dfoptim: Derivative-Free Optimization (original) (raw)
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: | |
| In views: | Optimization |
| CRAN checks: | dfoptim results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: | mvord |
|---|---|
| Reverse imports: | atRisk, calibrar, ConsReg, cops, DynTxRegime, foreSIGHT, matrisk, npcs, reReg, sklarsomega, stepPenal, stops |
| Reverse suggests: | afex, cxr, lme4, metadat, metafor, optimx, qra, ROI.plugin.optimx |
| Reverse enhances: | Rmpfr |
Linking:
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