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-4.7)dfoptim_2023.1.0.zip(r-4.6)dfoptim_2023.1.0.zip(r-4.5)
dfoptim_2023.1.0.tgz(r-4.6-any)dfoptim_2023.1.0.tgz(r-4.5-any)
dfoptim_2023.1.0.tar.gz(r-4.7-any)dfoptim_2023.1.0.tar.gz(r-4.6-any)
dfoptim_2023.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
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.

6.29 score 24 packages 257 scripts 13k downloads 8 mentions 5 exports 0 dependencies

Last updated from:a15cd5a8a8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK101
source / vignettesOK129
linux-release-x86_64OK91
macos-release-arm64OK162
macos-oldrel-arm64OK165
windows-develOK68
windows-releaseOK68
windows-oldrelOK73
wasm-releaseOK90

Exports:hjkhjkbmadsnmknmkb

Dependencies: