similar to: optim(method="SANN")

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) }

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

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

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 am trying to control the behavior of the SANN method in optim (R 2.14.1) via control$temp. In my toy tests it works; in my real use, it doesn't. As far as I can tell my code with different temp values is loaded; I even traced into the function that calls optim and verified temp had the value I had set. Could the fact that I have NaN's coming back from the objective function be a

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

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 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, 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 try this: p3dpairs <- function(x,x1, xlim=NULL,ylim=NULL,zlim=NULL,col=par("col"), pch=par("pch"), cex=par("cex"), ...){ if(is.matrix(x)){ z <- x[,3] y <- x[,2] x <- x[,1] } if(is.matrix(x1)){ z1 <- x1[,3] y1 <- x1[,2] x1 <- x1[,1] } if(missing(zlim)) { z.grid <-

> 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: I'm trying to use Rcmd SHLIB to compile a single file, sann.c, to get sann.dll. I was able to get make libR.a to work, after going into MkRules and changing the line DLLTOOL=$(BINPREF)dlltool -k --as $(AS) to read DLLTOOL=C:/MINGW-1.1/bin/dlltool -k --as $(AS) But now I get: C:\rw1051\src\gnuwin32>Rcmd SHLIB sann.c make: make: Command not found make: *** [libR] Error 127

optim ignores misspelled control parameters, so that trying to set (e.g.) "maxint=1000" in the control argument silently does nothing. The patch below (watch out for line breaks! also posted at http://www.zoo.ufl.edu/bolker/optim_patch.R , and http://www.zoo.ufl.edu/bolker/optim_new.R) adds three lines to optim.R that issue a warning if any names of elements of "control" fail

Hi R users, I am using the optim funciton to maximize a log likelihood function. My code is as follows: p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031, -0.00245, 3.366, 0.5885, -0.00008, 0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049, 0.1856, 0.00394, -0.00193, -0.889, 0.5379, -0.000063, 0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,

Hi, I'm having some difficulties formulating this question. But what I want, is to extract the options associated with a parameter for a function. e.g. method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN") in the optim function. So I would like to have a vector with c("Nelder-Mead", "BFGS", "CG",

I have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

I am trying to estimate a gamma function using real data and I am getting the following error messages. When I set a lower limit; the error message is "L-BFGS-B needs finite values of fn" ? For other method the error message is: Error in optim(x = c(0.105286666666667, 0.3472275, 2.057625, 0.329675,? : ? non-finite finite-difference value [1] The codes works fine for simulated data

Folks: I thought John Nash's comment below was profound and a possible Fortunes candidate: (Aside: I believe it applies to a great deal of what is discussed on this list, not just stochastic optimization.) Cheers, Bert ... (in the context of stochastic optimization) >... As with many tools in this domain, for effective use they > require more knowledge than many of their users

Hello everybody. I am trying to upgrade from R-1.9.0 to R-1.9.1 on a RedHat linux 2.4.18 system. I get the following error after "tar -xvzf R-1.9.1.tgz ; cd ./R-1.9.1/ ; ./configure" and "make" : [make works for 10 minutes ... snip ...] varExp text html latex varFixed text html latex varFunc