{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4CW6TGNXM5NZ4ZWYR7BFTSA56Z","short_pith_number":"pith:4CW6TGNX","schema_version":"1.0","canonical_sha256":"e0ade999b7675b9e66d88fc259c81df648dfd178327375422c4ed1799799d70a","source":{"kind":"arxiv","id":"2308.02145","version":1},"attestation_state":"computed","paper":{"title":"Optimization on Pareto sets: On a theory of multi-objective optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Abhishek Roy, Geelon So, Yi-An Ma","submitted_at":"2023-08-04T05:55:52Z","abstract_excerpt":"In multi-objective optimization, a single decision vector must balance the trade-offs between many objectives. Solutions achieving an optimal trade-off are said to be Pareto optimal: these are decision vectors for which improving any one objective must come at a cost to another. But as the set of Pareto optimal vectors can be very large, we further consider a more practically significant Pareto-constrained optimization problem, where the goal is to optimize a preference function constrained to the Pareto set.\n  We investigate local methods for solving this constrained optimization problem, whi"},"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":"2308.02145","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-08-04T05:55:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"85881314c255fe333ce05c6c7d9bb0110b544a960a14afb10f44c67445c137ff","abstract_canon_sha256":"5bebb734e26f9d982a8f2c14631a6e0107b7947730675a434a1e73bba99377a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:38.342642Z","signature_b64":"yGsw5surcJtl9b67EsrOaznsXtzAtclGX4TYZzmVD/fpkZCIsPnvkpZPAMu9nM92Pzby/CWRmEhYH95WRR2dDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0ade999b7675b9e66d88fc259c81df648dfd178327375422c4ed1799799d70a","last_reissued_at":"2026-07-05T06:37:38.342191Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:38.342191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimization on Pareto sets: On a theory of multi-objective optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Abhishek Roy, Geelon So, Yi-An Ma","submitted_at":"2023-08-04T05:55:52Z","abstract_excerpt":"In multi-objective optimization, a single decision vector must balance the trade-offs between many objectives. Solutions achieving an optimal trade-off are said to be Pareto optimal: these are decision vectors for which improving any one objective must come at a cost to another. But as the set of Pareto optimal vectors can be very large, we further consider a more practically significant Pareto-constrained optimization problem, where the goal is to optimize a preference function constrained to the Pareto set.\n  We investigate local methods for solving this constrained optimization problem, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.02145","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/2308.02145/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":"2308.02145","created_at":"2026-07-05T06:37:38.342256+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.02145v1","created_at":"2026-07-05T06:37:38.342256+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.02145","created_at":"2026-07-05T06:37:38.342256+00:00"},{"alias_kind":"pith_short_12","alias_value":"4CW6TGNXM5NZ","created_at":"2026-07-05T06:37:38.342256+00:00"},{"alias_kind":"pith_short_16","alias_value":"4CW6TGNXM5NZ4ZWY","created_at":"2026-07-05T06:37:38.342256+00:00"},{"alias_kind":"pith_short_8","alias_value":"4CW6TGNX","created_at":"2026-07-05T06:37:38.342256+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20619","citing_title":"SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front","ref_index":80,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z","json":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z.json","graph_json":"https://pith.science/api/pith-number/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/graph.json","events_json":"https://pith.science/api/pith-number/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/events.json","paper":"https://pith.science/paper/4CW6TGNX"},"agent_actions":{"view_html":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z","download_json":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z.json","view_paper":"https://pith.science/paper/4CW6TGNX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.02145&json=true","fetch_graph":"https://pith.science/api/pith-number/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/graph.json","fetch_events":"https://pith.science/api/pith-number/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/action/storage_attestation","attest_author":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/action/author_attestation","sign_citation":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/action/citation_signature","submit_replication":"https://pith.science/pith/4CW6TGNXM5NZ4ZWYR7BFTSA56Z/action/replication_record"}},"created_at":"2026-07-05T06:37:38.342256+00:00","updated_at":"2026-07-05T06:37:38.342256+00:00"}