{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:5LLLK7FUEWRH37JW2IJZIFZQLU","short_pith_number":"pith:5LLLK7FU","schema_version":"1.0","canonical_sha256":"ead6b57cb425a27dfd36d2139417305d107bca579595db1972595ba31a811b7e","source":{"kind":"arxiv","id":"1210.0563","version":1},"attestation_state":"computed","paper":{"title":"Sparse LMS via Online Linearized Bregman Iteration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Dmitri B. Chklovskii, Tao Hu","submitted_at":"2012-10-01T20:28:09Z","abstract_excerpt":"We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regularizer we arrive at a simple two-step iteration. We demonstrate that OLBI is bias free and compare its operation with existing sparse LMS algorithms by rederiving them in the online convex optimization framework. We perform convergence analysis of OLBI for white input signals and der"},"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":"1210.0563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-10-01T20:28:09Z","cross_cats_sorted":["cs.LG","math.IT","stat.ML"],"title_canon_sha256":"31fc0c15276ef06ff68946a048045df5db8c8b5c57705bb86012ee39ad501cee","abstract_canon_sha256":"e5b122b5ca9cfd1e0c32017e30232b847bb9ff673b016851492c31eb7c022121"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:44:14.910209Z","signature_b64":"SX32/sHYlXR0ZFlbSgt3TQuzfG3L/a0HC0iYtAFr/D1NpSeVLrOiI40hWqcFQ57aztDKSfryE8blr1DmEvVKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ead6b57cb425a27dfd36d2139417305d107bca579595db1972595ba31a811b7e","last_reissued_at":"2026-05-18T03:44:14.909687Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:44:14.909687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse LMS via Online Linearized Bregman Iteration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Dmitri B. Chklovskii, Tao Hu","submitted_at":"2012-10-01T20:28:09Z","abstract_excerpt":"We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regularizer we arrive at a simple two-step iteration. We demonstrate that OLBI is bias free and compare its operation with existing sparse LMS algorithms by rederiving them in the online convex optimization framework. We perform convergence analysis of OLBI for white input signals and der"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.0563","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":"1210.0563","created_at":"2026-05-18T03:44:14.909769+00:00"},{"alias_kind":"arxiv_version","alias_value":"1210.0563v1","created_at":"2026-05-18T03:44:14.909769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1210.0563","created_at":"2026-05-18T03:44:14.909769+00:00"},{"alias_kind":"pith_short_12","alias_value":"5LLLK7FUEWRH","created_at":"2026-05-18T12:26:56.085431+00:00"},{"alias_kind":"pith_short_16","alias_value":"5LLLK7FUEWRH37JW","created_at":"2026-05-18T12:26:56.085431+00:00"},{"alias_kind":"pith_short_8","alias_value":"5LLLK7FU","created_at":"2026-05-18T12:26:56.085431+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/5LLLK7FUEWRH37JW2IJZIFZQLU","json":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU.json","graph_json":"https://pith.science/api/pith-number/5LLLK7FUEWRH37JW2IJZIFZQLU/graph.json","events_json":"https://pith.science/api/pith-number/5LLLK7FUEWRH37JW2IJZIFZQLU/events.json","paper":"https://pith.science/paper/5LLLK7FU"},"agent_actions":{"view_html":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU","download_json":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU.json","view_paper":"https://pith.science/paper/5LLLK7FU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1210.0563&json=true","fetch_graph":"https://pith.science/api/pith-number/5LLLK7FUEWRH37JW2IJZIFZQLU/graph.json","fetch_events":"https://pith.science/api/pith-number/5LLLK7FUEWRH37JW2IJZIFZQLU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU/action/storage_attestation","attest_author":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU/action/author_attestation","sign_citation":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU/action/citation_signature","submit_replication":"https://pith.science/pith/5LLLK7FUEWRH37JW2IJZIFZQLU/action/replication_record"}},"created_at":"2026-05-18T03:44:14.909769+00:00","updated_at":"2026-05-18T03:44:14.909769+00:00"}