OOR: Optimistic Optimization in R

Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.

Version: 0.1.1
Depends: methods
Published: 2017-02-03
Author: M. Binois [cre, aut, trl] (R port), A. Carpentier [aut] (Matlab original), J.-B. Grill [aut] (Python original), R. Munos [aut] (Python and Matlab original), M. Valko [aut, ctb] (Python and Matlab original)
Maintainer: M. Binois <mickael.binois at chicagobooth.edu>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: no
CRAN checks: OOR results


Reference manual: OOR.pdf
Package source: OOR_0.1.1.tar.gz
Windows binaries: r-devel: OOR_0.1.1.zip, r-release: OOR_0.1.1.zip, r-oldrel: OOR_0.1.1.zip
OS X El Capitan binaries: r-release: OOR_0.1.1.tgz
OS X Mavericks binaries: r-oldrel: OOR_0.1.1.tgz


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