{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:C5EW3VWYXZU7DW333GJ2ESRUNK","short_pith_number":"pith:C5EW3VWY","schema_version":"1.0","canonical_sha256":"17496dd6d8be69f1db7bd993a24a346a9cf9ac251c3ba2e0edfcf14b37e61daf","source":{"kind":"arxiv","id":"2605.28757","version":1},"attestation_state":"computed","paper":{"title":"Learning Approximate Solutions to Multiparametric Generalized Nash Equilibrium Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"A. Bemporad, T. Tatarenko","submitted_at":"2026-05-27T17:18:11Z","abstract_excerpt":"We propose a learning-based approach for approximating solution mappings of multiparametric generalized Nash equilibrium problems (GNEPs) with coupling in both objectives and constraints. Rather than solving a standard regression problem on a training dataset of GNEP solutions, which are expensive and possibly difficult to collect, we use the Nikaido-Isoda (NI) gap function as a training loss, which requires only best-response data. To avoid bilevel optimization, a value-function surrogate approximates each agent's optimal best-response cost and is substituted into the NI loss, yielding a sing"},"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":"2605.28757","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-27T17:18:11Z","cross_cats_sorted":[],"title_canon_sha256":"e631a586bdc196e7076b97712fae5e5bf3ca7a9d1bbd56e8d06c85b14d0f86be","abstract_canon_sha256":"7ca2685f573be38154de9d7167fbe144d26ebfa9102c47e6d66951a4de139766"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:05:02.152062Z","signature_b64":"GDlhf3vZ79FYWIDKmzDwnvVx7Bna847mi22Q7oK5SOaOZ3WqvitHtAQXi3J39dxtGVyWdriR8w4TETdnJdVXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17496dd6d8be69f1db7bd993a24a346a9cf9ac251c3ba2e0edfcf14b37e61daf","last_reissued_at":"2026-05-28T02:05:02.151649Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:05:02.151649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Approximate Solutions to Multiparametric Generalized Nash Equilibrium Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"A. Bemporad, T. Tatarenko","submitted_at":"2026-05-27T17:18:11Z","abstract_excerpt":"We propose a learning-based approach for approximating solution mappings of multiparametric generalized Nash equilibrium problems (GNEPs) with coupling in both objectives and constraints. Rather than solving a standard regression problem on a training dataset of GNEP solutions, which are expensive and possibly difficult to collect, we use the Nikaido-Isoda (NI) gap function as a training loss, which requires only best-response data. To avoid bilevel optimization, a value-function surrogate approximates each agent's optimal best-response cost and is substituted into the NI loss, yielding a sing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28757","kind":"arxiv","version":1},"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/2605.28757/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":"2605.28757","created_at":"2026-05-28T02:05:02.151711+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28757v1","created_at":"2026-05-28T02:05:02.151711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28757","created_at":"2026-05-28T02:05:02.151711+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5EW3VWYXZU7","created_at":"2026-05-28T02:05:02.151711+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5EW3VWYXZU7DW33","created_at":"2026-05-28T02:05:02.151711+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5EW3VWY","created_at":"2026-05-28T02:05:02.151711+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/C5EW3VWYXZU7DW333GJ2ESRUNK","json":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK.json","graph_json":"https://pith.science/api/pith-number/C5EW3VWYXZU7DW333GJ2ESRUNK/graph.json","events_json":"https://pith.science/api/pith-number/C5EW3VWYXZU7DW333GJ2ESRUNK/events.json","paper":"https://pith.science/paper/C5EW3VWY"},"agent_actions":{"view_html":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK","download_json":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK.json","view_paper":"https://pith.science/paper/C5EW3VWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28757&json=true","fetch_graph":"https://pith.science/api/pith-number/C5EW3VWYXZU7DW333GJ2ESRUNK/graph.json","fetch_events":"https://pith.science/api/pith-number/C5EW3VWYXZU7DW333GJ2ESRUNK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK/action/storage_attestation","attest_author":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK/action/author_attestation","sign_citation":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK/action/citation_signature","submit_replication":"https://pith.science/pith/C5EW3VWYXZU7DW333GJ2ESRUNK/action/replication_record"}},"created_at":"2026-05-28T02:05:02.151711+00:00","updated_at":"2026-05-28T02:05:02.151711+00:00"}