{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:AVY4WW7Y4YQIQIQWGXQN7UORGX","short_pith_number":"pith:AVY4WW7Y","canonical_record":{"source":{"id":"2205.11638","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-23T21:09:41Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"1dab9d8ec7fb9c8a2fbafc7e4176c6a1f5dc680c0c8c18986875af602b8d62ec","abstract_canon_sha256":"6543e340ba905b3a9ac29e2d74a48367f10dfe8bee50ac438a66f80a38e61ca7"},"schema_version":"1.0"},"canonical_sha256":"0571cb5bf8e62088221635e0dfd1d135d2359ca3fdc0e57411086a530bde581c","source":{"kind":"arxiv","id":"2205.11638","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11638","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11638v2","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11638","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_12","alias_value":"AVY4WW7Y4YQI","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_16","alias_value":"AVY4WW7Y4YQIQIQW","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_8","alias_value":"AVY4WW7Y","created_at":"2026-07-05T07:28:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:AVY4WW7Y4YQIQIQWGXQN7UORGX","target":"record","payload":{"canonical_record":{"source":{"id":"2205.11638","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-23T21:09:41Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"1dab9d8ec7fb9c8a2fbafc7e4176c6a1f5dc680c0c8c18986875af602b8d62ec","abstract_canon_sha256":"6543e340ba905b3a9ac29e2d74a48367f10dfe8bee50ac438a66f80a38e61ca7"},"schema_version":"1.0"},"canonical_sha256":"0571cb5bf8e62088221635e0dfd1d135d2359ca3fdc0e57411086a530bde581c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:42.827542Z","signature_b64":"nVcDNq38vvNOwhq8H3BzC47/BEjRWqDHv7evQSXidI+/MP1WPHoAkhwl83AKxWVjIU4BmfMV1M2oEx7fjx/RBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0571cb5bf8e62088221635e0dfd1d135d2359ca3fdc0e57411086a530bde581c","last_reissued_at":"2026-07-05T07:28:42.827008Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:42.827008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.11638","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:28:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/PhuvQq3FZyfnUedZZ9vmIFpIfvmo/n2xWfwSs1j9qT2xnwusL84ZWr/B9GbqUFN9CfCTtsrMokQ1n3UlVU+Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T16:01:10.033471Z"},"content_sha256":"8d159ca2bd5c615600a1ecba7e16c4ffb792df01fb253bc9e481f577ad107fb2","schema_version":"1.0","event_id":"sha256:8d159ca2bd5c615600a1ecba7e16c4ffb792df01fb253bc9e481f577ad107fb2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:AVY4WW7Y4YQIQIQWGXQN7UORGX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DOGE-Train: Discrete Optimization on GPU with End-to-end Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Ahmed Abbas, Paul Swoboda","submitted_at":"2022-05-23T21:09:41Z","abstract_excerpt":"We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear programs. We use a combination of graph neural networks (GNN) and the Lagrange decomposition based algorithm FastDOG (Abbas and Swoboda 2022b). We make the latter differentiable for end-to-end training and use GNNs to predict its algorithmic parameters. This allows to retain the algorithm's theoretical properties including dual feasibility and guaranteed non-decrease in the lower bound while improving it via training. We overcome suboptimal fixed points of the basic solver by additional non-parametr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11638","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.11638/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":"2026-07-05T07:28:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZl3ilkdpIqhdvW0nB4v1Zz6xiZVRbMJWheCCTmS/iwjlfSIyogr3MM+IkTBf3DnCoONFLsyQA0G2Rm+4FLBDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T16:01:10.033851Z"},"content_sha256":"84cf37db656d2f775bcf2c94245d6866a956653a4840a5b07af8c1185998035c","schema_version":"1.0","event_id":"sha256:84cf37db656d2f775bcf2c94245d6866a956653a4840a5b07af8c1185998035c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/bundle.json","state_url":"https://pith.science/