{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FYL6WMT4SMPGH5AOXUHBLTX2A2","short_pith_number":"pith:FYL6WMT4","schema_version":"1.0","canonical_sha256":"2e17eb327c931e63f40ebd0e15cefa06ba08f9ad0f3c7bc94a2636f6eaa6b181","source":{"kind":"arxiv","id":"2505.19382","version":1},"attestation_state":"computed","paper":{"title":"Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Albert S. Berahas, Raghu Bollapragada, Shagun Gupta","submitted_at":"2025-05-26T00:50:41Z","abstract_excerpt":"In this paper, we propose a framework based on the Retrospective Approximation (RA) paradigm to solve optimization problems with a stochastic objective function and general nonlinear deterministic constraints. This framework sequentially constructs increasingly accurate approximations of the true problems which are solved to a specified accuracy via a deterministic solver, thereby decoupling the uncertainty from the optimization. Such frameworks retain the advantages of deterministic optimization methods, such as fast convergence, while achieving the optimal performance of stochastic methods w"},"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":"2505.19382","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2025-05-26T00:50:41Z","cross_cats_sorted":[],"title_canon_sha256":"71bfd598f6cd4d79f25d663336ea59a33a1cf38c318dd62fde66a5c3954fd0a9","abstract_canon_sha256":"b1abf86df2357cb25643169d72d0e134a13adacaefa5a57b7ba64bf94a0ec62f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:29.407806Z","signature_b64":"7vqzYg89+64Yarw153oOkHeSyKGlYYFFm//JA4Sr9K2pgyD5Jetai+PNhZTd+Fybp0rQ0BIXyKM7i4ixv8zQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e17eb327c931e63f40ebd0e15cefa06ba08f9ad0f3c7bc94a2636f6eaa6b181","last_reissued_at":"2026-07-05T11:09:29.407411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:29.407411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Albert S. Berahas, Raghu Bollapragada, Shagun Gupta","submitted_at":"2025-05-26T00:50:41Z","abstract_excerpt":"In this paper, we propose a framework based on the Retrospective Approximation (RA) paradigm to solve optimization problems with a stochastic objective function and general nonlinear deterministic constraints. This framework sequentially constructs increasingly accurate approximations of the true problems which are solved to a specified accuracy via a deterministic solver, thereby decoupling the uncertainty from the optimization. Such frameworks retain the advantages of deterministic optimization methods, such as fast convergence, while achieving the optimal performance of stochastic methods w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19382","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/2505.19382/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":"2505.19382","created_at":"2026-07-05T11:09:29.407467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.19382v1","created_at":"2026-07-05T11:09:29.407467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19382","created_at":"2026-07-05T11:09:29.407467+00:00"},{"alias_kind":"pith_short_12","alias_value":"FYL6WMT4SMPG","created_at":"2026-07-05T11:09:29.407467+00:00"},{"alias_kind":"pith_short_16","alias_value":"FYL6WMT4SMPGH5AO","created_at":"2026-07-05T11:09:29.407467+00:00"},{"alias_kind":"pith_short_8","alias_value":"FYL6WMT4","created_at":"2026-07-05T11:09:29.407467+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/FYL6WMT4SMPGH5AOXUHBLTX2A2","json":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2.json","graph_json":"https://pith.science/api/pith-number/FYL6WMT4SMPGH5AOXUHBLTX2A2/graph.json","events_json":"https://pith.science/api/pith-number/FYL6WMT4SMPGH5AOXUHBLTX2A2/events.json","paper":"https://pith.science/paper/FYL6WMT4"},"agent_actions":{"view_html":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2","download_json":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2.json","view_paper":"https://pith.science/paper/FYL6WMT4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.19382&json=true","fetch_graph":"https://pith.science/api/pith-number/FYL6WMT4SMPGH5AOXUHBLTX2A2/graph.json","fetch_events":"https://pith.science/api/pith-number/FYL6WMT4SMPGH5AOXUHBLTX2A2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2/action/storage_attestation","attest_author":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2/action/author_attestation","sign_citation":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2/action/citation_signature","submit_replication":"https://pith.science/pith/FYL6WMT4SMPGH5AOXUHBLTX2A2/action/replication_record"}},"created_at":"2026-07-05T11:09:29.407467+00:00","updated_at":"2026-07-05T11:09:29.407467+00:00"}