{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AC6ETWBKC4BDEQOQQHDSW5XNR3","short_pith_number":"pith:AC6ETWBK","schema_version":"1.0","canonical_sha256":"00bc49d82a17023241d081c72b76ed8ed9b270d0260bab410e662a4b759dc7f7","source":{"kind":"arxiv","id":"1908.11518","version":4},"attestation_state":"computed","paper":{"title":"Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Qihang Lin, Runchao Ma, Yangyang Xu","submitted_at":"2019-08-30T03:33:53Z","abstract_excerpt":"In this paper, an inexact proximal-point penalty method is studied for constrained optimization problems, where the objective function is non-convex, and the constraint functions can also be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-based method. The computa"},"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":"1908.11518","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-30T03:33:53Z","cross_cats_sorted":["cs.CC","cs.NA","math.NA"],"title_canon_sha256":"dd54347dd0ce78a27247af6fdfe9268c2ef8b91301c8a63b0fdd07a04fc37504","abstract_canon_sha256":"1c19838fe3558d750c07721db850ec13f48da7b0bd698e00f623f2bc6b0def66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:42.867806Z","signature_b64":"vypQtkJtnkUpCYz0t8HgrIyGABCiuaUuuyxh3ESDga3BKYc8WIV9fR8HJU+6NvW0QB3jJsAokjCl/ZWMMlMhDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00bc49d82a17023241d081c72b76ed8ed9b270d0260bab410e662a4b759dc7f7","last_reissued_at":"2026-07-05T01:55:42.867355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:42.867355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Qihang Lin, Runchao Ma, Yangyang Xu","submitted_at":"2019-08-30T03:33:53Z","abstract_excerpt":"In this paper, an inexact proximal-point penalty method is studied for constrained optimization problems, where the objective function is non-convex, and the constraint functions can also be non-convex. The proposed method approximately solves a sequence of subproblems, each of which is formed by adding to the original objective function a proximal term and quadratic penalty terms associated to the constraint functions. Under a weak-convexity assumption, each subproblem is made strongly convex and can be solved effectively to a required accuracy by an optimal gradient-based method. The computa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.11518","kind":"arxiv","version":4},"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/1908.11518/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":"1908.11518","created_at":"2026-07-05T01:55:42.867413+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.11518v4","created_at":"2026-07-05T01:55:42.867413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.11518","created_at":"2026-07-05T01:55:42.867413+00:00"},{"alias_kind":"pith_short_12","alias_value":"AC6ETWBKC4BD","created_at":"2026-07-05T01:55:42.867413+00:00"},{"alias_kind":"pith_short_16","alias_value":"AC6ETWBKC4BDEQOQ","created_at":"2026-07-05T01:55:42.867413+00:00"},{"alias_kind":"pith_short_8","alias_value":"AC6ETWBK","created_at":"2026-07-05T01:55:42.867413+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.11518","citing_title":"Inexact Proximal-Point Penalty Methods for Constrained Non-Convex Optimization","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3","json":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3.json","graph_json":"https://pith.science/api/pith-number/AC6ETWBKC4BDEQOQQHDSW5XNR3/graph.json","events_json":"https://pith.science/api/pith-number/AC6ETWBKC4BDEQOQQHDSW5XNR3/events.json","paper":"https://pith.science/paper/AC6ETWBK"},"agent_actions":{"view_html":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3","download_json":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3.json","view_paper":"https://pith.science/paper/AC6ETWBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.11518&json=true","fetch_graph":"https://pith.science/api/pith-number/AC6ETWBKC4BDEQOQQHDSW5XNR3/graph.json","fetch_events":"https://pith.science/api/pith-number/AC6ETWBKC4BDEQOQQHDSW5XNR3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3/action/storage_attestation","attest_author":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3/action/author_attestation","sign_citation":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3/action/citation_signature","submit_replication":"https://pith.science/pith/AC6ETWBKC4BDEQOQQHDSW5XNR3/action/replication_record"}},"created_at":"2026-07-05T01:55:42.867413+00:00","updated_at":"2026-07-05T01:55:42.867413+00:00"}