{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:U6TOZV2FOUKSXEYNW6HVH2YYAN","short_pith_number":"pith:U6TOZV2F","schema_version":"1.0","canonical_sha256":"a7a6ecd74575152b930db78f53eb1803406e1101d5be30ac70042ab06c80c80b","source":{"kind":"arxiv","id":"2410.04615","version":2},"attestation_state":"computed","paper":{"title":"Time-reversal solution of BSDEs in stochastic optimal control: a linear quadratic study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Amirhossein Taghvaei, Yuhang Mei","submitted_at":"2024-10-06T20:30:41Z","abstract_excerpt":"This paper addresses the numerical solution of backward stochastic differential equations (BSDEs) arising in stochastic optimal control. Specifically, we investigate two BSDEs: one derived from the Hamilton-Jacobi-Bellman equation and the other from the stochastic maximum principle. For both formulations, we analyze and compare two numerical methods. The first utilizes the least-squares Monte-Carlo (LSMC) approach for approximating conditional expectations, while the second leverages a time-reversal (TR) of diffusion processes. Although both methods extend to nonlinear settings, our focus is 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":"2410.04615","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-10-06T20:30:41Z","cross_cats_sorted":[],"title_canon_sha256":"35463197fb43988f0daba39c040e9c8e3b10503a571d0256b6e745f655e460fc","abstract_canon_sha256":"9f442d09bd021a57c592f3efa28a682013f1b231d036dd72088d47cc6bb7bf95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:23.679662Z","signature_b64":"SOKswpMjBfWXwWIOROIxLJm3V4hiCb8EY5auN/485ebOVO3Y6x4vFVpWmiNt9U4JZSfDDDpaUaqAw33U70PxDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7a6ecd74575152b930db78f53eb1803406e1101d5be30ac70042ab06c80c80b","last_reissued_at":"2026-07-05T10:28:23.679201Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:23.679201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time-reversal solution of BSDEs in stochastic optimal control: a linear quadratic study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Amirhossein Taghvaei, Yuhang Mei","submitted_at":"2024-10-06T20:30:41Z","abstract_excerpt":"This paper addresses the numerical solution of backward stochastic differential equations (BSDEs) arising in stochastic optimal control. Specifically, we investigate two BSDEs: one derived from the Hamilton-Jacobi-Bellman equation and the other from the stochastic maximum principle. For both formulations, we analyze and compare two numerical methods. The first utilizes the least-squares Monte-Carlo (LSMC) approach for approximating conditional expectations, while the second leverages a time-reversal (TR) of diffusion processes. Although both methods extend to nonlinear settings, our focus is o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04615","kind":"arxiv","version":2},"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/2410.04615/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":"2410.04615","created_at":"2026-07-05T10:28:23.679254+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.04615v2","created_at":"2026-07-05T10:28:23.679254+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04615","created_at":"2026-07-05T10:28:23.679254+00:00"},{"alias_kind":"pith_short_12","alias_value":"U6TOZV2FOUKS","created_at":"2026-07-05T10:28:23.679254+00:00"},{"alias_kind":"pith_short_16","alias_value":"U6TOZV2FOUKSXEYN","created_at":"2026-07-05T10:28:23.679254+00:00"},{"alias_kind":"pith_short_8","alias_value":"U6TOZV2F","created_at":"2026-07-05T10:28:23.679254+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.00617","citing_title":"Flow matching for stochastic linear control systems","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN","json":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN.json","graph_json":"https://pith.science/api/pith-number/U6TOZV2FOUKSXEYNW6HVH2YYAN/graph.json","events_json":"https://pith.science/api/pith-number/U6TOZV2FOUKSXEYNW6HVH2YYAN/events.json","paper":"https://pith.science/paper/U6TOZV2F"},"agent_actions":{"view_html":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN","download_json":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN.json","view_paper":"https://pith.science/paper/U6TOZV2F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.04615&json=true","fetch_graph":"https://pith.science/api/pith-number/U6TOZV2FOUKSXEYNW6HVH2YYAN/graph.json","fetch_events":"https://pith.science/api/pith-number/U6TOZV2FOUKSXEYNW6HVH2YYAN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN/action/storage_attestation","attest_author":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN/action/author_attestation","sign_citation":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN/action/citation_signature","submit_replication":"https://pith.science/pith/U6TOZV2FOUKSXEYNW6HVH2YYAN/action/replication_record"}},"created_at":"2026-07-05T10:28:23.679254+00:00","updated_at":"2026-07-05T10:28:23.679254+00:00"}