similar to: constrOptim with SANN

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

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

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)=

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 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

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

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/

Okay, looking at the docs, then it's not a bug, since the "..." argument is not actually documented as "other arguments passed to f or grad". However, that *is* how it's document in `optim', so one can see how this might cause some confusion. Now, it's not clear to me which other arguments need to be passed to `optim' except perhaps `hessian'. Am

I've moved this from r-help to r-bugs. If you reply, please be careful that replies go to the right place: r-bugs if your comment is specifically about the bug (and it contains the PR# in the subject that will be added when this is cc'd to r-devel), r-devel if general discussion, not both. On Wed, 14 Jul 2004 10:01:45 -0400, "Roger D. Peng" <rpeng@jhsph.edu> wrote :

Hello, I am using constrOptim to maximize a likelihood function (the values of my parameter vector must be between zero and one and must sum up to <=1). I am getting the error 'initial value in 'vmmin' is not finite'. I've tracked it down to a problem in the function 'R' defined within the constrOptim function. It is performing a check on my values, and its not

Hello, I have the following problem: I have a set of n matrix equations in the form of : [b1] = [A] * [b0] [b2] = [A] * [b1] etc. vertical vectors [b0], [b1], ... are GIVEN. We try to estimate matrix A. As there are many equations (more than cells in matrix A) the system has no solutions. A is transition matrix (stochastic matrix) or markov process, so the sum of each row = 1 and each entry is

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) }

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

Hello List I'm working on a combinatoric problem in which the object is to minimize the badness() of a vector. I think this class of problem is only soluble by optim() using method=SANN. The badness() of anything is >= 0, and when I've found a solution with zero badness, I want optim() to stop (carrying on beyond zero badness cannot improve the solution). Efficiency is crucial here.

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 %*%

I tried optim using the SANN algoithm. To start things out I tried the example of solving the "traveling salesman" problem as given in the documentation. The example works just fine. But if I comment out the line: set.seed(123) # chosen to get a good soln relatively quickly More often than not it doesn't converge to the optimum solution as shown in the example. Alos with trace on

Dear all, There appears to be a bug in how constrOptim handles ... arguments that are suppose to be passed to optim, according to the documentation. This means you can't get the hessian to be returned, for example (so this is a real problem, and not just a question of mistaken documentation). Looking at the code, it appears that a call to the user-defined f includes the ..., when the ...

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

I have a problem using optim, so I am hoping someone can help me out with it: Suppose I have the list: list(D,R,P) [[1]] V1 V2 V3 V4 1 0 1 0 1 2 1 1 0 0 3 1 0 1 0 4 0 0 1 1 5 0 1 0 1 6 1 1 0 0 7 1 0 1 0 8 0 0 1 1 9 1 0 1 0 10 0 0 1 1 [[2]] [1] 23 85 12 73 23 24 25 56 78 1200 [[3]] V1 1 25 2 80 3 15 4

Hello. I had previously posted a question concerning the optimization of a nonlinear function conditional on equality constraints. I was pointed towards the contrOptim function. However, I do not understand the syntax of this function with respect to specifying the constraints and so I don’t know if it is what I need. The command is: constrOptim(theta, f, grad,ui,ci,…). “theta” is the