{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:3IXMJ7VSX5UHGWAHJEMOOO74YV","short_pith_number":"pith:3IXMJ7VS","schema_version":"1.0","canonical_sha256":"da2ec4feb2bf687358074918e73bfcc55b7de13d411a99fc1f95f60f536d8336","source":{"kind":"arxiv","id":"1504.06020","version":1},"attestation_state":"computed","paper":{"title":"Network Newton-Part II: Convergence Rate and Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alejandro Ribeiro, Aryan Mokhtari, Qing Ling","submitted_at":"2015-04-23T01:28:49Z","abstract_excerpt":"The use of network Newton methods for the decentralized optimization of a sum cost distributed through agents of a network is considered. Network Newton methods reinterpret distributed gradient descent as a penalty method, observe that the corresponding Hessian is sparse, and approximate the Newton step by truncating a Taylor expansion of the inverse Hessian. Truncating the series at $K$ terms yields the NN-$K$ that requires aggregating information from $K$ hops away. Network Newton is introduced and shown to converge to the solution of the penalized objective function at a rate that is at lea"},"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":"1504.06020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2015-04-23T01:28:49Z","cross_cats_sorted":[],"title_canon_sha256":"fbde2163977e179243ea2ae20a0f55bd80e9df14ef11956b59106c21bcbd1778","abstract_canon_sha256":"fddfe8542ece04efb3af43f0e56458525df8a8fd83b508a1903b7ad4f79b357c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:02.879183Z","signature_b64":"VBLFWwSTgpqP+asJrYouza1BvCykpvm6g8WVtQ6sdNpaGajJeI/KvqmZlrNxzR6V9EKxoqngu0+DmhRVdJTIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da2ec4feb2bf687358074918e73bfcc55b7de13d411a99fc1f95f60f536d8336","last_reissued_at":"2026-05-18T02:18:02.878513Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:02.878513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Newton-Part II: Convergence Rate and Implementation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alejandro Ribeiro, Aryan Mokhtari, Qing Ling","submitted_at":"2015-04-23T01:28:49Z","abstract_excerpt":"The use of network Newton methods for the decentralized optimization of a sum cost distributed through agents of a network is considered. Network Newton methods reinterpret distributed gradient descent as a penalty method, observe that the corresponding Hessian is sparse, and approximate the Newton step by truncating a Taylor expansion of the inverse Hessian. Truncating the series at $K$ terms yields the NN-$K$ that requires aggregating information from $K$ hops away. Network Newton is introduced and shown to converge to the solution of the penalized objective function at a rate that is at lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.06020","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":"1504.06020","created_at":"2026-05-18T02:18:02.878603+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.06020v1","created_at":"2026-05-18T02:18:02.878603+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.06020","created_at":"2026-05-18T02:18:02.878603+00:00"},{"alias_kind":"pith_short_12","alias_value":"3IXMJ7VSX5UH","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_16","alias_value":"3IXMJ7VSX5UHGWAH","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_8","alias_value":"3IXMJ7VS","created_at":"2026-05-18T12:29:02.477457+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/3IXMJ7VSX5UHGWAHJEMOOO74YV","json":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV.json","graph_json":"https://pith.science/api/pith-number/3IXMJ7VSX5UHGWAHJEMOOO74YV/graph.json","events_json":"https://pith.science/api/pith-number/3IXMJ7VSX5UHGWAHJEMOOO74YV/events.json","paper":"https://pith.science/paper/3IXMJ7VS"},"agent_actions":{"view_html":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV","download_json":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV.json","view_paper":"https://pith.science/paper/3IXMJ7VS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.06020&json=true","fetch_graph":"https://pith.science/api/pith-number/3IXMJ7VSX5UHGWAHJEMOOO74YV/graph.json","fetch_events":"https://pith.science/api/pith-number/3IXMJ7VSX5UHGWAHJEMOOO74YV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV/action/storage_attestation","attest_author":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV/action/author_attestation","sign_citation":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV/action/citation_signature","submit_replication":"https://pith.science/pith/3IXMJ7VSX5UHGWAHJEMOOO74YV/action/replication_record"}},"created_at":"2026-05-18T02:18:02.878603+00:00","updated_at":"2026-05-18T02:18:02.878603+00:00"}