{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:BEZAXV332GLL3WFOR5A7NTRS3G","short_pith_number":"pith:BEZAXV33","schema_version":"1.0","canonical_sha256":"09320bd77bd196bdd8ae8f41f6ce32d99545cff8bd0df55c0e998596c46bef62","source":{"kind":"arxiv","id":"1608.07046","version":1},"attestation_state":"computed","paper":{"title":"Transient performance analysis of zero-attracting LMS","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.PF","authors_text":"Cedric Richard, David Brie, Jie Chen, Yingying Song","submitted_at":"2016-08-25T08:15:40Z","abstract_excerpt":"Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and $\\ell_1$-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its per"},"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":"1608.07046","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PF","submitted_at":"2016-08-25T08:15:40Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"cffc57b6d1cbb025c980deb49c911ad6ef0d2a41806f517f7e53385bdc74ee03","abstract_canon_sha256":"0c9e13d06314684e55dfeb79c597f3dda769de4b6aac0c6f4c2f773d0c09e246"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:19.742830Z","signature_b64":"gV2A4vLJecL9Pi6xzHMhsbPtKQ4br9lz0S7Ba93Tc5UME6wWfXPFEgPtzQO4NzPv14CDIjWxuXe3LlAfA9LXAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09320bd77bd196bdd8ae8f41f6ce32d99545cff8bd0df55c0e998596c46bef62","last_reissued_at":"2026-05-18T00:54:19.742451Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:19.742451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transient performance analysis of zero-attracting LMS","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"cs.PF","authors_text":"Cedric Richard, David Brie, Jie Chen, Yingying Song","submitted_at":"2016-08-25T08:15:40Z","abstract_excerpt":"Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and $\\ell_1$-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07046","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":"1608.07046","created_at":"2026-05-18T00:54:19.742506+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.07046v1","created_at":"2026-05-18T00:54:19.742506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07046","created_at":"2026-05-18T00:54:19.742506+00:00"},{"alias_kind":"pith_short_12","alias_value":"BEZAXV332GLL","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_16","alias_value":"BEZAXV332GLL3WFO","created_at":"2026-05-18T12:30:07.202191+00:00"},{"alias_kind":"pith_short_8","alias_value":"BEZAXV33","created_at":"2026-05-18T12:30:07.202191+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/BEZAXV332GLL3WFOR5A7NTRS3G","json":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G.json","graph_json":"https://pith.science/api/pith-number/BEZAXV332GLL3WFOR5A7NTRS3G/graph.json","events_json":"https://pith.science/api/pith-number/BEZAXV332GLL3WFOR5A7NTRS3G/events.json","paper":"https://pith.science/paper/BEZAXV33"},"agent_actions":{"view_html":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G","download_json":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G.json","view_paper":"https://pith.science/paper/BEZAXV33","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.07046&json=true","fetch_graph":"https://pith.science/api/pith-number/BEZAXV332GLL3WFOR5A7NTRS3G/graph.json","fetch_events":"https://pith.science/api/pith-number/BEZAXV332GLL3WFOR5A7NTRS3G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G/action/storage_attestation","attest_author":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G/action/author_attestation","sign_citation":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G/action/citation_signature","submit_replication":"https://pith.science/pith/BEZAXV332GLL3WFOR5A7NTRS3G/action/replication_record"}},"created_at":"2026-05-18T00:54:19.742506+00:00","updated_at":"2026-05-18T00:54:19.742506+00:00"}