similar to: choosing constraints for function optim method="L-BFGS-B" when they are in terms of other parameter values

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

Hi, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

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))

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 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, " (memory)", fill=TRUE, sep="") ?????????? return(ret) ?????? } ?????? xx

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

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, 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.

Full_Name: Jörg Polzehl Version: 1.5.1 OS: Windows 2000 Submission from: (NULL) (193.175.148.198) When calculating MLE's in a variance component model using constrained optimization, i.e. optim(...,method="L-BFGS-B",...) I observed an inproper behaviour in cases where the likelihood function was evalueted at the constraint. Parameters and value of the function at the constraint

Hello, I've just a little problem using the function optim. Here is the function I want to optimize : test_function(x){(exp(-0.06751 + 0.25473*((x[1]-350)/150) + 0.04455*((x[2]-40)/20) + 0.09399*((x[3]-400)/100) - 0.17238*((x[4]-250)/50)- 0.45984*((x[5]-550)/150)-0.39508*((x[1]-350)/150)* ((x[1]-350)/150) - 0.05116*((x[2]-40)/20)* ((x[2]-40)/20) - 0.27735*((x[3]-400)/100)*((x[3]-400)/100) -

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, 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

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 parent environment, I >> see the following unexpected behavior: >> >> makeFn <- function(){ >> ???? xx <- ret <- NA >> ???? fn <- function(x){ >> ??????? if(!is.na(xx)

Dear R-Help List, I am using optim, with method=L-BFGS-B, to maximize a likelihood inside a large simulation exercise. This runs fine for most simulated data sets, but for some reason, about 1 out of 100 times, optim will just hang. Using a dumb approach to the problem (i.e. printing the parameter values each time the function being maximized is evaluated), I tracked down when this happens,

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

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

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

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

I'm having some trouble with optim(method="L-BFGS-B"), and I'm not sure I have the ability to track down and fix what seem to be bugs within optim(). I'm bootstrapping an original data set and fitting a model to each bootstrapped data set. For some bootstrapped samples, optim() sets negative parameter values (despite the fact that I have explicitly set non-zero lower