{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MPCHSJTFT56A3MZDN26LQZQ7JY","short_pith_number":"pith:MPCHSJTF","schema_version":"1.0","canonical_sha256":"63c47926659f7c0db3236ebcb8661f4e20b74e094a188bf6703907a55938c854","source":{"kind":"arxiv","id":"2502.08835","version":1},"attestation_state":"computed","paper":{"title":"A Bundle-based Augmented Lagrangian Framework: Algorithm, Convergence, and Primal-dual Principles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Feng-Yi Liao, Yang Zheng","submitted_at":"2025-02-12T22:52:15Z","abstract_excerpt":"We propose a new bundle-based augmented Lagrangian framework for solving constrained convex problems. Unlike the classical (inexact) augmented Lagrangian method (ALM) that has a nested double-loop structure, our framework features a $\\textit{single-loop}$ process. Motivated by the proximal bundle method (PBM), we use a $\\textit{bundle}$ of past iterates to approximate the subproblem in ALM to get a computationally efficient update at each iteration. We establish sub-linear convergences for primal feasibility, primal cost values, and dual iterates under mild assumptions. With further regularity"},"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":"2502.08835","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-02-12T22:52:15Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"47e8c66c18a2e8f8dd1242e41aad3b075d96646b7da7a3227e9e80effc8bdc4f","abstract_canon_sha256":"231c9157ab8dd3f989608cd82669ba974e42d85e14e25eb9a01d95428c7e1ad7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:30.431444Z","signature_b64":"iOPMEwJGHzqVJ0h37pTg+jLD9knMjCVgs+ckmmQmPYWMwxTpStbciYZd3ZBIsoGqunEaCW2G+r7XPfx1wO2/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63c47926659f7c0db3236ebcb8661f4e20b74e094a188bf6703907a55938c854","last_reissued_at":"2026-07-05T10:13:30.430944Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:30.430944Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bundle-based Augmented Lagrangian Framework: Algorithm, Convergence, and Primal-dual Principles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Feng-Yi Liao, Yang Zheng","submitted_at":"2025-02-12T22:52:15Z","abstract_excerpt":"We propose a new bundle-based augmented Lagrangian framework for solving constrained convex problems. Unlike the classical (inexact) augmented Lagrangian method (ALM) that has a nested double-loop structure, our framework features a $\\textit{single-loop}$ process. Motivated by the proximal bundle method (PBM), we use a $\\textit{bundle}$ of past iterates to approximate the subproblem in ALM to get a computationally efficient update at each iteration. We establish sub-linear convergences for primal feasibility, primal cost values, and dual iterates under mild assumptions. With further regularity"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08835","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/2502.08835/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":"2502.08835","created_at":"2026-07-05T10:13:30.431000+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.08835v1","created_at":"2026-07-05T10:13:30.431000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08835","created_at":"2026-07-05T10:13:30.431000+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPCHSJTFT56A","created_at":"2026-07-05T10:13:30.431000+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPCHSJTFT56A3MZD","created_at":"2026-07-05T10:13:30.431000+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPCHSJTF","created_at":"2026-07-05T10:13:30.431000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2503.20142","citing_title":"Local Linear Convergence of the Alternating Direction Method of Multipliers for Semidefinite Programming under Strict Complementarity","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY","json":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY.json","graph_json":"https://pith.science/api/pith-number/MPCHSJTFT56A3MZDN26LQZQ7JY/graph.json","events_json":"https://pith.science/api/pith-number/MPCHSJTFT56A3MZDN26LQZQ7JY/events.json","paper":"https://pith.science/paper/MPCHSJTF"},"agent_actions":{"view_html":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY","download_json":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY.json","view_paper":"https://pith.science/paper/MPCHSJTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.08835&json=true","fetch_graph":"https://pith.science/api/pith-number/MPCHSJTFT56A3MZDN26LQZQ7JY/graph.json","fetch_events":"https://pith.science/api/pith-number/MPCHSJTFT56A3MZDN26LQZQ7JY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY/action/storage_attestation","attest_author":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY/action/author_attestation","sign_citation":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY/action/citation_signature","submit_replication":"https://pith.science/pith/MPCHSJTFT56A3MZDN26LQZQ7JY/action/replication_record"}},"created_at":"2026-07-05T10:13:30.431000+00:00","updated_at":"2026-07-05T10:13:30.431000+00:00"}