{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:57VJBD5CXH3KYQ34GURB2SLAQG","short_pith_number":"pith:57VJBD5C","schema_version":"1.0","canonical_sha256":"efea908fa2b9f6ac437c35221d49608186b1906563799e62ff93c01523f72240","source":{"kind":"arxiv","id":"1111.2667","version":2},"attestation_state":"computed","paper":{"title":"A note on the lack of symmetry in the graphical lasso","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ML","authors_text":"Bala Rajaratnam, Benjamin T. Rolfs","submitted_at":"2011-11-11T05:51:44Z","abstract_excerpt":"The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an L1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate a sparse inverse covariance matrix estimate, not only identify a graphical model but can also serve as intermediate inputs into multivariate procedures such as PCA, LDA, MANOVA, and others. The glasso indeed produces a covariance matrix estimate which solves the L1 penalized optimization problem in a dual sense; ho"},"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":"1111.2667","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-11-11T05:51:44Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"4e4f11b583d22328c9a97c727b3648f6c2281cc2c94a1ebaab445f9cd9b9352c","abstract_canon_sha256":"a3cd159b93d80f5ea59c246c5d0744cbdda563dc1d5926f481a937e52d2d55c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:50:22.991983Z","signature_b64":"nSSrPEY5cY7km/xzW3EgWRlp1IX5fQYAABrz2xr2JOHhGqyOaLauuQxoPECfpOCuuXFDN0WhKZBBSwNi0sIzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efea908fa2b9f6ac437c35221d49608186b1906563799e62ff93c01523f72240","last_reissued_at":"2026-05-18T03:50:22.991360Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:50:22.991360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A note on the lack of symmetry in the graphical lasso","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ML","authors_text":"Bala Rajaratnam, Benjamin T. Rolfs","submitted_at":"2011-11-11T05:51:44Z","abstract_excerpt":"The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an L1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate a sparse inverse covariance matrix estimate, not only identify a graphical model but can also serve as intermediate inputs into multivariate procedures such as PCA, LDA, MANOVA, and others. The glasso indeed produces a covariance matrix estimate which solves the L1 penalized optimization problem in a dual sense; ho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.2667","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":"1111.2667","created_at":"2026-05-18T03:50:22.991470+00:00"},{"alias_kind":"arxiv_version","alias_value":"1111.2667v2","created_at":"2026-05-18T03:50:22.991470+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.2667","created_at":"2026-05-18T03:50:22.991470+00:00"},{"alias_kind":"pith_short_12","alias_value":"57VJBD5CXH3K","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_16","alias_value":"57VJBD5CXH3KYQ34","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_8","alias_value":"57VJBD5C","created_at":"2026-05-18T12:26:20.644004+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/57VJBD5CXH3KYQ34GURB2SLAQG","json":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG.json","graph_json":"https://pith.science/api/pith-number/57VJBD5CXH3KYQ34GURB2SLAQG/graph.json","events_json":"https://pith.science/api/pith-number/57VJBD5CXH3KYQ34GURB2SLAQG/events.json","paper":"https://pith.science/paper/57VJBD5C"},"agent_actions":{"view_html":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG","download_json":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG.json","view_paper":"https://pith.science/paper/57VJBD5C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1111.2667&json=true","fetch_graph":"https://pith.science/api/pith-number/57VJBD5CXH3KYQ34GURB2SLAQG/graph.json","fetch_events":"https://pith.science/api/pith-number/57VJBD5CXH3KYQ34GURB2SLAQG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG/action/storage_attestation","attest_author":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG/action/author_attestation","sign_citation":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG/action/citation_signature","submit_replication":"https://pith.science/pith/57VJBD5CXH3KYQ34GURB2SLAQG/action/replication_record"}},"created_at":"2026-05-18T03:50:22.991470+00:00","updated_at":"2026-05-18T03:50:22.991470+00:00"}