similar to: Constraint Optimization problem.

Dear All, I am having trouble in using R function "constrOptim" to do constraint optimization. It seems that "constrOptim" calls function "optim" when it does the optimization, and "optim" allows us to set "method" to be "SANN" if we want to use simulated annealing. In "optim", the function allows us to set gradient to be

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

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 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,

Dear all R users, I am wondering if there are any function for Constraint optimization in R. Especially i am looking for a R - equivalent of "fmincon" function in MATLAB. Thanks and regards, Arun [[alternative HTML version deleted]]

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 }

Hello. I have some problems, when I try to model an optimization problem with some constraints. The original problem cannot be solved analytically, so I have to use routines like "Simulated Annealing" or "Sequential Quadric Programming". But to see how all this works in R, I would like to start with some simple problem to get to know the basics: The Problem: min f(x1,x2)=

Hi, I hope someone can help me with the following issue. I need find the minimum beta that satisfies the following: inf{beta>0 | f(x+beta*f(x))*f(x)<=0} where f() is a function and x is a sample statistic. Functions such as "nlminb" and "constrOptim" minimize a function and output the parameter (under parameter constraints). I need to minimize the parameter (also

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

> optim(c(1,1),LL,method="SANN",control=list(fnscale=-1),trans=trans,times=times) $par [1] 17.422635 -1.606859 How could i constraint that the parameters should be both positive in my maximizing problem? I check constrOptim but here i could only constraint the variables trans and times and not my parameters? many thanks, regards Christian

Hi, i need to minimize a quadratic function with boundary condidtions and one equality condition. In order to do that i converted the equality constraint into 2 inequality constaints and passed everything cia constrOptim, as the manual said: everything included in the ... will be passed to Optim that will pass it back to fn in case it does not need it. My code is the following: mat <-

Dear List, I have the code below, where I am using the constrained optimisation package, 'constrOptim.nl' to find the values of two values, b0 and b1. I have no problems when I enter further variable information DIRECTLY into the functions, fn, and heq. In this instance I require fn to have -0.0075 appended to it, and in the case of heq, h[1] has -0.2. library(alabama)

Dear helpers, I am using constrOptim to minimize a function subject to inequality constraint. It works well when the number of parameters to optimize is low (e.g. 4) but when they are more (e.g. 10) it does not produce the expected results. The function is minus the determinant of a binomial logit model with one explanatory variable. Calling ?xeta? the vector of explanatory variables the

Hello list reader, I am trying to form some constraints for an optimization I am working on. I think I have understand the use of the constraints in matrix form. I use them like: constr_mat<- -diag(2) constr_vec<- rep(-0.05,2) constr_mat<- rbind(constr_mat, diag(2)) constr_vec<- c(constr_vec, rep(-1, 2)) To get parameters in the interval [-1, 0.05]. (And this works so far) Now I

I am trying to optimize a likelihood function using constrOptim. I know from prior research that, e.g. x1>x2. Is there a way to include that constraint into the optimization routine, i.e. the ci constraint? The examples I found only use absolute numeric values for the constraint and not relative values. My attempts to include it into ci failed: e.g. ci=c(1, x[1]). Am I using the

Dear Helper, I used "constrOptim.nl" and got the value of par. The estimations looks good even if the number of iterations is only 16. But the values of hessian and convergence are both "NULL". I tested the objective function and gradient function by "optim" and didn't see any problem there. With these functions, "optim" gives the convergence value

Hello, and apologies for the upcoming naive questions. I am a biologist who is trying to teach himself the appropriate areas of math and stats. I welcome pointers to suggested background reading just as much as I do direct answers to my question. Let's say I have a function F() that takes variables (a,b,c,a1,b1,c1) and returns x, and I want to find the values of these variables that result in

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

Hello All, I am stuck in the following problem. Cexpt=c(0,25,50,100,150,300,250,125,40) t=c(0,0.2,0.4,0.6,1,4,8,12,24) theta0= vector of 6 parms (My initial parameter) A=Constraint matrix (hopefully 6*6) B= Constraint vector of length 6) Cfit=function(t,theta){ J(t)=function(theta,t) Cfit=function(J(t),constant) return(Cfit) } loss=function(theta,t,Cexpt) {

Hi Everyone, I'm trying to minimize a function using constrOptim with the simulated annealing method SANN. If I understand constrOptim well, it basically passes most of its arguments to optim while somehow enforcing the constraints. My problem is, that since SANN does not need gradients, when using optim with SANN, the gr argument of optim is used to specify a function to create the next