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: 2020.10-1
Depends: R (≥ 2.10.1)
Published: 2020-10-20
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)]
URL: https://coah.jhu.edu/people/Faculty_personal_Pages/Varadhan.html
NeedsCompilation: no
Materials: NEWS
In views: Optimization
CRAN checks: dfoptim results

Downloads:

Reference manual: dfoptim.pdf
Package source: dfoptim_2020.10-1.tar.gz
Windows binaries: r-devel: dfoptim_2020.10-1.zip, r-release: dfoptim_2020.10-1.zip, r-oldrel: dfoptim_2020.10-1.zip
macOS binaries: r-release: dfoptim_2020.10-1.tgz, r-oldrel: dfoptim_2020.10-1.tgz
Old sources: dfoptim archive

Reverse dependencies:

Reverse depends: BivarP, mvord
Reverse imports: ConsReg, cops, diffusion, DynTxRegime, matie, stepPenal
Reverse suggests: afex, garma, lme4, metafor, optimx, ROI.plugin.optimx, SACOBRA
Reverse enhances: Rmpfr

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