{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4RLZ2DNJ7UXWBJ3VKNO4ME67BF","short_pith_number":"pith:4RLZ2DNJ","schema_version":"1.0","canonical_sha256":"e4579d0da9fd2f60a775535dc613df0973c457366d703942db0b003b5244b311","source":{"kind":"arxiv","id":"2401.12490","version":3},"attestation_state":"computed","paper":{"title":"A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Arnesh Sujanani, Diego Cifuentes, Renato D.C. Monteiro","submitted_at":"2024-01-23T05:18:17Z","abstract_excerpt":"This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) method where the AL subproblems are solved by a novel hybrid low-rank (HLR) method. The recipe behind HLR is based on two key ingredients: 1) an adaptive inexact proximal point method with inner acceleration; 2) Frank-Wolfe steps to escape from spurious local stationary points. In contrast to the low-rank method of Burer and Monteiro, HALLaR finds a near-optimal solution (with provable complexity bounds) of SDP instances"},"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":"2401.12490","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-01-23T05:18:17Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"6f089ddf100bee3c1ed188f50804eeca120f0a39c14d91d6fe5f16db2c2b0b92","abstract_canon_sha256":"138f6ed52d7584b5763189ca7c151fd69e31d82eeb122f171bd6213daccc3b4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:58.771062Z","signature_b64":"pv5aHIs8GmTsv7yHQgJKaRAlqTvq1rc0gkK5XDAx7cIgRetfdAGdBFQF8xiYxrX7qJr8ARORDoDP/9hgnw8mAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4579d0da9fd2f60a775535dc613df0973c457366d703942db0b003b5244b311","last_reissued_at":"2026-07-05T07:56:58.770669Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:58.770669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Arnesh Sujanani, Diego Cifuentes, Renato D.C. Monteiro","submitted_at":"2024-01-23T05:18:17Z","abstract_excerpt":"This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) method where the AL subproblems are solved by a novel hybrid low-rank (HLR) method. The recipe behind HLR is based on two key ingredients: 1) an adaptive inexact proximal point method with inner acceleration; 2) Frank-Wolfe steps to escape from spurious local stationary points. In contrast to the low-rank method of Burer and Monteiro, HALLaR finds a near-optimal solution (with provable complexity bounds) of SDP instances"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.12490","kind":"arxiv","version":3},"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/2401.12490/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":"2401.12490","created_at":"2026-07-05T07:56:58.770731+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.12490v3","created_at":"2026-07-05T07:56:58.770731+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.12490","created_at":"2026-07-05T07:56:58.770731+00:00"},{"alias_kind":"pith_short_12","alias_value":"4RLZ2DNJ7UXW","created_at":"2026-07-05T07:56:58.770731+00:00"},{"alias_kind":"pith_short_16","alias_value":"4RLZ2DNJ7UXWBJ3V","created_at":"2026-07-05T07:56:58.770731+00:00"},{"alias_kind":"pith_short_8","alias_value":"4RLZ2DNJ","created_at":"2026-07-05T07:56:58.770731+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.07204","citing_title":"Rounding the Lov\\'asz Theta Function with a Value Function Approximation","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17089","citing_title":"A preconditioned augmented Lagrangian method for solving semidefinite programming problems","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF","json":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF.json","graph_json":"https://pith.science/api/pith-number/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/graph.json","events_json":"https://pith.science/api/pith-number/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/events.json","paper":"https://pith.science/paper/4RLZ2DNJ"},"agent_actions":{"view_html":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF","download_json":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF.json","view_paper":"https://pith.science/paper/4RLZ2DNJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.12490&json=true","fetch_graph":"https://pith.science/api/pith-number/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/graph.json","fetch_events":"https://pith.science/api/pith-number/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/action/storage_attestation","attest_author":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/action/author_attestation","sign_citation":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/action/citation_signature","submit_replication":"https://pith.science/pith/4RLZ2DNJ7UXWBJ3VKNO4ME67BF/action/replication_record"}},"created_at":"2026-07-05T07:56:58.770731+00:00","updated_at":"2026-07-05T07:56:58.770731+00:00"}