{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7PVPKKUZQB5ZMN7GY4QUTQMSJR","short_pith_number":"pith:7PVPKKUZ","schema_version":"1.0","canonical_sha256":"fbeaf52a99807b9637e6c72149c1924c4676a0379c6370981eed4cd8bf61aa54","source":{"kind":"arxiv","id":"1704.04135","version":2},"attestation_state":"computed","paper":{"title":"The truncated milstein method for stochastic differential equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Qian Guo, Rongxian Yue, Wei Liu, Xuerong Mao","submitted_at":"2017-04-13T13:45:13Z","abstract_excerpt":"Inspired by the truncated Euler-Maruyama method developed in Mao (J. Comput. Appl. Math. 2015), we propose the truncated Milstein method in this paper. The strong convergence rate is proved to be close to 1 for a class of highly non-linear stochastic differential equations. Numerical examples are given to illustrate the theoretical results."},"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":"1704.04135","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-04-13T13:45:13Z","cross_cats_sorted":[],"title_canon_sha256":"4f5c7f43ade4b9406116f80b021f9f300248ce44e2f96ec980ab0733f3fcb774","abstract_canon_sha256":"1b2ebc6f1f7ac44c417743f83f9c0858e6207b5a21dc7db3bc4d3e428e5bfad6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:48.746976Z","signature_b64":"M2Hh4HcVCgvxLe4jqC6OdtwWyNwy/1bikXL07VETvtr/cMZq7nkkUKgSlmnxnImRQxJkWZ85J2Kzv0FQs7qXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbeaf52a99807b9637e6c72149c1924c4676a0379c6370981eed4cd8bf61aa54","last_reissued_at":"2026-05-18T00:40:48.746347Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:48.746347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The truncated milstein method for stochastic differential equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Qian Guo, Rongxian Yue, Wei Liu, Xuerong Mao","submitted_at":"2017-04-13T13:45:13Z","abstract_excerpt":"Inspired by the truncated Euler-Maruyama method developed in Mao (J. Comput. Appl. Math. 2015), we propose the truncated Milstein method in this paper. The strong convergence rate is proved to be close to 1 for a class of highly non-linear stochastic differential equations. Numerical examples are given to illustrate the theoretical results."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04135","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":""},"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":"1704.04135","created_at":"2026-05-18T00:40:48.746419+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.04135v2","created_at":"2026-05-18T00:40:48.746419+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04135","created_at":"2026-05-18T00:40:48.746419+00:00"},{"alias_kind":"pith_short_12","alias_value":"7PVPKKUZQB5Z","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7PVPKKUZQB5ZMN7G","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7PVPKKUZ","created_at":"2026-05-18T12:31:05.417338+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/7PVPKKUZQB5ZMN7GY4QUTQMSJR","json":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR.json","graph_json":"https://pith.science/api/pith-number/7PVPKKUZQB5ZMN7GY4QUTQMSJR/graph.json","events_json":"https://pith.science/api/pith-number/7PVPKKUZQB5ZMN7GY4QUTQMSJR/events.json","paper":"https://pith.science/paper/7PVPKKUZ"},"agent_actions":{"view_html":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR","download_json":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR.json","view_paper":"https://pith.science/paper/7PVPKKUZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.04135&json=true","fetch_graph":"https://pith.science/api/pith-number/7PVPKKUZQB5ZMN7GY4QUTQMSJR/graph.json","fetch_events":"https://pith.science/api/pith-number/7PVPKKUZQB5ZMN7GY4QUTQMSJR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR/action/storage_attestation","attest_author":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR/action/author_attestation","sign_citation":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR/action/citation_signature","submit_replication":"https://pith.science/pith/7PVPKKUZQB5ZMN7GY4QUTQMSJR/action/replication_record"}},"created_at":"2026-05-18T00:40:48.746419+00:00","updated_at":"2026-05-18T00:40:48.746419+00:00"}