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.

Version: 2023.1.0
Depends: R (≥ 2.10.1)
Published: 2023-08-23
DOI: 10.32614/CRAN.package.dfoptim
Author: Ravi Varadhan[aut, cre], Johns Hopkins University, Hans W. Borchers[aut], ABB Corporate Research, and Vincent Bechard[aut], HEC Montreal (Montreal University)
Maintainer: Ravi Varadhan <ravi.varadhan at jhu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
In views: Optimization
CRAN checks: dfoptim results


Reference manual: dfoptim.pdf


Package source: dfoptim_2023.1.0.tar.gz
Windows binaries: r-devel: dfoptim_2023.1.0.zip, r-release: dfoptim_2023.1.0.zip, r-oldrel: dfoptim_2023.1.0.zip
macOS binaries: r-release (arm64): dfoptim_2023.1.0.tgz, r-oldrel (arm64): dfoptim_2023.1.0.tgz, r-release (x86_64): dfoptim_2023.1.0.tgz, r-oldrel (x86_64): dfoptim_2023.1.0.tgz
Old sources: dfoptim archive

Reverse dependencies:

Reverse depends: mvord
Reverse imports: atRisk, calibrar, ConsReg, cops, DynTxRegime, foreSIGHT, garma, matrisk, npcs, reReg, sklarsomega, stepPenal, stops
Reverse suggests: afex, cxr, lme4, metadat, metafor, optimx, qra, ROI.plugin.optimx
Reverse enhances: Rmpfr


Please use the canonical form https://CRAN.R-project.org/package=dfoptim to link to this page.