{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6WDLNRYOTGHW3QBNCUIDBZKYJ6","short_pith_number":"pith:6WDLNRYO","schema_version":"1.0","canonical_sha256":"f586b6c70e998f6dc02d151030e5584f8a8c6f779ba1daf0c1eba9ffe43be590","source":{"kind":"arxiv","id":"2210.05824","version":2},"attestation_state":"computed","paper":{"title":"Adapting Zeroth Order Algorithms for Comparison-Based Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daniel McKenzie, Isha Slavin","submitted_at":"2022-10-11T23:15:43Z","abstract_excerpt":"Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-world applications, this field has received little attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO). In this work we propose a relatively simple method for converting ZOO algorithms to CBO algorithms, thus greatly enlarging the pool of known algorithms for CBO. Via PyCUTEst, we benchmarked these algorithms against a suite of unconstrained problems. We then used hyperparameter tuning to de"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2210.05824","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2022-10-11T23:15:43Z","cross_cats_sorted":[],"title_canon_sha256":"1b9db13d5f054279439d015173179ae7a41f7911482f236d2737f0b03756ea56","abstract_canon_sha256":"9fac1e79bd1c8b48a9be8edc9a60610b89b871c32d14b2b642bc88fef397f548"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:13.205515Z","signature_b64":"2WjKzzLHJ3isjl1lYPDeKCtWXI6MWlpzuZMP9G0eBmDtCLplJ++mo52CXXLYu6uXAbbaEm7SDuxVlPHdtVK+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f586b6c70e998f6dc02d151030e5584f8a8c6f779ba1daf0c1eba9ffe43be590","last_reissued_at":"2026-07-05T05:54:13.205027Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:13.205027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adapting Zeroth Order Algorithms for Comparison-Based Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daniel McKenzie, Isha Slavin","submitted_at":"2022-10-11T23:15:43Z","abstract_excerpt":"Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-world applications, this field has received little attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO). In this work we propose a relatively simple method for converting ZOO algorithms to CBO algorithms, thus greatly enlarging the pool of known algorithms for CBO. Via PyCUTEst, we benchmarked these algorithms against a suite of unconstrained problems. We then used hyperparameter tuning to de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.05824","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.05824/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2210.05824","created_at":"2026-07-05T05:54:13.205085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.05824v2","created_at":"2026-07-05T05:54:13.205085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.05824","created_at":"2026-07-05T05:54:13.205085+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WDLNRYOTGHW","created_at":"2026-07-05T05:54:13.205085+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WDLNRYOTGHW3QBN","created_at":"2026-07-05T05:54:13.205085+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WDLNRYO","created_at":"2026-07-05T05:54:13.205085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11616","citing_title":"Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6","json":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6.json","graph_json":"https://pith.science/api/pith-number/6WDLNRYOTGHW3QBNCUIDBZKYJ6/graph.json","events_json":"https://pith.science/api/pith-number/6WDLNRYOTGHW3QBNCUIDBZKYJ6/events.json","paper":"https://pith.science/paper/6WDLNRYO"},"agent_actions":{"view_html":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6","download_json":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6.json","view_paper":"https://pith.science/paper/6WDLNRYO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.05824&json=true","fetch_graph":"https://pith.science/api/pith-number/6WDLNRYOTGHW3QBNCUIDBZKYJ6/graph.json","fetch_events":"https://pith.science/api/pith-number/6WDLNRYOTGHW3QBNCUIDBZKYJ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6/action/storage_attestation","attest_author":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6/action/author_attestation","sign_citation":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6/action/citation_signature","submit_replication":"https://pith.science/pith/6WDLNRYOTGHW3QBNCUIDBZKYJ6/action/replication_record"}},"created_at":"2026-07-05T05:54:13.205085+00:00","updated_at":"2026-07-05T05:54:13.205085+00:00"}