{"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/leapsandbounds-a-method-for-approximately","title":"LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration","arxiv_id":"1807.00755","date":"2018-07-02","proceeding":"ICML 2018","authors":["Gellért Weisz","András György","Csaba Szepesvári"],"abstract":"We consider the problem of configuring general-purpose solvers to run\nefficiently on problem instances drawn from an unknown distribution. The goal\nof the configurator is to find a configuration that runs fast on average on\nmost instances, and do so with the least amount of total work. It can run a\nchosen solver on a random instance until the solver finishes or a timeout is\nreached. We propose LeapsAndBounds, an algorithm that tests configurations on\nrandomly selected problem instances for longer and longer time. We prove that\nthe capped expected runtime of the configuration returned by LeapsAndBounds is\nclose to the optimal expected runtime, while our algorithm's running time is\nnear-optimal. Our results show that LeapsAndBounds is more efficient than the\nrecent algorithm of Kleinberg et al. (2017), which, to our knowledge, is the\nonly other algorithm configuration method with non-trivial theoretical\nguarantees. Experimental results on configuring a public SAT solver on a new\nbenchmark dataset also stand witness to the superiority of our method.","url_abs":"http://arxiv.org/abs/1807.00755v1","url_pdf":"http://arxiv.org/pdf/1807.00755v1.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":"leapsandbounds-a-method-for-approximately","repo_url":"https://github.com/drgrhm/alg_config","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.00755","atlas_url":"https://app.syntology.ai/?focus=1807.00755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.00755"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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