similar to: samin and vmmin

Dear list, I was trying to use the R API for optimization method "Simulated annealing" void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex); but I encountered the following problem: The implementation of the function samin (as seen in src/main/optim.c) passes its void * argument "ex" into the function

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 I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Dear listers, Am currently using MCMC approaches to estimate some parameters of my model. One parameter has to be updated using a tuned gamma distribution. So at each iteration I estimate the mean and variance of the density of the gamma approximation using "vmmin" (i also supply the gradient argument). For moderate replications the procedure works, but if I increase them R crashes.

I am calling 'vmmin' several times from a C function (which is called via .C). It works very well, except for memory consumption. The cause is that vmmin allocates memory via R_alloc, and this memory is not freed as vmmin exits. Instead all the allocated memory is freed on return of the .C call. In one application, I have 2000 functions of 500 variables each to minimize. In each call to

Error in optim(method = "BFGS", c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : initial value in 'vmmin' is not finite I am running a logit model with latent class segments. I successfully got estimates for 2 segments. However when I tried to increase the no. of segments, I got this error message at the end. I checked my code again but can't find anything wrong. Is this error

Dear r helpers I run the following code for nested logit and got a message that Error in optim(c(0, 0, 0, 0, 0.1, -2, -0.2), fr, hessian = TRUE, method = "BFGS") : initial value in 'vmmin' is not finite What does this mean? and how can I correct it? Thank you June > yogurt = read.table("yogurtnp.csv", header=F,sep=",")> attach(yogurt)>

Dear r helpers, I'm trying to run the models of the BIOMOD package and got this message: * Error in nnet.default (x, y, w,...): **initial value in 'vmmin' is not finite* What does this mean? and how can I correct it? Thank you *Andreia* [[alternative HTML version deleted]]

I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error: > lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr) Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, : initial value in 'vmmin' is not

I am using ESS on a unix system for my analysis. My R environment contains a 90118 by 94 dataframe. I am trying to calculate the mean of a column in this data frame and I am getting the following error: Error: can not allocate a vector of size 704 Kb I have tried options(memory=1000000000000000000) and this does not help. when I call gc() this is what is returned > gc() used

Hi all. I'm just starting to explore ordinal multinomial regression. My dataset is 300,000 rows, with an outcome (ordinal factor from 1 to 9) and five independent variables (all continuous). My first stab at it was this: pomod <- polr(Npf ~ o_stddev + o_skewness + o_kurtosis + o_acl_1e + dispersal, rlc, Hess=TRUE) And that worked; I got a good model fit. However, a variety of other

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

Hi there, I am trying to plot some output from a FORTRAN (ifort) program using R (2.5.1) under batch mode. In the FORTRAN code, I call R in batch mode to execute a script called fig1.R using something like PROGRAM test USE IFPORT IMPLICIT NONE DO !Some function which makes an output file called ~/fortran_output.txt CALL myfunc() !System call to R plotting routine CALL SYSTEM

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 r-helpers, I'm experiencing some problems in fitting a maximum likelihood binomial model to some of my data. The error is in optim, which founds: Error in optim(par = c(0.2, 0.5), fn = function (p) : initial value in 'vmmin' is not finite Yes, I know it's a common problem, and I've carefully searched and readed all the posts about the issue, But, I can't find a

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

I want to get some "simple" logic regression examples to work before exploring a hard problem. I can get results, but I'm having some problems using "cat" to save the logic regression equation to a disk file. Consider this: # Simple Logic Regression Example # efg, 23 Feb 2005 library(LogicReg) # Create simulated data with known logic equation: # "noise"

I'm preparing some code to compute the optimal geometry of stressed solids. The core of the calculations is the optimization of elastic energy using the simulated annealing method implemented in the R optim() rutine. I've defined a function to compute this "energy" scalar (the fn parameter for optim) and prepared a list with the arrays defining the geometry and the elastic