similar to: Nelder-Mead with output of simplex vertices

Dear list,   I am looking for an R-only implementation of a Nelder-Mead process that can find local maxima of a spatially distributed variable, e.g. height, on a spatial grid, and outputs the coordinates of the new point during each evaluation. I have found two previous threads about this topic, and was wondering if something similar has been implemented since those messages were posted.   Thank

Hi, I have written an R translation of C.T. Kelley's Matlab version of the Nelder-Mead algorithm. This algorithm is discussed in detail in his book "Iterative methods for optimization" (SIAM 1999, Chapter 8). I have tested this relatively extensively on a number of smooth and non-smooth problems. It performs well, in general, and it almost always outperforms optim's

All, In looking at `optim', it doesn't appear that it is possible to impose nonlinear constraints on Nelder- Mead. I am sufficiently motivated to try to code something in C from scratch and try to call it from R.... Does anyone have some good references to barrier and/or penalization methods for Nelder-Mead? I would ideally like some papers with pseudocode for method(s) that are in

Dear UseRs, Is there a way to define the lower-upper bounds for parameters fitted by optim using the Nelder-Mead method ? Thanks, Arnaud [[alternative HTML version deleted]]

Dear all, I?m having some problems getting optim with method="Nelder-Mead" to work properly. It seems like there is no way of controlling the step size, and the step size seems to depend on the *difference* between the initial values, which makes no sense. Example: f=function(xy, mu1, mu2) { print(xy) dnorm(xy[1]-mu1)*dnorm(xy[2]-mu2) } f1=function(xy) -f(xy, 0,

I have a Master's student working on a project which involves estimating parameters of a certain model via maximum likelihood, with the maximization being done via optim(). A phenomenon has occurred which I am at a loss to explain. If we use certain pairs of starting values for optim(), it simply returns those values as the ``optimal'' values, although they are definitely not

I don't want to re-invent the wheel, and I'm trying to code up something that does a Nelder-Mead simplex method to minimise a non-linear objective function. (I'm porting something I originally wrote in matlab, using the optimisation toolbox funciton fmins). Is there already something available to do this included in R? Do people have suggestions on the best way to do this? Thanks,

In the optim() documentation, the control parameter "maxit" says "There is no other stopping criteria." That should be "are", or "criterion" ... While I'm at it -- poking at the code a little bit, it looks as if the initial simplex is set from the initial point by displacing each parameter value by max(0.1,0.1*pmax(fabs(Bvec))), where Bvec is the

Dear R-users, I am trying to port to R something that I wrote in Matlab to perform model parameter optimization using the Nelder-Mead simplex method (fminsearch). I read the help on ?optim (which seems to be the way to go) as well as a bunch of posts on the topic, but I would like to make sure about something before I spend to much time trying to reproduce something that is not possible. The

Hello, I am attempting to use optim under the default Nelder-Mead algorithm for model fitting, minimizing a Chi^2 statistic whose value is determined by a .C call to an external shared library compiled from C & C++ code. My problem has been that the R session will immediately crash upon starting the simplex run, without it taking a single step. This is strange, as the .C call itself works,

Dear R-er, For a non-linear optimisation, I used optim() with BFGS method but it stopped regularly before to reach a true mimimum. It was not a problem with limit of iterations, just a local minimum. I was able sometimes to reach better minimum using several rounds of optim(). Then I moved to optimx() to do the different optim rounds automatically using "Nelder-Mead" and

Dear R users, I am pleased to announce the release of three new R packages: optimbase, optimsimplex, and neldermead. - optimbase provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear

Dear R users, I am pleased to announce the release of three new R packages: optimbase, optimsimplex, and neldermead. - optimbase provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear

I am using R 2.5.0 on windows XP and trying to fit copula. I see the following code works for some users, however my code crashes on the chol. Any suggestions? > mycop <- tCopula(param=0.5, dim=8, dispstr="ex", df=5) > x <- rcopula(mycop, 1000) > myfit <- fitCopula(x, mycop, c(0.6, 10), optim.control=list(trace=1), method="Nelder-Mead")

Hi! I'm trying to figure out how to use optim... I get some really strange results, so I guess I got something wrong. I defined the following function which should be minimized: errorFunction <- function(localShifts,globalShift,fileName,experimentalPI,lambda) { lambda <- 1/sqrt(147) # error <- abs(errHuber(localShifts,globalShift, #

Dears useRs, I am using the mle function but this gives me the follow erros that I don't understand. Perhaps there is someone that can help me. thank you for you atention. Bernardo. > erizo <- read.csv("Datos_Stokes_1.csv", header = TRUE) > head(erizo) EDAD TALLA 1 0 7.7 2 1 14.5 3 1 16.9 4 1 13.2 5 1 24.4 6 1 22.5 > TAN <-

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 R-Help, I am using R to do functional outlier detection (using PCA to reduce to 2 dimensions - the functional boxplot methodology used in the Rainbow package), and using Hscv.diag function to calculate the bandwidth matrix where this line of code is run: result <- optim(diag(Hstart), scv.mat.temp, method = "Nelder-Mead", control = list(trace = as.numeric(verbose))) Within the

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",

Hi, I'm having a problem converting a Matlab program into R. The R code works almost all the time, but about 4% of the time R's optim function gets stuck on a local minimum whereas matlab's fminsearch function does not (or at least fminsearch finds a better minimum than optim). My understanding is that both functions default to Nelder-Mead optimization, but what's different about