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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.

Type Size Name Uploaded Downloads Labels
conda 68.7 kB | noarch/r-oor-0.1.4-r43h142f84f_0.tar.bz2  1 year and 2 months ago 23 main
conda 58.8 kB | noarch/r-oor-0.1.3-r42h142f84f_0.tar.bz2  2 years and 9 months ago 52 main
conda 58.8 kB | noarch/r-oor-0.1.2-r36h6115d3f_0.tar.bz2  5 years and 23 days ago 119 main

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