{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DL5OCMEMVSJNL7QXIYGXJEUTX4","short_pith_number":"pith:DL5OCMEM","schema_version":"1.0","canonical_sha256":"1afae1308cac92d5fe17460d749293bf18207b3502760fad1f8e6af66f339a1c","source":{"kind":"arxiv","id":"2406.05846","version":3},"attestation_state":"computed","paper":{"title":"Fast and Certifiable Trajectory Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"math.OC","authors_text":"Heng Yang, Jay Sarva, Ling Liang, Shucheng Kang, Xiaoyang Xu","submitted_at":"2024-06-09T16:34:03Z","abstract_excerpt":"We propose semidefinite trajectory optimization (STROM), a framework that computes fast and certifiably optimal solutions for nonconvex trajectory optimization problems defined by polynomial objectives and constraints. STROM employs sparse second-order Lasserre's hierarchy to generate semidefinite program (SDP) relaxations of trajectory optimization. Different from existing tools (e.g., YALMIP and SOSTOOLS in Matlab), STROM generates chain-like multiple-block SDPs with only positive semidefinite (PSD) variables. Moreover, STROM does so two orders of magnitude faster. Underpinning STROM is cuAD"},"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":"2406.05846","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2024-06-09T16:34:03Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"a8fc559a5e5db6d4d10098a2fd5590f422b9d5fc57e678ad8efb1c0437502759","abstract_canon_sha256":"0822c206a4076ecc48ab065b7f45cf8c493342b7dcf2413fe14191b0b74e29c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:02:21.027141Z","signature_b64":"r2N7ElnSOnXPkKOxNMHwxRketxlywd0TsYvOoliOh9FrC/dhj+IVjr2QnfY0eUCqNnCPkcLgmVyZmTNoJLjGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1afae1308cac92d5fe17460d749293bf18207b3502760fad1f8e6af66f339a1c","last_reissued_at":"2026-07-05T09:02:21.026680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:02:21.026680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and Certifiable Trajectory Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"math.OC","authors_text":"Heng Yang, Jay Sarva, Ling Liang, Shucheng Kang, Xiaoyang Xu","submitted_at":"2024-06-09T16:34:03Z","abstract_excerpt":"We propose semidefinite trajectory optimization (STROM), a framework that computes fast and certifiably optimal solutions for nonconvex trajectory optimization problems defined by polynomial objectives and constraints. STROM employs sparse second-order Lasserre's hierarchy to generate semidefinite program (SDP) relaxations of trajectory optimization. Different from existing tools (e.g., YALMIP and SOSTOOLS in Matlab), STROM generates chain-like multiple-block SDPs with only positive semidefinite (PSD) variables. Moreover, STROM does so two orders of magnitude faster. Underpinning STROM is cuAD"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05846","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/2406.05846/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":"2406.05846","created_at":"2026-07-05T09:02:21.026737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05846v3","created_at":"2026-07-05T09:02:21.026737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05846","created_at":"2026-07-05T09:02:21.026737+00:00"},{"alias_kind":"pith_short_12","alias_value":"DL5OCMEMVSJN","created_at":"2026-07-05T09:02:21.026737+00:00"},{"alias_kind":"pith_short_16","alias_value":"DL5OCMEMVSJNL7QX","created_at":"2026-07-05T09:02:21.026737+00:00"},{"alias_kind":"pith_short_8","alias_value":"DL5OCMEM","created_at":"2026-07-05T09:02:21.026737+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10579","citing_title":"LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4","json":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4.json","graph_json":"https://pith.science/api/pith-number/DL5OCMEMVSJNL7QXIYGXJEUTX4/graph.json","events_json":"https://pith.science/api/pith-number/DL5OCMEMVSJNL7QXIYGXJEUTX4/events.json","paper":"https://pith.science/paper/DL5OCMEM"},"agent_actions":{"view_html":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4","download_json":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4.json","view_paper":"https://pith.science/paper/DL5OCMEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05846&json=true","fetch_graph":"https://pith.science/api/pith-number/DL5OCMEMVSJNL7QXIYGXJEUTX4/graph.json","fetch_events":"https://pith.science/api/pith-number/DL5OCMEMVSJNL7QXIYGXJEUTX4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4/action/storage_attestation","attest_author":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4/action/author_attestation","sign_citation":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4/action/citation_signature","submit_replication":"https://pith.science/pith/DL5OCMEMVSJNL7QXIYGXJEUTX4/action/replication_record"}},"created_at":"2026-07-05T09:02:21.026737+00:00","updated_at":"2026-07-05T09:02:21.026737+00:00"}