{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QQY47XGFGKNNIIIFF6RLP4U725","short_pith_number":"pith:QQY47XGF","schema_version":"1.0","canonical_sha256":"8431cfdcc5329ad421052fa2b7f29fd775119c63bcad58cfe718a1d0342ea75d","source":{"kind":"arxiv","id":"2104.06675","version":2},"attestation_state":"computed","paper":{"title":"FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alejandro Carderera, Mathieu Besan\\c{c}on, Sebastian Pokutta","submitted_at":"2021-04-14T07:38:22Z","abstract_excerpt":"We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is designed with flexibility and high-performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia's unique multiple dispatch feature, and interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl."},"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":"2104.06675","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-04-14T07:38:22Z","cross_cats_sorted":[],"title_canon_sha256":"1a223c3ba4192d825f6dbc47670b7e686a68edb41bc48394c6e52b9c2c1b1221","abstract_canon_sha256":"f075c2e0a11b4e6ef925f84f13d00e1340ffe53165be0a0f9d11f7655d3714b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:01.559105Z","signature_b64":"RZi9gaOS8WECpHJ45J94z3279mzhCXgcg7HoyYtMU+ikf3/IIJVugeHTU7Ne9saUsW1jJA4Gpi6oX+6s4EeCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8431cfdcc5329ad421052fa2b7f29fd775119c63bcad58cfe718a1d0342ea75d","last_reissued_at":"2026-07-05T03:20:01.558618Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:01.558618Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alejandro Carderera, Mathieu Besan\\c{c}on, Sebastian Pokutta","submitted_at":"2021-04-14T07:38:22Z","abstract_excerpt":"We present FrankWolfe.jl, an open-source implementation of several popular Frank-Wolfe and Conditional Gradients variants for first-order constrained optimization. The package is designed with flexibility and high-performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia's unique multiple dispatch feature, and interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.06675","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/2104.06675/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":"2104.06675","created_at":"2026-07-05T03:20:01.558677+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.06675v2","created_at":"2026-07-05T03:20:01.558677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.06675","created_at":"2026-07-05T03:20:01.558677+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQY47XGFGKNN","created_at":"2026-07-05T03:20:01.558677+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQY47XGFGKNNIIIF","created_at":"2026-07-05T03:20:01.558677+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQY47XGF","created_at":"2026-07-05T03:20:01.558677+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.12886","citing_title":"Can outcome communication explain Bell nonlocality?","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725","json":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725.json","graph_json":"https://pith.science/api/pith-number/QQY47XGFGKNNIIIFF6RLP4U725/graph.json","events_json":"https://pith.science/api/pith-number/QQY47XGFGKNNIIIFF6RLP4U725/events.json","paper":"https://pith.science/paper/QQY47XGF"},"agent_actions":{"view_html":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725","download_json":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725.json","view_paper":"https://pith.science/paper/QQY47XGF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.06675&json=true","fetch_graph":"https://pith.science/api/pith-number/QQY47XGFGKNNIIIFF6RLP4U725/graph.json","fetch_events":"https://pith.science/api/pith-number/QQY47XGFGKNNIIIFF6RLP4U725/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725/action/storage_attestation","attest_author":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725/action/author_attestation","sign_citation":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725/action/citation_signature","submit_replication":"https://pith.science/pith/QQY47XGFGKNNIIIFF6RLP4U725/action/replication_record"}},"created_at":"2026-07-05T03:20:01.558677+00:00","updated_at":"2026-07-05T03:20:01.558677+00:00"}