similar to: optim(…, method=‘L-BFGS-B’) stops with an error message while violating the lower bound

Hi, Mark et al.: Thanks, Mark. Three comments: 1. Rvmmin was one of the methods I tried after Ravi directed me to optimx. It returned NAs for essentially everything. See my email of this subject stamped 4:43 PM Central time = 21:43 UTC. 2. It would be interesting to know if the current algorithm behind optim and optimx with

I'll not copy all the previous material on this thread to avoid overload. The summary is that all the methods Spencer has tried have some issues. The bad news: This is not uncommon with optimization methods, in part because the problems are "hard", in part because getting them implemented and linked to an interfacing approach like R is very tedious and prone to omissions and

Hello! I am trying to create an R optimization routine for a task that's currently being done using Excel (lots of tables, formulas, and Solver). However, otpim seems to be finding a local minimum. Example data, functions, and comparison with the solution found in Excel are below. I am not experienced in optimizations so thanks a lot for your advice! Dimitri ### 2 Inputs:

Hi Spencer: See the link below about L-BFGS-B below because I had problems with it a good while back (and I think the link description is the cause but I can't prove it ) so eventually I moved to the Rvmmin(b) package. It's a package but really an algorithm. Rvmmin(b) uses a variable-metric algorithm similar to that of L-BFGS-B but without the problem below. It's not surprisingly a

Have you tried "optimx" package that John Nash and I wrote? The main purpose is to be able to readily compare multiple optimizers on a particular class of problems and see which one seems to do the best. It doesn't include nloptr, but most other optimizers are there. Ravi ________________________________________ From: R-devel <r-devel-bounces at r-project.org> on behalf of

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

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

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.

Hi, Currently I am trying to solve a minimization problem using optim as method Nelder-Mead. However, Neldel-Mead needs many iterations until it finally converges. I have set $control.trace and $control.report such that I can see the value of the function at each iteration. I do see that I set the convergence criteria to strict in the sense that the function value does not change much. However,

Hey, R users I do not know how to describe my question. I am a new user for R and write the following?code for a dynamic labor economics?model and use OPTIM to get optimizations and parameter values. the following code does not work due to the?equation: ?? wden[,i]<-dnorm((1-regw[,i])/w[5])/w[5] where w[5]?is one of the parameters (together with vector a, b and other elements in vector

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

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Hej, Calling newuoa (from the minqa package) makes R crash when the package rgl is loaded first. This however only on certain selected data. The data used for testing (saved to 'bugs.R'): xvals = c(1,2,4,5,7,8,9,10,11,12,14,15,16,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36) yvals =

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

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

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.) -------------------------------------------------------------------------------------------- >

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

Hi all, I am trying to rebuild the results of a study using a different data set. I'm using about 450 observations. The code I've written seems to work well, but I have some troubles minimizing the negative of the LogLikelyhood function using 5 free parameters. As starting values I am using the result of the paper I am rebuiling. The system.time of the calculation of the function is

Thanks for all your great answers. The app I?m working on is indeed an exploratory data analysis tool for gene expression, which requires a bunch of bioconductor packages. I guess for now, my best solution is to divide my app into modules and load/unload packages as the user switch from one module to another. This brought me another question: it seems that unload package with the