the release of the new version of DEoptim (version 2.0-0) which is now available from CRAN. The DEoptim package [3] performs Differential Evolution (DE) minimization, a genetic algorithm-based optimization technique [2,3]. This allows robust minimization over a continuous (bounded or not) domain. The new DEoptim function calls a C implementation of the DE

I am trying to figure out how parameters are defined in a function that is used by DEOptim. That is, when I set upper and lower bounds for DEOptim how does it know which element of the function to apply those bounds to? For example, in DEOptim call below, when DEOptim goes into "optimfxn" how does it know what element of "optimfxn" to apply the upper and lower bounds to?

(the same mail was sent to the author) When I called the function DEoptim with control=list(strategy=1) or control=list(strategy=2) I got the error: Error in mui[rtd + 1, i] : incorrect number of dimensions Analysis of the source code of the DEoptim reveals the following fragment if (con$strategy > 5) st <- con$strategy - 5 ## binomial crossover else { st <-

Hi list: This is my first time to post my question on the list. Thanks for your help. I am solving a quadratic programming using R. Here is my question: w = arg min 0.5*w'Mw - w'N s. t. sum(w) = 1; w>0 note: w is weight vector, each w_i must >=0, and the sum of w =1. Here is my R code: A <-matrix(c(2.26,1.26,1.12,1.12,2.27,1.13,1.12,1.13,2.2),3,3); B <-

Dear R users, I'd like to announce a new package on CRAN called ``denstrip''. It implements ``density strips'' and other graphical methods for illustrating and comparing distributions in a compact fashion. Posterior distributions of parameters are often summarised using point and line drawings of means and credible intervals. This is common, for example, in multiple

Dear R users, I'd like to announce a new package on CRAN called ``denstrip''. It implements ``density strips'' and other graphical methods for illustrating and comparing distributions in a compact fashion. Posterior distributions of parameters are often summarised using point and line drawings of means and credible intervals. This is common, for example, in multiple

Hi all I would like to know a bit more about illustrating an a4 page within R. Finally I would like to have a pdf with multiple pages containing some statistics. I'm already able to produce a pdf, containing a plot in the center of one a4 page. As a next step I would like to set a title on top of this a4 page (maybe with absolute positions, if not possible to set it relative?). The plots then

Hello all, I posted a similar question recently but I suspect that it was not well enough formulated to trigger any answers. So I try again: Is there a way to produce boxplots (or something similar) that uses the estimated fixed effects of an lme{nlme} object? When I want to know the mean, I use estimable{gmodels} together with an appropriate matrix. I did look for a similar tool to estimable in

Dear R Community, I am trying to optimize a water quality model that I am using. Based on conversations with others more familiar with what I am doing I plan to implement DEOptim to do this. The water quality model is interfaced through a GUI. I have the input file necessary to alter parameters and run the model as a text file. To do the optimization I have figured out the general procedure

Before I start reinventing the wheel, has anything been done or is there any work ongoing to provide a P/Invoke based interface to parts or all of libFLAC or even a native port of libFLAC to C#/.NET? Regards, Dave -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.xiph.org/pipermail/flac-dev/attachments/20051221/2b360697/attachment.html

Hi R-Core, i am using nnet and DEoptim, Xcc=matrix(rnorm(100,0.5,0.08),50,2) Ycr=matrix(rnorm(50,0.2,0.05),50,1) pred_regm1 <- function(A) { A1=A[1] A2=A[2] A3=A[3] regm1 <- nnet(Xcc,Ycr,entropy=T,size=A1,decay=A2,maxit=2000,trace=F,Hess=T,rang=A3,skip=T) dif=sum((predict(regm1,Xcc)-Ycr)^2) return(dif) } somar=DEoptim(pred_regm1,c(1,0.00001,0.01), c(25,0.999,0.95),

Dear R list, I use the constrOptim to maximize a function with four constriants but the answer does not leave from the starting value and there is only one outer iteration. The function is defined as follows: tm<-function(p){ p1<-p[1]; p2<-p[2]; p3<-p[3]; p4<-1-p1-p2-p3; p1*p2*p3*p4} ##the constraints are p1>=0; p2>=0; p3>=0 and p4>=0 i.e. p1+p2+p3<=1

Dear all R2.10 WinXP I have a dataset dealing with the way different wheat cultivars build their yield. Wheat ears are organised in spikelets where the spikelets can be numbered from the bottom, with even numbers on one side and odd on the other. I know how many kernels there were in each spikelet after some months spent counting them... Now I want to illustrate the differences between the

Hello everyone, I have come across a bit of an odd problem: I am currently analysing data PCR reaction data of which part is behaving exponential. I would like to fit the exponential part to the following: y0 + alpha * E^t In which Y0 is the groundphase, alpha a fluorescence factor, E the efficiency of the reaction & t is time (in cycles) I can get this to work for most of my reactions,

Hi there, I am trying to fit the following model with a sum of exponentials - y ~ Ae^(-md) + B e^(-nd) + c the model has 5 parameters A, b, m, n, c I am using nls to fit the data and I am using DEoptim package to pick the most optimal start values - fm4 <- function(x) x[1] + x[2]*exp(x[3] * -dist) + x[4]*exp(x[5] * -dist) fm5 <- function(x) sum((wcorr-fm4(x))^2) fm6 <- DEoptim(fm5,

All, I am looking for an optimization library that does well on something as chaotic as the Schwefel function: schwefel <- function(x) sum(-x * sin(sqrt(abs(x)))) With these guys, not much luck: > optim(c(1,1), schwefel)$value [1] -7.890603 > optim(c(1,1), schwefel, method="SANN", control=list(maxit=10000))$value [1] -28.02825 > optim(c(1,1), schwefel, lower=c(-500,-500),

All, I am looking for an optimization library that does well on something as chaotic as the Schwefel function: schwefel <- function(x) sum(-x * sin(sqrt(abs(x)))) With these guys, not much luck: > optim(c(1,1), schwefel)$value [1] -7.890603 > optim(c(1,1), schwefel, method="SANN", control=list(maxit=10000))$value [1] -28.02825 > optim(c(1,1), schwefel, lower=c(-500,-500),