{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:3BZQ6GVLSIKBP3PDO7FSNUVQPW","short_pith_number":"pith:3BZQ6GVL","schema_version":"1.0","canonical_sha256":"d8730f1aab921417ede377cb26d2b07d80a5c51b9944fd6b9c00d3615e8631fd","source":{"kind":"arxiv","id":"1408.4445","version":3},"attestation_state":"computed","paper":{"title":"Robust Sample Average Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Dimitris Bertsimas, Nathan Kallus, Vishal Gupta","submitted_at":"2014-08-19T19:57:18Z","abstract_excerpt":"Sample average approximation (SAA) is a widely popular approach to data-driven decision-making under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong asymptotic performance guarantees. Similar guarantees, however, do not typically hold in finite samples. In this paper, we propose a modification of SAA, which we term Robust SAA, which retains SAA's tractability and asymptotic properties and, additionally, enjoys strong finite-sample performance guarantees. The key to our method is linking SAA, distributionally robust optimization, and hypothesis testing of goodness-o"},"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":"1408.4445","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-08-19T19:57:18Z","cross_cats_sorted":[],"title_canon_sha256":"7c79145b74e59c9d3bce0cceaec9ab8e5651453d411e9c1046fd247f6aa5099e","abstract_canon_sha256":"0222ea267b36bf725d82f3ab5fb0a00896d592462f4b454c70826ab40b60bc4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:40.798489Z","signature_b64":"kTD3neIJJEmRhV8s2iU4g/lgbNKLii90ZXlIIe/E2ss8zq72nbPUZdvkmvVtHqmaGM+m1txbImce2LXNiVj7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8730f1aab921417ede377cb26d2b07d80a5c51b9944fd6b9c00d3615e8631fd","last_reissued_at":"2026-05-18T01:00:40.797865Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:40.797865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Sample Average Approximation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Dimitris Bertsimas, Nathan Kallus, Vishal Gupta","submitted_at":"2014-08-19T19:57:18Z","abstract_excerpt":"Sample average approximation (SAA) is a widely popular approach to data-driven decision-making under uncertainty. Under mild assumptions, SAA is both tractable and enjoys strong asymptotic performance guarantees. Similar guarantees, however, do not typically hold in finite samples. In this paper, we propose a modification of SAA, which we term Robust SAA, which retains SAA's tractability and asymptotic properties and, additionally, enjoys strong finite-sample performance guarantees. The key to our method is linking SAA, distributionally robust optimization, and hypothesis testing of goodness-o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.4445","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1408.4445","created_at":"2026-05-18T01:00:40.797962+00:00"},{"alias_kind":"arxiv_version","alias_value":"1408.4445v3","created_at":"2026-05-18T01:00:40.797962+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.4445","created_at":"2026-05-18T01:00:40.797962+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BZQ6GVLSIKB","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BZQ6GVLSIKBP3PD","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BZQ6GVL","created_at":"2026-05-18T12:28:11.866339+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/3BZQ6GVLSIKBP3PDO7FSNUVQPW","json":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW.json","graph_json":"https://pith.science/api/pith-number/3BZQ6GVLSIKBP3PDO7FSNUVQPW/graph.json","events_json":"https://pith.science/api/pith-number/3BZQ6GVLSIKBP3PDO7FSNUVQPW/events.json","paper":"https://pith.science/paper/3BZQ6GVL"},"agent_actions":{"view_html":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW","download_json":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW.json","view_paper":"https://pith.science/paper/3BZQ6GVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1408.4445&json=true","fetch_graph":"https://pith.science/api/pith-number/3BZQ6GVLSIKBP3PDO7FSNUVQPW/graph.json","fetch_events":"https://pith.science/api/pith-number/3BZQ6GVLSIKBP3PDO7FSNUVQPW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW/action/storage_attestation","attest_author":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW/action/author_attestation","sign_citation":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW/action/citation_signature","submit_replication":"https://pith.science/pith/3BZQ6GVLSIKBP3PDO7FSNUVQPW/action/replication_record"}},"created_at":"2026-05-18T01:00:40.797962+00:00","updated_at":"2026-05-18T01:00:40.797962+00:00"}