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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.", "License": "LGPL", "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": { "Date": "2026-10-10 14:12:40 UTC", "User": "root" }, "Author": "M. Binois [cre, aut, trl] (R port),\nA. Carpentier [aut] (Matlab original),\nJ.-B. Grill [aut] (Python original),\nR. Munos [aut] (Python and Matlab original),\nM. Valko [aut, ctb] (Python and Matlab original)", "Maintainer": "M. 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