similar to: bounds violations, infinite loops in optim/L-BFGS-B (PR#671)

>>>>> Spencer Graves <spencer.graves at prodsyse.com> >>>>> on Sat, 8 Oct 2016 18:03:43 -0500 writes: [.............] > 2. It would be interesting to know if the > current algorithm behind optim and optimx with > method='L-BFGS-B' incorporates Morales and Nocedal (2011) > 'Remark on ?Algorithm 778:

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

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, 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

Dear kind R-experts. Does anybody have an experience to use optim function? If yes, what is the main difference between two method "BFGS" vs "L-BFGS-B"? I used "BFGS" method and got what I wanted. But when I used "L-BFGS-B" the error message said that "L-BFGS-B needs finite values of fn". So that means "BFGS" method can handle even if fn

The the use of optim with the L-BFGS-B method for the following simple function gives erroneous results. Any help appreciated! Best, Bob Reilly # Code: V=function(p){ p1=p[1];p2=p[2] y=p1*p2-.4*(p1+p2) return(-y)} p=c(.2,.2) # p=c(.8,.8) max=optim(p,V,method = "L-BFGS-B",lower=c(0,0),upper=c(1,1)) max1=optim(max$par,V,method = "L-BFGS-B",lower=c(0,0),upper=c(1,1))

Hi all! I'm running some simulations and I need to estimate some paramaters with optim( ), in some cases optim stops with the next message: "L-BFGS-B needs finite values of 'fn' " I would like to know how to include and "if" condition when this happen, could it be something like: myfun <- optim(....) # run my function

On 02/05/2019 21:35, Florian Gerber wrote: > Dear all, > > when using optim() for a function that uses the parent environment, I > see the following unexpected behavior: > > makeFn <- function(){ > ??? xx <- ret <- NA > ??? fn <- function(x){ > ?????? if(!is.na(xx) && x==xx){ > ?????????? cat("x=", xx, ", ret=", ret, "

Hi, When using method L-BFGS-B along with a parscale argument, should the lower and upper bounds provided be on the scaled or unscaled values? Thanks. Cheers, -- Seb

Hi, I am using optim with a L-BFGS-B method to minimize a function. As I've understood, the way to specify a tolerance for stopping optimization is through "factr" argument. My function, is by construction, minimal when equal to 1. I wonder if there is any way to pass this info to "optim". If not, how "factr" argument works (I am quite confused about the

Hi, my call of optim() with the L-BFGS-B method ended with the following error message: ERROR: ABNORMAL_TERMINATION_IN_LNSRCH Further tracing shows: Line search cannot locate an adequate point after 20 function and gradient evaluations final value 0.086627 stopped after 7 iterations Could someone pls tell me whether it is possible to increase the limit of 20 evaluations? Is it even worth

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 R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt =

Hi Tom, I am not entirely sure what the problem, you haven't been very specific. If you want general linear constraints on your parameters, ie linear combinations of parameters summing to some value, constrOptim may be of help. hth, ingmar Ingmar Visser Developmental Processes Research Group Department of Psychology University of Amsterdam http://users.fmg.uva.nl/ivisser/ -----Original

I have a function of several variables which I wish to minimise over four variables, two of the upper bounds for which are defined in terms of other variables in the model over which minimisation will take place. I cannot work out how to code this in such a way as to avoid getting an error message when I run the code. If anyone can provide any assistance I will be most grateful. Best Regards

Your results below make it look like a bug in optim(): it is not duplicating a value when it should, so changes to x affect xx as well. Duncan Murdoch On 03/05/2019 4:41 a.m., Serguei Sokol wrote: > On 03/05/2019 10:31, Serguei Sokol wrote: >> On 02/05/2019 21:35, Florian Gerber wrote: >>> Dear all, >>> >>> when using optim() for a function that uses the

Hello, A couple of ideas... Im not clear on your whole problem however... Consider making use of the lgamma function, which returns the natural log of the gamma function. This may help. The gamma function gets awfully, big very fast. Also multivariable likelihoods can be bumpy like a mountain range, with minor peaks and valleys. It is possible that your likelihood has such a shape. Maybe

Sorry, I deleted my old post. Pasting the new query below. Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here

Hi all, I am trying to find estimates for 7 parameters of a model which should fit real data. I have a function for the negative log likelihood (NLL) of the data. With optim(method="L-BFGS-B",lower=0) I am now minimizing the NLL to find the best fitting parameters. My problem is that the algorithm does not converge for certain data sets. I have read that one should scale the fn