{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BGHS3PN427JTXAXF26RUHGMMRJ","short_pith_number":"pith:BGHS3PN4","schema_version":"1.0","canonical_sha256":"098f2dbdbcd7d33b82e5d7a343998c8a4a639391ffe60c24ea426c645b6e3836","source":{"kind":"arxiv","id":"1807.02227","version":3},"attestation_state":"computed","paper":{"title":"Polynomial time algorithm for optimal stopping with fixed accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.OC","q-fin.CP","q-fin.MF"],"primary_cat":"math.PR","authors_text":"David A. Goldberg, Yilun Chen","submitted_at":"2018-07-06T02:53:45Z","abstract_excerpt":"The problem of high-dimensional path-dependent optimal stopping (OS) is important to multiple academic communities and applications. Modern OS tasks often have a large number of decision epochs, and complicated non-Markovian dynamics, making them especially challenging. Standard approaches, often relying on ADP, duality, deep learning and other heuristics, have shown strong empirical performance, yet have limited rigorous guarantees (which may scale exponentially in the problem parameters and/or require previous knowledge of basis functions or additional continuity assumptions). Although past "},"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":"1807.02227","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2018-07-06T02:53:45Z","cross_cats_sorted":["cs.DS","math.OC","q-fin.CP","q-fin.MF"],"title_canon_sha256":"7c140139819f37c0db2524de81f9b67adc9197b49d55e7e141fb37ef6b653863","abstract_canon_sha256":"7a1b717230544b39ce3b180bafe6f7f3426bbf8a96b1897d86ee40482cb20185"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:19:06.205283Z","signature_b64":"O/gJDOShuRuUrIwSZROgcoJRxBxGSTj5vmMG5efUkflX23poMWr8/HhcWLJvxZ/dBTzFCZtYYYOs7wPZMa7jAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"098f2dbdbcd7d33b82e5d7a343998c8a4a639391ffe60c24ea426c645b6e3836","last_reissued_at":"2026-07-05T08:19:06.204797Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:19:06.204797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Polynomial time algorithm for optimal stopping with fixed accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.OC","q-fin.CP","q-fin.MF"],"primary_cat":"math.PR","authors_text":"David A. Goldberg, Yilun Chen","submitted_at":"2018-07-06T02:53:45Z","abstract_excerpt":"The problem of high-dimensional path-dependent optimal stopping (OS) is important to multiple academic communities and applications. Modern OS tasks often have a large number of decision epochs, and complicated non-Markovian dynamics, making them especially challenging. Standard approaches, often relying on ADP, duality, deep learning and other heuristics, have shown strong empirical performance, yet have limited rigorous guarantees (which may scale exponentially in the problem parameters and/or require previous knowledge of basis functions or additional continuity assumptions). Although past "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02227","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1807.02227/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":"1807.02227","created_at":"2026-07-05T08:19:06.204856+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02227v3","created_at":"2026-07-05T08:19:06.204856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02227","created_at":"2026-07-05T08:19:06.204856+00:00"},{"alias_kind":"pith_short_12","alias_value":"BGHS3PN427JT","created_at":"2026-07-05T08:19:06.204856+00:00"},{"alias_kind":"pith_short_16","alias_value":"BGHS3PN427JTXAXF","created_at":"2026-07-05T08:19:06.204856+00:00"},{"alias_kind":"pith_short_8","alias_value":"BGHS3PN4","created_at":"2026-07-05T08:19:06.204856+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1906.09431","citing_title":"Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ","json":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ.json","graph_json":"https://pith.science/api/pith-number/BGHS3PN427JTXAXF26RUHGMMRJ/graph.json","events_json":"https://pith.science/api/pith-number/BGHS3PN427JTXAXF26RUHGMMRJ/events.json","paper":"https://pith.science/paper/BGHS3PN4"},"agent_actions":{"view_html":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ","download_json":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ.json","view_paper":"https://pith.science/paper/BGHS3PN4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02227&json=true","fetch_graph":"https://pith.science/api/pith-number/BGHS3PN427JTXAXF26RUHGMMRJ/graph.json","fetch_events":"https://pith.science/api/pith-number/BGHS3PN427JTXAXF26RUHGMMRJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ/action/storage_attestation","attest_author":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ/action/author_attestation","sign_citation":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ/action/citation_signature","submit_replication":"https://pith.science/pith/BGHS3PN427JTXAXF26RUHGMMRJ/action/replication_record"}},"created_at":"2026-07-05T08:19:06.204856+00:00","updated_at":"2026-07-05T08:19:06.204856+00:00"}