{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesopt-a-bayesian-optimization-library-for","title":"BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits","arxiv_id":"1405.7430","date":"2014-05-29","proceeding":null,"authors":["Ruben Martinez-Cantin"],"abstract":"BayesOpt is a library with state-of-the-art Bayesian optimization methods to\nsolve nonlinear optimization, stochastic bandits or sequential experimental\ndesign problems. Bayesian optimization is sample efficient by building a\nposterior distribution to capture the evidence and prior knowledge for the\ntarget function. Built in standard C++, the library is extremely efficient\nwhile being portable and flexible. It includes a common interface for C, C++,\nPython, Matlab and Octave.","url_abs":"http://arxiv.org/abs/1405.7430v1","url_pdf":"http://arxiv.org/pdf/1405.7430v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bayesopt-a-bayesian-optimization-library-for","repo_url":"https://github.com/rmcantin/bayesopt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"experimental-design","task_name":"Experimental Design"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}