{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:M4NCW7S7W6OQYYAOLSHZWIACBW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f3fd276d3fbd0d7299b2e8362a8f16447a9eeb16df985578a2cf3448f7f20634","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-08-15T23:01:41Z","title_canon_sha256":"6db471aae79d794ef39a544e85b10d3c7a5b53a093d26de81cee180b36b67d8f"},"schema_version":"1.0","source":{"id":"2408.08449","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08449","created_at":"2026-07-05T09:48:34Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08449v2","created_at":"2026-07-05T09:48:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08449","created_at":"2026-07-05T09:48:34Z"},{"alias_kind":"pith_short_12","alias_value":"M4NCW7S7W6OQ","created_at":"2026-07-05T09:48:34Z"},{"alias_kind":"pith_short_16","alias_value":"M4NCW7S7W6OQYYAO","created_at":"2026-07-05T09:48:34Z"},{"alias_kind":"pith_short_8","alias_value":"M4NCW7S7","created_at":"2026-07-05T09:48:34Z"}],"graph_snapshots":[{"event_id":"sha256:09418683702655f5db732dc14bd0e3358718d65f4c542191a541c61967c2bc78","target":"graph","created_at":"2026-07-05T09:48:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2408.08449/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixed-integer rounding (MIR) cutting planes (cuts) are effective at improving the strength of a linear relaxation for mixed-integer linear programming (MIP) problems. The cuts in this family are derived by aggregating constraints then rounding coefficients, but finding the strongest MIR cuts requires optimizing a costly MIP for the aggregation step, so in practice, heuristic strategies for separating fractional points are employed. We propose to improve MIR cut generation in the context of a common scenario in applications, where constraints remain fixed but costs are varied. We present a hybr","authors_text":"Aleksandr M. Kazachkov, Arnaud Deza, Elias B. Khalil, Oscar Guaje","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-08-15T23:01:41Z","title":"Machine Learning for Optimization-Based Separation of Mixed-Integer Rounding Cuts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08449","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b9a6a5755e0ac7366ad3f2f3e0f9c826ce87f1261310f64e8a4e01c67baab340","target":"record","created_at":"2026-07-05T09:48:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f3fd276d3fbd0d7299b2e8362a8f16447a9eeb16df985578a2cf3448f7f20634","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-08-15T23:01:41Z","title_canon_sha256":"6db471aae79d794ef39a544e85b10d3c7a5b53a093d26de81cee180b36b67d8f"},"schema_version":"1.0","source":{"id":"2408.08449","kind":"arxiv","version":2}},"canonical_sha256":"671a2b7e5fb79d0c600e5c8f9b20020da4a50b7b3150d60a9668fccefab5602a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"671a2b7e5fb79d0c600e5c8f9b20020da4a50b7b3150d60a9668fccefab5602a","first_computed_at":"2026-07-05T09:48:34.979437Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:48:34.979437Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dF7NH+XOwn7w62oWoCDSRbTGfc0CltOTp0n8694rE9SvfSxN5myF3klbHQ9CKDc1zweVgkHhY3eZJeStFp9FAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:48:34.979829Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.08449","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9a6a5755e0ac7366ad3f2f3e0f9c826ce87f1261310f64e8a4e01c67baab340","sha256:09418683702655f5db732dc14bd0e3358718d65f4c542191a541c61967c2bc78"],"state_sha256":"a853ab6408b42b2efd85463fbcde816c4a2f5b5df923569cd77da4a4fddb59f2"}