{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:2B3GH2ZVXYRAPEDDRZ3QXPVCBS","short_pith_number":"pith:2B3GH2ZV","schema_version":"1.0","canonical_sha256":"d07663eb35be220790638e770bbea20c822bb342985acaeb8bba76f64797d2c2","source":{"kind":"arxiv","id":"1801.00329","version":3},"attestation_state":"computed","paper":{"title":"ZOOpt: Toolbox for Derivative-Free Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chao Qian, Hong Qian, Yang Yu, Yi-Qi Hu, Yu-Ren Liu","submitted_at":"2017-12-31T18:06:25Z","abstract_excerpt":"Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides efficient derivative-free solvers and is designed easy to use. ZOOpt provides single-machine parallel optimization on the basis of python core and multi-machine distributed optimization for time-consuming tasks by incorporating with the Ray framework -- a famous platform for building dis"},"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":"1801.00329","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2017-12-31T18:06:25Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82fef970ae17ffc2ff999039a984f5a4a7fb8329adf30d076438f7785dd84c51","abstract_canon_sha256":"74430456e80ed82efcccdf0155c7d374ba7e1b42cac487a20fb5e62ad03a21ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:14.370659Z","signature_b64":"F4SqZb+/unNehswXwLnHfGnRmPTcQ7KBlsOJBqd7TFtTr+tftyR/LFABJVRpiyIL2jzM3xLBok02kZx/YL9ZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d07663eb35be220790638e770bbea20c822bb342985acaeb8bba76f64797d2c2","last_reissued_at":"2026-07-05T05:01:14.370159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:14.370159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ZOOpt: Toolbox for Derivative-Free Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chao Qian, Hong Qian, Yang Yu, Yi-Qi Hu, Yu-Ren Liu","submitted_at":"2017-12-31T18:06:25Z","abstract_excerpt":"Recent advances in derivative-free optimization allow efficient approximation of the global-optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides efficient derivative-free solvers and is designed easy to use. ZOOpt provides single-machine parallel optimization on the basis of python core and multi-machine distributed optimization for time-consuming tasks by incorporating with the Ray framework -- a famous platform for building dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00329","kind":"arxiv","version":3},"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/1801.00329/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":"1801.00329","created_at":"2026-07-05T05:01:14.370218+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.00329v3","created_at":"2026-07-05T05:01:14.370218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00329","created_at":"2026-07-05T05:01:14.370218+00:00"},{"alias_kind":"pith_short_12","alias_value":"2B3GH2ZVXYRA","created_at":"2026-07-05T05:01:14.370218+00:00"},{"alias_kind":"pith_short_16","alias_value":"2B3GH2ZVXYRAPEDD","created_at":"2026-07-05T05:01:14.370218+00:00"},{"alias_kind":"pith_short_8","alias_value":"2B3GH2ZV","created_at":"2026-07-05T05:01:14.370218+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.31272","citing_title":"Algorithmic Recourse of In-Context Learning for Tabular Data","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS","json":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS.json","graph_json":"https://pith.science/api/pith-number/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/graph.json","events_json":"https://pith.science/api/pith-number/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/events.json","paper":"https://pith.science/paper/2B3GH2ZV"},"agent_actions":{"view_html":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS","download_json":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS.json","view_paper":"https://pith.science/paper/2B3GH2ZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.00329&json=true","fetch_graph":"https://pith.science/api/pith-number/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/graph.json","fetch_events":"https://pith.science/api/pith-number/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/action/storage_attestation","attest_author":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/action/author_attestation","sign_citation":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/action/citation_signature","submit_replication":"https://pith.science/pith/2B3GH2ZVXYRAPEDDRZ3QXPVCBS/action/replication_record"}},"created_at":"2026-07-05T05:01:14.370218+00:00","updated_at":"2026-07-05T05:01:14.370218+00:00"}