{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XMLBP4UHOI5O5YQZXSNHMWW56D","short_pith_number":"pith:XMLBP4UH","schema_version":"1.0","canonical_sha256":"bb1617f287723aeee219bc9a765addf0ec0056f71a257c4111e1f55d61c6053e","source":{"kind":"arxiv","id":"2607.11460","version":1},"attestation_state":"computed","paper":{"title":"First-Order Methods for Distributionally Robust Constrained Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hubert Villuendas, J\\'er\\^ome Malick, Mathieu Besan\\c{c}on","submitted_at":"2026-07-13T12:09:01Z","abstract_excerpt":"We consider constrained optimization problems in which input data are affected by estimation errors. In such settings, Wasserstein distributionally robust optimization provides a principled framework to mitigate model risk by optimizing against worst-case distributions within Wasserstein ambiguity sets. However, the numerical resolution of the resulting problems remains challenging, especially in constrained and combinatorial settings. In this paper, we propose a tractable stochastic approach based on two key ingredients: (i) an entropic regularization of the distributionally robust value func"},"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":"2607.11460","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-07-13T12:09:01Z","cross_cats_sorted":[],"title_canon_sha256":"92f8c6d62bf6a8df609666b91e91cfa415b73992b890f7d97c41027d637df5b2","abstract_canon_sha256":"41e2741898cd0854a48d9b8cc7edf34ab7fef4df92cee54c5dd81b92c6865e3a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T02:22:04.324486Z","signature_b64":"V8hx+kErCo4obpKPZllko8cJU0PfXeVM5bcWHgedHuZCo4Sihpvmop53ROIVAwtxuz86/vZL/kfqFKV3MS++AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb1617f287723aeee219bc9a765addf0ec0056f71a257c4111e1f55d61c6053e","last_reissued_at":"2026-07-14T02:22:04.323636Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T02:22:04.323636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"First-Order Methods for Distributionally Robust Constrained Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hubert Villuendas, J\\'er\\^ome Malick, Mathieu Besan\\c{c}on","submitted_at":"2026-07-13T12:09:01Z","abstract_excerpt":"We consider constrained optimization problems in which input data are affected by estimation errors. In such settings, Wasserstein distributionally robust optimization provides a principled framework to mitigate model risk by optimizing against worst-case distributions within Wasserstein ambiguity sets. However, the numerical resolution of the resulting problems remains challenging, especially in constrained and combinatorial settings. In this paper, we propose a tractable stochastic approach based on two key ingredients: (i) an entropic regularization of the distributionally robust value func"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11460","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/2607.11460/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":"2607.11460","created_at":"2026-07-14T02:22:04.324079+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11460v1","created_at":"2026-07-14T02:22:04.324079+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11460","created_at":"2026-07-14T02:22:04.324079+00:00"},{"alias_kind":"pith_short_12","alias_value":"XMLBP4UHOI5O","created_at":"2026-07-14T02:22:04.324079+00:00"},{"alias_kind":"pith_short_16","alias_value":"XMLBP4UHOI5O5YQZ","created_at":"2026-07-14T02:22:04.324079+00:00"},{"alias_kind":"pith_short_8","alias_value":"XMLBP4UH","created_at":"2026-07-14T02:22:04.324079+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/XMLBP4UHOI5O5YQZXSNHMWW56D","json":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D.json","graph_json":"https://pith.science/api/pith-number/XMLBP4UHOI5O5YQZXSNHMWW56D/graph.json","events_json":"https://pith.science/api/pith-number/XMLBP4UHOI5O5YQZXSNHMWW56D/events.json","paper":"https://pith.science/paper/XMLBP4UH"},"agent_actions":{"view_html":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D","download_json":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D.json","view_paper":"https://pith.science/paper/XMLBP4UH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11460&json=true","fetch_graph":"https://pith.science/api/pith-number/XMLBP4UHOI5O5YQZXSNHMWW56D/graph.json","fetch_events":"https://pith.science/api/pith-number/XMLBP4UHOI5O5YQZXSNHMWW56D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D/action/storage_attestation","attest_author":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D/action/author_attestation","sign_citation":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D/action/citation_signature","submit_replication":"https://pith.science/pith/XMLBP4UHOI5O5YQZXSNHMWW56D/action/replication_record"}},"created_at":"2026-07-14T02:22:04.324079+00:00","updated_at":"2026-07-14T02:22:04.324079+00:00"}