similar to: Constrained Nelder-Mead

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

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

Greetings! I want to follow the evolution of a Nelder-Mead function minimisation (a function of 2 variables). Hence each simplex will have 3 vertices. Therefore I would like to have a function which can output the coordinates of the 3 vertices after each new simplex is generated. However, there seems to be no way (which I can detect) of extracting this information from optim() (the

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,

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 helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

Hi r-users, I would like to solve system of nonlinear equation using BBsolve function and below is my code.  I have 4 parameters and I have 4 eqns. mgf_gammasum <- function(p) { t  <- rep(NA, length(p)) mn <- 142.36 vr <- 9335.69 sk <- 0.8139635 kur <- 3.252591 rh  <- 0.896 # cumulants k1 <- p[1]*(p[2]+p[3]) k2 <- p[1]*(2*p[2]*p[3]*p[4] +p[2]^2+p[3]^2) k3 <-

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

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 <-

Dear all, I've worked with optim before but never encountered this error message: Nelder-Mead direct search function minimizer 0.23 0Error: subscript out of bounds The error seems to depend on the initial parameter values. However, strangely (I think), I recieve this error message when I have given very good initial values - the values which were returned when I gave worse values. An

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")

i want to estimate parameters with maximum likelihood method with contraints (contant numbers). for example sum(Ai)=0 and sum(Bi)=0 i have done it without the constraints but i realised that i have to use the contraints. Without constraints(just a part-not complete): skellamreg_LL=function(parameters,z,design) { n=length(z); mu=parameters[1]; H=parameters[2]; Apar=parameters[3:10];

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

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