{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DSEUYV62Z2OP2PZNMSL4AGRIBR","short_pith_number":"pith:DSEUYV62","schema_version":"1.0","canonical_sha256":"1c894c57dace9cfd3f2d6497c01a280c43c920db26e9836d2a04a2fc9f8d97fc","source":{"kind":"arxiv","id":"2402.02596","version":3},"attestation_state":"computed","paper":{"title":"Dual Interior Point Optimization Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Mathieu Tanneau, Michael Klamkin, Pascal Van Hentenryck","submitted_at":"2024-02-04T20:06:20Z","abstract_excerpt":"In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of how to design optimization proxies -- machine learning models that produce high-quality solutions -- has recently received significant attention. Orthogonal to this research thread which focuses on learning primal solutions, this paper studies how to learn dual feasible solutions that complement primal approaches and provide quality guarantees. The paper makes two distinct contributions. First, to train dual linear op"},"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":"2402.02596","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-04T20:06:20Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"88adbe564b853dbd9fa47979e7d5ecf6052b88cb071c046a845f7bc29b726da0","abstract_canon_sha256":"a71a57f0d408a0a4d6afa1b8e06799ae231464df2d5df18b0ba6847f491dfa89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:26.195793Z","signature_b64":"0oEn4Z/TEl5lrhaafO1OmJLr/u7dAl+DpTAj09QerudIIyh1Ojgxt9XIbBamO7IVEwzTFgGmhd2RWo5k6W9oCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c894c57dace9cfd3f2d6497c01a280c43c920db26e9836d2a04a2fc9f8d97fc","last_reissued_at":"2026-07-05T10:13:26.193840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:26.193840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual Interior Point Optimization Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Mathieu Tanneau, Michael Klamkin, Pascal Van Hentenryck","submitted_at":"2024-02-04T20:06:20Z","abstract_excerpt":"In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of how to design optimization proxies -- machine learning models that produce high-quality solutions -- has recently received significant attention. Orthogonal to this research thread which focuses on learning primal solutions, this paper studies how to learn dual feasible solutions that complement primal approaches and provide quality guarantees. The paper makes two distinct contributions. First, to train dual linear op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02596","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/2402.02596/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":"2402.02596","created_at":"2026-07-05T10:13:26.193914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.02596v3","created_at":"2026-07-05T10:13:26.193914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02596","created_at":"2026-07-05T10:13:26.193914+00:00"},{"alias_kind":"pith_short_12","alias_value":"DSEUYV62Z2OP","created_at":"2026-07-05T10:13:26.193914+00:00"},{"alias_kind":"pith_short_16","alias_value":"DSEUYV62Z2OP2PZN","created_at":"2026-07-05T10:13:26.193914+00:00"},{"alias_kind":"pith_short_8","alias_value":"DSEUYV62","created_at":"2026-07-05T10:13:26.193914+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/DSEUYV62Z2OP2PZNMSL4AGRIBR","json":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR.json","graph_json":"https://pith.science/api/pith-number/DSEUYV62Z2OP2PZNMSL4AGRIBR/graph.json","events_json":"https://pith.science/api/pith-number/DSEUYV62Z2OP2PZNMSL4AGRIBR/events.json","paper":"https://pith.science/paper/DSEUYV62"},"agent_actions":{"view_html":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR","download_json":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR.json","view_paper":"https://pith.science/paper/DSEUYV62","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.02596&json=true","fetch_graph":"https://pith.science/api/pith-number/DSEUYV62Z2OP2PZNMSL4AGRIBR/graph.json","fetch_events":"https://pith.science/api/pith-number/DSEUYV62Z2OP2PZNMSL4AGRIBR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR/action/storage_attestation","attest_author":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR/action/author_attestation","sign_citation":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR/action/citation_signature","submit_replication":"https://pith.science/pith/DSEUYV62Z2OP2PZNMSL4AGRIBR/action/replication_record"}},"created_at":"2026-07-05T10:13:26.193914+00:00","updated_at":"2026-07-05T10:13:26.193914+00:00"}