{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:A566YEM7N4XQTH53JZN6RNNIFY","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":"682b122b3b88f2bc6f0d54b0bba75749f2e2a8f171c8a719de195fb28ca81de1","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2021-02-20T18:07:33Z","title_canon_sha256":"582b46b4d6f56f398849c5c1b478ead32904fe01b933f9de3a0ca5840b2e6819"},"schema_version":"1.0","source":{"id":"2103.04748","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.04748","created_at":"2026-07-05T02:37:11Z"},{"alias_kind":"arxiv_version","alias_value":"2103.04748v2","created_at":"2026-07-05T02:37:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.04748","created_at":"2026-07-05T02:37:11Z"},{"alias_kind":"pith_short_12","alias_value":"A566YEM7N4XQ","created_at":"2026-07-05T02:37:11Z"},{"alias_kind":"pith_short_16","alias_value":"A566YEM7N4XQTH53","created_at":"2026-07-05T02:37:11Z"},{"alias_kind":"pith_short_8","alias_value":"A566YEM7","created_at":"2026-07-05T02:37:11Z"}],"graph_snapshots":[{"event_id":"sha256:2b26e1d520c870841b2a47fb7684788d37f30906e0f793fba4612749d568a10d","target":"graph","created_at":"2026-07-05T02:37:11Z","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/2103.04748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many mathematical optimization algorithms fail to sufficiently explore the solution space of high-dimensional nonlinear optimization problems due to the curse of dimensionality. This paper proposes generative models as a complement to optimization algorithms to improve performance in problems with high dimensionality. To demonstrate this method, a conditional generative adversarial network (C-GAN) is used to augment the solutions produced by a genetic algorithm (GA) for a 311-dimensional nonconvex multi-objective mixed-integer nonlinear optimization. The C-GAN, composed of two networks with th","authors_text":"Michael D. Lepech, Pouya Rezazadeh Kalehbasti, Samarpreet Singh Pandher","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2021-02-20T18:07:33Z","title":"Augmenting High-dimensional Nonlinear Optimization with Conditional GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.04748","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:c8fea652db9b80efe3fff38db6141b5ba22c57fcb724e3b8fb2ffffd8ff1204c","target":"record","created_at":"2026-07-05T02:37:11Z","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":"682b122b3b88f2bc6f0d54b0bba75749f2e2a8f171c8a719de195fb28ca81de1","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2021-02-20T18:07:33Z","title_canon_sha256":"582b46b4d6f56f398849c5c1b478ead32904fe01b933f9de3a0ca5840b2e6819"},"schema_version":"1.0","source":{"id":"2103.04748","kind":"arxiv","version":2}},"canonical_sha256":"077dec119f6f2f099fbb4e5be8b5a82e0b5f286015ab81844382ba5224f8db1a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"077dec119f6f2f099fbb4e5be8b5a82e0b5f286015ab81844382ba5224f8db1a","first_computed_at":"2026-07-05T02:37:11.059200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:37:11.059200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vxLV4hUX8kF6dCnTzKe/070PATYifJTgn19rsmGyBOeAp72VLLKuYQvObOBTYcZKMQPnCjhdldGKvd2yEhK/CA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:37:11.059849Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.04748","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8fea652db9b80efe3fff38db6141b5ba22c57fcb724e3b8fb2ffffd8ff1204c","sha256:2b26e1d520c870841b2a47fb7684788d37f30906e0f793fba4612749d568a10d"],"state_sha256":"3524befea1036ca5b5e490c32ded54c7989ab696f8a002f71ef822c216681c19"}