similar to: Optimization with constraint.

Hello. I have some further problems with modelling an optimization problem in R: How can I model some optimization problem in R with a linear objective function with subject to some nonlinear constraints? I would like to use "optim" or "constrOptim", maybe with respect to methods like "Simulated Annealing" or "Sequential Quadric Programming" or something

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 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

I'm using the function constrOptim together with the "SANN" method and my objective function (f) has two parameters. One of the parameters needs be into (2^(-10), 2^4) range and the other into (2^(-2), 2^12) range. How can I do it using constrOptim?? Thank you André Rossi Alertas do Yahoo! Mail em seu celular. Saiba mais em http://br.mobile.yahoo.com/mailalertas/

Hello useRs, I'm using the command "solnp" in package "Rsolnp" to solve a general nonlinear programming problem. But I got an error that " the leading minor of order 15 is not positive definite ". Can anybody tell what may cause this error? Does it have something to do with the starting values? Thanks a lot!!! Xiaoxi [[alternative HTML version

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

1. 566 squeue_enter(squeue_t *sqp, mblk_t *mp, mblk_t *tail, uint32_t cnt, Why cnt is declared as uint32_t (fixed size?) Why not just int or uint_t? 554 * squeue_enter() - enter squeue sqp with mblk mp (which can be 555 * a chain), while tail points to the end and cnt in number of 556 * mblks in the chain. It is not quite clear what is a tail (and why it is needed as well) 558 * For

Dear list, The task view on optimization does not reference a package for non linear constrained optimization problems. Stefan Theussl told me to look at the Rsolnp package, but unfortunately it is not very clear what method is R ported. (The authors ported the matlab code of Yinyu Ye http://www.stanford.edu/~yyye/ <http://www.stanford.edu/%7Eyyye/>) Currently I'm looking for an

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do

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 Mr Graves, Thank you very much for your response. Nobody else from this mailing list ventured to reply to me for the two weeks since I posted my question. "nlminb" and "optim" are just optimization procedures. What I need is not just optimization, but a nonlinear CURVE FITTING procedure. If there is some way to perform nonlinear curve fitting with the

Hello, I'm trying to run constrOptim. It returns to me an error about the fact that constraints arguments (ui and ci) are non compatibles: > optout= constrOptim(startparams, f=ImpulseSS, grad=grImpulse, ui=UI, ci=CI, data=gexp[k,], t=t) Error in ui %*% theta : non-conformable arguments I would like to point out that I can calculate that product in the command line: > UI %*%

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 R-Users, I'm searching for somebody who can support me or even likes to collaborate with me in setting up an R-package for "constrained maximim log-likelihood" parameter estimation. For example fitting the parameters of a MA(1)-APARCH(1,1) model for a time series of 17'000 points (e.g. the famous Ding-Granger-Engle mode) takes about 10 minutes with the existing

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 }

> 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

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

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

Please advice on the package I should use to run a linear regression model (weighted least squared) with linear equality constraint. I initially tried "constrOptim" but it turned out that it only supported inequality linear constraint. Thank you very much in advance. Cheers, Jon -- View this message in context:

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