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  "Title": "Optimistic Optimization in R",
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  "Date": "2020-03-23",
  "Authors@R": "c(\nperson(\"M.\", \"Binois\", role = c(\"cre\", \"aut\", \"trl\"), comment = \"R port\", email = \"mickael.binois@inria.fr\"),\nperson(\"A.\", \"Carpentier\", role = \"aut\", comment = \"Matlab original\"),\nperson(\"J.-B.\", \"Grill\", role = \"aut\", comment = \"Python original\"),\nperson(\"R.\", \"Munos\", role = \"aut\", comment = \"Python and Matlab original\"),\nperson(\"M.\", \"Valko\", role = c(\"aut\", \"ctb\"), comment = \"Python and Matlab original\"))",
  "Description": "Implementation of optimistic optimization methods for\nglobal optimization of deterministic or stochastic functions.\nThe algorithms feature guarantees of the convergence to a\nglobal optimum. They require minimal assumptions on the (only\nlocal) smoothness, where the smoothness parameter does not need\nto be known. They are expected to be useful for the most\ndifficult functions when we have no information on smoothness\nand the gradients are unknown or do not exist. Due to the weak\nassumptions, however, they can be mostly effective only in\nsmall dimensions, for example, for hyperparameter tuning.",
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  "Repository": "https://mbinois.r-universe.dev",
  "Date/Publication": "2023-08-22 07:49:06 UTC",
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