Displaying 5 results from an estimated 5 matches for "nlopt".
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nloptr
2013 Jun 16
4
can't install rugarch and nloptr packages in R 3.01 opensuse linux
I can't install rugarch package because installation of nloptr package fails .
I use opensuse 12.3
# uname -a
Linux candide 3.7.10-1.11-desktop #1 SMP PREEMPT Thu May 16 20:27:27 UTC 2013 (adf31bb) x86_64 x86_64 x86_64 GNU/Linux
my gcc version is 4.8.1
I compiled and installed R 3.01 . then I tried to install rugarch package but it fails because it can'...
2025 Feb 05
1
Error when loading optimx package and a solution
Dear R-help members
Since this morning (5/2/2025), I get an error when I try to load optimx
package. I solve it and I send my solution but if someone has a better
idea or understand what's happened, I will be most happy to know.
MacOSX 15.3
R 4.4.2
nlopt installed using
brew install nlopt
Here are the steps to produce the error and a solution to solve it. Some
parts are in French. I don't know how to "ask" to R to not use French
for some system messages.
Marc
> library("optimx")
Erreur : le chargement du package ou...
2025 Feb 05
1
Error when loading optimx package and a solution
Hi,
That looks like the dependency on nlopt for nloptr (used by several of the
solvers) was somehow missing. There is SUPPOSED to be a check of what is installed
and a warning issued, but possibly one got missed. Even some base packages
do sometimes need the external libraries manually installed, and I find I'm
doing that after various O...
2014 Sep 07
1
lbfgsb from C/C++
Hi,
I would like to call R's lbfgsb function from my C/C++ code by including
R_ext/Applic.h and linking against libR.
Currently, I am allocating memory for x (and the other input arrays for
lbfgsb) in my C/C++ code via malloc/new. However, this gives a segmentation
fault when executing the program.
I tried to allocate x via PROTECT(x = NEW_NUMERIC(n)); x_p =
NUMERIC_POINTER(x);.
This compiles
2023 Aug 13
4
Noisy objective functions
...----------------------------
1 0.21 0.32 0.13 0.00 0.00
3 0.52 0.63 0.50 0.00 0.00
10 0.81 0.91 0.87 0.00 0.00
Solvers: nmk = dfoptim::nmk, anms = pracma::anms [both Nelder-Mead codes]
neldermead = nloptr::neldermead,
ucminf = ucminf::ucminf, optim_BFGS = optim with method "BFGS"
Read the table as follows: `nmk` will be successful in 21% of the
trials, while for example `optim` will never come close to the true
minimum.
I think it is reasonable to assume that gradient-based met...