search for: abnormal_termination_in_lnsrch

...: L-BFGS-B needs finite values of 'fn' In addition: Warning message: Leading minor of size 1 of downdated X'X is indefinite Error in .local(object, ...) : Leading 2 minor of Omega[[1]] not positive definite In addition: Warning messages: 1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200, 2: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, msMaxIter = 200, thanks for any help. -eduardo

I am using the function optim and I get the error message ABNORMAL_TERMINATION_IN_LNSRCH. Reason for this could be a scaling problem. Thus, I used parscale in order to scale the parameters. But I still have the error message. For example, with parscale=c(rep(1,n), 0.01,1,0.01): return(optim(c(mu1,b,k,phi), neg2loglikelihood, method = "L-BFGS-B", lower=c(rep(-...

[sorry, forgot some details...] I'm using optim(param, fun, method='L-BFGS-B', lower=lo, upper=up) to minimize a certain function. Often the minimization ends with the message: ERROR: ABNORMAL_TERMINATION_IN_LNSRCH What is optim() trying to say? What have I to change in my function to make the minimization succeed? Do you think using BBoptim() instead of optim() changes anything? Thanks for your help! mario -- Ing. Mario Valle Data Analysis and Visualization Group | http://www.cscs.ch/~mva...

...st5", "bg2cost6"))) > factanal.fit.uls(covmat,2) $par bg2cost1 bg2cost2 bg2cost3 bg2cost4 bg2cost5 bg2cost6 0.7454829 0.7191459 0.6969019 0.7611750 0.6940870 0.6930580 $value [1] 0.02167674 $counts function gradient 21 21 $convergence [1] 52 $message [1] "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH" -- Ronggui Huang Department of Sociology Fudan University, Shanghai, China

Hi: I'm using function lmer from package lme4, and I get this message: " There were 12 warnings (use warnings() to see them)" So I checked them: Warnings 1 to 11 said: 1: optim returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = structure(list(maxIter = 50, ... and Warning 12 said: 12: IRLS iterations for glmm did not converge in: lmer(Enfermo ~ Bloque + Trat * Var * dia + (1 | IDfruto), data = desinf, ... There was no error message in the call of lmer function...

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 doing so? My function(s) to...

...2:z2, data, poisson, method="Laplace") Both model 1 and 2 run fine. For model 3, I got error message: ---------------------------------- Error in fn(par, ...) : Unable to invert singular factor of downdated X'X In addition: Warning messages: 1: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: LMEopt(x = mer, value = cv) 2: Leading minor of size 1 of downdated X'X is indefinite ---------------------------------- What is going on here? Any workarounds? Thanks! Best, Shige

...c a1 b1 c1 est 0.50843845 4.519553 90.11219 3.0334775 0.6799724 88.06625 3.5105745 se 0.05461006 9.646768 11.02222 0.3451899 9.8708639 10.15148 0.1797819 Everything looks fine and dandy, but ... > mixwb3.nl1$convergence [1] 52 > mixwb3.nl1$message [1] "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH" So, my questions are: 1) How to obtain se for estimated parameters from the Nelder-Mead method? 2) Is the error message in the L-BFGS-B method serious? How to circumvent it? Any thoughts and suggestions? TIA, Richard Yang Northern Forestry Centre / Centre de foresterie du Nord C...

...day I then take totuperr/totupnum, totdownerr/totdownnum and add them both to my final error term. I'm constraining two of my parameters (to be between 0 and 1), and so it seems I have to use L-BFGS-B. Unfortunately, when optim gets close to the minimum, it usually bails with 52: "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH" or sometimes 1: "NEW_X". Occasionally it succeeds. I assume this error is something to do with the error terms disappearing when you get reasonably good parameters. Is that plausible? If so does anybody know how to fix it? Thanks, Jon Phillips

...is that the solution must be very fast to compute (e.g. 1/3 second for vector of 5000). I coded something up using the L-BFGS-B method of optim where I penalized values between (0, minx) with a parabolic cost function. While reasonably fast and accurate, I occasionally get the message "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH". I believe this is because the gradient is discontinuous at 'minx', so optim finds the gradient calculation unsatisfactory around that value. Not supplying the gradient avoids the error (by using a finite-difference model), but is unacceptably slow. Does anyone have an idea for a mor...

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:

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

...60866 -0.8442 0.39853 time -0.346605 0.026666 -12.9979 < 2e-16 *** sigma 2.608 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Warning message: optim or nlminb returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: LMEopt(x = mer, value = cv) The R routine correctly identifies the treatment effect as not significant. However the parameter estimates are poor. Likelihood Ratio Testing Accurate calculation of the log-likelihood value is desirable so that hypothesis testing can be ca...

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) -