{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LHLLSNZDPMZOUA2JD4R2EAA3LG","short_pith_number":"pith:LHLLSNZD","schema_version":"1.0","canonical_sha256":"59d6b937237b32ea03491f23a2001b5997fb93bf531b83d102a9e9662c51a776","source":{"kind":"arxiv","id":"1811.05730","version":1},"attestation_state":"computed","paper":{"title":"Subsampled Inexact Newton methods for minimizing large sums of convex functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Natasa Krejic, Natasa Krklec Jerinkic, Stefania Bellavia","submitted_at":"2018-11-14T11:28:14Z","abstract_excerpt":"This paper deals with the minimization of large sum of convex functions by Inexact Newton (IN) methods employing subsampled functions, gradients and Hessian approximations. The Conjugate Gradient method is used to compute the inexact Newton step and global convergence is enforced by a nonmonotone line search procedure. The aim is to obtain methods with affordable costs and fast convergence. Assuming strongly convex functions, R-linear convergence and worst-case iteration complexity of the procedure are investigated when functions and gradients are approximated with increasing accuracy. A set o"},"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":"1811.05730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-11-14T11:28:14Z","cross_cats_sorted":[],"title_canon_sha256":"58546306db034cbf9720ce4ec1218c394587027b56cb87f3f087418df427be12","abstract_canon_sha256":"10fc66c4cf62e03bfb2684bdad1edb9187833507cadb842d2067869843c2f372"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:42.216030Z","signature_b64":"pNsw3DsSfob++Jj07ua8BCPNdOQeQYiMK+JHnUUzat5b5bgrwrHu1tg8LcoKXBim3+/v2qSkotIu+/xxHI2VBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59d6b937237b32ea03491f23a2001b5997fb93bf531b83d102a9e9662c51a776","last_reissued_at":"2026-05-18T00:00:42.215612Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:42.215612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Subsampled Inexact Newton methods for minimizing large sums of convex functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Natasa Krejic, Natasa Krklec Jerinkic, Stefania Bellavia","submitted_at":"2018-11-14T11:28:14Z","abstract_excerpt":"This paper deals with the minimization of large sum of convex functions by Inexact Newton (IN) methods employing subsampled functions, gradients and Hessian approximations. The Conjugate Gradient method is used to compute the inexact Newton step and global convergence is enforced by a nonmonotone line search procedure. The aim is to obtain methods with affordable costs and fast convergence. Assuming strongly convex functions, R-linear convergence and worst-case iteration complexity of the procedure are investigated when functions and gradients are approximated with increasing accuracy. A set o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05730","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":"1811.05730","created_at":"2026-05-18T00:00:42.215673+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05730v1","created_at":"2026-05-18T00:00:42.215673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05730","created_at":"2026-05-18T00:00:42.215673+00:00"},{"alias_kind":"pith_short_12","alias_value":"LHLLSNZDPMZO","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LHLLSNZDPMZOUA2J","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LHLLSNZD","created_at":"2026-05-18T12:32:37.024351+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/LHLLSNZDPMZOUA2JD4R2EAA3LG","json":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG.json","graph_json":"https://pith.science/api/pith-number/LHLLSNZDPMZOUA2JD4R2EAA3LG/graph.json","events_json":"https://pith.science/api/pith-number/LHLLSNZDPMZOUA2JD4R2EAA3LG/events.json","paper":"https://pith.science/paper/LHLLSNZD"},"agent_actions":{"view_html":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG","download_json":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG.json","view_paper":"https://pith.science/paper/LHLLSNZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05730&json=true","fetch_graph":"https://pith.science/api/pith-number/LHLLSNZDPMZOUA2JD4R2EAA3LG/graph.json","fetch_events":"https://pith.science/api/pith-number/LHLLSNZDPMZOUA2JD4R2EAA3LG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG/action/storage_attestation","attest_author":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG/action/author_attestation","sign_citation":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG/action/citation_signature","submit_replication":"https://pith.science/pith/LHLLSNZDPMZOUA2JD4R2EAA3LG/action/replication_record"}},"created_at":"2026-05-18T00:00:42.215673+00:00","updated_at":"2026-05-18T00:00:42.215673+00:00"}