{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XDTCZT25J2CE5G7W4CLCQXYRCO","short_pith_number":"pith:XDTCZT25","schema_version":"1.0","canonical_sha256":"b8e62ccf5d4e844e9bf6e096285f111393489a0a595de6ee701c75fc26cfc60a","source":{"kind":"arxiv","id":"1702.01850","version":2},"attestation_state":"computed","paper":{"title":"Convergence rate bounds for a proximal ADMM with over-relaxation stepsize parameter for solving nonconvex linearly constrained problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jefferson G. Melo, Max L.N. Goncalves, Renato D.C. Monteiro","submitted_at":"2017-02-07T02:27:37Z","abstract_excerpt":"This paper establishes convergence rate bounds for a variant of the proximal alternating direction method of multipliers (ADMM) for solving nonconvex linearly constrained optimization problems. The variant of the proximal ADMM allows the inclusion of an over-relaxation stepsize parameter belonging to the interval $(0,2)$. To the best of our knowledge, all related papers in the literature only consider the case where the over-relaxation parameter lies in the interval $(0,(1+\\sqrt{5})/2)$."},"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":"1702.01850","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-02-07T02:27:37Z","cross_cats_sorted":[],"title_canon_sha256":"3d147ba5ac98ef6e1f57ad27f54b356cf1da00fa384dcba108db59b9d9098d6e","abstract_canon_sha256":"4e6193efb32ed5144ae1e11a0b7706a804cef04823d03624b00c707f1736abc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:23.879861Z","signature_b64":"tD5mVhllHIh3BoBdS4erGbbVEsSCZ4FuJCrzcGJeaV/7N/IdylZO1PVC2cI+Jz+XIem6h1GB/DY3NpFn4vPyDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8e62ccf5d4e844e9bf6e096285f111393489a0a595de6ee701c75fc26cfc60a","last_reissued_at":"2026-05-18T00:31:23.879362Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:23.879362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convergence rate bounds for a proximal ADMM with over-relaxation stepsize parameter for solving nonconvex linearly constrained problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jefferson G. Melo, Max L.N. Goncalves, Renato D.C. Monteiro","submitted_at":"2017-02-07T02:27:37Z","abstract_excerpt":"This paper establishes convergence rate bounds for a variant of the proximal alternating direction method of multipliers (ADMM) for solving nonconvex linearly constrained optimization problems. The variant of the proximal ADMM allows the inclusion of an over-relaxation stepsize parameter belonging to the interval $(0,2)$. To the best of our knowledge, all related papers in the literature only consider the case where the over-relaxation parameter lies in the interval $(0,(1+\\sqrt{5})/2)$."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.01850","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":"1702.01850","created_at":"2026-05-18T00:31:23.879444+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.01850v2","created_at":"2026-05-18T00:31:23.879444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.01850","created_at":"2026-05-18T00:31:23.879444+00:00"},{"alias_kind":"pith_short_12","alias_value":"XDTCZT25J2CE","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"XDTCZT25J2CE5G7W","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"XDTCZT25","created_at":"2026-05-18T12:31:53.515858+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.12015","citing_title":"Accelerated Symmetric ADMM and Its Applications in Signal Processing","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO","json":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO.json","graph_json":"https://pith.science/api/pith-number/XDTCZT25J2CE5G7W4CLCQXYRCO/graph.json","events_json":"https://pith.science/api/pith-number/XDTCZT25J2CE5G7W4CLCQXYRCO/events.json","paper":"https://pith.science/paper/XDTCZT25"},"agent_actions":{"view_html":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO","download_json":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO.json","view_paper":"https://pith.science/paper/XDTCZT25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.01850&json=true","fetch_graph":"https://pith.science/api/pith-number/XDTCZT25J2CE5G7W4CLCQXYRCO/graph.json","fetch_events":"https://pith.science/api/pith-number/XDTCZT25J2CE5G7W4CLCQXYRCO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO/action/storage_attestation","attest_author":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO/action/author_attestation","sign_citation":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO/action/citation_signature","submit_replication":"https://pith.science/pith/XDTCZT25J2CE5G7W4CLCQXYRCO/action/replication_record"}},"created_at":"2026-05-18T00:31:23.879444+00:00","updated_at":"2026-05-18T00:31:23.879444+00:00"}