Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
| Version: | 2018.2-1 |
| Depends: | R (≥ 2.10.1) |
| Published: | 2018-04-02 |
| Author: | Ravi Varadhan, Johns Hopkins University, and Hans W. Borchers, ABB Corporate Research. |
| Maintainer: | Ravi Varadhan <ravi.varadhan at jhu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.html |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | Optimization |
| CRAN checks: | dfoptim results |
| Reference manual: | dfoptim.pdf |
| Package source: | dfoptim_2018.2-1.tar.gz |
| Windows binaries: | r-devel: dfoptim_2018.2-1.zip, r-release: dfoptim_2018.2-1.zip, r-oldrel: dfoptim_2018.2-1.zip |
| macOS binaries: | r-release: dfoptim_2018.2-1.tgz, r-oldrel: dfoptim_2018.2-1.tgz |
| Old sources: | dfoptim archive |
| Reverse depends: | BivarP, mvord |
| Reverse imports: | ConsReg, cops, diffusion, DynTxRegime, matie, stepPenal |
| Reverse suggests: | afex, garma, lme4, metafor, optimx, ROI.plugin.optimx, SACOBRA |
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