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/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":"2026-07-07T16:01:10Z","links":{"resolver":"https://pith.science/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX","bundle":"https://pith.science/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/bundle.json","state":"https://pith.science/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AVY4WW7Y4YQIQIQWGXQN7UORGX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:AVY4WW7Y4YQIQIQWGXQN7UORGX","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":"6543e340ba905b3a9ac29e2d74a48367f10dfe8bee50ac438a66f80a38e61ca7","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-23T21:09:41Z","title_canon_sha256":"1dab9d8ec7fb9c8a2fbafc7e4176c6a1f5dc680c0c8c18986875af602b8d62ec"},"schema_version":"1.0","source":{"id":"2205.11638","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.11638","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"arxiv_version","alias_value":"2205.11638v2","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.11638","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_12","alias_value":"AVY4WW7Y4YQI","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_16","alias_value":"AVY4WW7Y4YQIQIQW","created_at":"2026-07-05T07:28:42Z"},{"alias_kind":"pith_short_8","alias_value":"AVY4WW7Y","created_at":"2026-07-05T07:28:42Z"}],"graph_snapshots":[{"event_id":"sha256:84cf37db656d2f775bcf2c94245d6866a956653a4840a5b07af8c1185998035c","target":"graph","created_at":"2026-07-05T07:28:42Z","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/2205.11638/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear programs. We use a combination of graph neural networks (GNN) and the Lagrange decomposition based algorithm FastDOG (Abbas and Swoboda 2022b). We make the latter differentiable for end-to-end training and use GNNs to predict its algorithmic parameters. This allows to retain the algorithm's theoretical properties including dual feasibility and guaranteed non-decrease in the lower bound while improving it via training. We overcome suboptimal fixed points of the basic solver by additional non-parametr","authors_text":"Ahmed Abbas, Paul Swoboda","cross_cats":["math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-23T21:09:41Z","title":"DOGE-Train: Discrete Optimization on GPU with End-to-end Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.11638","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":"sha256:8d159ca2bd5c615600a1ecba7e16c4ffb792df01fb253bc9e481f577ad107fb2","target":"record","created_at":"2026-07-05T07:28:42Z","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":"6543e340ba905b3a9ac29e2d74a48367f10dfe8bee50ac438a66f80a38e61ca7","cross_cats_sorted":["math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-23T21:09:41Z","title_canon_sha256":"1dab9d8ec7fb9c8a2fbafc7e4176c6a1f5dc680c0c8c18986875af602b8d62ec"},"schema_version":"1.0","source":{"id":"2205.11638","kind":"arxiv","version":2}},"canonical_sha256":"0571cb5bf8e62088221635e0dfd1d135d2359ca3fdc0e57411086a530bde581c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0571cb5bf8e62088221635e0dfd1d135d2359ca3fdc0e57411086a530bde581c","first_computed_at":"2026-07-05T07:28:42.827008Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:28:42.827008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nVcDNq38vvNOwhq8H3BzC47/BEjRWqDHv7evQSXidI+/MP1WPHoAkhwl83AKxWVjIU4BmfMV1M2oEx7fjx/RBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:28:42.827542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.11638","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8d159ca2bd5c615600a1ecba7e16c4ffb792df01fb253bc9e481f577ad107fb2","sha256:84cf37db656d2f775bcf2c94245d6866a956653a4840a5b07af8c1185998035c"],"state_sha256":"505f5e142ffd313fde336a4f761c6105a1f7eae90d31d6dbfd2eacaee35b46c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xkOvJZhH4bXK07nZb5WZ0sSMaNy5tGsjXZQG8xp+4j4SJUvsCBEDscUqjqYdEVYrKwRm9W8zrjdePEEHKxRJCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T16:01:10.036033Z","bundle_sha256":"2b64d9869756a1210dfceb35e2fc1d22ce643fa036bf30b6ac1385dc6c730909"}}