{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VASB7OFPXK24RRE2RPMWZMRCLK","short_pith_number":"pith:VASB7OFP","schema_version":"1.0","canonical_sha256":"a8241fb8afbab5c8c49a8bd96cb2225a8625090de7ec7b972d0966bc1ef0834b","source":{"kind":"arxiv","id":"2109.07081","version":2},"attestation_state":"computed","paper":{"title":"Optimizing Trajectories with Closed-Loop Dynamic SQP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jean-Jacques Slotine, Sumeet Singh, Vikas Sindhwani","submitted_at":"2021-09-15T05:13:08Z","abstract_excerpt":"Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e.g., control bounds, obstacle avoidance). However, a na$\\\"i$ve implementation of NLP algorithms, e.g., shooting-based sequential quadratic programming (SQP), may suffer from slow convergence -- caused from natural instabilities of the underlying system manifesting as poor numerical stability within the"},"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":"2109.07081","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-09-15T05:13:08Z","cross_cats_sorted":[],"title_canon_sha256":"22844aaabbf99e97c9a47b14bc15ef8f41df4f35194805e644ecc7081c1f2526","abstract_canon_sha256":"9ddf2d30132c70e88ea4f228f567f1f76a76d07f8f24abf4675765c2bbcf271a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:29.568388Z","signature_b64":"OfNc74FcDOYCO+Vl50YZTEfayFWf9Q/HZsVGhXFQsPZSeQxxtlzb+u5metVtsiUwmji8GXlgGlERxxUNZWh9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8241fb8afbab5c8c49a8bd96cb2225a8625090de7ec7b972d0966bc1ef0834b","last_reissued_at":"2026-07-05T04:20:29.567986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:29.567986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Trajectories with Closed-Loop Dynamic SQP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jean-Jacques Slotine, Sumeet Singh, Vikas Sindhwani","submitted_at":"2021-09-15T05:13:08Z","abstract_excerpt":"Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e.g., control bounds, obstacle avoidance). However, a na$\\\"i$ve implementation of NLP algorithms, e.g., shooting-based sequential quadratic programming (SQP), may suffer from slow convergence -- caused from natural instabilities of the underlying system manifesting as poor numerical stability within the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.07081","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/2109.07081/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":"2109.07081","created_at":"2026-07-05T04:20:29.568051+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.07081v2","created_at":"2026-07-05T04:20:29.568051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.07081","created_at":"2026-07-05T04:20:29.568051+00:00"},{"alias_kind":"pith_short_12","alias_value":"VASB7OFPXK24","created_at":"2026-07-05T04:20:29.568051+00:00"},{"alias_kind":"pith_short_16","alias_value":"VASB7OFPXK24RRE2","created_at":"2026-07-05T04:20:29.568051+00:00"},{"alias_kind":"pith_short_8","alias_value":"VASB7OFP","created_at":"2026-07-05T04:20:29.568051+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/VASB7OFPXK24RRE2RPMWZMRCLK","json":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK.json","graph_json":"https://pith.science/api/pith-number/VASB7OFPXK24RRE2RPMWZMRCLK/graph.json","events_json":"https://pith.science/api/pith-number/VASB7OFPXK24RRE2RPMWZMRCLK/events.json","paper":"https://pith.science/paper/VASB7OFP"},"agent_actions":{"view_html":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK","download_json":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK.json","view_paper":"https://pith.science/paper/VASB7OFP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.07081&json=true","fetch_graph":"https://pith.science/api/pith-number/VASB7OFPXK24RRE2RPMWZMRCLK/graph.json","fetch_events":"https://pith.science/api/pith-number/VASB7OFPXK24RRE2RPMWZMRCLK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK/action/storage_attestation","attest_author":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK/action/author_attestation","sign_citation":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK/action/citation_signature","submit_replication":"https://pith.science/pith/VASB7OFPXK24RRE2RPMWZMRCLK/action/replication_record"}},"created_at":"2026-07-05T04:20:29.568051+00:00","updated_at":"2026-07-05T04:20:29.568051+00:00"}