{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:O64MALBGM4TSNOHZPTVY4G4Q36","short_pith_number":"pith:O64MALBG","canonical_record":{"source":{"id":"2107.10201","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-07-21T16:43:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9f7ec39cab4dc937536049cdefad4882d0fb12880385d5f0d5cf74b7e3293730","abstract_canon_sha256":"f443beb62805c10b2a62406b7209ed56d0581f8757143b324a83de1a2e95d6e7"},"schema_version":"1.0"},"canonical_sha256":"77b8c02c26672726b8f97ceb8e1b90df9f6345f18fab19f1495aceb28dd8e4c3","source":{"kind":"arxiv","id":"2107.10201","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10201","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10201v3","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10201","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"O64MALBGM4TS","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"O64MALBGM4TSNOHZ","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"O64MALBG","created_at":"2026-07-05T04:24:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:O64MALBGM4TSNOHZPTVY4G4Q36","target":"record","payload":{"canonical_record":{"source":{"id":"2107.10201","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-07-21T16:43:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9f7ec39cab4dc937536049cdefad4882d0fb12880385d5f0d5cf74b7e3293730","abstract_canon_sha256":"f443beb62805c10b2a62406b7209ed56d0581f8757143b324a83de1a2e95d6e7"},"schema_version":"1.0"},"canonical_sha256":"77b8c02c26672726b8f97ceb8e1b90df9f6345f18fab19f1495aceb28dd8e4c3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:24:55.917550Z","signature_b64":"rxBPbP+WbtTn1OpfR4QoZQbQL1uh6W2hpgQ1jm7pg9IQ62nA1Fwm50uE9nMECawNpohMTF8jKYP1i++SLMxjDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77b8c02c26672726b8f97ceb8e1b90df9f6345f18fab19f1495aceb28dd8e4c3","last_reissued_at":"2026-07-05T04:24:55.916990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:24:55.916990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.10201","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KEH+/8oauEkVrdSCc8/O3gJg+Da0+ralgtpRv09+S0+/GErP4J0fYJe7zdeyPradcmq0CKmqOVvIzQzBTuTGBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:57:59.092403Z"},"content_sha256":"ffec7e688f0e25b66873d646762b7a933248455ba6367110ac893654ecd27b98","schema_version":"1.0","event_id":"sha256:ffec7e688f0e25b66873d646762b7a933248455ba6367110ac893654ecd27b98"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:O64MALBGM4TSNOHZPTVY4G4Q36","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Ira Ktena, Nicolas Sonnerat, Pengming Wang, Sergey Bartunov, Vinod Nair","submitted_at":"2021-07-21T16:43:46Z","abstract_excerpt":"Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment. In this paper we consider a learning-based LNS approach for mixed integer programs (MIPs). We train a Neural Diving model to represent a probability distribution over assignments, which, together with an off-the-shelf MIP solver, generates an initial assignment. Formulating the subsequent search steps as a Markov Decision Process, we train a Neural Neighb"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10201","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/2107.10201/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:24:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"veIuohvW97NI4wfVFjlLyCFT+z7jv77WnozARYFxhfcF0+/VCw9ILHCWjFAE6XAciVoPIFS2wOt5pnP1ImPyDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:57:59.092943Z"},"content_sha256":"9ec7ae9a1b16306eff51b25d1c758ff461f3860a0634733ddc4780bbbacef83f","schema_version":"1.0","event_id":"sha256:9ec7ae9a1b16306eff51b25d1c758ff461f3860a0634733ddc4780bbbacef83f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O64MALBGM4TSNOHZPTVY4G4Q36/bundle.json","state_url":"https://pith.science/pith/O64MALBGM4TSNOHZPTVY4G4Q36/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O64MALBGM4TSNOHZPTVY4G4Q36/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T06:57:59Z","links":{"resolver":"https://pith.science/pith/O64MALBGM4TSNOHZPTVY4G4Q36","bundle":"https://pith.science/pith/O64MALBGM4TSNOHZPTVY4G4Q36/bundle.json","state":"https://pith.science/pith/O64MALBGM4TSNOHZPTVY4G4Q36/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O64MALBGM4TSNOHZPTVY4G4Q36/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:O64MALBGM4TSNOHZPTVY4G4Q36","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":"f443beb62805c10b2a62406b7209ed56d0581f8757143b324a83de1a2e95d6e7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-07-21T16:43:46Z","title_canon_sha256":"9f7ec39cab4dc937536049cdefad4882d0fb12880385d5f0d5cf74b7e3293730"},"schema_version":"1.0","source":{"id":"2107.10201","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10201","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10201v3","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10201","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_12","alias_value":"O64MALBGM4TS","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_16","alias_value":"O64MALBGM4TSNOHZ","created_at":"2026-07-05T04:24:55Z"},{"alias_kind":"pith_short_8","alias_value":"O64MALBG","created_at":"2026-07-05T04:24:55Z"}],"graph_snapshots":[{"event_id":"sha256:9ec7ae9a1b16306eff51b25d1c758ff461f3860a0634733ddc4780bbbacef83f","target":"graph","created_at":"2026-07-05T04:24:55Z","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/2107.10201/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment. In this paper we consider a learning-based LNS approach for mixed integer programs (MIPs). We train a Neural Diving model to represent a probability distribution over assignments, which, together with an off-the-shelf MIP solver, generates an initial assignment. Formulating the subsequent search steps as a Markov Decision Process, we train a Neural Neighb","authors_text":"Ira Ktena, Nicolas Sonnerat, Pengming Wang, Sergey Bartunov, Vinod Nair","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-07-21T16:43:46Z","title":"Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10201","kind":"arxiv","version":3},"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:ffec7e688f0e25b66873d646762b7a933248455ba6367110ac893654ecd27b98","target":"record","created_at":"2026-07-05T04:24:55Z","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":"f443beb62805c10b2a62406b7209ed56d0581f8757143b324a83de1a2e95d6e7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2021-07-21T16:43:46Z","title_canon_sha256":"9f7ec39cab4dc937536049cdefad4882d0fb12880385d5f0d5cf74b7e3293730"},"schema_version":"1.0","source":{"id":"2107.10201","kind":"arxiv","version":3}},"canonical_sha256":"77b8c02c26672726b8f97ceb8e1b90df9f6345f18fab19f1495aceb28dd8e4c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77b8c02c26672726b8f97ceb8e1b90df9f6345f18fab19f1495aceb28dd8e4c3","first_computed_at":"2026-07-05T04:24:55.916990Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:55.916990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rxBPbP+WbtTn1OpfR4QoZQbQL1uh6W2hpgQ1jm7pg9IQ62nA1Fwm50uE9nMECawNpohMTF8jKYP1i++SLMxjDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:55.917550Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.10201","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ffec7e688f0e25b66873d646762b7a933248455ba6367110ac893654ecd27b98","sha256:9ec7ae9a1b16306eff51b25d1c758ff461f3860a0634733ddc4780bbbacef83f"],"state_sha256":"696a7074beeb74af1caca9bcc20b59a9114c8478a1c57e4c49163260ba3f1164"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UBlYdydcAnplQ4AYxVHVwaQVdrcWcaCX9QYpYU5F5dBcfeafCb7J8vhXncBs772tUNyFemM7BZQlXWmx4bn0Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T06:57:59.098533Z","bundle_sha256":"c8443f986dbe849bd7e15aeca7bd9c5d39710d48df341a98c96ca1c89aa59ecc"}}