search for: rcgmin

Dear list, I am using optim() function to MLE ~55 parameters, but it is very slow to converge (~ 25 min), whereas I can do the same in ~1 sec. using ADMB, and ~10 sec using MS EXCEL Solver. Are there any tricks to speed up? Are there better optimization functions? Thanks Toshihide "Hamachan" Hamazaki, $B_@:j=S=((JPhD Alaska Department of Fish and Game:

...t;http://R-Forge.R-project.org") I have received the following information: package 'numDeriv' successfully unpacked and MD5 sums checked package 'BB' successfully unpacked and MD5 sums checked package 'ucminf' successfully unpacked and MD5 sums checked package 'Rcgmin' successfully unpacked and MD5 sums checked package 'Rvmmin' successfully unpacked and MD5 sums checked package 'minqa' successfully unpacked and MD5 sums checked package 'setRNG' successfully unpacked and MD5 sums checked package 'optimx' successfully unpack...

...ing. Stefan Theussl and others have the R Optimization Infrastructure. There are some differences of focus in these projects. For the present poster, I cannot unfortunately help much on GNLM details, but I can say that there are trial versions of methods that will handle bounds (packages minqa, Rcgmin and Rvmmin) that accept bounds, along with existing L-BFGS-B in optim(). Rcgmin and Rvmmin also accept "masks", that is fixed parameters. These routines are about to go into CRAN. It appears GNLM is not a CRAN package. Does it permit substitution of optimizer? That is something those...

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

...where. I was the source (in Pascal) of Nelder-Mead, BFGS and CG algorithms in optim(). BFGS is still mostly competitive, and Nelder-Mead is useful for initial exploration of an optimization problem, but CG was never very good, right from the mid-1970s well before it was interfaced to R. By contrast Rcgmin works rather well considering how similar it is in nature to CG. Yet I continue to see use and even recommendations of these tools in inappropriate circumstances. Given that it would break too many other packages and examples to drop the existing tools, should we at least add short notes in the ma...

...e source (in Pascal) of Nelder-Mead, BFGS and CG algorithms in optim(). > BFGS is still mostly competitive, and Nelder-Mead is useful for initial exploration > of an optimization problem, but CG was never very good, right from the mid-1970s > well before it was interfaced to R. By contrast Rcgmin works rather well > considering how similar it is in nature to CG. Yet I continue to see use and > even recommendations of these tools in inappropriate circumstances. > > Given that it would break too many other packages and examples to drop the > existing tools, should we at least...

...do so in a later version after we figure out what advice to give on where it is appropriate to apply it. In the meantime, I would suggest that BFGS or L-BFGS-B are the closest options in optim() and generally perform quite well. There are updates to BFGS and CG on CRAN in the form of Rvmmin and Rcgmin which are all-R implementations with box constraints too. UCMINF is a very similar implementation of the unconstrained algorithm that seems to have the details done rather well -- though BFGS in optim() is based on my work, I actually find UCMINF often does better. There's also nlm and nlmin...

...>> algorithms in optim(). BFGS is still mostly competitive, >> and Nelder-Mead is useful for initial exploration of an >> optimization problem, but CG was never very good, right >> from the mid-1970s well before it was interfaced to R. By >> contrast Rcgmin works rather well considering how similar >> it is in nature to CG. Yet I continue to see use and even >> recommendations of these tools in inappropriate >> circumstances. >> >> Given that it would break too many other packages and >> exa...

Hello: The development version of Ecdat on R-Forge contains a vignette in which optim(?, method=?L-BFGS-B?) stops with an error message while violating the lower bound. To see all the details, try the following: install.packages("Ecdat", repos="http://R-Forge.R-project.org") Then do "help(pac=Ecdat)" -> "User guides, package

Hi R users, I am trying to restrct the range of two of the parameters in a maximization problem. Both parameters should be between -1 and 1. As far as I know, if I choose the estimation method ="L-BFGS-B" under Optim, I can restrict the parameter space. However, the "L-BFGS-B" always require finite values of the loglik function and cannot get around of the problem if an

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

...that became Nelder-Mead, CG, and BFGS in optim() in the mid 1970s. CG never did as well as I would like, but the other two routines turned out pretty well. However, in nearly 40 years, there are a few improvements, particularly in handling bounds and masks (fixed parameters). For all-R routines see Rcgmin and Rvmmin. Rather than directly use the optim() routines in C, you may want to use some more modern ones, but the choice may be dependent on your problem. JN On 06/09/2012 06:00 AM, r-devel-request at r-project.org wrote: > Message: 2 > Date: Fri, 8 Jun 2012 09:40:17 -0400 > From: Edwar...

While lm() is a linear modeling, the constraints make it easier to solve with a nonlinear tool. Both my packages Rvmmin and Rcgmin (I recommend the R-forge versions as more up-to-date) have bounds constraints and "masks" i.e., fixed parameters. I am actually looking for example problems of this type that are more recent than the ones that got me into this 30 years ago. Do contact me off-list if you have something th...

Hi. I just noticed the paper by Morales and Nocedal Remark on "Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound Constrained Optimization". TOMS 2011; 38(1): 7 http://www.ece.northwestern.edu/~morales/PSfiles/acm-remark.pdf which describes a couple of improvements (speed and accuracy) to the original Netlib code which AFAICT is that still used by optim() via f2c.

Dear all, R offers integration mechanism with different programming languages like C, C++, Fortran, .NET etc. Therefore I am curious on, for heavy numerical computation which language is the fastest? Is there any study? I specially want to know because, if there is some study saying that C is the fastest language for numerical computation then I would change some of my R code into C. Thanks for

Hi all, I'm trying to figure out the effective differences between BFGS and L-BFGS-B are, besides the obvious that L-BFGS-B should be using a lot less memory, and the user can provide box constraints. 1) Why would you ever want to use BFGS, if L-BFGS-B does the same thing but use less memory? 2) If i'm optimizing with respect to a variable x that must be non-negative, a common approach

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Dear R users I am trying to use OPTIMX(OPTIM) for nonlinear optimization. There is no error in my code but the results are so weird (see below). When I ran via OPTIM, the results are that Initial values are that theta0 = 0.6 1.6 0.6 1.6 0.7. (In fact true vales are 0.5,1.0,0.8,1.2, 0.6.) -------------------------------------------------------------------------------------------- >

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

Dear R users When I use OPTIM with BFGS, I've got a significant result without an error message. However, when I use OPTIMX with BFGS( or spg), I've got the following an error message. ---------------------------------------------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, gr=gr.fy, method="BFGS", >