{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7KZHVMNR7E5R3IK2HHMEYMPV7W","short_pith_number":"pith:7KZHVMNR","schema_version":"1.0","canonical_sha256":"fab27ab1b1f93b1da15a39d84c31f5fdadf48f6ab580a32240bca400409a4efb","source":{"kind":"arxiv","id":"2508.02969","version":1},"attestation_state":"computed","paper":{"title":"Quantum Hamiltonian Descent based Augmented Lagrangian Method for Constrained Nonconvex Nonlinear Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Lei Fan, Mingze Li, Zhu Han","submitted_at":"2025-08-05T00:15:54Z","abstract_excerpt":"Nonlinear programming (NLP) plays a critical role in domains such as power energy systems, chemical engineering, communication networks, and financial engineering. However, solving large-scale, nonconvex NLP problems remains a significant challenge due to the complexity of the solution landscape and the presence of nonlinear nonconvex constraints. In this paper, we develop a Quantum Hamiltonian Descent based Augmented Lagrange Method (QHD-ALM) framework to address largescale, constrained nonconvex NLP problems. The augmented Lagrange method (ALM) can convert a constrained NLP to an unconstrain"},"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":"2508.02969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-08-05T00:15:54Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"64ad85a8e5cb9b1d89136544d77d474c64a7cae5a8046722032e7dc69d3b5b05","abstract_canon_sha256":"29dd1c801916953896c01d6aea043f4ecc2672a6e30a36f4b5cd7906f3dfc7b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:33.766577Z","signature_b64":"QvCIxdVqUwqoJyOsLBGjv4apfc0NYmUI7zot0Hg7jh1vetmJoUCLPZwiN+z1kYSTripZ1frZHfyYkO5wl5qwBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fab27ab1b1f93b1da15a39d84c31f5fdadf48f6ab580a32240bca400409a4efb","last_reissued_at":"2026-07-05T11:48:33.766090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:33.766090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Hamiltonian Descent based Augmented Lagrangian Method for Constrained Nonconvex Nonlinear Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Lei Fan, Mingze Li, Zhu Han","submitted_at":"2025-08-05T00:15:54Z","abstract_excerpt":"Nonlinear programming (NLP) plays a critical role in domains such as power energy systems, chemical engineering, communication networks, and financial engineering. However, solving large-scale, nonconvex NLP problems remains a significant challenge due to the complexity of the solution landscape and the presence of nonlinear nonconvex constraints. In this paper, we develop a Quantum Hamiltonian Descent based Augmented Lagrange Method (QHD-ALM) framework to address largescale, constrained nonconvex NLP problems. The augmented Lagrange method (ALM) can convert a constrained NLP to an unconstrain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02969","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/2508.02969/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":"2508.02969","created_at":"2026-07-05T11:48:33.766159+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02969v1","created_at":"2026-07-05T11:48:33.766159+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02969","created_at":"2026-07-05T11:48:33.766159+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KZHVMNR7E5R","created_at":"2026-07-05T11:48:33.766159+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KZHVMNR7E5R3IK2","created_at":"2026-07-05T11:48:33.766159+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KZHVMNR","created_at":"2026-07-05T11:48:33.766159+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/7KZHVMNR7E5R3IK2HHMEYMPV7W","json":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W.json","graph_json":"https://pith.science/api/pith-number/7KZHVMNR7E5R3IK2HHMEYMPV7W/graph.json","events_json":"https://pith.science/api/pith-number/7KZHVMNR7E5R3IK2HHMEYMPV7W/events.json","paper":"https://pith.science/paper/7KZHVMNR"},"agent_actions":{"view_html":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W","download_json":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W.json","view_paper":"https://pith.science/paper/7KZHVMNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02969&json=true","fetch_graph":"https://pith.science/api/pith-number/7KZHVMNR7E5R3IK2HHMEYMPV7W/graph.json","fetch_events":"https://pith.science/api/pith-number/7KZHVMNR7E5R3IK2HHMEYMPV7W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W/action/storage_attestation","attest_author":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W/action/author_attestation","sign_citation":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W/action/citation_signature","submit_replication":"https://pith.science/pith/7KZHVMNR7E5R3IK2HHMEYMPV7W/action/replication_record"}},"created_at":"2026-07-05T11:48:33.766159+00:00","updated_at":"2026-07-05T11:48:33.766159+00:00"}