similar to: constrained optimisation without second order derivatives? - lnsrch error

[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?

Hi everyone, this should be pretty basic but I need asking for help as I got stuck. I am running simple linear regression models on R with k regressors where k > 1. In order to automate my code I packed all the regressors in a matrix X so that lm(y~X) will always produce the results I want regardless of the variables in X. I am new to R but I found this advice somewhere so I guess it is

Hi, I wonder why my attempt to extend an existing loess fit to a new data set is producing error. I was trying the following: dat = read.csv(choose.files()) x = dat[,2]; y = dat[,1] x.sort = sort(x) y.loess = loess(y~x, span=0.75) # For testing the above fit with a new dataset: test = read.csv(choose.files()) # test data new_x = test [,1]; new_y = test[,2] new_x.sort = sort(new_x) predicted

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",

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hello Thanks for looking into this. I can't be very confident what the knock on result of a change like that would be, especially on architectures that are not Arm. What I can do though, is run some benchmarks and look at that results. Using this patch: --- a/lib/Transforms/InstCombine/InstructionCombining.cpp +++ b/lib/Transforms/InstCombine/InstructionCombining.cpp @@ -150,6 +150,9 @@

Greetings, A couple general questions regarding the use of splines to interpolate depth profile data. Here is an example of a set of depths, with associated attributes for a given soil profile, along with a function for calculating midpoints from a set of soil horizon boundaries: #calculate midpoints: mid <- function(x) { for( i in 1:length(x)) { if( i > 1) { a[i] = (x[i] -

Example: define i8 @narrow_add(i8 %x, i8 %y) { %x32 = zext i8 %x to i32 %y32 = zext i8 %y to i32 %add = add nsw i32 %x32, %y32 %tr = trunc i32 %add to i8 ret i8 %tr } With no data-layout or with an x86 target where 8-bit integer is in the data-layout, we reduce to: $ ./opt -instcombine narrowadd.ll -S define i8 @narrow_add(i8 %x, i8 %y) { %add = add i8 %x, %y ret i8 %add } But on

Hello, I have been trying for weeks to compile a 64-bit Rblas. I started with ATLAS where I have had success in the past, but 64 bit was not behaving, and as each compilation takes between 9 and 12 hours, "test, check, and revise" was not going to be really viable. I therefore switched to OpenBLas (OPBL). I was successful in compiling R-3.0.1 and an OPBL-based BLAS for Windows 64bit

Hi, I'm Luca from Savona, Italy. I'm using Compiz and I'm writing an application with Xlib to draw two windows, let's say window_A and window_B. I would like to keep fixed the relative position between two established corners of them that they have when mapped. So that if I move or resize one of the window, the other if necessary automatically moves to observe the condition.

Thanks for the perf testing. I assume that DAG legalization is equipped to handle these cases fairly well, or someone would've complained by now... FWIW (and at least some of this can be blamed on me), instcombine already does the narrowing transforms without checking shouldChangeType() for binops like and/or/xor/udiv. The justification was that narrower ops are always better for

Hi Luca, In recent versions of compiz (0.9.10) if you have lazy positioning on (org.compiz.move 'lazy-positioning') other clients won't get a configure notification until the end of the move. This is a performance optimization to avoid a slow path on the nvidia drivers with vsync. If you want such notifications you need to turn this option off. Either that or implement this as a

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

Hello, I need to solve a non-linear optimization with non-linear constraints. The 'nlm' routine does not seem to allow constraints. Is there a package for solving such problems in R? Thanks, John. -- ========================================== John Janmaat Department of Economics Acadia University, Wolfville, NS, B0P 1X0 (902)585-1461 All opinions stated are personal, unless

Hi, i have to optimise a function f(a,b), with a, b vectors in R^d such that a and b are orthogonal, that is a'b=0. Anybody has a suggestion? Thanks, in advance, for your help, Giovanna _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]

I'm relatively new to R, but I'm attempting to do a non-linear maximum likelihood estimation (mle) in R, with the added problem that I have a non-linear constraint. The basic problem is linear in the parameters (a_i) and has only one non-linear component, b, with the problem being linear when b = 0 and non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f

Dear R users, I have a problem. I would like to solve the following: I have pL = 1/(1+e^(-b0+b1)) pM = 1/(1+e^(-b0)) pH = 1/(1+e^(-b0-b1)) My target function is TF= mean(pL,pM,pH) which must equal 0.5% My non-linear constraint is nl.Const = 1-(pM/pH), which must equal 20%, and would like the values of both b0 and b1 where these conditions are met. I have searched widely for an answer,

Dear All I have a constrained optimisation problem, I want to maximise the following function t(weights) %*% CovarianceMatrix %*% weights for the weights, subject to constraints on each element within the weights & the weights vector summing to 1. i.e. weights = (x1, x2, x3), where x1 is within some given range (a +b, a - b). I have tried to do this using the optim function in R,

_______________________________________________________________________________________ Hi, I was just wondering if there was any available R code that could handle general constrained optimisation problems. At the moment I'm using nlminb and optim, both of which allow box constraints on the parameters, but ideally I'd like to be able to specify more general constraints on the solution

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