{"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/on-the-convergence-of-prior-guided-zeroth","title":"On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms","arxiv_id":"2107.10110","date":"2021-07-21","proceeding":"NeurIPS 2021 12","authors":["Shuyu Cheng","Guoqiang Wu","Jun Zhu"],"abstract":"Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been made to integrate prior information into the gradient estimation procedure based on finite differences, with promising empirical results. However, their convergence properties are not well understood. This paper makes an attempt to fill up this gap by analyzing the convergence of prior-guided ZO algorithms under a greedy descent framework with various gradient estimators. We provide a convergence guarantee for the prior-guided random gradient-free (PRGF) algorithms. Moreover, to further accelerate over greedy descent methods, we present a new accelerated random search (ARS) algorithm that incorporates prior information, together with a convergence analysis. Finally, our theoretical results are confirmed by experiments on several numerical benchmarks as well as adversarial attacks.","url_abs":"https://arxiv.org/abs/2107.10110v2","url_pdf":"https://arxiv.org/pdf/2107.10110v2.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":"on-the-convergence-of-prior-guided-zeroth","repo_url":"https://github.com/csy530216/pg-zoo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"random-search","method_name":"Random Search"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2107.10110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10110"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/csy530216/pg-zoo","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"d171f7a8a7f40924","entry":"cosine_similarity","repo":"csy530216/pg-zoo","repo_kind":"official","path":"numerical_benchmarks/exp.py","file_url":"https://github.com/csy530216/pg-zoo/blob/HEAD/numerical_benchmarks/exp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d171f7a8a7f40924"}},{"code_sha256_prefix":"b4e63da00b5070c6","entry":"normalize","repo":"csy530216/pg-zoo","repo_kind":"official","path":"numerical_benchmarks/exp.py","file_url":"https://github.com/csy530216/pg-zoo/blob/HEAD/numerical_benchmarks/exp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b4e63da00b5070c6"}},{"code_sha256_prefix":"82e4eb0b6767c960","entry":"set_function","repo":"csy530216/pg-zoo","repo_kind":"official","path":"numerical_benchmarks/exp.py","file_url":"https://github.com/csy530216/pg-zoo/blob/HEAD/numerical_benchmarks/exp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"82e4eb0b6767c960"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}