similar to: good numerical optimization to use in R?

Hello R users, Can anyone recommend me any package that can be used to solve linear programming subject to nonlinear equality/inequality constraints. Rdonlp2 is now unavailable due to licensing issue. Thanks, Xiaoxi [[alternative HTML version deleted]]

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

Previous versions have this question have partially bounced. I apologize if parts of this are showing up multiple times on the list. Another try ... There was at one time an R package called Rdonlp2 for solving constrained nonlinear programming problems. Both the objective function and the constraints could be nonlinear in the decision variables. The package is no longer in the CRAN list.

Hi. I need some advises on how to use R to find pi (i is the index) with the following objective function and constraint: max (sum i)[ f(ai, bi, pi) * g(ci, di, pi) * Di ] s.t. (sum i)[ f(ai, bi, pi) * Di * pi] / (sum i)[ f(ai, bi, pi) * Di ] <= constant f and g are diffentiable. So, I am thinking of optim with method = "BFGS"? But wonder how to include the

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 R helpers I have a question regarding the constrainOptim. I'm coding the nested logit and would like to set a bound of rho to (0,1] as an extreme value distribution where rho = exp(lambda)/1+exp(lambda) I wonder if I can do that directly in optim (say rho > 0 & <= 1) or need to use constrainOptim I read the help but still don't know how to set ui and ci Thanks, June

Dear All, I would like to minimise a nonlinear function subject to linear inequality constraints as part of an R program. I have been using the constrOptim function. I have tried all of the methods that come with Optim, but nothing finds the correct solution. If I use the correct solution as the vector of starting values, though, my program does output the correct solution and optimum - the

Dear R wizards: I am playing (and struggling) with the example in the constrOptim function. simple example. let's say I want to constrain my variables to be within -1 and 1. I believe I want a whole lot of constraints where ci is -1 and ui is either -1 or 1. That is, I have 2*N constraints. Should the following work? N=10 x= rep(1:N) ci= rep(-1, 2*N) ui= c(rep(1, N), rep(-1, N))

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_______________________________________________________________________________________ Hi, I was just wondering if there was any available R code that could handle general constrained optimisation problems. At the moment I'm using nlminb and optim, both of which allow box constraints on the parameters, but ideally I'd like to be able to specify more general constraints on the solution

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

I''m looking for a R function which can maximise this logliklihood function, under the constraits a>0 e b>0 f<-function(param){ a<-param[1] b <-param[2] log(prod)-(a*s2)-(b*s)-n*log(1-((0.5*b/sqrt(a))*(exp((b^2)/(4*a)))*((sqrt(pi ))*(1-pnorm(-b/(2*sqrt(a)), mean=0, sd=1)))))} I''ve tried maxlik constrOptim e donlp2 but without success. Thanks so

Dear all, I'm looking for a way to solve a simple optimization problem with a nonlinear constraint. An example would be max x s.t. y = x * T ^(x-1) where y and T are known values. optim() and constrOptim() do only allow for box or linear constraints, so I did not succedd here. I also found hints to donlp2 but this does not seem to be available anymore. Any hints are welcome,

Hi there, I have a piece of Matlab code I use to optimise a trding strategy. If there are any Matlab/R specialists out there, I would appreciate your help in doing the exact same optimisation in R. I suspect I would use nlm() in R but am not sure where to define my constraints. I have attached my Matlab code below for reference. Many thanks. Constraints function [c,ceq]=TriskellConstraints(X)

Hi, I am having trouble using constrOptim. My target is to do a portfolio optimization and there some constraints have to be fulfilled. 1) The weight of each share of the portfolio has to be greater than 0 2) The sum of these weights has to be 1 I am able to fulfill either the first or the second constraint but not both. One simple way would be to fulfill the first constraint by using optim as

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Hello, I've got to compute a minimization equation under an equality constraint (Min g(x1,x2,x3) with x1+x2=const). The Constroptim function does not authorize an equality condition but only inequality conditions. Which function can I use instead? Thank you very much for your help. Gael Robert - +33 1 42 14 27 96 ****************************************************************** This

Is there a package in R that handles general linear (in-)equality + box constrained optimization? If it is not there, could anyone advise me which way to go? And/or point me to packages that solve these problems partially? best, ingmar -- Ingmar Visser Department of Psychology, University of Amsterdam Roetersstraat 15, 1018 WB Amsterdam The Netherlands http://users.fmg.uva.nl/ivisser/ tel:

Hello All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =