{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AHR6OPRMDRSQZN47LQMLFWDG7L","short_pith_number":"pith:AHR6OPRM","schema_version":"1.0","canonical_sha256":"01e3e73e2c1c650cb79f5c18b2d866faec8583d2a94465ade27583739b9cf578","source":{"kind":"arxiv","id":"1802.05374","version":2},"attestation_state":"computed","paper":{"title":"A Progressive Batching L-BFGS Method for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Dheevatsa Mudigere, Hao-Jun Michael Shi, Jorge Nocedal, Ping Tak Peter Tang, Raghu Bollapragada","submitted_at":"2018-02-15T01:02:36Z","abstract_excerpt":"The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and quasi-Newton updating yields useful quadratic models of the objective function. All of this appears to call for a full batch approach, but since small batch sizes give rise to faster algorithms with better generalization properties, L-BFGS is currently not considered an algorithm of choice for large-scale machine learning applications. One need not, however, choose between the two extremes represented by the full batch "},"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":"1802.05374","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-02-15T01:02:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"679b936bc4a3d7884646dec9c72d2a36a95dff4e5f6839d885c0e553439002dd","abstract_canon_sha256":"fa213ad8fbc2f189baf556cae59fb4685d7f6807b540bfdc379bff4168b814f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:40.326627Z","signature_b64":"YsyCkEN+MTwp0s+HFujuT0/rNVVTyRv5wiaqCSHdG61IlNoSR3ePO0XGf1ysNPh/sE2iRD5FJdW+jN33i4hPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01e3e73e2c1c650cb79f5c18b2d866faec8583d2a94465ade27583739b9cf578","last_reissued_at":"2026-05-18T00:14:40.325970Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:40.325970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Progressive Batching L-BFGS Method for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Dheevatsa Mudigere, Hao-Jun Michael Shi, Jorge Nocedal, Ping Tak Peter Tang, Raghu Bollapragada","submitted_at":"2018-02-15T01:02:36Z","abstract_excerpt":"The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and quasi-Newton updating yields useful quadratic models of the objective function. All of this appears to call for a full batch approach, but since small batch sizes give rise to faster algorithms with better generalization properties, L-BFGS is currently not considered an algorithm of choice for large-scale machine learning applications. One need not, however, choose between the two extremes represented by the full batch "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05374","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":""},"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":"1802.05374","created_at":"2026-05-18T00:14:40.326065+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.05374v2","created_at":"2026-05-18T00:14:40.326065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05374","created_at":"2026-05-18T00:14:40.326065+00:00"},{"alias_kind":"pith_short_12","alias_value":"AHR6OPRMDRSQ","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AHR6OPRMDRSQZN47","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AHR6OPRM","created_at":"2026-05-18T12:32:13.499390+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/AHR6OPRMDRSQZN47LQMLFWDG7L","json":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L.json","graph_json":"https://pith.science/api/pith-number/AHR6OPRMDRSQZN47LQMLFWDG7L/graph.json","events_json":"https://pith.science/api/pith-number/AHR6OPRMDRSQZN47LQMLFWDG7L/events.json","paper":"https://pith.science/paper/AHR6OPRM"},"agent_actions":{"view_html":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L","download_json":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L.json","view_paper":"https://pith.science/paper/AHR6OPRM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.05374&json=true","fetch_graph":"https://pith.science/api/pith-number/AHR6OPRMDRSQZN47LQMLFWDG7L/graph.json","fetch_events":"https://pith.science/api/pith-number/AHR6OPRMDRSQZN47LQMLFWDG7L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L/action/storage_attestation","attest_author":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L/action/author_attestation","sign_citation":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L/action/citation_signature","submit_replication":"https://pith.science/pith/AHR6OPRMDRSQZN47LQMLFWDG7L/action/replication_record"}},"created_at":"2026-05-18T00:14:40.326065+00:00","updated_at":"2026-05-18T00:14:40.326065+00:00"}