{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:WPWG3TMTLJN5YK6IM65LUK5MDE","short_pith_number":"pith:WPWG3TMT","schema_version":"1.0","canonical_sha256":"b3ec6dcd935a5bdc2bc867baba2bac1928cc6978627a32b5de79d3ab274a4f28","source":{"kind":"arxiv","id":"1401.1754","version":1},"attestation_state":"computed","paper":{"title":"Greedy Strategies for Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Guergana Petrova, Hao Nguyen","submitted_at":"2014-01-08T17:06:34Z","abstract_excerpt":"We investigate two greedy strategies for finding an approximation to the minimum of a convex function $E$ defined on a Hilbert space $H$. We prove convergence rates for these algorithms under suitable conditions on the objective function $E$. These conditions involve the behavior of the modulus of smoothness and the modulus of uniform convexity of $E$."},"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":"1401.1754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-01-08T17:06:34Z","cross_cats_sorted":[],"title_canon_sha256":"a791bbd41e98338b8cc0811574112b70bd5da322dc95ac676ef4e3dc8e96a0ac","abstract_canon_sha256":"b95b8b88bfc507a58bb0738a1a28d806bff24ee042e2c959192cd3990e722d16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:02:57.904125Z","signature_b64":"zgXv3R6OVTU7RIA6X5cu//fIRMUCd7CpJhg9DNki230IAyTgnD7JNUoa1t/baLPvuYnPKtXAdA9Sn9lgC61CBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3ec6dcd935a5bdc2bc867baba2bac1928cc6978627a32b5de79d3ab274a4f28","last_reissued_at":"2026-05-18T03:02:57.903390Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:02:57.903390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Greedy Strategies for Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Guergana Petrova, Hao Nguyen","submitted_at":"2014-01-08T17:06:34Z","abstract_excerpt":"We investigate two greedy strategies for finding an approximation to the minimum of a convex function $E$ defined on a Hilbert space $H$. We prove convergence rates for these algorithms under suitable conditions on the objective function $E$. These conditions involve the behavior of the modulus of smoothness and the modulus of uniform convexity of $E$."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1401.1754","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1401.1754","created_at":"2026-05-18T03:02:57.903508+00:00"},{"alias_kind":"arxiv_version","alias_value":"1401.1754v1","created_at":"2026-05-18T03:02:57.903508+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1401.1754","created_at":"2026-05-18T03:02:57.903508+00:00"},{"alias_kind":"pith_short_12","alias_value":"WPWG3TMTLJN5","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_16","alias_value":"WPWG3TMTLJN5YK6I","created_at":"2026-05-18T12:28:54.890064+00:00"},{"alias_kind":"pith_short_8","alias_value":"WPWG3TMT","created_at":"2026-05-18T12:28:54.890064+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE","json":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE.json","graph_json":"https://pith.science/api/pith-number/WPWG3TMTLJN5YK6IM65LUK5MDE/graph.json","events_json":"https://pith.science/api/pith-number/WPWG3TMTLJN5YK6IM65LUK5MDE/events.json","paper":"https://pith.science/paper/WPWG3TMT"},"agent_actions":{"view_html":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE","download_json":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE.json","view_paper":"https://pith.science/paper/WPWG3TMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1401.1754&json=true","fetch_graph":"https://pith.science/api/pith-number/WPWG3TMTLJN5YK6IM65LUK5MDE/graph.json","fetch_events":"https://pith.science/api/pith-number/WPWG3TMTLJN5YK6IM65LUK5MDE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE/action/storage_attestation","attest_author":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE/action/author_attestation","sign_citation":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE/action/citation_signature","submit_replication":"https://pith.science/pith/WPWG3TMTLJN5YK6IM65LUK5MDE/action/replication_record"}},"created_at":"2026-05-18T03:02:57.903508+00:00","updated_at":"2026-05-18T03:02:57.903508+00:00"}