{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:65HLJPHWVWAGEZTHUWAOFKD2ED","short_pith_number":"pith:65HLJPHW","schema_version":"1.0","canonical_sha256":"f74eb4bcf6ad80626667a580e2a87a20c673a01a04061e770d02f5c0ae7b9977","source":{"kind":"arxiv","id":"1405.0601","version":1},"attestation_state":"computed","paper":{"title":"Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fernando De la Torre, Xuehan Xiong","submitted_at":"2014-05-03T15:37:27Z","abstract_excerpt":"Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, t"},"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":"1405.0601","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-03T15:37:27Z","cross_cats_sorted":[],"title_canon_sha256":"1b3359907c5aad568e16817dde9c68fd289951fa76ccf7394b9bbafabd7a9c2a","abstract_canon_sha256":"c92a4a6796cbb7737d47effb779b7985359d849f452c4d75a22fe9d67e42fd2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:41.114035Z","signature_b64":"lg+/xwfQkPdeCEzHJK87zkFHbk6zrq/pSyiYGYYiXlArd3+Tg3v2Xkd9smOA8C3OeHeULV8LXLh5NZC8u3rYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f74eb4bcf6ad80626667a580e2a87a20c673a01a04061e770d02f5c0ae7b9977","last_reissued_at":"2026-05-18T02:52:41.113539Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:41.113539Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fernando De la Torre, Xuehan Xiong","submitted_at":"2014-05-03T15:37:27Z","abstract_excerpt":"Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.0601","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":""},"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":"1405.0601","created_at":"2026-05-18T02:52:41.113614+00:00"},{"alias_kind":"arxiv_version","alias_value":"1405.0601v1","created_at":"2026-05-18T02:52:41.113614+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.0601","created_at":"2026-05-18T02:52:41.113614+00:00"},{"alias_kind":"pith_short_12","alias_value":"65HLJPHWVWAG","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_16","alias_value":"65HLJPHWVWAGEZTH","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_8","alias_value":"65HLJPHW","created_at":"2026-05-18T12:28:16.859392+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/65HLJPHWVWAGEZTHUWAOFKD2ED","json":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED.json","graph_json":"https://pith.science/api/pith-number/65HLJPHWVWAGEZTHUWAOFKD2ED/graph.json","events_json":"https://pith.science/api/pith-number/65HLJPHWVWAGEZTHUWAOFKD2ED/events.json","paper":"https://pith.science/paper/65HLJPHW"},"agent_actions":{"view_html":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED","download_json":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED.json","view_paper":"https://pith.science/paper/65HLJPHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1405.0601&json=true","fetch_graph":"https://pith.science/api/pith-number/65HLJPHWVWAGEZTHUWAOFKD2ED/graph.json","fetch_events":"https://pith.science/api/pith-number/65HLJPHWVWAGEZTHUWAOFKD2ED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED/action/storage_attestation","attest_author":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED/action/author_attestation","sign_citation":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED/action/citation_signature","submit_replication":"https://pith.science/pith/65HLJPHWVWAGEZTHUWAOFKD2ED/action/replication_record"}},"created_at":"2026-05-18T02:52:41.113614+00:00","updated_at":"2026-05-18T02:52:41.113614+00:00"}