{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:KGYICDQNSUKDJPP55AQNDXZVRI","short_pith_number":"pith:KGYICDQN","schema_version":"1.0","canonical_sha256":"51b0810e0d951434bdfde820d1df358a3ca5426d4a325d7ebb573276b43ea8c7","source":{"kind":"arxiv","id":"1410.4754","version":2},"attestation_state":"computed","paper":{"title":"Parallel and Distributed Methods for Nonconvex Optimization-Part I: Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.MA","authors_text":"Francisco Facchinei, Gesualdo Scutari, Lorenzo Lampariello, Peiran Song","submitted_at":"2014-10-17T15:15:37Z","abstract_excerpt":"In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints. The algorithm solves a sequence of (separable) strongly convex problems and mantains feasibility at each iteration. Convergence to a stationary solution of the original nonconvex optimization is established. Our framework is very general and flexible; it unifies several existing Successive Convex Approximation (SCA)-based algorithms such as (proximal) gradient or Newton type methods, block coordinate (parallel) descent schemes, differen"},"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":"1410.4754","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MA","submitted_at":"2014-10-17T15:15:37Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"bca2f8fc8684e2533972f2c26dd81b9ced7bee8ce603c968d11bf859cc9be5ef","abstract_canon_sha256":"76e8ae9ec6e3e3061f589dda5098f3fb4c1fcecad6253cd551922b5a0ebd5588"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:52.364954Z","signature_b64":"zn57m3nYqEql6F72h+Vms3HoHHLKTZU2xUMnI99NVc+pzNP4ySFNm2ZQX/d9sm8eDb8HHQKCZxlPi4Lu2cNmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51b0810e0d951434bdfde820d1df358a3ca5426d4a325d7ebb573276b43ea8c7","last_reissued_at":"2026-05-18T01:22:52.364435Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:52.364435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel and Distributed Methods for Nonconvex Optimization-Part I: Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.MA","authors_text":"Francisco Facchinei, Gesualdo Scutari, Lorenzo Lampariello, Peiran Song","submitted_at":"2014-10-17T15:15:37Z","abstract_excerpt":"In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints. The algorithm solves a sequence of (separable) strongly convex problems and mantains feasibility at each iteration. Convergence to a stationary solution of the original nonconvex optimization is established. Our framework is very general and flexible; it unifies several existing Successive Convex Approximation (SCA)-based algorithms such as (proximal) gradient or Newton type methods, block coordinate (parallel) descent schemes, differen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.4754","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":"1410.4754","created_at":"2026-05-18T01:22:52.364524+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.4754v2","created_at":"2026-05-18T01:22:52.364524+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.4754","created_at":"2026-05-18T01:22:52.364524+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGYICDQNSUKD","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGYICDQNSUKDJPP5","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGYICDQN","created_at":"2026-05-18T12:28:35.611951+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/KGYICDQNSUKDJPP55AQNDXZVRI","json":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI.json","graph_json":"https://pith.science/api/pith-number/KGYICDQNSUKDJPP55AQNDXZVRI/graph.json","events_json":"https://pith.science/api/pith-number/KGYICDQNSUKDJPP55AQNDXZVRI/events.json","paper":"https://pith.science/paper/KGYICDQN"},"agent_actions":{"view_html":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI","download_json":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI.json","view_paper":"https://pith.science/paper/KGYICDQN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.4754&json=true","fetch_graph":"https://pith.science/api/pith-number/KGYICDQNSUKDJPP55AQNDXZVRI/graph.json","fetch_events":"https://pith.science/api/pith-number/KGYICDQNSUKDJPP55AQNDXZVRI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI/action/storage_attestation","attest_author":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI/action/author_attestation","sign_citation":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI/action/citation_signature","submit_replication":"https://pith.science/pith/KGYICDQNSUKDJPP55AQNDXZVRI/action/replication_record"}},"created_at":"2026-05-18T01:22:52.364524+00:00","updated_at":"2026-05-18T01:22:52.364524+00:00"}