Package: OOR
Type: Package
Title: Optimistic Optimization in R
Version: 0.1.4
Date: 2020-03-23
Authors@R: c(
  person("M.", "Binois", role = c("cre", "aut", "trl"), comment = "R port", email = "mickael.binois@inria.fr"),
  person("A.", "Carpentier", role = "aut", comment = "Matlab original"),
  person("J.-B.", "Grill", role = "aut", comment = "Python original"),
  person("R.", "Munos", role = "aut", comment = "Python and Matlab original"),
  person("M.", "Valko", role = c("aut", "ctb"), comment = "Python and Matlab original"))
Description: 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.
License: LGPL
Depends: methods
URL: http://github.com/mbinois/OOR
BugReports: http://github.com/mbinois/OOR/issues
RoxygenNote: 7.2.3
Repository: https://mbinois.r-universe.dev
Date/Publication: 2023-08-22 07:49:06 UTC
RemoteUrl: https://github.com/mbinois/oor
RemoteRef: HEAD
RemoteSha: 5517a187de53afc4c300ea33184a7113c60cfc74
NeedsCompilation: no
Packaged: 2026-07-04 11:50:39 UTC; root
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@inria.fr>