{"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/llama-leveraging-learning-to-automatically","title":"LLAMA: Leveraging Learning to Automatically Manage Algorithms","arxiv_id":"1306.1031","date":"2013-06-05","proceeding":null,"authors":["Lars Kotthoff"],"abstract":"Algorithm portfolio and selection approaches have achieved remarkable\nimprovements over single solvers. However, the implementation of such systems\nis often highly customised and specific to the problem domain. This makes it\ndifficult for researchers to explore different techniques for their specific\nproblems. We present LLAMA, a modular and extensible toolkit implemented as an\nR package that facilitates the exploration of a range of different portfolio\ntechniques on any problem domain. It implements the algorithm selection\napproaches most commonly used in the literature and leverages the extensive\nlibrary of machine learning algorithms and techniques in R. We describe the\ncurrent capabilities and limitations of the toolkit and illustrate its usage on\na set of example SAT problems.","url_abs":"http://arxiv.org/abs/1306.1031v3","url_pdf":"http://arxiv.org/pdf/1306.1031v3.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":"llama-leveraging-learning-to-automatically","repo_url":"https://bitbucket.org/lkotthoff/llama","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"llama-leveraging-learning-to-automatically","repo_url":"https://github.com/kvrigor/algosel-rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"llama-leveraging-learning-to-automatically","repo_url":"https://github.com/MS-P3/code3/tree/main/llama","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}