{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FRVWPA6MP7FQENNIESFXWDPMU6","short_pith_number":"pith:FRVWPA6M","schema_version":"1.0","canonical_sha256":"2c6b6783cc7fcb0235a8248b7b0deca7befb087fe49a565fa3c3c77a1df2a50f","source":{"kind":"arxiv","id":"2503.02985","version":1},"attestation_state":"computed","paper":{"title":"Regularization for Covariance Parameterization of Direct Data-Driven LQR Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Alessandro Chiuso, Feiran Zhao, Florian D\\\"orfler","submitted_at":"2025-03-04T20:22:57Z","abstract_excerpt":"As the benchmark of data-driven control methods, the linear quadratic regulator (LQR) problem has gained significant attention. A growing trend is direct LQR design, which finds the optimal LQR gain directly from raw data and bypassing system identification. To achieve this, our previous work develops a direct LQR formulation parameterized by sample covariance. In this paper, we propose a regularization method for the covariance-parameterized LQR. We show that the regularizer accounts for the uncertainty in both the steady-state covariance matrix corresponding to closed-loop stability, and 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":"2503.02985","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SY","submitted_at":"2025-03-04T20:22:57Z","cross_cats_sorted":["cs.SY","math.OC"],"title_canon_sha256":"c65c5343cdc99a15e3697ad17376bfa6b082c929202e2f8888d227a4cc6a1a51","abstract_canon_sha256":"4cbaf5b9eee1b14f33649a561870fe500826830dbbfb04da14f0645fb5b4eca5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:36.339897Z","signature_b64":"xX8kxdvX9klPxLex8xmQL+7ZftmhKp2ewpdZFcyjKx7gVylxRT2P+3LFvM6kx8HRkHDwN25ed+dpKtHRkuUrBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c6b6783cc7fcb0235a8248b7b0deca7befb087fe49a565fa3c3c77a1df2a50f","last_reissued_at":"2026-07-05T10:24:36.339066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:36.339066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Regularization for Covariance Parameterization of Direct Data-Driven LQR Control","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","math.OC"],"primary_cat":"eess.SY","authors_text":"Alessandro Chiuso, Feiran Zhao, Florian D\\\"orfler","submitted_at":"2025-03-04T20:22:57Z","abstract_excerpt":"As the benchmark of data-driven control methods, the linear quadratic regulator (LQR) problem has gained significant attention. A growing trend is direct LQR design, which finds the optimal LQR gain directly from raw data and bypassing system identification. To achieve this, our previous work develops a direct LQR formulation parameterized by sample covariance. In this paper, we propose a regularization method for the covariance-parameterized LQR. We show that the regularizer accounts for the uncertainty in both the steady-state covariance matrix corresponding to closed-loop stability, and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02985","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.02985/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":"2503.02985","created_at":"2026-07-05T10:24:36.339178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02985v1","created_at":"2026-07-05T10:24:36.339178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02985","created_at":"2026-07-05T10:24:36.339178+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRVWPA6MP7FQ","created_at":"2026-07-05T10:24:36.339178+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRVWPA6MP7FQENNI","created_at":"2026-07-05T10:24:36.339178+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRVWPA6M","created_at":"2026-07-05T10:24:36.339178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.03706","citing_title":"Policy Gradient Adaptive Control for the LQR: Indirect and Direct Approaches","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6","json":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6.json","graph_json":"https://pith.science/api/pith-number/FRVWPA6MP7FQENNIESFXWDPMU6/graph.json","events_json":"https://pith.science/api/pith-number/FRVWPA6MP7FQENNIESFXWDPMU6/events.json","paper":"https://pith.science/paper/FRVWPA6M"},"agent_actions":{"view_html":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6","download_json":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6.json","view_paper":"https://pith.science/paper/FRVWPA6M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02985&json=true","fetch_graph":"https://pith.science/api/pith-number/FRVWPA6MP7FQENNIESFXWDPMU6/graph.json","fetch_events":"https://pith.science/api/pith-number/FRVWPA6MP7FQENNIESFXWDPMU6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6/action/storage_attestation","attest_author":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6/action/author_attestation","sign_citation":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6/action/citation_signature","submit_replication":"https://pith.science/pith/FRVWPA6MP7FQENNIESFXWDPMU6/action/replication_record"}},"created_at":"2026-07-05T10:24:36.339178+00:00","updated_at":"2026-07-05T10:24:36.339178+00:00"}