Package: dfoptim 2023.1.0

dfoptim: Derivative-Free Optimization

Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.

Authors:Ravi Varadhan[aut, cre], Johns Hopkins University, Hans W. Borchers[aut], ABB Corporate Research, and Vincent Bechard[aut], HEC Montreal

dfoptim_2023.1.0.tar.gz
dfoptim_2023.1.0.zip(r-4.5)dfoptim_2023.1.0.zip(r-4.4)dfoptim_2023.1.0.zip(r-4.3)
dfoptim_2023.1.0.tgz(r-4.5-any)dfoptim_2023.1.0.tgz(r-4.4-any)dfoptim_2023.1.0.tgz(r-4.3-any)
dfoptim_2023.1.0.tar.gz(r-4.5-noble)dfoptim_2023.1.0.tar.gz(r-4.4-noble)
dfoptim_2023.1.0.tgz(r-4.4-emscripten)dfoptim_2023.1.0.tgz(r-4.3-emscripten)
dfoptim.pdf |dfoptim.html
dfoptim/json (API)
NEWS

# Install 'dfoptim' in R:
install.packages('dfoptim', repos = c('https://rvaradhan.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.66 score 19 packages 195 scripts 5.2k downloads 8 mentions 5 exports 0 dependencies

Last updated 2 years agofrom:a15cd5a8a8. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 13 2025
R-4.5-winOKFeb 13 2025
R-4.5-macOKFeb 13 2025
R-4.5-linuxOKFeb 13 2025
R-4.4-winOKFeb 13 2025
R-4.4-macOKFeb 13 2025
R-4.3-winOKFeb 13 2025
R-4.3-macOKFeb 13 2025

Exports:hjkhjkbmadsnmknmkb

Dependencies: