similar to: Question about Constraint Optimization

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

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 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/

Dear R users, I am running a maximum likelihood model with optim. I chose the simulated annealing method (method="SANN"). SANN is not performing bad, but I guess it would be much more effecive if I could set the `parscale' parameter. The help sais: `parscale' A vector of scaling values for the parameters. Optimization is performed on `par/parscale' and these

Hello, I wonder why the control parameter REPORT is not supported by method SANN. Looking into optim.c I found an internal constant: #define STEPS 100 ... and decreasing this to 10 helped me fine-tuning the annealing parameters in an actual problem. Is there any reason why not passing nREPORT to samin and setting something like: STEPS = nREPORT / tmax Thomas P. -- Thomas Petzoldt

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

Hi all I am trying to solve a combinatorial optimization problem. Basically, I can reduce my problem into the next problem: 1.- Given a NxN grid of points, with some values in each cell 2.- Find the combination of K points on the grid such that, the maximum mean value is obtained I took the Travel SalesMan problem example in ?optim documentation. I am not sure if I have understood correctly

> 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, 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

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

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, 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, I need an advice in the following problem. I have to maximize two functions of the form f1(x)=f(y1,x,alpha1,beta1) and f2(x)=(y2,x,alpha2,beta2), the maximization is with respect to alpha1, alpha2, beta1, beta2. I can maximize each function separately using nlm. The problem is that I have to add the constraint of the form g(alpha1)=g(alpha2). The total number of parameters is

Hello, I have a bigger problem in calculating the Maximum Likelihood Estimator regarding the degree of freedom of a multivariate skew-t copula. First of all I would like to describe what this is all about, so that you can understand my problem: I have 2 time series with more than 3000 entries each. I would like to calculate a multivariate skew-t Copula that fits this time series. Notice:

Hi I hope this is the right forum - if not, point me please to a better one. I am using R 2.3.0 on Linux, SuSE 10. I have a question concerning mle (method="BFGS"). I have a few models which I am fitting to existing data points. I realised, that the likelihood is quite sensitive to the start values for one parameter. I am wondering: what is the best approach to identify the right

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

I think SANN method in optim() is failing to report that it has not converged. Here is an example genrose.f<- function(x, gs=NULL){ # objective function ## One generalization of the Rosenbrock banana valley function (n parameters) n <- length(x) if(is.null(gs)) { gs=100.0 } fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2) return(fval) }

Hello R-Help, I'm using R to do some optimization, specifically using the optim method with method = 'SANN' (simulated annealing). I read the help file, and noticed that this method does not include restarts/reheats, which I think would help my optimization significantly. Does anyone know of an implementation that does this? I searched both the internet and the help archives and

Hi, I think that there something wrong with the 'constrOptim' max/minimization function because she doesn?t send extra arguments to 'optim' call. Fact: When I use optim in a f(x,theta)-like function, everything goes ok. But using constrOptim with the same function leads to error... Proof: Make a small change in the 'Rosenbrock Banana function' (taken from the Examples