{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LDY5FWNPP4GUGSHELVTRUXYOPU","short_pith_number":"pith:LDY5FWNP","schema_version":"1.0","canonical_sha256":"58f1d2d9af7f0d4348e45d671a5f0e7d3a19282f2db9178578af4afa24fe5c90","source":{"kind":"arxiv","id":"1706.02374","version":3},"attestation_state":"computed","paper":{"title":"A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Ernest K. Ryu, Wotao Yin, Yanli Liu","submitted_at":"2017-06-07T20:35:49Z","abstract_excerpt":"In this paper, we present a method for identifying infeasible, unbounded, and pathological conic programs based on Douglas-Rachford splitting, or equivalently ADMM. When an optimization program is infeasible, unbounded, or pathological, the iterates of Douglas-Rachford splitting diverge. Somewhat surprisingly, such divergent iterates still provide useful information, which our method uses for identification. In addition, for strongly infeasible problems the method produces a separating hyperplane and informs the user on how to minimally modify the given problem to achieve strong feasibility. A"},"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":"1706.02374","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-07T20:35:49Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"fcafaa1cdd8cedd377d7128eb760d0c23a1b1a49b0411530439a6abfd2fc65f8","abstract_canon_sha256":"a6fd34d8f3d69539eacefbafab9e3cb2cc611e71aa83e9c2db33b8c34d557069"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:50.604793Z","signature_b64":"OII0nisSWoAnqB3m+MAxMuSzs/PPsq1HXMO091dzmsrDrMYINBjG2hvs/9fHZICO94UY5VugjsYjEmYUHn6FBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58f1d2d9af7f0d4348e45d671a5f0e7d3a19282f2db9178578af4afa24fe5c90","last_reissued_at":"2026-05-18T00:32:50.604181Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:50.604181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Ernest K. Ryu, Wotao Yin, Yanli Liu","submitted_at":"2017-06-07T20:35:49Z","abstract_excerpt":"In this paper, we present a method for identifying infeasible, unbounded, and pathological conic programs based on Douglas-Rachford splitting, or equivalently ADMM. When an optimization program is infeasible, unbounded, or pathological, the iterates of Douglas-Rachford splitting diverge. Somewhat surprisingly, such divergent iterates still provide useful information, which our method uses for identification. In addition, for strongly infeasible problems the method produces a separating hyperplane and informs the user on how to minimally modify the given problem to achieve strong feasibility. A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02374","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":""},"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":"1706.02374","created_at":"2026-05-18T00:32:50.604281+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.02374v3","created_at":"2026-05-18T00:32:50.604281+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02374","created_at":"2026-05-18T00:32:50.604281+00:00"},{"alias_kind":"pith_short_12","alias_value":"LDY5FWNPP4GU","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LDY5FWNPP4GUGSHE","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LDY5FWNP","created_at":"2026-05-18T12:31:28.150371+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/LDY5FWNPP4GUGSHELVTRUXYOPU","json":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU.json","graph_json":"https://pith.science/api/pith-number/LDY5FWNPP4GUGSHELVTRUXYOPU/graph.json","events_json":"https://pith.science/api/pith-number/LDY5FWNPP4GUGSHELVTRUXYOPU/events.json","paper":"https://pith.science/paper/LDY5FWNP"},"agent_actions":{"view_html":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU","download_json":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU.json","view_paper":"https://pith.science/paper/LDY5FWNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.02374&json=true","fetch_graph":"https://pith.science/api/pith-number/LDY5FWNPP4GUGSHELVTRUXYOPU/graph.json","fetch_events":"https://pith.science/api/pith-number/LDY5FWNPP4GUGSHELVTRUXYOPU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU/action/storage_attestation","attest_author":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU/action/author_attestation","sign_citation":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU/action/citation_signature","submit_replication":"https://pith.science/pith/LDY5FWNPP4GUGSHELVTRUXYOPU/action/replication_record"}},"created_at":"2026-05-18T00:32:50.604281+00:00","updated_at":"2026-05-18T00:32:50.604281+00:00"}