{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HOKA2QY2A6PDKHZYVDUV6KMFLK","short_pith_number":"pith:HOKA2QY2","schema_version":"1.0","canonical_sha256":"3b940d431a079e351f38a8e95f29855a822f85ba83ae40069e8a626593e6a76b","source":{"kind":"arxiv","id":"2011.00667","version":1},"attestation_state":"computed","paper":{"title":"Asynchronous Parallel Stochastic Quasi-Newton Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"math.OC","authors_text":"Chunjiang Zhu, Guannan Liang, Jinbo Bi, Qianqian Tong, Xingyu Cai","submitted_at":"2020-11-02T01:20:00Z","abstract_excerpt":"Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm"},"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":"2011.00667","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2020-11-02T01:20:00Z","cross_cats_sorted":["cs.DC"],"title_canon_sha256":"d96cff928119010d413550f8f632afee60b809adaf043768868c0030353422fd","abstract_canon_sha256":"b1a6a7f88b5b41873a13f4d69a1deee6d30f72661c2c5c0b979c269d1c86f0b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:48:18.357433Z","signature_b64":"3GNlRwfPWfn+28rKiWUktC29k/H2BIVGRy7Sh57O31y+9XISIUuk3QHlrpw9X+LAim+KDFs5loemO5Y34i9MDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b940d431a079e351f38a8e95f29855a822f85ba83ae40069e8a626593e6a76b","last_reissued_at":"2026-07-05T01:48:18.357001Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:48:18.357001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Asynchronous Parallel Stochastic Quasi-Newton Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC"],"primary_cat":"math.OC","authors_text":"Chunjiang Zhu, Guannan Liang, Jinbo Bi, Qianqian Tong, Xingyu Cai","submitted_at":"2020-11-02T01:20:00Z","abstract_excerpt":"Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.00667","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.00667/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":"2011.00667","created_at":"2026-07-05T01:48:18.357068+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.00667v1","created_at":"2026-07-05T01:48:18.357068+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.00667","created_at":"2026-07-05T01:48:18.357068+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOKA2QY2A6PD","created_at":"2026-07-05T01:48:18.357068+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOKA2QY2A6PDKHZY","created_at":"2026-07-05T01:48:18.357068+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOKA2QY2","created_at":"2026-07-05T01:48:18.357068+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/HOKA2QY2A6PDKHZYVDUV6KMFLK","json":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK.json","graph_json":"https://pith.science/api/pith-number/HOKA2QY2A6PDKHZYVDUV6KMFLK/graph.json","events_json":"https://pith.science/api/pith-number/HOKA2QY2A6PDKHZYVDUV6KMFLK/events.json","paper":"https://pith.science/paper/HOKA2QY2"},"agent_actions":{"view_html":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK","download_json":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK.json","view_paper":"https://pith.science/paper/HOKA2QY2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.00667&json=true","fetch_graph":"https://pith.science/api/pith-number/HOKA2QY2A6PDKHZYVDUV6KMFLK/graph.json","fetch_events":"https://pith.science/api/pith-number/HOKA2QY2A6PDKHZYVDUV6KMFLK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK/action/storage_attestation","attest_author":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK/action/author_attestation","sign_citation":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK/action/citation_signature","submit_replication":"https://pith.science/pith/HOKA2QY2A6PDKHZYVDUV6KMFLK/action/replication_record"}},"created_at":"2026-07-05T01:48:18.357068+00:00","updated_at":"2026-07-05T01:48:18.357068+00:00"}