similar to: supplying gradient to constrOptim()

Dear all, There appears to be a bug in how constrOptim handles ... arguments that are suppose to be passed to optim, according to the documentation. This means you can't get the hessian to be returned, for example (so this is a real problem, and not just a question of mistaken documentation). Looking at the code, it appears that a call to the user-defined f includes the ..., when the ...

>>>>> Duncan Murdoch >>>>> on Sun, 26 Mar 2023 12:41:03 -0400 writes: > On 26/03/2023 11:54 a.m., J C Nash wrote: >> A tangential email discussion with Simon U. has >> highlighted a long-standing matter that some tools in the >> base R distribution are outdated, but that so many >> examples and other tools may use

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:

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

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do

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", >

I've moved this from r-help to r-bugs. If you reply, please be careful that replies go to the right place: r-bugs if your comment is specifically about the bug (and it contains the PR# in the subject that will be added when this is cc'd to r-devel), r-devel if general discussion, not both. On Wed, 14 Jul 2004 10:01:45 -0400, "Roger D. Peng" <rpeng@jhsph.edu> wrote :

Dear all, There have been one or two questions posted to the list regarding the optim error "non-finite finite-difference value [4]." The error apparently means that the 4th element of the gradient is non-finite. My question is what part(s) of my program should I fiddle with in an attempt to fix it? Starting values? Something in the log-likelihood itself? Perhaps the data

Okay, looking at the docs, then it's not a bug, since the "..." argument is not actually documented as "other arguments passed to f or grad". However, that *is* how it's document in `optim', so one can see how this might cause some confusion. Now, it's not clear to me which other arguments need to be passed to `optim' except perhaps `hessian'. Am

Optim's Nelder-Mead works correctly for this example. > optim(par=10, fn=fn, method="Nelder-Mead") x=10, ret=100.02 (memory) x=11, ret=121 (calculate) x=9, ret=81 (calculate) x=8, ret=64 (calculate) x=6, ret=36 (calculate) x=4, ret=16 (calculate) x=0, ret=0 (calculate) x=-4, ret=16 (calculate) x=-4, ret=16 (memory) x=2, ret=4 (calculate) x=-2, ret=4 (calculate) x=1, ret=1

Hi, We (Paul Gilbert and I) have just released a new R package on CRAN called "BB" (stands for Barzilai-Borwein) that provides functionality for solving large-scale (and small-scale) nonlinear system of equations. Until now, R didn't have any functionality for solving nonlinear systems. We hope that this package fills that need. We also have an implementation of the

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Hi all! The objective function I want to minimize contains about 10 to 20 variables, maybe more in the future. I never solved such problems in R, so I had no idea about the efficiency of R's optimization functions. I know doing loop in R is quite slow, so I am not sure whether this shortage influences the speed of R's optimization functions. I would be very appreciated if anyone could

Trying to use constrOptim to minimize the sum of squared deviations. I put the objective function in as: sum((x %*% Y - Z)^2) so i'm trying to get values for x to minimize the sum of the squared deviations between the product of x and Y and Z. Anyways i have no problem using this when x is a 3x1 test variable. it works great with the constraints and everything. when i actually use it on

Dear R wizards: I am playing (and struggling) with the example in the constrOptim function. simple example. let's say I want to constrain my variables to be within -1 and 1. I believe I want a whole lot of constraints where ci is -1 and ui is either -1 or 1. That is, I have 2*N constraints. Should the following work? N=10 x= rep(1:N) ci= rep(-1, 2*N) ui= c(rep(1, N), rep(-1, N))

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, #

Yes, I think you are right. I was at first confused by the fact that after the optim() call, > environment(fn)$xx [1] 10 > environment(fn)$ret [1] 100.02 so not 9.999, but this could come from x being assigned the final value without calling fn. -pd > On 3 May 2019, at 11:58 , Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > Your results below make it look like a

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,

Dear R-help list, I am working on an optimization with R by evaluating a likelihood function that contains lots of Gamma calculations (BGNBD: Hardie Fader Lee 2005 Management Science). Since I am forced to implement lower bounds for the four parameters included in the model, I chose the optim() function mith L-BFGS-B as method. But the likelihood often returns inf-values which L-BFGS-B

Dear friends. I use nls() and encounter the following puzzling problem: I have a function f(a,b,c,x), I have a data vector of x and a vectory y of realized value of f. Case1 I tried to estimate c with (a=0.3, b=0.5) fixed: nls(y~f(a,b,c,x), control=list(maxiter = 100000, minFactor=0.5 ^2048),start=list(c=0.5)). The error message is: "number of iterations exceeded maximum of